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Automation & Control Systems
A. H. Tahoun, M. Arafa
Summary: This paper addresses the leader-follower tracking problem in multi-agent networks with unknown uncertainties. By designing distributed adaptive observers and controllers, the tracking path can be estimated and good tracking performance is achieved.
Article
Mathematics, Interdisciplinary Applications
Hua Zhang
Summary: This paper presents an adaptive controller for MIMO chaotic systems with system uncertainties and unknown control direction. Matrix decomposition theory and Nussbaum-type function are used to handle the unknown control direction, and a proportional integral (PI) law is proposed to update the parameters of the fuzzy system. The stability of the controlled system is strictly proven, and simulation results are provided.
Article
Automation & Control Systems
Wenrui Shi, Mingzhe Hou, Mingrui Hao
Summary: This paper proposes a synthesis of an asymptotic tracking controller for strict-feedback nonlinear systems with unknown control direction. The proposed design procedure combines dynamic surface control (DSC) technique, Nussbaum gain technique (NGT), and fuzzy logic systems (FLSs). The design overcomes the issues of 'differential explosion' and unknown control direction, and achieves asymptotic tracking.
Article
Computer Science, Artificial Intelligence
Dapeng Li, Hong-Gui Han, Jun-Fei Qiao
Summary: This study develops an adaptive neural controller for nonlinear strict-feedback systems subject to state-dependent constraint boundaries. The controller employs nonlinear state-dependent mapping and a radial basis function neural network to estimate unknown system dynamics. The Nussbaum gain technique is integrated into the controller design to remove the effect of unknown control direction. Based on Lyapunov analysis, the developed control strategy ensures bounded closed-loop signals and achieves constraints on system states and tracking error.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Mechanical
Mohammad Al Janaideh, Almuatazbellah M. Boker, Micky Rakotondrabe
Summary: This study proposes a new output-feedback tracking control scheme for a class of precision motion systems with unknown hysteresis nonlinearity and linear dynamics. The effectiveness of the method is demonstrated through simulation and experimental results.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Ahlem Dorgham, Mohamed Ali Hammami
Summary: This paper investigates the problem of global practical tracking control for a family of uncertain nonlinear systems with unknown output function. A high-gain observer is used to reconstruct the unmeasured system states and handle the unknown output function. An adaptive output feedback controller is designed based on the observer to guarantee reference trajectory tracking. The effectiveness of the proposed design scheme is illustrated with a practical example.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Xiang Liu, Yang Wu, Nailong Wu, Huaicheng Yan, Yueying Wang
Summary: This article addresses the issues of unmeasurable states and actuator hysteresis in multi-input multi-output nonstrict-feedback nonlinear systems. It proposes a neural network observer to estimate the unmeasurable states and uses radial basis function neural networks to approximate the nonlinear terms. A variable separation technique is employed to solve the algebraic loop problem and a command filter design technique reduces the complexity. A finite-time performance function is implemented to ensure the tracking error enters a preassigned range within a specified time. The effectiveness of the developed controller is demonstrated through simulation examples.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Panpan Yang, Xingwen Chen, Xiangmo Zhao, Jiacheng Song
Summary: The study investigates an event-triggered tracking control method for large-scale high order nonlinear uncertain systems, utilizing a neural observer to estimate unmeasurable states and reducing communication burden through event-trigger strategy, eventually designing an observer-based adaptive event-triggered controller to achieve output tracking.
NONLINEAR DYNAMICS
(2021)
Article
Mathematics
Xiongfeng Deng, Yiming Yuan, Lisheng Wei, Binzi Xu, Liang Tao
Summary: This paper addresses the tracking control problem of nonstrict-feedback systems with unknown control gains. The dynamic surface control method, Nussbaum gain function control technique, and radial basis function neural network are applied for the design of virtual control laws and adaptive control laws. An adaptive neural tracking control law is proposed in the last step. Through stability analysis, it is proved that the proposed control law can guarantee bounded signals in the closed-loop system and convergence of the tracking error.
Article
Automation & Control Systems
Yize Mi, Jianyong Yao
Summary: This article addresses the tracking control problem of a class of control-affine nonlinear systems subject to input saturation, parametric uncertainties, and unmodeled uncertainties. A nested-saturation-function-based controller integrated with feedforward model compensation is proposed. A saturated linear extended state observer (SLESO) and parameter adaptation law are used to compensate for unmodeled uncertainties and parametric uncertainties respectively. The proposed scheme guarantees steady-state tracking performance through uncertainty compensation and effectively addresses the conservativeness issue in the input saturation problem by online-updating the available unsaturated region, improving transient performance. The proposed scheme ensures asymptotic stability under constant unmodeled uncertainties and ultimate boundedness under time-varying unmodeled uncertainties. Simulation studies are presented to demonstrate the effectiveness of the proposed approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Hong Cheng, Xiucai Huang, Hongwei Cao
Summary: This paper proposes a method to achieve asymptotic tracking control for uncertain nonlinear strict-feedback systems with unknown time-varying delays and unknown control direction. The Lyapunov-Krasovskii functional is used to deal with the time delays, and the neural network is applied to compensate for the unknown terms arising from the derivative of the Lyapunov-Krasovskii functional. An NN-based adaptive control scheme is constructed based on backstepping technique, and the output tracking error is ensured to converge to zero asymptotically. The proposed method settles the singularity issue commonly encountered in coping with time delay problems and improves the transient performance with proper choice of design parameters.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yumeng Cao, Ning Zhao, Ning Xu, Xudong Zhao, Fawaz E. Alsaadi
Summary: This paper investigates an adaptive neural network event-triggered tracking problem for uncertain switched nonlinear systems. A triggering mechanism based on tracking error and minimal approximation technology are introduced to reduce complexity in controller design. Simulation results validate the effectiveness of the proposed strategy in saving communication resources.
Article
Computer Science, Artificial Intelligence
Jiapeng Liu, Qing-Guo Wang, Jinpeng Yu
Summary: This paper presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation. The scheme addresses uncertainties in subsystems by using command filters to reconstruct virtual control functions, and employs a piecewise continuous function to deal with the unknown input saturation problem. An event-triggered tracking controller is developed using adaptive neural network technique. Simulation studies validate the effectiveness of the controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Duc Thien Tran, Hoang Vu Dao, Kyoung Kwan Ahn
Summary: An adaptive synchronization sliding mode control is proposed in this paper to deal with parameter variations, external disturbance, and unknown control directions in a dual-arm robot. The control utilizes cross-coupling error and sliding mode control to ensure position synchronization of the dual-arm manipulator. An extended state observer is employed to handle the lumped uncertainties caused by the mentioned factors, enhancing the stability of the controlled system. Additionally, a Nussbaum gain function is integrated to address the issue of unknown control direction. Lyapunov stability theory is used to demonstrate the stability of the controlled system. Simulations in MATLAB Simulink are conducted using a dual 3-DOF manipulator system to validate the effectiveness of the proposed control.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Linghuan Kong, Xinbo Yu, Shuang Zhang
Summary: This paper proposes a neural networks-based learning policy for strict-feedback nonlinear systems with symmetric and asymmetric constraints. By introducing a state-constrained function, systems with and without constraints can be handled in a unified manner, avoiding discontinuous actions. Through the use of Nussbaum gain technique and NNs-based approximation technique, the control method effectively deals with unknown signs of control gains and matched, mismatched uncertainties.
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Engineering, Electrical & Electronic
Jafar Zarei, Ehsan Shokri
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(2015)
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Elham Kowsari, Behrooz Safarinejadian, Jafar Zarei
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(2016)
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Automation & Control Systems
Hossein Hassani, Jafar Zarei, Mohammad Mehdi Arefi, Roozbeh Razavi-Far
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2017)
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Jafar Zarei, Abdolrahman Ramezani
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(2018)
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Hossein Hassani, Jafar Zarei, Mohammed Chadli, Jianbin Qiu
IEEE TRANSACTIONS ON CYBERNETICS
(2017)
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Engineering, Electrical & Electronic
Mohsen Bahreini, Jafar Zarei
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Mohsen Bahreini, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif
2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
(2017)
Proceedings Paper
Engineering, Industrial
Elham Kowsari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif
IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
(2017)
Proceedings Paper
Engineering, Industrial
Jafar Zarei, Mahmood Tabatabaei, Roozbeh Razavi-Far, Mehrdad Saif
IECON 2017 - 43RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Roozbeh Razavi-Far, Maryam Farajzadeh-Zanjani, Shokoofeh Zare, Mehrdad Saif, Jafar Zarei
2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE)
(2017)
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Mohsen Bahreini, Jafar Zarei
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(2016)
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Hossein Hassani, Jafar Zarei
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(2016)
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Engineering, Multidisciplinary
Jafar Zarei, Mohammad Mehdi Arefi, Hossein Hassani
2015 6TH INTERNATIONAL CONFERENCE ON MODELING, SIMULATION, AND APPLIED OPTIMIZATION (ICMSAO)
(2015)
Proceedings Paper
Computer Science, Cybernetics
M. Farajzadeh-Zanjani, R. Razavi-Far, M. Saif, J. Zarei, V. Palade
2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
(2015)
