Publications
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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),
p. 136–144.
doi:
10.1016/j.jai.2025.02.002.
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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.
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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.
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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.
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Hua, Minh Tuan; Sveen, Emil Mühlbradt; Schlanbusch, Siri Marte & Sanfilippo, Filippo
(2024).
Robust-Adaptive Two-Loop Control for Robots with Mixed Rigid-Elastic Joints.
In NN, . (Eds.),
International Conference on Intelligent Robots and Systems (IROS).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798350377705.
p. 11332–11339.
doi:
10.1109/IROS58592.2024.10802178.
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In robotics, while rigid joints are common due to their accuracy and fast response ability, elastic joints are wellknown for their safety when interacting with the environment. To harmonise the advantages of these joint types, robots with mixed rigid-elastic joints can be considered. In this paper, a robust-adaptive two-loop control algorithm is proposed to control this type of hybrid robots when there are uncertainties in system parameters. In the outer loop, a robust control algorithm is proposed to deal with the uncertainties in the parameters of the joint dynamics, together with an adaptive controller for the rigid joints. In the inner loop, another robust control algorithm is proposed to handle the uncertainties in system parameters of the elastic joint’s motor contribution, and a similar adaptive control algorithm is presented to manipulate the elastic joints’ motors. The stability of the system is assured by Lyapunov’s stability theory. Finally, simulations are conducted to verify the proposed control algorithm.
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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.
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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.
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View all works in NVA
Published
Apr. 16, 2024 11:42 AM
