Teaching
- MAS411 Industrial IT
- MAS508 Control theory
- MAS247 Industrial IT
Research
- Performance monitoring
- Control and automation
Publications
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Murtaza, Aitzaz Ahmed; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo
(2026).
Tool wear prediction using multi-domain features and quantised attention embedded CNN.
Measurement: Sensors.
47.
doi:
10.1016/j.measen.2026.102013.
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In precision manufacturing, accurate prediction of tool wear is pivotal for maintaining production quality and efficiency. This paper introduces a novel predictive model that employs a multi-branch convolutional neural network (multi-CNN) enhanced by an attention mechanism and Bayesian optimisation to forecast tool wear with high accuracy. The proposed approach is evaluated using the publicly available dataset, which comprises force and torque measurements acquired from machining processes. By incorporating time-domain (TD), frequency-domain (FD), and discrete wavelet transform (DWT) features, the model comprehensively analyses signals from machining processes, enabling robust predictions. The attention mechanism strategically emphasises significant features, augmenting the model’s predictive capability. Demonstrated results show superior performance with a mean absolute error (MAE) of 2.8, root mean squared error (RMSE) of 3.4, and an R2 score of 0.986, significantly outperforming conventional models. Additionally, post-training quantisation optimises the model for deployment on edge computing devices, maintaining effectiveness while being lightweight, ideal for real-time applications in resource-constrained environments. This study exemplifies how advanced machine learning techniques can improve predictive maintenance in manufacturing which not only enhances operational efficiency but also reduces downtime.
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Sharjil, Muhammad & Aftab, Muhammad Faisal
(2026).
Linear parameter varying based adaptive model predictive control for power down regulation of floating offshore wind turbines in region–3.
Ocean Engineering.
ISSN 0029-8018.
363,
p. 126496–126496.
doi:
10.1016/j.oceaneng.2026.126496.
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Floating offshore wind turbines (FOWTs) enable access to abundant deep-water wind resources and are rapidly gaining traction worldwide as more and more floating wind farms are getting deployed. However, the existing farm-level control strategies often rely on simplified turbine models that overlook the coupled aero-hydro-servo-elastic dynamics of FOWTs. At the wind farm level, power setpoints are allocated using methods such as greedy algorithms, induction control, or wake steering, with the latter two requiring individual turbines to operate in power curtailment mode, i.e. downregulation or derating. This however, can impact platform motions, fatigue loads, and mooring stresses, if plant-wise wind farm control is not optimized. Unlike land-based turbines, FOWTs exhibit unique control challenges such as changes in platform pitch damping sign in region-3, which restrict control bandwidth up to certain wind speeds above rated wind, and impacts power regulation. The systematic design of individual turbine-level controllers for tracking varying power references under these dynamics remains underexplored, particularly with high-fidelity validation. To address this gap, we propose an adaptive model predictive control (AMPC) scheme based on gridded linear parameter-varying (LPV) model, constructed from high-fidelity simulations and scheduled by wind speed and derating level, with gap-metric theory guiding model selection. The control scheme is implemented in MATLAB/Simulink, and tested on the NREL 5 MW OC3 sparbouy in OpenFAST, and results demonstrate improved power tracking and load reduction under varying test conditions compared to down regulation capable gain-scheduled PI control.
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Darowski, Michal; Aftab, Muhammad Faisal; Walker, David; Li, Hongyu & Omlin, Christian Walter Peter
(2025).
Data-driven material removal rate estimation in bonnet polishing process.
IFAC-PapersOnLine.
ISSN 2405-8971.
59(6),
p. 211–216.
doi:
10.1016/j.ifacol.2025.07.147.
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Bonnet polishing is an ultra-precision polishing technique used for manufacturing components utilized in optics, electronics, and Scientific instrumentation, where sub-nanometer accuracy is required. However, the process is not fully deterministic and requires multiple process-metrology iterations. In modern computer numerically controlled (CNC) machines, polishing is performed by moderating the bonnet tool dwell time at each location based on the input parameters and material removal rate (MRR). While the MRR is typically treated as constant once established, it continuously evolves due to the process’s dynamic nature and changing conditions. This variability in MRR impacts the convergence of the polishing process, necessitating repeated surface processing and resulting in increased manufacturing time and cost. In this work, we present a data-driven approach to estimate the amount of material removed during the pre-polishing routine in bonnet polishing. The estimations are based on the force exerted by the bonnet tool on a polished surface along the three dimensions. Measurements were obtained using a bespoke force table with load sensors across three axes, mounted on the Zeeko IRP600 machine table. The results demonstrate the Effectiveness of this data-driven approach for estimating MRR, achieving a mean absolute error of 0.0541 µm and a mean absolute percentage error of 5.89% across the test set.
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Sharjil, Muhammad & Aftab, Muhammad Faisal
(2025).
Design and Analysis of Gain-Scheduled Collective Pitch Control for Down Regulation in Floating Offshore Wind Turbines.
IEEE Conference on Control Technology and Applications (CCTA).
ISSN 2768-0762.
p. 238–243.
doi:
10.1109/ccta53793.2025.11151326.
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As Floating Offshore Wind Turbines (FOWTs) take on a growing share in global renewable energy generation, they will eventuallyj be required to support the grid through ancillary services. This will be achieved through down-regulating (DR) the FOWT by varying the output electrical power through Active Power Control (APC). The FOWT control schemes for power maximization, derived from land-based wind-turbine (LWT) have bandwidth limitations due to the existence of right-half-plane zeros (RHPZs) in Region 3. Conventionally this problem is solved by detuning the land-based controllers but this significantly impacts the power quality. This paper proposes a novel gain-schedule design methodology for Collective Pitch Control (CPC), based on linear model dynamics in Region 3 for DR operation of FOWT. Parallel compensation through feedback of nacelle fore-aft velocity in generator torque is used to address the RHPZs and to achieve control bandwidth similar to LWT counterparts. Two operational strategies (OS) for power command tracking are compared for distributing demanded power, i.e., constant generator torque or angular speed references. System performance is evaluated through both linear and nonlinear analysis in Region 3 considering up to 20% DR. OpenFAST toolbox in MATLAB/SIMULINK is used under varying wind conditions, using RegA and RegD standard power signals by the PJM regional transmission organization for nonlinear analysis. Results demonstrate the proposed solutions' effectiveness in FOWTs for achieving power quality alongside system stability.
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Aftab, Muhammad Faisal; Bounoua, Wahiba; Zafar, Muhammad Hamza & Sanfilippo, Filippo
(2025).
Improved Stiction Detection via Hybrid Residual Embedded Inception Module Networks.
IFAC-PapersOnLine.
ISSN 2405-8971.
59(6),
p. 145–150.
doi:
10.1016/j.ifacol.2025.07.136.
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Zafar, Muhammad Hamza; Langås, Even Falkenberg; Aftab, Muhammad Faisal & Sanfilippo, Filippo
(2024).
Enhanced Intrusion Detection in Robot Operating Systems via Grid Search Based Multi-Head Attention Stacked Convolutional Network.
IEEE International Conference on Automation Science and Engineering.
ISSN 2161-8070.
p. 3880–3885.
doi:
10.1109/CASE59546.2024.10711785.
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Bounoua, Wahiba & Aftab, Muhammad Faisal
(2024).
Multivariate singular spectrum analysis and detrended fluctuation analysis for plant-wide oscillations denoising.
IFAC-PapersOnLine.
ISSN 2405-8971.
58(14),
p. 676–681.
doi:
10.1016/j.ifacol.2024.08.415.
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Oscillations are considered the most important indicator of poorly performing control loops. However, noise and other disturbances conceal these oscillations, thus making the detection task quite difficult. Furthermore, the efficiency of most detection and diagnosis techniques proposed in the literature is reduced considerably in the presence of noise. Therefore, denoising is recommended to make the detection task more straightforward. In this work, the multivariate singular spectrum analysis (MSSA) is employed to denoise the plant-wide oscillatory control loops. This approach stands in contrast to existing methods that typically focus on addressing noise in individual control loops. In order to improve the efficiency of MSSA, detrended fluctuation analysis (DFA) is incorporated to select only the significant components and eliminate the noise to provide a noise-free version of the multivariate data. The effectiveness of the proposed MSSA-DFA method has been verified using a numerical example and real industrial plant data.
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Bounoua, Wahiba & Aftab, Muhammad Faisal
(2024).
Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops.
Journal of Process Control.
ISSN 0959-1524.
138.
doi:
10.1016/j.jprocont.2024.103226.
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The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.
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Sterk-Hansen, Alexander; Saghaug, Bjørn; Hagen, Daniel & Aftab, Muhammad Faisal
(2024).
A ROS 2 and TwinCAT Based Digital Twin Framework for Mechatronics Systems.
International Conference on Control, Mechatronics and Automation.
ISSN 2837-5114.
p. 485–490.
doi:
10.1109/ICCMA59762.2023.10374978.
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This paper presents a digital twin framework as a first step towards developing digital twin-based control systems for autonomous lifting and load-handling operations. The proposed digital twin framework has been divided into three main parts: a) a real-time control and simulation environment using TwinCAT 3 running on a Beckhoff embedded PC with EtherCAT Fieldbus, b) a ROS 2 interface (middleware), and c) a virtual environment using the state-of-the-art game engine software NVIDIA Isaac Sim. The investigated case study demonstrates that the proposed architecture offers scalability and forms a promising baseline for further developing a general-purpose digital twin development platform suitable for developing autonomous control systems for various mechatronics systems and applications.
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Murtaza, Aitzaz Ahmed; Saher, Amina; Zafar, Muhammad Hamza; Moosavi, Syed Kumayl Raza; Aftab, Muhammad Faisal & Sanfilippo, Filippo
(2024).
Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study.
Results in Engineering (RINENG).
24.
doi:
10.1016/j.rineng.2024.102935.
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Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter
(2023).
Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks.
IFAC-PapersOnLine.
ISSN 2405-8971.
56(2),
p. 11687–11692.
doi:
10.1016/j.ifacol.2023.10.523.
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Valve stiction is one of the major causes of poorly performing industrial control loops. Stiction occurs when the static friction exceeds the dynamic friction during a direction change or when the stem is at rest. Recently, machine learning techniques were employed to detect the presence of stiction. These techniques required the use of multiple signals from the control loop in order to extract the key features to distinguish stiction cases from healthy or other malfunctions cases. In this paper, a new image-generating method, named the Poincaré plot, is proposed to feed the convolutional neural network (CNN) that only needs one signal from the control loop. The Poincaré plot is a powerful technique that can reveal the complexity of the process by evaluating the correlation within a single time series. The proposed Poincaré plot-based CNN showed satisfactory results in detecting stiction in real industrial applications as compared to other machine learning techniques present in the literature.
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Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter
(2023).
Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops.
Computers and Chemical Engineering.
ISSN 0098-1354.
172.
doi:
10.1016/j.compchemeng.2023.108173.
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Detrended Fluctuation Analysis (DFA) is a reliable and assumption-free approach for gauging the complexity
of a time series. In this paper, an online oscillations detection paradigm is presented, which integrates the
potential of DFA in detecting abnormal coherent fluctuations with the Empirical Wavelet Transform (EWT)
efficiency in extracting the characteristics of oscillations. However, the standard EWT fails to separate modes
oscillating at close frequencies, resulting in an incorrect decomposition. Furthermore, the lack of an appropriate
stopping criterion frequently results in the signal being over-decomposed into several inconsequential components. Therefore, owing to the capability of DFA to differentiate between fluctuations stemming from noise and
coherent fluctuations arising from genuine oscillations, an Improved EWT (IEWT) is presented to mitigate these
issues and accurately extract only compelling oscillating modes. The proposed DFA-based IEWT framework is
verified on simulated applications and data from real industrial processes, illustrating its effectiveness.
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Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter
(2022).
Controller performance monitoring: A survey of problems and a review of approaches from a data-driven perspective with a focus on oscillations detection and diagnosis.
Industrial & Engineering Chemistry Research.
ISSN 0888-5885.
61(49),
p. 17735–17765.
doi:
10.1021/acs.iecr.2c02785.
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Optimal operations of industrial control systems require rigorous monitoring to ensure safety, increase profitability, and minimize plant maintenance downtime. Thus, controller performance monitoring has been actively pursued by the research community, resulting in increased research publications over the past two decades. The availability of large data sets, the so-called big data era, has led impetus to the data-driven domain of controller performance monitoring. In this paper, a comprehensive review of the data-driven research in the CPM domain is presented. To illustrate the rationale behind the research efforts, a succinct explanation of the faults observed in control loops and their influence on overall controller performance is also presented. The paper then provides the publicly available data repositories adopted by most researchers to assess and compare their performance monitoring techniques realistically. Moreover, a review of the most eminent techniques proposed so far concerning both detection and diagnosis phases is presented.
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Bukhari, Syeda Halima Zahra; Hovd, Morten; Aftab, Muhammad Faisal & Winkler, Jan
(2021).
Design of parallel compensator and stabilizing controller to mitigate non-minimum phase behaviour of the Czochralski process.
IFAC-PapersOnLine.
ISSN 2405-8971.
53(2),
p. 11710–11715.
doi:
10.1016/j.ifacol.2020.12.669.
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This paper addresses the design of a parallel compensator and a stabilizing controller for the simplified crystal growth dynamics of the Czochralski (CZ) process, i.e., the process for the production of monocrystalline silicon ingots of uniform diameter. The diameter control of the produced ingots is achieved by a CCD camera measurement used to sense the radius of the boundary between the base of the growing crystal and the surrounding glowing meniscus — a raised melt surface connecting the crystal ingot with the flatter melt surface. Due to the intrinsic nature of the process, the bright ring radius measurement signal exhibits a non-minimum phase behaviour. A combination of the parallel compensator and a stabilizing controller is designed, such that the former provides for the mitigation of non-minimum phase behaviour, while the latter combined with the formal yields a suitable closed-loop performance.
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Khan, Muhammad Arshad; Aftab, Muhammad Faisal; Ahmed, Ejaz & Youn, Iljoong
(2019).
Robust Differential Steering Control System for an Independent Four Wheel Drive Electric Vehicle.
International Journal of Automotive Technology.
ISSN 1229-9138.
20(1),
p. 87–97.
doi:
10.1007/s12239-019-0008-9.
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2019).
Diagnosis of plant-wide oscillations by combining multivariate empirical mode decomposition and delay vector variance.
Journal of Process Control.
ISSN 0959-1524.
83,
p. 177–186.
doi:
10.1016/j.jprocont.2019.01.001.
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Kölle, Konstanze; Aftab, Muhammad Faisal; Andersson, Leif Erik; Fougner, Anders Lyngvi & Stavdahl, Øyvind
(2019).
Data driven filtering of bowel sounds using multivariate empirical mode decomposition.
Biomedical engineering online.
18(28),
p. 1–20.
doi:
10.1186/s12938-019-0646-1.
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Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of rather sudden bursts. Realistic recordings of abdominal sounds are contaminated with noise and artifacts from which the bowel sounds must be differentiated. Methods The proposed intrinsic mode function-fractal dimension (IMF-FD) filtering utilizes the property of the multivariate empirical mode decomposition (MEMD) to behave as a series of band pass filters. The MEMD decomposes the abdominal signal into its different frequency components. The resulting intrinsic mode functions (IMFs) are modulated in amplitude and frequency where transient sonic events occur. Based on the complexity of the IMFs, measured by their fractal dimension (FD) in sliding windows, the information-carrying IMFs are selected. The filtered signal is formed as the superposition of all selected IMFs. The IMF-FD filter not only enhances the non-linear components of the original signal but also segments them from the rest. Another important aspect of this work is that typical artifacts that occur in the same frequency range as bowel sounds can be subsequently eliminated by heuristic rules. Conclusions The method is tested on a realistic, contaminated data set with promising performance: close to 100% of the manually labeled bowel sounds are identified.
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Khan, Muhammad Arshad; Aftab, Muhammad Faisal; Ahmad, Ejaz & Youn, Iljoong
(2019).
Active steering control system for an independent wheel drive electric vehicle.
International Journal of Vehicle Design.
ISSN 0143-3369.
79(4),
p. 273–291.
doi:
10.1504/IJVD.2019.103597.
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2018).
Improved oscillation detection via noise-assisted data analysis.
Control Engineering Practice.
ISSN 0967-0661.
81,
p. 162–171.
doi:
10.1016/j.conengprac.2018.08.019.
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Oscillation detection is usually a precursor to more advanced performance monitoring steps such as plant wide oscillation detection and root cause detection. Therefore any false or missed detection can have serious implications. Oscillation detection is a challenging problem due to the presence of noise and multiple modes in the plant data. This paper presents an improved and robust automatic oscillation detection algorithm based on noise-assisted data analysis that can handle multiple oscillatory modes in the presence of both coloured and white noise along with non-stationary effects. The dyadic filter bank property of multivariate empirical mode decomposition has been used to accurately detect the oscillations and to calculate the associated characteristics. This work improves upon the existing auto covariance function based methods. The robustness and reliability of the proposed scheme is demonstrated via simulation and industrial case studies.
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2018).
Plant-wide oscillation detection using multivariate empirical mode decomposition.
Computers and Chemical Engineering.
ISSN 0098-1354.
117,
p. 320–330.
doi:
10.1016/j.compchemeng.2018.06.007.
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Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies.
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Andersson, Leif Erik; Scibilia, Francesco; Copland, Luke; Aftab, Muhammad Faisal & Imsland, Lars Struen
(2018).
Analysis of Iceberg Drift Trajectories Using the Multivariate Empirical Mode Decomposition.
ISOPE - International Offshore and Polar Engineering Conference. Proceedings.
ISSN 1098-6189.
2018-June,
p. 1653–1660.
doi:
10.1109/ccta.2017.8062605.
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In this study we show how to detect and extract the tidal and inertial oscillation from iceberg velocity data by using the multivariate empirical model decomposition. Due to the similar frequencies of both oscillations in regions subject to drifting icebergs, this is an extremely challenging filtering problem. The method is tested on two iceberg drift trajectories from the east coast of Canada, one at about 51◦N and one at about 76.5 ◦N. The two icebergs differ in latitudinal location, such that the inertial and tidal oscillations in the first data set have a slightly different frequency and in the other they are approximately the same. For the latter case a second filtering stage has to be included that uses tidal current information from a tidal current model. Finally we show how the multivariate empirical model decomposition can be used to analyze connections between current, wind and iceberg velocities. This information may help to improve either the ocean current and meteorological models or the iceberg drift model.
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2017).
Convergent cross mapping (CCM) based approach for isolating the source of plant-wide disturbances,
2017 IEEE Conference on Control Technology and Applications (CCTA).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781509021826.
p. 1492–1498.
doi:
10.1109/CCTA.2017.8062669.
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Disturbances originating in one control loop of a large industrial plant can propagate far from the source, giving rise to plant-wide oscillations. The underlying interactions among the different control loops make it hard to identify the origin of such large scale disturbances. This paper studies the application of the convergent cross mapping (CCM) based technique to isolate the source of a plant-wide disturbance. The proposed scheme exploits the cause and effect relationships among the affected variables to find the source of disturbance. The states of the causative factors are estimated from the effect variable and the directionality of information flow is established using the correlation between the original and estimated signal. The method is applied to the industrial case study and is shown to be effective in isolating the disturbance origin.
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2017).
Detecting non-linearity induced oscillations via the dyadic filter bank property of multivariate empirical mode decomposition.
Journal of Process Control.
ISSN 0959-1524.
60,
p. 68–81.
doi:
10.1016/j.jprocont.2017.08.005.
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Non-linearity induced oscillations in control loops are characterized by the presence of higher order harmonics. In this paper the dyadic filter bank property of the multivariate empirical mode decomposition (MEMD) is exploited to reveal the harmonic content of the oscillatory signal to indicate the presence of non-linearity. Once the harmonics are identified the extent of non-linearity is evaluated automatically using degree of non-linearity measure (DNL) introduced in our previous work [11]. Although detection of non-linearity via harmonics is an old concept; any automatic method has still not been reported. Moreover, the existing methods suffer from the restrictive assumption of signal stationarity. The proposed method is more robust in identifying the non-linearity induced oscillations and is adaptive and data driven in nature and thus requires no a priori assumption about the underlying process dynamics. The proposed method can also differentiate among the different sources of multiple oscillations, for example combinations of nonlinearity and linear sources or two nonlinear sources. Apart from detecting the non-linearities the proposed method can also contribute in locating the source of non-linearity, thereby reducing the maintenance time to a considerable extent. The robustness and effectiveness of the proposed method is established using industrial case studies and results are compared with existing methods based on higher order statistics [7] and surrogate based methods [8].
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2017).
A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops.
IFAC-PapersOnLine.
ISSN 2405-8971.
50(1),
p. 7975–7980.
doi:
10.1016/j.ifacol.2017.08.992.
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Non-linear time series analysis based methods are a popular choice for industrial control loop data analysis. In this paper a delay vector variance (DVV) based approach is presented to analyze the source of oscillations in an industrial control loop. The method is capable of differentiating between the linear and non-linear causes of oscillations and can also help in isolating the source of non-linearity. The automatic determination of embedding dimensions is augmented with the DVV analysis to make it more robust and reliable. The efficacy of the proposed method is established using simulation as well as industrial case studies.
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Bukhari, Halima Zahra; Aftab, Muhammad Faisal; Winkler, Jan & Hovd, Morten
(2017).
Adaptive Nonlinear Control of the Czochralski Process Via Integration of Second Order Sliding Mode and Iterative Learning Control.
In Mareels, Iven & Tan, Ying (Ed.),
Proceedings of the 2017 Asian Control Conference.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9781509015726.
p. 2732–2737.
doi:
10.1109/ASCC.2017.8287609.
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This paper utilises nonlinear second-order sliding mode (SOSM) combined with an iterative learning control (ILC) scheme for the control of interface dynamics primarily focused on diameter control in the Czochralski (CZ) process. The sliding mode control (SMC) or variable structure control (VSC) is well suited for systems with bounded and unstructured parametric uncertainties. The SOSM is therefore employed to attain robustness in the control despite unknown system dynamics. On the other hand, iterative learning control is incorporated as an intelligent module of the designed controller to better adapt to repetitive operation even in the presence of unknown parameters and bounded disturbances. Since CZ is a batch process that repeats itself every time a new crystal ingot is produced, repeatability renders learnability to the designed controller.
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Hagen, Daniel; Eide, Marcus; Liland, Tollak & Aftab, Muhammad Faisal
(2026).
Mobile Manipulator with Passive Mechanical Coupling for Autonomous Object-Handling.
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Andersson, Leif Erik; Scibilia, Francesco; Copland, Luke; Aftab, Muhammad Faisal & Imsland, Lars Struen
(2018).
Analysis of Iceberg Drift Trajectories Using the Multivariate Empirical Mode Decomposition - presentation.
Full text in Research Archive
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Aftab, Muhammad Faisal; Hovd, Morten & Sivalingam, Selvanathan
(2017).
Convergent cross mapping (CCM) based approach for isolating the source of plant-wide disturbances.
Full text in Research Archive
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Bukhari, Halima Zahra; Aftab, Muhammad Faisal; Hovd, Morten & Winkler, Jan
(2017).
Adaptive Nonlinear Control of the Czochralski Process Via Integration of Second Order Sliding Mode and Iterative Learning Control.
Full text in Research Archive
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Andersson, Leif Erik; Aftab, Muhammad Faisal; Scibilia, Francesco & Imsland, Lars Struen
(2017).
Forecasting using multivariate empirical mode decomposition — Applied to iceberg drift forecast - presentation.
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Published
Apr. 16, 2024 11:23 AM
- Last modified
June 1, 2024 4:12 PM
