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Shu-Yi Wei, Yuan-Xin Li
Summary: This paper investigates an adaptive finite-time command filtered control scheme for nonlinear systems with asymmetric time-varying full state constraints and input quantization. By fusing the backstepping control method and command filter tech-nique, a novel adaptive neural networks (NNs) command filtered control approach is proposed, along with a modified error compensation mechanism and a smooth nonlinear transformation. The effectiveness of the theoretical results is illustrated through simulations.
INFORMATION SCIENCES
(2022)
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Automation & Control Systems
Yulin Li, Ben Niu, Guangdeng Zong, Jinfeng Zhao, Xudong Zhao
Summary: This article proposes an adaptive neural finite-time control strategy for stochastic nonlinear systems, which combines neural network approximation and backstepping technique, constructs a time-varying barrier Lyapunov function, and solves the difficulty arising from saturation nonlinearity.
With the proposed control strategy, it is guaranteed that system signals are bounded, the reference signal is tracked within a finite time, and system states do not violate constraints.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
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Computer Science, Information Systems
Xin Jin, Yuan-Xin Li
Summary: This article investigates the fuzzy adaptive control design for a class of stochastic nonstrict feedback nonlinear systems, introducing a bounded estimation method, smooth functions, and barrier Lyapunov functions to ensure the controlled system's performance and stability. The proposed asymptotic tracking control scheme shows superior performance in an illustrative simulation instance.
INFORMATION SCIENCES
(2021)
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Computer Science, Artificial Intelligence
Yan Zhang, Jian Guo, Zhengrong Xiang
Summary: This article develops an adaptive finite-time tracking control scheme for a category of uncertain nonlinear systems with asymmetric time-varying full-state constraints and actuator failures. The original constrained nonlinear system is transformed into an equivalent "unconstrained" one using the uniform barrier function (UBF). By introducing a new coordinate transformation and employing neural network to approximate the unknown nonlinear function, the control design can handle more general state constraints. The developed finite-time control method ensures bounded signals in the closed-loop system and convergence of the output tracking error to a small neighborhood of the origin.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
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Automation & Control Systems
Qiming Lin, Pingfang Zhou, Dengping Duan
Summary: This paper proposes a finite-time command-filtered backstepping control method for nonlinear systems with input delay and time-varying asymmetric full-state constraints. By using a novel finite-time delay compensation mechanism and command-filtered backstepping design, the challenges caused by the backstepping approach and full-state constraints are effectively addressed.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
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Computer Science, Information Systems
Xiaohang Su, C. L. Philip Chen, Zhi Liu
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INFORMATION SCIENCES
(2021)
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Computer Science, Artificial Intelligence
Jiaxin Zhang, Kewen Li, Yongming Li
Summary: This paper presents an adaptive neural network optimized control strategy for full vehicle active suspension system, addressing the complexity and nonlinearity of automotive suspension system control using hydraulic actuators. The proposed method develops virtual and actual optimal controllers based on backstepping technique and the identifier-actor-critic structure, along with Barrier Lyapunov functions to prevent system state constraints. Simulation results demonstrate optimal and satisfactory control of the full-car system, highlighting the superiority of the proposed method.
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Automation & Control Systems
Chengpeng Li, Qinyuan Ren, Zuhua Xu, Jun Zhao, Chunyue Song
Summary: In this paper, an adaptive finite-time impedance control strategy based on optimised backstepping (OB) technique is proposed. The proposed method overcomes the drawback of existing OB methods by constructing simplified reinforcement learning (RL) updating laws and relaxing the persistence excitation requirement. The effectiveness of the proposed method is demonstrated through a simulation example with environment-robot interaction.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
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Automation & Control Systems
Yudi Wang, Guangdeng Zong, Dong Yang, Kaibo Shi
Summary: This study proposes an adaptive neural networks controller based on the barrier Lyapunov function methodology and backstepping technique for finite-time tracking control of nonstrict feedback nonlinear systems. The controller ensures bounded closed-loop system signals and convergence of tracking error to a sufficiently modest area around the origin under full state constraints. An example involving an electromechanical system is provided to validate the effectiveness of the proposed methods.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
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Automation & Control Systems
Huifang Min, Shengyuan Xu, Zhengqiang Zhang
Summary: This article discusses the adaptive finite-time tracking control for state constrained stochastic nonlinear systems with parametric uncertainties and input saturation, using a combination of various methods to ensure stability and tracking performance of the system.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
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Automation & Control Systems
Wei Zhao, Yanjun Liu, Lei Liu
Summary: A new fuzzy adaptive control method is proposed for strict feedback nonlinear systems with immeasurable states and full constraints, utilizing a fuzzy logic system to design the approximator. The use of an integral barrier Lyapunov function ensures state constraint bounds and reduces conservativeness. The proposed method combines adaptive backstepping technique and fuzzy output feedback to ensure bounded signals and convergence of tracking error. Simulation results demonstrate the effectiveness of the control scheme.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
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Automation & Control Systems
Chunxiao Wang, Yuqiang Wu, Fenghe Wang, Yan Zhao
Summary: This paper presents a novel control design approach to address the tracking control problem of nonlinear systems with parametric uncertainties and constraints, achieving system stability and satisfaction of full-state constraints.
INTERNATIONAL JOURNAL OF CONTROL
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Engineering, Electrical & Electronic
Ze Li, Wangyao Xu, Jinpeng Yu, Chengxi Wang, Guozeng Cui
Summary: This paper studies the problem of finite-time heading tracking control for surface vehicles with controller faults and full state constraints. Fuzzy logical systems and command filtered backstepping method are used to approximate the unknown nonlinear dynamics and construct an adaptive controller respectively. The Nussbaum function is employed to solve the unknown control direction problem. A new control scheme based on barrier Lyapunov functions and finite-time control strategy is proposed.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
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Mathematics
Yinlong Hou, Xiaoling Xu, Ruixia Liu, Xiangyun Bai, Hui Liu
Summary: This paper studies the adaptive finite-time fuzzy control issue of uncertain nonlinear systems with asymmetric constraints. A distinct function is designed to mitigate excessive breach of the full-state boundaries without modifying the controller structure or making additional assumptions about virtual control. By employing approximating functions using fuzzy logic systems and dynamic surface control technology, the unknown nonlinear functions of the controller strategy are estimated. Simulation examples confirm the effectiveness of the proposed control scheme in keeping all states within the predefined regions.
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Automation & Control Systems
Fazhan Tao, Pengyu Fan, Zhumu Fu, Nan Wang, Yueyang Wang
Summary: An adaptive fuzzy fixed time control scheme is developed for stochastic pure-feedback nonlinear systems with full state constraints in this paper. The proposed scheme guarantees the boundedness and convergence of all signals in the systems within a fixed time, as verified by simulation examples.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
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Chunxiao Wang, Yuqiang Wu, Guangdeng Zong
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Chunxiao Wang, Yuqiang Wu, Jiangbo Yu
IET CONTROL THEORY AND APPLICATIONS
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Chunxiao Wang, Yuqiang Wu, Jiangbo Yu
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2017)
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Automation & Control Systems
Chunxiao Wang, Yuqiang Wu, Zhongcai Zhang
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2018)
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Automation & Control Systems
Chunxiao Wang, Fenghe Wang, Jiali Yu
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
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Automation & Control Systems
Chunxiao Wang, Jinling Du, Jiangbo Yu
IET CONTROL THEORY AND APPLICATIONS
(2019)
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Automation & Control Systems
Chunxiao Wang, Yuqiang Wu, Fenghe Wang, Yan Zhao
Summary: This paper presents a novel control design approach to address the tracking control problem of nonlinear systems with parametric uncertainties and constraints, achieving system stability and satisfaction of full-state constraints.
INTERNATIONAL JOURNAL OF CONTROL
(2021)
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Jiali Yu, Xiong Dai, Wenshuang Chen, Chunxiao Wang, Jin Qi
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
(2020)
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Automation & Control Systems
Chun-xiao Wang, Lu Qi, Jia-yun Liu, Jia-li Yu
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2020)
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Automation & Control Systems
Chunxiao Wang, Lu Qi, Xiao Yu, Jiali Yu
Summary: The study introduces a tracking control method for a class of partial state constrained nonlinear systems. By utilizing state transformation and cross backstepping control, both constrained and unconstrained controllers are designed simultaneously, effectively solving the coupling problem and avoiding computation explosion. Dynamic surface control is used to ensure error signals converge to zero and maintain time-varying constraints on system partial states.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
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Automation & Control Systems
Chunxiao Wang, Lu Qi, Yan Zhao, Jiali Yu
Summary: This article studies the tracking control problem for a class of uncertain nonlinear system with unknown control coefficients. By introducing a hyperbolic tangent function to approximate the saturated input function and constructing an auxiliary system to compensate the approximation error, adaptive controllers are constructed using the barrier Lyapunov function based adaptive backstepping control. The use of dynamic surface control improves the tracking performance of the system.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
