Jing Zhou

• Bandi, Prasad; Saikrishna, P. S. & Zhou, Jing (2026). Quantized Observer-Based Controller Under Network Imperfections: Application to Scalable Fog Computing Network. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 73(9), s. 13613–13624. doi: 10.1109/TIE.2026.3675096. Fulltekst i vitenarkiv Vis sammendrag

  This article presents a less conservative quantized observer-based control framework for linear parameter-varying (LPV) systems operating under network-induced imperfections, with application to a scalable fog computing network (SFCN) for real-time vision data processing in autonomous vehicles (AVs). The considered SFCN supports time-varying vision workloads offloaded by the AV onboard vision system and is subject to practical constraints such as limited communication bandwidth, quantization effects, packet losses, and bidirectional network-induced delays. First, the dynamic behavior of the SFCN is captured using an LPV model obtained via system identification techniques, enabling the representation of workload variations and auto-scaling mechanisms of fog nodes. Based on this model, observer-based state-feedback load balancing controllers are designed within an input-to-state stability (ISS) framework to explicitly address quantization effects, packet losses, and delays in both measurement and control channels. Both dependent and independent observer–controller structures are investigated, and sufficient conditions are derived to ensure stability and desirable transient performance. In particular, independent controller designs are formulated that incorporate state estimates from the proposed observers, leading to reduced conservatism and enhanced robustness compared to conventional approaches. The resulting stability conditions are expressed in terms of decay rates, robustness margins, and ISS gains for both observer and controller dynamics. The effectiveness of the proposed control framework is validated through real-time experimental results, demonstrating improved load balancing performance and timely processing of time-varying vision data under network imperfections.

• Schlanbusch, Siri Marte & Zhou, Jing (2026). Predictor-based attitude control for rigid body systems with input delays. Automatica. ISSN 0005-1098. 187. doi: 10.1016/j.automatica.2026.112893. Fulltekst i vitenarkiv Vis sammendrag

  Time-delays are a prevalent challenge for systems controlled over networks. This paper addresses the attitude tracking control problem of rigid bodies subject to input delays, where the attitude is parameterized by unit quaternions. A predictor-feedback controller is developed to compensate the delay, and achieve tracking. Two different cases are considered, where in the first case a stability result is given based on Lyapunovs method. In the second case, a dual to Lyapunov’s stability theorem based on a density function is used for analyzing the system stability for convergence of almost all solutions to the desired equilibrium. The theoretical results are validated through experiments on a two-degrees-of-freedom (2-DOF) helicopter system, illustrating the effectiveness of the proposed approach.

• Vinod, Dinsha & Zhou, Jing (2026). Adaptive Event-Triggered Control for Uncertain Polytopic NLPV Systems: An Application in Robotic Vision. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 73(6), s. 9245–9255. doi: 10.1109/tie.2026.3651341. Fulltekst i vitenarkiv Vis sammendrag

  This article presents the design of an adaptive event-triggered mechanism (AETM) for a specific class of uncertain polytopic nonlinear parameter-varying (NLPV) systems with Lipschitz nonlinearity. The primary objective of the proposed control design is to ensure the D-stability of the nominal system while concurrently minimizing the frequency of control updates with flexible design requirements. This is achieved by leveraging the principles of Lyapunov functions and linear matrix inequalities (LMIs). The efficacy of the developed control framework is validated on a mobile robot integrated with a distributed fog computing platform (DFCP) within the context of warehouse applications. The experimental findings substantiate the effectiveness of the control theory in auto-scaling the fog computing nodes for processing the vision data acquired from a mobile robot within the service time constraints.

• Vinod, Dinsha & Zhou, Jing (2025). Adaptive Event-Triggered and Self-Triggered Control for a Discrete-Time Saturated Polytopic LPV Systems. IEEE Conference on Industrial Informatics. ISSN 1935-4576. 23. doi: 10.1109/INDIN64977.2025.11278969. Fulltekst i vitenarkiv Vis sammendrag

  This paper delineates the development of two sophisticated control strategies: Adaptive Event-Triggered Control (AETC) and Adaptive Self-Triggered Control (ASTC), specifically designed for a discrete-time polytopic Linear Parameter Varying (LPV) system subjected to input saturation. Initially, a novel AETC algorithm is introduced, which utilizes a parameter-varying Lyapunov function specifically tailored for the polytopic LPV system with input saturation constraint. Subsequently, the ASTC is proposed to mitigate the necessity for continuously monitoring measurement errors. The theoretical advancements presented herein are experimentally validated on a distributed fog computing platform (DFP) for efficient offloading of vision data acquired from a mobile robot in a warehouse environment.

• Østerholt, Lars; Gundersen, Are; Zhou, Jing & Hagen, Daniel (2025). Real-Time Markerless Vision-Based Control of Knuckle Boom Cranes via Embedded AI. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. 2025. doi: 10.1109/iccma67641.2025.11369496. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a load-handling system for a knuckle boom crane, integrating edge-AI-based computer vision, real-time control, and Screw theory kinematics. Utilizing YOLOv6-N for object detection and a camera-driven control system, the approach enables payload navigation in industrial environments without external markers. Key contributions include a markerless vision-based framework, real-time PLC implementation for control, and experimental validation of autonomous navigation across four distinct landing zones, demonstrating robust performance in dynamic settings. All without the need for external infrastructure. The system’s design minimizes setup complexity and enhances adaptability to diverse conditions.

• Vinod, Dinsha & Zhou, Jing (2025). Adaptive event-triggered control for resource-based auto-scaling of an uncertain distributed fog computing platform. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/iecon58223.2025.11221340. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the design of an adaptive event-triggered controller to facilitate the efficient auto-scaling of the distributed fog computing platform (DFP), while meticulously considering resource constraints, disturbances, and uncertainties. A data-driven discrete polytopic linear parameter-varying (PLPV) model effectively addresses the service requirements of DFP with time-varying characteristics. The controller gains are formulated utilizing linear matrix inequalities (LMIs) to guarantee the DR stability of the system. Finally, the results are experimentally validated on a DFP to offload the images from an autonomous mobile robot (AMR) specifically designed for vision applications in a warehouse environment. Furthermore, the validation results are systematically compared to a static event-triggered control approach for discrete PLPV systems.

• Zhu, Mingda; Eidsvik, Johannes Arnesen; Landsverk, Ronny; Hagen, Daniel & Zhou, Jing (2025). Rapid Model-Based Design and Real-Time Digital Twin Integration: An Offshore Robot Case Study. Proceedings of the IEEE International Symposium on Industrial Electronics. s. 1–6. doi: 10.1109/isie62713.2025.11124748. Fulltekst i vitenarkiv Vis sammendrag

  The rapid advancement of digitalization has led to the widespread adoption of digital twins across various industries. While digital twins have demonstrated significant potential in many applications, challenges remain in their implementation, particularly in real-time control. Therefore, this paper addresses the challenges by proposing a DT-based method for fast prototyping of real-time control in medium-scale industrial robots, leveraging rapid model-based design. With Speedgoat and the User Datagram Protocol communication protocol, the proposed approach enables real-time data exchange and control execution. The effectiveness of the method is demonstrated through four case studies by utilizing the Comau robot at the Norwegian Motion Laboratory.

• Zhu, Mingda; Han, Peihua; Landsverk, Ronny; Strandene, Jan Christian Bjerke; Hagen, Daniel & Zhou, Jing (2025). A Data-driven Approach to Payload Heave Motion Prediction under Irregular Waves, IEEE Industrial Electronics Society Annual On-Line Conference (ONCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350357981. doi: 10.1109/ONCON62778.2024.10931669. Fulltekst i vitenarkiv Vis sammendrag

  Effective handling of the payload with an offshore crane is critical for ensuring safety and efficiency in the offshore operation. Accurate modelling of payload heave motion is essential for safeguarding transportation and improving feedback control for active heave compensation. Traditional kinematic modelling fails to account for the nonlinear dynamics of waveinduced motion. This paper proposes a data-driven method,leveraging real motion data from the Norwegian Motion Lab, to predict payload heave positions based on ship heave positions. Extensive experiments are carried out using a diverse dataset under various sea states, and the proposed method is evaluated against three methods from different disciplines. The results demonstrate that the data-driven approach outperforms the compared methods, showing a better overall performance across multiple wave scenarios.

• Schlanbusch, Siri Marte; Zhou, Jing & Schlanbusch, Rune (2025). Quantized Predictor-Based Tracking Control for Input Delay Systems: Application to a Helicopter System. IEEE Conference on Decision and Control. Proceedings. ISSN 0743-1546. doi: https:/ieeexplore.ieee.org/document/10886553. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, the tracking control problem is studied for systems with quantized signals and communication delays. A bounded type of quantizer is introduced to reduce the communication transmission burden, where both control inputs and states are quantized. A predictor-feedback controller is designed to compensate for delays in the inputs, while boundedness of the tracking errors are achieved and practical stability is ensured. An application to a helicopter system is presented to show the effectiveness of the proposed predictor-based controller in a closed-loop configuration, and to illustrate the main findings.

• Zhu, Mingda; Nguyen, Hoang Nam Du; Han, Peihua; Zhou, Jing & Huynh, van Khang (2025). A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs, 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis - WEMDCD. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331520748. doi: 10.1109/WEMDCD61816.2025.11014178. Fulltekst i vitenarkiv Vis sammendrag

  Maintenance has been critical in energy and marine systems, ensuring safety, reliability and efficiency. Fault detection, a cornerstone of prognostics and health management systems, faces challenges due to limited faulty data. This study proposes a semi-supervised Variational Autoencoder (VAE) with Long Short-Term Memory Networks for fault detection. The method is demonstrated via an example of low-severity inter-turn short-circuit faults in permanent magnet synchronous machines. Trained only on healthy data, the model can generate anomaly scores based on the VAE's reconstruction error. The model is tested on time-series current data under varying conditions, achieving high detection accuracy. Comparative analysis shows its superiority over traditional methods such as One-Class Support Vector Machine, Isolation Forest and Autoencoder.

• Sveen, Emil Mühlbradt; Zhou, Jing; Ebbesen, Morten Kjeld & Poursina, Mohammad (2025). Decentralised adaptive learning-based control of robot manipulators with unknown parameters. Journal of Automation and Intelligence (JAI). 4(2), s. 136–144. doi: 10.1016/j.jai.2025.02.002. Fulltekst i vitenarkiv Vis sammendrag

  This paper studies motor joint control of a 4-degree-of-freedom (DoF) robotic manipulator using learning-based Adaptive Dynamic Programming (ADP) approach. The manipulator’s dynamics are modelled as an open-loop 4-link serial kinematic chain with 4 Degrees of Freedom (DoF). Decentralised optimal controllers are designed for each link using ADP approach based on a set of cost matrices and data collected from exploration trajectories. The proposed control strategy employs an off-line, off-policy iterative approach to derive four optimal control policies, one for each joint, under exploration strategies. The objective of the controller is to control the position of each joint. Simulation and experimental results show that four independent optimal controllers are found, each under similar exploration strategies, and the proposed ADP approach successfully yields optimal linear control policies despite the presence of these complexities. The experimental results conducted on the Quanser Qarm robotic platform demonstrate the effectiveness of the proposed ADP controllers in handling significant dynamic nonlinearities, such as actuation limitations, output saturation, and filter delays.

• Vinod, Dinsha & Zhou, Jing (2025). Event-Based Control for Discrete Polytopic LPV Systems With AI Inference. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 72(9), s. 9497–9507. doi: 10.1109/TIE.2025.3536560. Fulltekst i vitenarkiv Vis sammendrag

  This article presents an in-depth exploration of event-based control schemes, focusing on event-triggered control (ETC) and self-triggered control (STC) for discrete polytopic linear parameter-varying (LPV) systems with time-varying characteristics. The primary objective of the proposed ETC design is to ensure optimal system performance while minimizing the frequency of control updates. The article first introduces a standard ETC algorithm for discrete-time polytopic LPV systems, leveraging a parameter-varying Lyapunov function. Subsequently, a dynamic ETC algorithm is proposed to extend the inter-event time (IET) associated with the standard ETC. Furthermore, standard and dynamic STC algorithms are developed for the polytopic LPV systems to reduce the periodic sampling by evaluating the subsequent control update based on the preceding triggered state. The control gains are designed using multiobjective linear matrix inequalities (LMIs). Lastly, the efficacy of the developed control theory is demonstrated through its implementation on a distributed fog computing platform (DFCP) in warehouse applications. The experimental results demonstrate the effectiveness of auto-scaling fog computing nodes in processing vision data obtained from a mobile robot operating within a warehouse environment subject to service time constraints.

• Shen, Jiajun; Wang, Wei; Zhou, Jing & Lü, Jinhu (2025). Adaptive safety with Control Barrier Functions and triggered Batch Least-Squares Identifier. Automatica. ISSN 0005-1098. 173. doi: 10.1016/j.automatica.2024.112059. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, a triggered Batch Least-Squares Identifier (BaLSI) based adaptive safety control scheme is proposed for uncertain systems with potentially conflicting control objectives and safety constraints. A relaxation term is added to the Quadratic Programs (QP) combining the transformed Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs), to mediate the potential conflict. The existing Lyapunov-based adaptive laws designed to guarantee specific properties of the Lyapunov functions, may grow unboundedly under the effects of the relaxation term. The proposed adaptive law is designed by processing system inputs and outputs, to avoid unbounded estimates and overparameterization problems in the existing results. A safety-triggered condition is presented, based on which the forward invariant property of the safe set is shown and Zeno behavior can be excluded. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive control scheme.

• Nama, Ajay Nagendra; Beferull-Lozano, Baltasar; Saad, Leila Ben & Zhou, Jing (2024). Node-Variant Graph Filter Based Multi-Agent Deep Reinforcement Learning for Aligned Game and Wireless Environments. IEEE Conference on Industrial Electronics and Applications. 19. doi: 10.1109/ICIEA61579.2024.10665191. Fulltekst i vitenarkiv
• Sveen, Emil Mühlbradt & Zhou, Jing (2024). Learning Based Adaptive Dynamic Programming of a Four-Bar Mechanism with Linear Approximation. IEEE Conference on Industrial Electronics and Applications. doi: 10.1109/ICIEA61579.2024.10665201. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the use of a learning-based approach of Adaptive Dynamic Programming (ADP) on a highly nonlinear four-bar mechanism which uses simple initial evaluation of nonlinear dynamics to increase both controller performance and robustness of algorithm convergence. This approach is based on the assumption of a gravity-induced moment on the crank, with an empirically identified stable equilibrium that is used as a linearising approximation in a state feedback signal. Previous work related to this study shows significant algorithm convergence boundedness, especially related to the initial crank position. In the results, a convergence analysis is provided with a direct relation to this of previous work, and further investigations of controller methods are also provided to evaluate the efficiency of the linear approximation. The results, when compared to the previous study related to the four-bar mechanism and learning-based ADP, show that, although simple and approximate, the presented case can significantly reduce the boundedness of the system and algorithm parameters that was previously found to be most important for convergence failure when using ADP on nonlinear systems.

• Singh, Jayant; Zhou, Jing; Beferull-Lozano, Baltasar; Yan, Shijun & Khan, Shalman (2024). Haptic Teleoperation Framework for Learning Task Space Fine Manipulation Skills. IEEE Conference on Industrial Electronics and Applications. doi: 10.1109/ICIEA61579.2024.10665270. Fulltekst i vitenarkiv Vis sammendrag

  This paper introduces a framework for teaching robot fine-manipulation skills by combining haptic teleoperation with Probabilistic Movement Primitives (ProMPs). Addressing the challenges of executing precise tasks such as snap-joint manipulation, our framework utilises ROS2 alongside a cartesian compliance controller for task space operation. This setup facilitates the intuitive guidance of robots by human operators, capturing the detailed movements critical for fine-manipulation tasks. Through experiments with the UR5e 6-DOF collaborative robot arm and a 3DSystems Touch haptic device, we illustrate the accurate representation and replication of the human demonstrated tasks.

• Vinod, Dinsha & Zhou, Jing (2024). An event-triggered MPC scheme for discrete pLPV systems: Application in auto-scaling of compute nodes in a fog cluster. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. s. 1–6. doi: https:/ieeexplore.ieee.org/document/10906025. Fulltekst i vitenarkiv Vis sammendrag

  This paper proposes a control-theoretic approach for discrete polytopic linear parameter varying (pLPV) systems with time-varying characteristics. The approach employs event-triggered model predictive control (ET-MPC) to uphold the performance while minimising the frequency of controller updates. Initially, a model predictive control (MPC) is designed to predict the system’s dynamics in the future. An event-triggering mechanism is also incorporated in the design, which uses a parameter-varying Lyapunov function to lessen the burden of online optimization. The design of an ET-MPC is based on the global stability and feasibility of the pLPV system. Finally, the control-theoretic approach is experimentally validated on a distributed fog computing platform to enable the auto-scaling of compute nodes for mobile robot vision applications.

• Nandam, Sreevidya; Nathala, Sai Sree; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Classification of Anomalies in Industrial Machines utilizing Machine Sounds and Deep Learning, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665175. Fulltekst i vitenarkiv
• Saji, Sidharth; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Underwater Debris Detection using Visual Images and YOLOv8n for Marine Pollution Monitoring, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10664718. Fulltekst i vitenarkiv
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Vessel Type Classification utilizing Underwater Acoustic Data and Deep Learning, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665252. Fulltekst i vitenarkiv
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Vis alle 8 forfattere av denne artikkelen] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging, 2024 IEEE 19th Conference on Industrial Electronics and Applications - ICIEA. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350360868. doi: 10.1109/ICIEA61579.2024.10665263. Fulltekst i vitenarkiv
• Landsverk, Ronny; Zhou, Jing & Hagen, Daniel (2024). Antiswing Control and Trajectory Planning for Offshore Cranes: Design and Experiments. MIC Journal: Modeling, Identification and Control. ISSN 0332-7353. 45(4), s. 115–126. doi: 10.4173/mic.2024.4.1. Fulltekst i vitenarkiv
• Li, Xiaolei; Wen, Changyun; Wang, Jiange & Zhou, Jing (2024). Adaptive Coordination of Networked Lagrangian Systems with Nonsmooth Actuator Nonlinearities. IEEE Transactions on Automatic Control. ISSN 0018-9286. doi: 10.1109/TAC.2024.3471860. Fulltekst i vitenarkiv
• Mahipathi, Ashoka Chakravarthi; Bethi, Pardhasaradhi; Pathipati, Srihari; D’Souza, John; Jena, Paramananda & Zhou, Jing [Vis alle 7 forfattere av denne artikkelen] (2024). Communication-aided Target State Estimation in a Cooperative Radar-Communication System. IEEE Transactions on Radar Systems. 2, s. 832–848. doi: 10.1109/TRS.2024.3452869. Fulltekst i vitenarkiv
• Vinod, Dinsha & Zhou, Jing (2024). Event-Triggered and Self-Triggered Control for Discrete Polytopic Linear Parameter-Varying Systems with Exogenous Inputs. IEEE Xplore Digital Library. doi: 10.1109/ISIE54533.2024.10595715. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a control-theoretic approach for discrete polytopic linear parameter varying (PLPV) systems using event-triggered control (ETC) and self-triggered control (STC) strategies. The approaches guarantee the desired performance while reducing the frequency of controller updates. First, the paper presents an ETC for discrete PLPV systems with exogenous input using a parameter-varying Lyapunov function. The gains of ETC and disturbance attenuation are solved using multi-objective linear matrix inequalities (LMIs). Moreover, to address the issue of periodic sampling and the additional hardware required in ETC, this paper proposes a control-theoretic approach using STC for a discrete PLPV system. Finally, the developed approaches are experimentally validated on a distributed fog computing platform for auto-scaling the compute nodes for mobile robot vision applications.

• Vinod, Dinsha; Zhou, Jing; Beferull-Lozano, Baltasar & Cenkeramaddi, Linga Reddy (2024). Event-Triggered Control for a Fog Computing Platform with AI-based Inference. IEEE Xplore Digital Library. doi: 10.1109/ICIT58233.2024.10540954. Fulltekst i vitenarkiv Vis sammendrag

  This paper addresses the control-theoretic approach of an autonomous fog computing platform to process the of-ftoaded mobile robot vision data for warehouse applications. The approach entails polytopic linear parameter varying (PLPV) modelling of the fog platform and the design of an event-triggered control to process the vision data within a specified time frame called service time, by auto-scaling the computing nodes. The PLPV model is developed using dynamic mobile robot vision data with image resolution and frame rate as parameters. The developed theory is experimentally validated on a mobile robot that navigates in a warehouse application environment, and the results are presented to show the efficacy of the proposed controller in meeting the service time requirements.

• Schlanbusch, Siri Marte & Zhou, Jing (2024). Adaptive Super-Twisting Sliding Mode Control of Uncertain Underactuated Cranes. IEEE Xplore Digital Library. doi: 10.1109/ICIT58233.2024.10540715. Fulltekst i vitenarkiv
• Yu, Mengze; Wang, Wei; Huang, Jiangshuai; Wen, Changyun & Zhou, Jing (2024). Decentralized Adaptive Secure Control of Uncertain Nonlinear Time-Varying Interconnected Systems Against Sensor and Actuator Attacks. IEEE Transactions on Cybernetics. ISSN 2168-2267. 54(9), s. 5012–5025. doi: 10.1109/TCYB.2024.3373198. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2024). Adaptive predictor-based control for a helicopter system with input delays: Design and experiments. Journal of Automation and Intelligence (JAI). 3(1), s. 50–56. doi: 10.1016/j.jai.2024.02.001. Fulltekst i vitenarkiv
• RIPOLL, ÁLVARO ÚBEDA; Jiang, Zhiyu; Verma, Amrit Shankar & Zhou, Jing (2023). Active damage mitigation of the blade leading edge erosion for a wind turbine during rainfall events. IOP Conference Series: Materials Science and Engineering. ISSN 1757-8981. 1294(012001). doi: 10.1088/1757-899X/1294/1/012001. Fulltekst i vitenarkiv Vis sammendrag

  Leading edge erosion (LEE) of wind turbine blades is a major concern for the wind turbine industry. LEE leads to increased blade surface roughness and incurs significant repair and maintenance costs. To address this issue, the present paper proposes an active LEE mitigation method by operating wind turbines in an erosion-safe mode with reduced rotor speed during precipitation. The proposed method involves first numerically modelling the NREL 5-MW reference wind turbine in openFAST. Then, during a regular operation in the presence of rainfall, the erosion-safe mode is designed, implemented and initiated with a modified blade pitch controller in Matlab-Simulink. To evaluate the effectiveness of this mode, the long-term rainfall distribution of the De Kooy site in the Netherlands is examined. The analysis considers a comparison of power output, erosion lifetime, and levelized cost of energy (LCOE) with and without the proposed safe modes. The study shows that the erosion-safe mode can significantly extend the erosion lifetime at the cost of reduced power generation. The controller settings affect the wind turbine performance, and a reduced rotor speed of 11 revolutions per minute is recommended considering the LCOE (308.64€/MWh). While the proposed mitigation method shows promise, it requires further refinement for real-life implementation in wind turbines.

• Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar (2023). Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON51785.2023.10312543. Fulltekst i vitenarkiv Vis sammendrag

  While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of MultiAgent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.

• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive Control of an Uncertain 2-DOF Helicopter System with Input Delays. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, we consider a 2-degrees-of-freedom (DOF) helicopter system subject to input delays and uncertain system parameters. To address this challenge in control design, we develop an adaptive predictor-feedback control law. The control law is designed to compensate for a known delay while considering the system uncertainty. Stability of the closed-loop system is established, where tracking is achieved. We demonstrate the effectiveness of our proposed control approach through simulations of the helicopter system, where the input delays are compensated in the control-loop.

• Sveen, Emil Mühlbradt & Zhou, Jing (2023). Adaptive Learning Based Motor Control of an Unknown Robot Manipulator. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. Fulltekst i vitenarkiv Vis sammendrag

  Adaptive dynamic programming, optimal control, robot manipulator, unknown dynamics, noise Abstract—This paper focuses on motor joint control using learning based Adaptive Dynamic Programming (ADP). The dynamics of a 2-link robot manipulator is presented. The system is regarded as a linear system and the optimal control is developed using linear quadratic regulator (LQR) and ADP methods. An off-line, off-policy controller iteration approach is used to find two optimal controllers, one for each motor joint of the robot. The optimal controller is developed to control the joint position without knowing the dynamic model. Simulation results show that two independent optimal linear control policies are learned simultaneously under a common exploration strategy, and the efficiency of ADP is verified.

• Li, Zifei; Han, Zhen; Wang, Wei & Zhou, Jing (2023). Observer-Based Adaptive Attack Reconstruction for a Class of Uncertain Systems. I IEEE, . (Red.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350331820. doi: 10.1109/IECON51785.2023.10312718. Fulltekst i vitenarkiv
• Landsverk, Ronny; Zhou, Jing & Hagen, Daniel (2023). Antiswing Control and Trajectory Planning for Offshore Cranes. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON51785.2023.10312101. Fulltekst i vitenarkiv Vis sammendrag

  Safe handling of heavy payloads in an offshore environment requires careful crane maneuvering to avoid collision with obstacles and other equipment. The residual swing from a hanging payload can ultimately lead to danger and high cost failures if not properly dealt with. This paper investigates an openloop control method for eliminating the payload swing for hanging loads on offshore knuckle-boom cranes. A trajectory tracking method is designed for payload swing suppression for open-loop control and based on the iterative learning algorithm. It is shown that the proposed anti-swing control method guarantees asymptotic convergence of the swing, the angular velocity, and the angular acceleration of the payload using Lyapunov techniques. The simulation results show the superior performance of the proposed anti-swing control method.

• Nama, Ajay Nagendra; Saad, Leila Ben; Beferull-Lozano, Baltasar & Zhou, Jing (2023). Neighborhood Graph Filters Based Graph Convolutional Neural Networks for Multi-Agent Deep Reinforcement Learning. I IEEE, . (Red.), IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350331820. doi: 10.1109/IECON51785.2023.10311938. Fulltekst i vitenarkiv Vis sammendrag

  Multi-agent deep reinforcement learning (MADRL), where a group of agents inside multi-agent systems cooperate to achieve a common goal, has been shown useful in many applications such as collaborative robots, autonomous driving or video games involving teams. In this paper, we propose two multi-agent deep reinforcement learning (MADRL) frameworks for value function factorization built by using Graph Convolutional Neural Networks (GCNN) based on neighborhood graph filters (NGFs). These MADRL frameworks are based on the paradigm of centralized training with decentralized execution (CTDE). In this work, we show that the superior stability of the NGFs as compared to standard graph filters leads also to superior performance for the MADRL algorithms. In the first MADRL framework, the NGF-based GCNN is used to predict the local q-value at each of the agents, while in the other one, the NGF-based GCNN is used to mix the local q-values to generate a global Q-value. We have compared the performance of NGF-based GCNNs over state-of-the-art graph neural networks for value function factorization in the MADRL framework for the StarCraft II and Coalition Structure Generation problems. The results show that the proposed MARL frameworks outperform the existing state-of-art architectures.

• Sveen, Emil Mühlbradt & Zhou, Jing (2023). Applications of Linear Adaptive Dynamic Programming (ADP) to a Non-linear Four-bar Mechanism. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. s. 177–182. doi: 10.1109/ICCMA59762.2023.10374822. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive quantized control of uncertain nonlinear rigid body systems. Systems & control letters (Print). ISSN 0167-6911. 175. doi: 10.1016/j.sysconle.2023.105513. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the attitude tracking control problem for uncertain nonlinear rigid body systems, where both inputs and states are quantized. It is common in networked control systems that sensor and control signals are quantized before they are transmitted via a communication network. An adaptive backstepping control algorithm is developed for a class of uncertain multiple-input multiple-output (MIMO) systems where a class of sector bounded quantizers is considered. It is shown that all the closed-loop signals are ensured uniformly bounded and tracking is achieved. Further, the tracking errors are shown to converge towards a compact set containing the origin and the set can be made small by the choice of the quantization parameters and the control parameters. For illustration of the proposed control scheme, experiments were conducted on a 2 degrees-of-freedom (DOF) helicopter system.

• Kumar, Ravi; Dale, Jørgen; Singh, Jayant & Zhou, Jing (2023). Design and Development of an Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. s. 233–238. doi: 10.1109/ICCMA59762.2023.10374949. Fulltekst i vitenarkiv
• Zhou, Jing & Schlanbusch, Siri Marte (2022). Adaptive Quantized Control of Offshore Underactuated Cranes with Uncertainty. IEEE International Conference on Control and Automation. ISSN 1948-3449. s. 297–302. doi: 10.1109/ICCA54724.2022.9831937. Fulltekst i vitenarkiv Vis sammendrag

  The anti-swing control of offshore cranes presents much more challenges. Most existing controllers for offshore cranes are designed based on linearized dynamics and require the accurate values of the plant parameters. In this paper, an adaptive sliding mode control scheme is investigated for a nonlinear underactuated crane system with unmodeled dynamics. The proposed control method can ensure asymptotic stability and does not need linearization of the complicated nonlinear dynamic equations during controller design and stability analysis. To reduce the communication burden in a network, a uniform quantizer is introduced in the input communication channel. A quantized adaptive sliding mode control scheme is further developed for the underactuated cranes to compensate for the effects of input quantization and uncertain parameters. The proposed controller together with the quantizer ensures the asymptotic stability of the closed-loop system in the sense of signal boundedness and zero stabilization error. Numerical simulations are conducted to illustrate the effectiveness of proposed schemes.

• Singh, Jayant; Zhou, Jing; Beferull-Lozano, Baltasar & Tyapin, Ilya (2022). Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9969046. Fulltekst i vitenarkiv Vis sammendrag

  Multi-Agent Reinforcement Learning (MARL) algorithms based on the Centralised Training Decentralised Execution (CTDE) approach have seen a great deal of interest in recent years. Most of the recent works focus on a specific class of environments built on the StartCraft Multi-Agent Challenge (SMAC) environment suite. However, experiments with the PettingZoo multi-particle environments show poor performance in an interesting subset of tasks. In this paper, the nature of these environments and the reward structures are analyzed. It shows that poor performance in these tasks is not due to a lack of representation power in the individual Q function or mixing functions, but rather a result of convergence to a suboptimal equilibrium of dual channel rewards and the issue of agent-reward decoupling that can be a common problem for many MARL environments. We present reward curriculum-based versions of QMIX and VDN as a solution to these problems and compare their results with the standard algorithms. The results show a clear performance gain in terms of a common cumulative reward metric.

• Schlanbusch, Siri Marte; Aamo, Ole Morten & Zhou, Jing (2022). Attitude Control of a 2-DOF Helicopter System with Input Quantization and Delay. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON49645.2022.9968994. Fulltekst i vitenarkiv Vis sammendrag

  In this paper the attitude tracking control problem of a 2 degrees-of-freedom helicopter system with network induced constraints is studied. A predictor feedback control law is developed to compensate a known delay in the communication, where the inputs are quantized before transmitted over the network. Stability of the closed-loop system is established, where tracking is achieved with bounded tracking errors due to the network issues. The developed predictor-based controller is experimentally tested on the helicopter system, where we demonstrate that tracking is achieved in presence of both input delay and quantization.

• Borge, Jørgen; Wad, Martin Sauar; Økter, Martin Bjaadal; Ottestad, Morten; Rudolfsen, Morten H. & Zhou, Jing (2022). Design and Implementation of Mechatronics Home Lab for Undergraduate Mechatronics Teaching, 2022 IEEE 17th International Conference on Control & Automation (ICCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665495721. s. 291–296. doi: 10.1109/ICCA54724.2022.9831876. Fulltekst i vitenarkiv Vis sammendrag

  The field of mechatronics is a multidisciplinary field of engineering, where the combination of physical components and theory from several engineering fields is applied to build complex machines. Mechatronics education is an active learning process through practical laboratory exercises and problem-based learning. This paper presents the design and implementation of a mechatronics home lab to support undergraduate mechatronics teaching. The purpose is to support theoretical teaching in mechatronics with a low-cost, 3D-printable platform where the students can experiment and practice instrumentation and control theory with a practical problem-based approach. Five projects were introduced for experiment implementation of the developed home lab. Throughout these experiments, it is intended to facilitate the understanding of theories and concepts in mechatronics, and enhance the ability to design and implementation of experiments, the collection and analysis of data, and the conducting of simulation in MATLAB.

• Yakkati, Rakesh Reddy; Pardhasaradhi, Bethi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification, 2022 IEEE International Symposium on Smart Electronic Systems. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350399226. s. 532–535. doi: 10.1109/iSES54909.2022.00116. Fulltekst i vitenarkiv Vis sammendrag

  The global navigation satellite system (GNSS) provides accurate position, velocity, and time data all over the world. However, GNSS are susceptible to multipath effects in suburban, urban, and indoor environments. Furthermore, it is vulnerable to intentional interference such as jamming and spoofing, which can result in either no or false position estimates. This study focuses on classifying received GNSS signals in a multi-correlation GNSS receiver as interference-free, multipath, jamming, or spoofing. To classify the GNSS signal, the average power and distortion correlation features are extracted from the multi-correlation output. Various machine learning algorithms such as neural networks, support vector machines, nearest neighbors, kernel approxi-mation, decision trees, discriminant analysis, naive Bayes, and ensemble classifiers are investigated and quantified using test accuracy and confusion matrix.

• Huang, Jiangshuai; Wang, Wei & Zhou, Jing (2021). Adaptive Control Design for Underactuated Cranes with Guaranteed Transient Performance: Theoretical Design and Experimental Verification. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 69(3), s. 2822–2832. doi: 10.1109/TIE.2021.3065835. Fulltekst i vitenarkiv
• Wang, Wei; Zhou, Jing; Wen, Changyun & Long, Jiang (2021). Adaptive Backstepping Control of Uncertain Nonlinear Systems with Input and State Quantization. IEEE Transactions on Automatic Control. ISSN 0018-9286. 67(12), s. 6754–6761. doi: 10.1109/TAC.2021.3131958. Fulltekst i vitenarkiv Vis sammendrag

  Although it is common in network control systems that the sensor and control signals are transmitted via a common communication network, no result is available in investigating the stabilization problem for uncertain nonlinear systems with both input and state quantization. The issue is solved in this article, by presenting an adaptive backstepping based control algorithm for the systems with sector bounded input/state quantizers. In addition to overcome the difficulty to proceed recursive design of virtual controls with quantized states, the relation between the input signal and error state need be well established to handle the effects due to input quantization. It is shown that all closed-loop signals are ensured uniformly bounded and all states will converge to a compact set. Experimental results are provided to validate the effectiveness of the proposed control scheme.

• Schlanbusch, Siri Marte; Zhou, Jing & Schlanbusch, Rune (2021). Adaptive Attitude Control of a Rigid Body with Input and Output Quantization. IEEE transactions on industrial electronics (1982. Print). ISSN 0278-0046. 69(8), s. 8296–8305. doi: 10.1109/TIE.2021.3105999. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, the adaptive attitude tracking the problem of a rigid body is investigated where the input and output are transmitted via a network. To reduce the communication burden in a network, a quantizer is introduced in both uplink and downlink communication channels. An adaptive backstepping-based control scheme is developed for a class of multiple-input and multiple-output (MIMO) rigid body systems. The proposed control algorithm can overcome the difficulty to proceed with the recursive design of virtual controls with quantized output vector and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector output quantization and input quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the effectiveness of the proposed control scheme.

• Yu, Mengze; Wang, Wei; Zhou, Jing & Tong, Yongxin (2021). Adaptive Backstepping based Secure Control for P-normal Form of Second-Order Nonlinear Systems Against Deception Attacks. IEEE Conference on Decision and Control. Proceedings. ISSN 0743-1546. s. 1802–1807. doi: 10.1109/CDC45484.2021.9683733. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2021). Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization. I Plapper, Peter (Red.), 2021 IEEE The 9th International Conference on Control, Mechatronics and Automation. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728137865. doi: 10.1109/ICCMA54375.2021.9646184. Fulltekst i vitenarkiv Vis sammendrag

  This paper studies the attitude tracking control for an uncertain 2-degrees of freedom helicopter system where the inputs and the states are quantized. An adaptive backstepping based control scheme is proposed to handle the effect of quantization for tracking of reference angles for pitch and yaw. All closed-loop signals are ensured uniformly bounded and the tracking errors will converge to a compact set containing the origin. Experiments on the helicopter system illustrate the proposed control scheme.

• Wang, Wei; Long, Jiang; Zhou, Jing; Huang, Jiangshuai & Wen, Changyun (2021). Adaptive backstepping based consensus tracking of uncertain nonlinear systems with event-triggered communication. Automatica. ISSN 0005-1098. 133. doi: 10.1016/j.automatica.2021.109841. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the consensus tracking problem for a class of uncertain high-order nonlinear systems with parametric uncertainties and event-triggered communication. Under a directed communication condition, a totally distributed adaptive backstepping based control scheme is presented. Specifically, a decentralized triggering condition is adopted in this paper such that continuous monitoring of neighboring states, as required in some existing results, can be avoided. Besides, to handle the non-differentiability problem of virtual controllers, which arises from the utilization of neighboring states collected only at the triggering instants, the virtual controllers in each recursive step are firstly designed with continuous communication. Then, the partial derivatives of these designed virtual controllers are adopted to construct distributed adaptive consensus controllers for the event based communication case. It is shown that with the presented distributed adaptive consensus control scheme and even-triggered communication mechanism, all the closed-loop signals are uniformly bounded and the output consensus tracking errors will converge to a compact set. Besides, the tracking performance in the mean square sense can be improved by appropriately adjusting design parameters.

• Schlanbusch, Siri Marte; Zhou, Jing & Schlanbusch, Rune (2021). Adaptive Backstepping Attitude Control of a Rigid Body with State Quantization, Proceedings of 60th IEEE Conference on Decision and Control. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728113982. s. 372–377. doi: 10.1109/CDC45484.2021.9683579. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, the attitude tracking control problem of a rigid body is investigated where the states are quantized. An adaptive backstepping based control scheme is developed and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector state quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the proposed control scheme.

• Landsverk, Ronny; Zhou, Jing; Hovland, Geir & Zhang, Houxiang (2020). Modeling of Offshore Crane and Marine Craft in Wave Motion. IEEE Conference on Industrial Electronics and Applications. s. 1463–1470. doi: 10.1109/ICIEA48937.2020.9248110. Fulltekst i vitenarkiv Vis sammendrag

  Safe handling of heavy payloads in an offshore environment requires careful planning and depends on the interaction between a crane and a vessel. This paper investigates the coupled dynamics between a multipurpose crane with payload, and an offshore carrying vessel. A classical multi-body model is derived using holonomic constraints and Newton-Euler kinetics. The resulting index-3 system of differential-algebraic equation (DAE) is transformed into an index-1 system and solved using commonly used numerical ode solvers. Numerical simulations are carried out to show that the proposed models behave in a physically realistic manner.

• Schlanbusch, Siri Marte & Zhou, Jing (2020). Adaptive Backstepping Control of a 2-DOF Helicopter System with Uniform Quantized Inputs. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON). ISSN 1553-572X. doi: 10.1109/IECON43393.2020.9254497. Fulltekst i vitenarkiv Vis sammendrag

  This paper proposes a new adaptive controller for a 2-Degree of Freedom (DOF) helicopter system in the presence of input quantization. The inputs are quantized by uniform quantizers. A nonlinear mathematical model is derived for the 2-DOF helicopter system based on Euler-Lagrange equations, where the system parameters and the control coefficients are uncertain. A new adaptive control algorithm is developed by using backstepping technique to track the pitch and yaw position references independently. Only quantized input signals are used in the system which reduces communication rate and cost. It is shown that not only the ultimate stability is guaranteed by the proposed controller, but also the designers can tune the design parameters in an explicit way to obtain the required closed loop behavior. Experiments are carried out on the Quanser helicopter system to validate the effectiveness, robustness and control capability of the proposed scheme.

• Zhuang, Wenxiu; Zhou, Jing; Liu, Zhitao & Su, Hongye (2020). Event-Triggered Adaptive Control for a Class of Nonlinear Systems with Unknown Time-Varying Parameters. IEEE Conference on Industrial Electronics and Applications. s. 1324–1329. doi: 10.1109/ICIEA48937.2020.9248250. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, we investigate the event-triggered output tracking problem for a class of nonlinear systems with unknown time-varying parameters. To reduce the communication burden, an event-triggered adaptive control method is proposed by introducing hyperbolic tangent functions in the controller to compensate for the effects of the time-varying parameters. New estimation laws are developed to estimate the bounds of time-varying parameters, where no any prior information about the bounds of unknown time-varying parameters is required. An additional term is introduced in the parameter estimation law to compensate for the effects of unknown time-varying parameters. The proposed control scheme can effectively reduce the communication burden while maintain global stability of the closed-loop system, which means that all the signals are bounded. And the tracking error converges towards an adjustable set, which can be exactly expressed with the user-defined parameters. Simulation results are given to show the performance of the proposed method.

• Huang, Jiangshuai; Wang, Wei; Wen, Changyun; Zhou, Jing & Li, Guoqi (2020). Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems: a unified approach. Automatica. ISSN 0005-1098. 118. doi: 10.1016/j.automatica.2020.109021. Fulltekst i vitenarkiv
• Xiangyang, Xu; Xiang, Li; Hui, Zhang & Zhou, Jing (2020). Event-Triggered Robust Control of an Integrated Motor-Gearbox Powertrain System for a Connected Vehicle under CAN and DOS-Induced Delays. SAE technical paper series. ISSN 0148-7191. 2020(February). doi: 10.4271/2020-01-5016. Fulltekst i vitenarkiv
• Zhou, Jing (2019). Adaptive Tracking Control of Nonlinear Time-Varying Systems with Unknown Control Coefficients and Unknown Time-Varying Parameters. I al., N.N. et (Red.), Proceedings of 58th IEEE Conference on Decision and Control (CDC 2019). IEEE Press. ISSN 9781728113975. s. 1025–1030. doi: 10.1109/CDC40024.2019.9029616. Fulltekst i vitenarkiv
• Wang, Wei; Wen, Changyun; Huang, Jiangshuai & Zhou, Jing (2019). Adaptive consensus of uncertain nonlinear systems with event triggered communication and intermittent actuator faults. Automatica. ISSN 0005-1098. 111, s. 1–11. doi: 10.1016/j.automatica.2019.108667. Fulltekst i vitenarkiv
• Zhou, Jing; Wen, Chang-Yun; Wang, Wei & Yang, Fan (2019). Adaptive Backstepping Control of Nonlinear Uncertain Systems With Quantized States. IEEE Transactions on Automatic Control. ISSN 0018-9286. 64(11), s. 4756–4763. doi: 10.1109/TAC.2019.2906931. Fulltekst i vitenarkiv
• Zhuang, Wenxiu; Wen, Changyun; Zhou, Jing; Liu, Zhitao & Su, Hongye (2019). Event-triggered robust adaptive control for discrete time uncertain systems with unmodelled dynamics and disturbances. IET Control Theory & Applications. ISSN 1751-8644. 13(18), s. 3124–3131. doi: 10.1049/iet-cta.2019.0308. Fulltekst i vitenarkiv
• Long, Jiang; Wang, Wei; Huang, Jiangshuai; Zhou, Jing & Liu, Kexin (2019). Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Communication Topology. IEEE Transactions on Cybernetics. ISSN 2168-2267. 51(8), s. 4050–4061. doi: 10.1109/TCYB.2019.2940284. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2019). Adaptive Backstepping Control of a 2-DOF Helicopter. I Herder, J. & HosseinNia, Hassan (Red.), Proceedings, 2019 IEEE 7th International Conference on Control, Mechatronics and Automation. IEEE Press. ISSN 9781728137865. s. 210–215. doi: 10.1109/ICCMA46720.2019.8988761. Fulltekst i vitenarkiv
• Fjetland, Andreas Kvalbein; Zhou, Jing; Abeyrathna, Kuruge Darshana & Gravdal, Jan Einar (2019). Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning. IEEE Conference on Industrial Electronics and Applications. doi: 10.1109/ICIEA.2019.8833850. Fulltekst i vitenarkiv Vis sammendrag

  An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up on, and quantifying, subtle patterns in time series given enough data. In this paper, a new model is developed using Long Short-Term Memory (LSTM), a Recurrent Deep Neural Network, for kick detection and influx size estimation during drilling operations. The proposed detection methodology is based on simulated drilling dataand involves detecting and quantifying the influx of fluids between fractured formations and the well bore. The results show that the proposed methods are effective both to detect and estimate the influx size during drilling operations, so that corrective actions can be taken before any major problem occurs.

Se alle arbeider i NVA

• Zhou, Jing (2024). 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798350360868. 2000 s. Fulltekst i vitenarkiv
• Zhou, Jing (2024). IEEE International Symposium on Industrial Electronics (ISIE). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798350394085. 1000 s. Fulltekst i vitenarkiv
• Zhou, Jing (2024). IEEE International Conference on Industrial Technology (ICIT). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798350340266. 1800 s. Fulltekst i vitenarkiv
• Singh, Jayant; Zhou, Jing & Beferull-Lozano, Baltasar (2023). Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy Adaptation. Fulltekst i vitenarkiv
• Zhou, Jing (2023). 2023 The 11th International Conference on Control, Mechatronics and Automation (ICCMA 2023). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9781665490481. Fulltekst i vitenarkiv
• Zhou, Jing (2023). 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798350315684. 497 s. Fulltekst i vitenarkiv
• Zhou, Jing; Xing, Lantao & Wen, Changyun (2021). Adaptive Control of Dynamic Systems with Uncertainty and Quantization. CRC Press. ISBN 9781032009810. 249 s. doi: 10.1201/9781003176626. Fulltekst i vitenarkiv
• Zhou, Jing (2020). 15th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers (IEEE). ISBN 9781728151687. 200 s. Fulltekst i vitenarkiv

Se alle arbeider i NVA

• Wathne, Joakim Holtskog; Hansen, Michael Rygaard & Zhou, Jing (2025). Benefits of Secondary Control over Primary Control for AHC Winches. Fulltekst i vitenarkiv
• Zhou, Jing (2025). Data-driven control and applications. doi: https:/irce.org/2025.html. Fulltekst i vitenarkiv
• Zhou, Jing (2025). Adaptive Control with Quantization. doi: https:/ecc25.euca-ecc.org/. Fulltekst i vitenarkiv Vis sammendrag

  In control engineering, information and sensing technologies are essential components of real-time measurement systems, providing variable resolution quantitative measurements in control systems, such as in electric power grid systems, intelligent transportation systems, vehicle coordination systems, and offshore mechatronics. Typically, these control systems rely on communication channels. An important aspect is to use quantization schemes with sufficient precision while maintaining low communication rates.This talk explores the challenges and advancements in quantized control, focusing on the theoretical foundations of stability analysis and the interplay between quantization and control system design. Specifically, it addresses the adaptive control of dynamic systems in the presence of uncertainties, nonlinear dynamics, and quantization. The discussion highlights how adaptive design and Lyapunov-based analysis can be applied systematically to mitigate, offering a unified and systematic framework for tackling these challenges.

• Zhou, Jing (2025). Data-Driven Adaptive and Quantized Control - Theory and Applications. doi: https:/ieeexplore.ieee.org/xpl/conhome/1835066/all-proceedings. Fulltekst i vitenarkiv
• Ellore, Ram Chandra Kamavaram; Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2025). A Lightweight CNN for Real-Time Domestic Animal Sound Classification on IoT Devices Using Time-Frequency Imaging. Fulltekst i vitenarkiv
• Sirisilla, Sacharitha; Bethi, Pardhasaradhi; Zhou, Jing; Jha, Ajit & Cenkeramaddi, Linga Reddy (2025). Lightweight 2D CNN Fusion for Human Activity Recognition Using Multi-Transceiver CSI Data on Edge Devices. Fulltekst i vitenarkiv
• Saji, Sidharth; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Underwater Debris Detection using Visual Images and YOLOv8n for Marine Pollution Monitoring. Fulltekst i vitenarkiv
• Nandam, Sreevidya; Nathala, Sai Sree; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Classification of Anomalies in Industrial Machines utilizing Machine Sounds and Deep Learning. Fulltekst i vitenarkiv
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Dayal, Aveen; Manikandan, M. Sabarimalai; Zhou, Jing & Cenkeramaddi, Linga Reddy (2024). Vessel Type Classification utilizing Underwater Acoustic Data and Deep Learning. Fulltekst i vitenarkiv
• Nathala, Sai Sree; Yakkati, Rakesh Reddy; Breland, Daniel Skomedal; Yeduri, Sreenivasa Reddy; Manikandan, M. Sabarimalai & Jha, Ajit [Vis alle 8 forfattere av denne artikkelen] (2024). A Deep CNN-based Hand Gestures Recognition using High-Resolution Thermal Imaging. Fulltekst i vitenarkiv
• Zhou, Jing (2023). Adaptive Quantized Control and Applications. Fulltekst i vitenarkiv
• Zhou, Jing (2023). Adaptive Control and Robotics. Fulltekst i vitenarkiv
• Yakkati, Rakesh Reddy; Bethi, Pardhasaradhi; Zhou, Jing & Cenkeramaddi, Linga Reddy (2022). A Machine Learning Based GNSS Signal Classification. Fulltekst i vitenarkiv
• Zhou, Jing (2021). Adaptive control of dynamic systems with quantization. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2020). Adaptive Backstepping Control in the Presence of Quantization: Application to a 2-DOF Helicopter System. Fulltekst i vitenarkiv
• Zhou, Jing (2020). Adaptive Control of Systems with Quantization. Fulltekst i vitenarkiv
• Hatletvedt, John Kenneth; Gjerstad, Kristian & Zhou, Jing (2018). Modelling of a Hydro-Pneumatic System for Heave Compensation. Fulltekst i vitenarkiv
• Gaustad, Eskil Gresen; Knausgård, Kristian Muri & Zhou, Jing (2025). Supervisory Control Design for a SES Vessel. Universitetet i Agder. Fulltekst i vitenarkiv
• Zhou, Jing; Togba, Henrik Hansen & Finnvold, Jakob Underdal (2024). Modeling and Control of Hybrid energy storage system for integrated use in industrial microgrids. Universitetet i Agder. Fulltekst i vitenarkiv
• Zhou, Jing; Skaar, Kevin Hermann & Solheim, Rodion (2024). Scanning and path planning for surface processing industrial tanks. Universitetet i Agder. Fulltekst i vitenarkiv
• Zhou, Jing; Kumar, Ravi; Dale, Jørgen & Singh, Jayant (2023). Design and Development of a low-cost Anthropomorphic Gripper for Service Robotics and Prosthetic Applications. Universitetet i Agder. Fulltekst i vitenarkiv
• Zhou, Jing; RIPOLL, ÁLVARO ÚBEDA & Jiang, Zhiyu (2023). Active control of the blade leading edge erosion for an onshore wind turbine during rainfall events. Universitetet i Agder. Fulltekst i vitenarkiv
• Schlanbusch, Siri Marte & Zhou, Jing (2023). Adaptive Control of Systems with Quantization and Time Delays. Universitetet i Agder. ISSN 9788284271286. Fulltekst i vitenarkiv Vis sammendrag

  This thesis addresses problems relating to tracking control of nonlinear systems in the presence of quantization and time delays. Motivated by the importance in areas such as networked control systems (NCSs) and digital systems, where the use of a communication network in NCS introduces several constraints to the control system, such as the occurrence of quantization and time delays. Quantization and time delays are of both practical and theoretical importance, and the study of systems where these issues arises is thus of great importance. If the system also has parameters that vary or are uncertain, this will make the control problem more complicated. Adaptive control is one tool to handle such system uncertainty. In this thesis, adaptive backstepping control schemes are proposed to handle uncertainties in the system, and to reduce the effects of quantization. Different control problems are considered where quantization is introduced in the control loop, either at the input, the state or both the input and the state. The quantization introduces difficulties in the controller design and stability analysis due to the limited information and nonlinear characteristics, such as discontinuous phenomena. In the thesis, it is analytically shown how the choice of quantization level affects the tracking performance, and how the stability of the closed-loop system equilibrium can be achieved by choosing proper design parameters. In addition, a predictor feedback control scheme is proposed to compensate for a time delay in the system, where the inputs are quantized at the same time. Experiments on a 2-degrees of freedom (DOF) helicopter system demonstrate the different developed control schemes.

• Zhou, Jing & Nilsen, Espen (2022). Robot localization. Universitetet i Agder. Fulltekst i vitenarkiv Vis sammendrag

  This master's thesis presents the application of a robot localization process for a terminal tractor. To determine an accurate robot localization is essential for autonomous operations. This thesis investigates the use of non-visual localization sensors to estimate the position and orientation. There are several different methods for robot localization, but this thesis focuses on the use of sensors such as Global Position System (GPS), Inertial Measurement Unit (IMU), and wheel odometry. The contribution of this thesis is to develop accurate robot localization, which can be used for autonomous vehicles. This has been done by investigating all individual sensors, as well as the overall performance of sensor fusion. This thesis gives an indication of the performance of the individual sensors and also the number of sensors necessary for maintaining acceptable performance. The robot localization software was implemented on the Robot Operating System (ROS) framework using Python, C++, Extensible Markup Language (XML), and Cmake as programming languages. The robot localization process was tested by simulating the vehicle in Gazebo. By simulating the movement of the vehicle and simultaneously collecting measurement data from the sensors, a comparison was made with ground truth values for further analysis. The results of the robot localization from the wheel odometry was not accurate enough. There was a deviation in the measurement data for linear motion trajectory, which affected the overall performance of the robot localization. However, the IMU sensor showed highly accurate performance for the robot localization. The conclusion of the master thesis was that the implementation of the robot localization on the terminal tractor was a success, but the accuracy of the robot localization process during linear motion was not adequate. Based on this, more sensors should be incorporated into the system.

• Zhou, Jing; Vange, Tom Erik & Tyapin, Ilya (2021). Model-free object grasping : Model-free object grasping with a learning-free approach. Universitetet i Agder. Fulltekst i vitenarkiv Vis sammendrag

  The industry standards and capability are constantly advancing and pushing forward to increase data collection, efficiency, profit, and quality as well as decrease downtime, injuries, and hazards as much as possible. In recent years, robot systems have received more attention in the context of a large number of industrial applications, such as automotive manufacturing, additive manufacturing, assembly, quality inspection, and co-packing. The collaboration between multiple robots and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities. With the use of robots and their abilities could efficiency, profit, safety, and quality be further increased, potentially revolutionizing the industry and production. This project was supported in part by DEEPCOBOT Project. DEEPCOBOT, Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems, are a research project funded by IKTPLUSS under Grant 306640/O70 from the Research Council of Norway. The project will investigate the design of a new generation of decentralized data-driven Deep Learning based controllers for multiple coexisting collaborative robots, which interact both between them-selves and with human operators in order to collectively learn from each other’s experiences and perform cooperatively different complex tasks in large-scale industrial environments. This is motivated by the increasing demand of automation in industry, especially the demand of a safer and more efficient collaboration between multiple robots and human operators to integrate the best of human abilities and robotic automation.

• Zhou, Jing; Kumar, Ravi; Radicke, Jan-Philip; Eidhammer, Eirik & Tyapin, Ilya (2021). Multi-Robot Cell Operation using Robot Operating System. Universitetet i Agder. Fulltekst i vitenarkiv
• Zhou, Jing; Ims, Benjamin Årøy; Enehaug, Bjørn; Skår, Eirik Magnus & Tyapin, Ilya (2021). HUMAN-COBOT SEQUENTIAL COOPERATION. Universitetet i Agder. Fulltekst i vitenarkiv
• Boginskis, Ilja; Hmdoun, Hmdoun Abker Ibrahim & Zhou, Jing (2020). Modeling, Simulation and Control for Marine Crane Operations. Universitetet i Agder. Fulltekst i vitenarkiv Vis sammendrag

  Marine cranes play an important part in offshore operations. They are expected to perform awide range of different tasks, such as handling the load safely offshore. Nowadays, the offshoreoil fields and wind farms are detected and developed, thus more and more offshore platforms arebuilt. The scale and capacity of crane vessels are developing rapidly to fulfill the construction needof offshore platforms and offshore wind farms. Heavy crane, placed on-board vessel, is affected byvessel motions induced by environmental forces such as wind, waves and current, and vice versa.Therefore, the investigation of coupled dynamics between heavy crane and vessel, position controlof vessel, and heave compensation is important and needs to be analyzed thoroughly. This masterproject focus on Modeling, Simulation and Control for Marine Crane Operations. The model wasdeveloped in Matlab, Simulink and Simscape Multi-body.It is several ways to developed Control designs for an offshore crane, but in this thesis the controltask only concerns position control of the crane. The main goal is to determine the most suitablecontroller design for this tasks.A trajectory planning scheme is developed that can damp out unexpected payload swing. Craneend-effector is controlled to follow a trajectory planning scheme in x-direction with as small er-ror between desired and measured position as possible. The desired position is a movement from23.15m which is the x-coordinate when the crane arm is fully extended, to 15m.

• Zhou, Jing & Bjørnarå, Christopher (2020). Pipe joint position identification. Universitetet i Agder. Fulltekst i vitenarkiv

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