Article
Computer Science, Artificial Intelligence
Tianliang Zhang, Rui Bai, Yongming Li
Summary: This article focuses on the practically predefined-time adaptive fuzzy quantized control for nonlinear stochastic systems with actuator dead zone. The fuzzy logic systems are used to approximate uncertain nonlinear functions. A novel stochastic predefined-time control scheme is proposed to reduce the control parameters and increase the robustness of the closed-loop system. The adaptive fuzzy controller is designed based on the stochastic predefined-time stabilization theory to configure the upper bound of the expected settling time arbitrarily.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yan Zhang, Mohammed Chadli, Zhengrong Xiang
Summary: In this article, the adaptive fuzzy predefined-time tracking control problem for a class of nonlinear systems with output hysteresis is investigated. An inverse model is utilized to capture the output hysteresis phenomenon, and the Nussbaum-type function technique is utilized to overcome the difficulty of unknown time-varying control gain caused by output hysteresis. An adaptive fuzzy control scheme under the backstepping framework is developed using the predefined-time stability criterion. The feasibility of the developed scheme is verified by an example of an electromechanical system.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Mechanical
Zhilong Yu, Yinghui Li, Maolong Lv, Jing Chang, Binbin Pei
Summary: This work investigates the anti-saturation attitude tracking control for the tailless aircraft with guaranteed output constraints in the presence of uncertainties. The proposed predefined-time adaptive backstepping attitude control scheme effectively handles complex issues, asymmetric time-varying output constraints, and actuator faults/failures. The control scheme ensures convergence of all signals in the closed-loop system to a residual set around the origin within a predefined time.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Zhibao Song, Lihong Gao, Zhen Wang, Ping Li
Summary: This article studies adaptive neural control for multiple-input-multiple-output (MIMO) nonlinear systems with asymmetric input saturation, dead zone, and full state-function constraints. It introduces a suitable transformation to overcome the dead zone and saturation nonlinearity, and radial basis function neural networks to approximate the unknown nonlinear functions. Additionally, the Nussbaum function and time-varying barrier Lyapunov function are applied to handle the unknown control gains and full state-function constraints. A universal adaptive neural control scheme based on the backstepping method is presented, ensuring that the state-function constraints of the closed-loop system are not violated, all signals of the closed-loop system are bounded, and the tracking error converges to a small neighborhood containing the origin. The effectiveness of the proposed control scheme is verified through application to a mass-spring-damper system and a numerical example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Mechanical
Xin Zhou, Chuang Gao, Zhi-gang Li, Xin-yu Ouyang, Li-bing Wu
Summary: This paper presents a novel control scheme for finite-time tracking control of nonlinear systems using fuzzy logic systems and fuzzy state observer, and converts non-smooth input dead-zone and saturated nonlinearity to affine form via the mean value theorem.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Aerospace
Yong Hao, Zhixian Lin, Ziyi Su, Yunfei Xiao, Bing Huang
Summary: This paper proposes a robust adaptive predefined-time control scheme for spacecraft rendezvous maneuvers, considering input saturation and external disturbances. By using a constructive performance function to impose time-varying constraints on system errors and employing a nonlinear mapping method to transform the constrained problem into an unconstrained one, the design process is significantly simplified. The introduction of compensation signals through an auxiliary system optimizes the steady-state performance of the system and properly handles the input saturation constraint. Simulation results validate the effectiveness of the proposed control scheme.
JOURNAL OF AEROSPACE ENGINEERING
(2022)
Article
Automation & Control Systems
Di-Hua Zhai, Yuanqing Xia
Summary: This article develops a guaranteed predefined performance control scheme for robotic manipulators with input saturation and unknown uncertainties. The proposed scheme provides convenient and quantifiable control by integrating the performance function and an adaptive anti-saturation compensator.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Aerospace
Zhilong Yu, Yinghui Li, Maolong Lv, Jing Chang, Binbin Pei
Summary: This paper proposes a predefined-time fault-tolerant attitude control methodology for tailless aircraft. It compensates for the adverse influence caused by actuator fault and input saturation simultaneously using a novel predefined-time performance function and bound-based adaptation laws. The stability analysis of the closed-loop system is conducted using a piecewise continuous Lyapunov function, and it is proven that the attitude tracking errors can converge to a predefined residual set within a predefined time. Comparative simulations validate the effectiveness and superiority of the developed control scheme.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Zhao Zhang, Lingxi Peng, Jianing Zhang, Xiaowei Wang
Summary: In this study, a backstepping-based fault-tolerant controller was developed for a robotic manipulator system with input and output constraints. The controller considered the system's output constraints, actuator characteristics, actuator failures, and external disturbances, and utilized neural networks and disturbance observers for compensation and elimination. Simulation results demonstrated the effectiveness of the proposed controller.
Article
Automation & Control Systems
Daniele Astolfi, Angelo Alessandri, Luca Zaccarian
Summary: A redesign paradigm for stable estimators introduces nonlinearity with adaptive thresholds, improving sensitivity to measurement noise while maintaining convergence properties. This redesign applies to various state estimators and has been confirmed effective through simulation results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Engineering, Mechanical
Shijia Kang, Peter Xiaoping Liu, Huanqing Wang
Summary: This article addresses the problem of fuzzy adaptive fixed-time control for nonstrict-feedback nonlinear systems with input saturation and dead zone. The unknown nonlinear functions are modeled using the universal approximation properties of fuzzy logic systems. A command filter-based fixed-time adaptive fuzzy control strategy is presented based on the backstepping framework and fixed-time control theory. The computational explosion problem in the backstepping scheme is resolved using the command filter technique, and the errors arising from command filters are reduced using an error compensation mechanism. The non-smooth input saturation and dead zone nonlinearities are approximated and transformed into an affine form. The fixed-time convergence of the tracking error and the boundedness of the closed-loop signals are theoretically proven. Simulation results demonstrate the effectiveness of the proposed method.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Zhenyu Gao, Yi Zhang, Ge Guo
Summary: This article focuses on the problem of fixed-time prescribed performance platoon control for heterogeneous vehicles with unknown dead-zone and actuator saturation. An equivalent transformation is developed to approximate the actuator nonlinearity, reducing the complexity of controller design. A modified prescribed performance function is presented to guarantee the convergence of tracking errors to a predetermined region in a fixed time. A novel adaptive sliding mode control scheme is developed, ensuring individual vehicle stability and string stability in fixed time. Numerical simulations demonstrate the effectiveness of the proposed control scheme.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Weihao Pan, Xianfu Zhang, Hanfeng Li
Summary: This paper focuses on the event-triggered output feedback control problem for a class of large-scale nonlinear time-delay systems with asymmetric dead-zone input. Without knowing the precise priori knowledge of dead-zone parameters, an event-triggered output feedback control strategy is developed based on the dynamic gain design approach. It is proved by the Lyapunov analysis that all signals of the closed-loop system are globally bounded and the system states converge to a bounded region which is adjustable.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Engineering, Ocean
Caoyang Yu, Yiming Zhong, Lian Lian, Xianbo Xiang
Summary: This paper addresses the importance of accurate depth tracking control for underwater vehicles and proposes the use of fuzzy approximation and adaptive sliding mode control techniques to address hydrodynamics uncertainty, thruster dead-zone, and saturation. By introducing the gradient projection method and online translation compensation, an adaptive bounded depth control is achieved to ensure the control output always stays within the permitted range.
APPLIED OCEAN RESEARCH
(2021)
Article
Engineering, Mechanical
Zong-Yao Sun, Kai Zhang, Chih-Chiang Chen, Qinghua Meng
Summary: This paper investigates the problem of robust output feedback control for a class of time-delay nonlinear systems with unknown continuous time varying output function. A new robust control scheme is constructed by introducing coordinate transformations and modifying a double-domination method cooperating with integral Lyapunov functions. The proposed scheme guarantees that all closed-loop signals are ultimately uniform bounded and is finally applied to a two-stage chemical reactor system with dead-zone input and external disturbances to illustrate its effectiveness and performances.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Yujia Wang, Jiae Yang, Xuebo Yang, Tong Wang
Summary: This paper presents an adaptive neural network-based fault-tolerant control strategy to solve the tracking control problem of a three degrees of freedom (3-DOF) helicopter with unknown actuator faults, model uncertainties, and external time-varying disturbances. By utilizing neural networks to approximate and compensate for unknown functions, and introducing hyperbolic tangent functions to reduce negative effects, the proposed strategy improves the tracking performance of the closed-loop system.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Min Ma, Kangkang Sun, Tong Wang, Jianbin Qiu
Summary: This article introduces a novel adaptive fuzzy risk-sensitive control method for stochastic nonlinear systems, which ensures the desired cost level of the risk-sensitive index by solving a specified HJB equation. The proposed method reduces the conservatism of existing adaptive robust control methods and guarantees the input-to-state stability of the system. The effectiveness of the approach is verified through a simulation example of a one-link manipulator.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhumu Fu, Nan Wang, Shuzhong Song, Tong Wang
Summary: This article investigates the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems and proposes a novel control strategy using fuzzy logic systems and backstepping design technique to guarantee system stability and achieve performance objectives.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Min Ma, Tong Wang, Runsheng Guo, Jianbin Qiu
Summary: This article investigates the neural-network based backstepping control problem for autonomous marine vehicles perturbed by external disturbances, considering the actuator dead-zone phenomenon. A command filtering compensation strategy is proposed to cope with complexity explosion and decrease tracking errors, ensuring satisfactory tracking performance and boundedness of the closed-loop system signals. Simulation studies further demonstrate the effectiveness of the proposed control method.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Jianbin Qiu, Min Ma, Tong Wang
Summary: This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). The effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Wenshan Bi, Tong Wang
Summary: This article presents an adaptive fuzzy decentralized control algorithm for large-scale interconnected nonlinear systems with unmodeled dynamics. The algorithm utilizes a fuzzy logic system (FLS) to identify unknown nonlinear functions and introduces dynamic signals to compensate for the effect of unmodeled dynamics. A novel adaptive state feedback control algorithm is proposed using a Lyapunov function, and a fuzzy state observer with FLSs is constructed for output feedback. The algorithm ensures the semi-global uniformly ultimately bounded (SGUUB) stability of the system and guarantees bounded signals. Numerical and practical simulation examples are provided to demonstrate the feasibility of the control algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jianbin Qiu, Tong Wang, Kangkang Sun, Imre J. Rudas, Huijun Gao
Summary: This article focuses on the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. The finite-time prescribed performance is considered to meet practical application requirement. A disturbance observer is proposed to estimate the external disturbance, and the stability of the closed-loop system is proven.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Nan Wang, Zhumu Fu, Shuzhong Song, Tong Wang
Summary: This article studies the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints. By introducing the mean value theorem and finite-time-stablelike function, the pure-feedback form is transformed into a system strict-feedback case, and integral barrier Lyapunov functions are used to ensure that the state variables remain within the prescribed constraints. Fuzzy logic systems are utilized to approximate nonlinear uncertainties, achieving convergence of output tracking error and semiglobal ultimate uniform boundedness of signals in the system.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Zhaoke Ning, Tong Wang, Kai Zhang
Summary: This paper investigates event-triggered security control and fault detection for nonlinear systems, where output signals are wirelessly transmitted to the control and detection module. Quantization before data transmission and deception attacks during data transmission are considered. A dynamic event-triggered protocol is proposed to ease the data transmission pressure of wireless networks, and the triggering threshold changes according to the system state. By considering the dynamic event-triggered protocol, signal quantization, and randomly occurring deception attacks, an integrated dynamic output feedback controller and fault detection filter module is developed, ensuring stochastic stability and guaranteed levels of security control and detection performance.
INFORMATION SCIENCES
(2022)
Article
Mathematics
Jiae Yang, Yujia Wang, Tong Wang, Xuebo Yang
Summary: This paper focuses on the tracking control problem for a family of fractional-order systems with unknown drift functions and unknown time delays. By employing fuzzy logic systems, the unknown functions are approximated and compensated while mitigating the adverse effects of time-varying delays and approximation errors. Stability analysis shows that the tracking error can converge to a small neighborhood of the origin. Simulation confirms the effectiveness of the presented control strategy.
Article
Automation & Control Systems
Runsheng Guo, Kangkang Sun, Tong Wang, Jianbin Qiu, Danlei Chu
Summary: This article investigates the boundary control problem for a class of 2 x 2 hyperbolic partial differential equation systems with uncertain transport speeds, considering both state feedback and output feedback cases. The least-square method is used for finite-time parameter estimation based on parametric models. Proper actuation signals are designed via contradiction method in parameter estimation to avoid singular problems. Control laws are designed using the backstepping method for both state-feedback and output-feedback cases, with the estimated parameters. Simulation studies are provided to demonstrate the efficiency of the results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Wenshan Bi, Tong Wang, Xinghu Yu
Summary: This article introduces an adaptive fuzzy control algorithm for a class of large-scale switched fractional-order nonlinear nonstrict feedback systems. The algorithm uses fuzzy-logic systems to approximate unknown nonlinear functions and presents a virtual control law based on fractional Lyapunov stability rules. A fuzzy adaptive decentralized control method is developed under the technique of the Lyapunov function to ensure the stability and control performance of the proposed systems. Simulation results are provided to demonstrate the feasibility and effectiveness of the proposed method.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Tong Wang, Nan Wang, Jianbin Qiu, Concettina Buccella, Carlo Cecati
Summary: In this article, we address the tracking control problem for a class of stochastic nonlinear systems with output feedback signal. The controlled plant is assumed to be affected by unknown dead-zone input. By modeling the unknown dead-zone input function as a time-varying nonlinear function and a bounded disturbance and selecting appropriate design parameters, we demonstrate that the effect of the unknown dead zone can be compensated for. Furthermore, a fuzzy state observer is designed to estimate the unknown system states using fuzzy logic modeling technique, and Lyapunov stability analysis shows that the controlled plant is bounded in probability, and all signals in the closed-loop system are globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, a simulation example of a one-link manipulator is presented to demonstrate the effectiveness of the proposed control strategy.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yujia Wang, Tong Wang, Xuebo Yang, Jiae Yang
Summary: In this article, a control strategy for nonlinear systems is proposed, which combines the gradient descent-Barzilai Borwein algorithm and radial basis function neural network. The main advantages of this strategy are the online updating of neural network parameters and the simplification of controller design process, reducing the number of parameters that need to be adjusted.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tong Wang, Yujia Wang, Xuebo Yang, Jiae Yang
Summary: This article develops a novel cost function to solve the optimal tracking control problem for a class of nonlinear systems with known system dynamics. By designing a specific cost function related to tracking errors and their derivatives, the assumption and obstacles in traditional problems are removed, and the controller design process is simplified. Comparative simulations on an inverted pendulum system demonstrate the effectiveness and advantages of the proposed optimal tracking control strategy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
