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
Automation & Control Systems
Jing Na, Yongfeng Lv, Kaiqiang Zhang, Jun Zhao
Summary: This article proposes an ADP method for optimal tracking control of nonlinear systems using a neural network identifier and critic. The combination of static control and online training of the critic NN improves control response effectively.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Information Systems
Qingkun Yu, Xiqin He, Libing Wu, Liangdong Guo
Summary: This paper addresses the design of neural network observer and adaptive finite-time tracking controller for uncertain nonlinear systems with event-triggered inputs and unknown dead-zone constraints. By improving the finite-time command filter backstepping technique and developing an adaptive output feedback event triggering mechanism, the goal of finite-time convergence is achieved and network bandwidth is effectively saved.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yanli Liu, Hongjun Ma
Summary: This article investigates the problem of adaptive output feedback control for stochastic switched nonlinear systems with dead-zone output and asymmetric tracking constraints. A type of smooth approximate model is proposed, and the Nussbaum-type function is utilized to develop the control design. The control design is successfully completed by introducing an improved first-order filter into the barrier Lyapunov function analysis.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Mingjie Cai, Peng Shi, Jinpeng Yu
Summary: This brief studies the control design method for a class of non-strict feedback nonlinear systems, taking into consideration uncertain nonlinearities and unknown non-symmetrical input dead-zone. By combining the finite-time command filtered backstepping technique with a neural network-based methodology, a novel finite-time adaptive control approach is proposed. The effectiveness of the control scheme is verified through numerical simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
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
Computer Science, Theory & Methods
Chengjie Huang, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper proposes a consensus control scheme for a leader-follower multiagent system with a fuzzy dead zone constraint. By using smooth functions and control analysis methods, the problem of constrained actuator and convergence of tracking errors in multiagent systems is effectively solved.
FUZZY SETS AND SYSTEMS
(2022)
Article
Automation & Control Systems
Kewen Li, Yongming Li
Summary: This article focuses on the problem of performance-guaranteed adaptive fuzzy optimal compensator control for stochastic affine nonlinear systems with dead-zone and unknown nonlinear dynamics. By introducing a feedforward fuzzy compensator and adaptive learning algorithm, the developed control scheme ensures the uniform ultimate boundedness of signals in the controlled system and precise tracking performance.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yu, Tongwen Chen
Summary: This article proposes an adaptive tracking controller based on radial basis function neural networks (RBFNNs) for nonlinear plants with unmatched uncertainties and smooth reference signals. Valid RBFNN adaptive control is introduced, ensuring that all closed-loop arguments of the involved RBFNNs remain inside their corresponding compact sets. A novel iterative design method is proposed and embedded into the traditional backstepping approach to obtain valid RBFNN adaptive controllers.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Rohollah Moghadam, Sarangapani Jagannathan
Summary: This article introduces an actor-critic neural network-based online optimal adaptive regulation method for a class of nonlinear continuous-time systems. The method considers known state and input delays, as well as uncertain system dynamics. The temporal difference error (TDE) dependent on state and input delays is derived using actual and estimated value function and reinforcement learning. The critic neural network (NN) weights are tuned at each sampling instant based on the instantaneous integral TDE. A novel identifier is used to estimate the control coefficient matrices and obtain the estimated control policy. The boundedness of various components in the system is proved through Lyapunov analysis, and simulation results demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Hang Su, Qiang Zhang
Summary: This paper investigates the problem of tracking control for a class of nonstrict-feedback nonlinear systems with input quantization, asymmetric fuzzy dead zones, and unknown control directions. A useful coordinate transformation is proposed to tackle the issue caused by the unknown control coefficients, followed by transforming the researched system into an equivalent one. By combining the nonlinear decomposition of an asymmetric hysteresis quantizer and a simplified asymmetric dead zone model, a feasible connection between system input and control signal is established. Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and a novel adaptive fuzzy control scheme is proposed via backstepping technique, ensuring bounded signals and minimal tracking error. Simulation results demonstrate the effectiveness of the proposed algorithm.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Haihan Wang, Guangdeng Zong, Dong Yang, Jianwei Xia
Summary: This article investigates the adaptive fault-tolerant tracking control problem for nonlinear systems with prescribed performance and input dead-zone. By constructing a new composite variable and designing an adaptive neural network observer, this method effectively addresses the effects of sensor failure and unknown nonlinear functions, limiting the tracking error within the performance boundary.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Zhengqiang Zhang, Chen Yang, Shuzhi Sam Ge
Summary: In this article, a decentralized adaptive control strategy is proposed for large-scale nonlinear systems with interconnections. Each subsystem includes state delays, asymmetric dead-zone inputs, and time-varying disturbances. The decentralized adaptive controller is developed to handle uncertain functions in each subsystem. Robust adaptive control schemes are addressed for matched and mismatched time-delay nonlinearities. The effectiveness of the proposed method is demonstrated through a simulation example.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Liuliu Zhang, Peng Wang, Changchun Hua
Summary: This paper focuses on the control problem of time-delay nonlinear high-order fully actuated (HOFA) systems, considering unmodeled dynamics and unknown dead-zone input. The objective is to design an adaptive controller using the HOFA systems approach. To achieve this, technical obstacles need to be overcome. Firstly, the supply rate is changed to deal with unmodeled dynamics in the HOFA systems. Secondly, an adaptive dead-zone inverse is constructed to compensate for the unknown asymmetrical dead-zone input nonlinearity. The controller is designed using the HOFA systems approach, and it is proved that all states of the systems converge to a bounded region based on the Lyapunov-Krasovskii functions. Finally, simulation results demonstrate the effectiveness of the designed adaptive controller.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Yanxian Chen, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper presents an adaptive fuzzy control scheme for switched nonlinear systems with uncertain dead-zone, unknown nonlinearities, and immeasurable states. The proposed novel mode-dependent fuzzy dead-zone model (MFDM) can accurately describe uncertain dead-zones and improves tracking performance. The feasibility of the control technique is demonstrated through simulation examples.
