Article
Computer Science, Artificial Intelligence
Faxiang Zhang, Yang-Yang Chen
Summary: This article addresses the containment control problem of multiagent systems under directed topologies, transforming unknown higher order nonlinear dynamics into simpler models with unknown affine terms, and using fuzzy logic systems and fuzzy state observers to approximate these unknown terms. The proposed fully distributed fuzzy adaptive containment control law is shown to effectively control the system and ensure stable convergence of containment errors to a small neighborhood of the origin.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Ze Zhu, Zhanxia Zhu
Summary: This paper proposes a backstepping model-free adaptive control algorithm for trajectory tracking control of second-order nonlinear systems. The algorithm combines model-free adaptive control with backstepping control and achieves asymptotic and stable convergence of system velocity error. The algorithm also possesses advantages such as simple structure, model independence, and strong robustness. Simulation results on a planar two-link space robot arm model verify the efficacy of the algorithm in achieving trajectory tracking control.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yang Zhou, Shubo Wang
Summary: This paper investigates asymptotic tracking control of nonlinear robotic systems with prescribed performance. The control strategy is developed based on a modified prescribed performance function (PPF) and fuzzy logic system (FLS) to approximate the unknown dynamics. A robust integral of the sign of the error (RISE) term is incorporated into the control design to achieve asymptotic convergence. Numerical simulation and experimental results validate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Kaixin Lu, Zhi Liu, Yaonan Wang, C. L. Philip Chen
Summary: A new fixed-time adaptive fuzzy control scheme is proposed in this study, which achieves fixed-time stability and convergence of tracking error while avoiding the requirement of prior knowledge of system dynamics.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Danping Zeng, Zhi Liu, Yaonan Wang, C. L. Philip Chen, Yun Zhang, Zongze Wu
Summary: This paper presents a fuzzy adaptive inverse optimal control strategy for optimal control of uncertain switched systems. By developing alternative methods and introducing new conditions, the inverse optimality of the cost function and stability of the switched system are analyzed.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hongjing Liang, Guangliang Liu, Huaguang Zhang, Tingwen Huang
Summary: This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. The use of radial basis function neural networks to approximate unknown nonlinear functions and a dynamic signal to handle design difficulties in unmodeled dynamics were highlighted. A novel event-triggered control protocol was proposed to reduce communication burden and achieve convergence of follower outputs to a neighborhood of the leader's output, while ensuring bounded signals in the closed-loop system. An illustrative simulation example was provided to verify the efficacy of the proposed algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Jing Wu, Wei Sun, Shun-Feng Su, Yuqiang Wu
Summary: This study introduces an adaptive quantized control scheme for uncertain strict-feedback nonlinear systems with unknown control directions. By combining backstepping technique and Lyapunov stability theory, a systematic analysis method is designed to overcome obstacles related to quantized input signals and unknown control directions. The effectiveness and feasibility of the control scheme are verified through simulation examples, demonstrating the boundedness of all signals and convergence of tracking error to a small domain of origin.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Xiaoyu Guo, Chenliang Wang, Zhen Dong, Zhengtao Ding
Summary: In this article, a novel output-feedback adaptive containment control scheme is proposed for a class of heterogeneous multiagent systems. The proposed scheme successfully overcomes the difficulties jointly caused by the unknown control gain matrices, unknown parameters, and unknown jumps introduced by actuator faults. Simulation results illustrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Shubo Wang
Summary: This article investigates an adaptive robust control scheme based on barrier Lyapunov function for nonaffine nonlinear systems with unknown dynamics. The scheme converts the nonaffine system into an affine system and reconstructs the immeasurable states using a high-gain observer. It also incorporates a robust integral and a barrier Lyapunov function in the control design to reject unknown dynamics.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Cungen Liu, Xiaoping Liu, Huanqing Wang, Yucheng Zhou, Chuang Gao
Summary: This paper investigates the problem of adaptive control for unknown mixed high-order fully actuated (HOFA) nonlinear systems. To address parametric uncertainties, a tuning function is utilized to design an adaptive update law without parameter overestimation. Based on the full-actuation feature of HOFA nonlinear systems and Lyapunov stability theory, an adaptive controller is constructed and its stability is analyzed. Finally, the effectiveness of the control strategy is verified through simulation results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Romeo Ortega, Rafael Cisneros, Lei Wang, Arjan van der Schaft
Summary: This paper addresses the problem of indirect adaptive control of nonlinear systems. By estimating system parameters and designing a controller, the system can be regulated and tracked. The proposed approach shows better performance compared to other existing solutions.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Kewen Li, Yongming Li, Guangdeng Zong
Summary: This article proposes a fuzzy adaptive fixed-time decentralized control design algorithm for nonlinear interconnected systems with stochastic disturbances and unknown nonlinearities. By combining stability theory and control techniques, it is proven that the closed-loop system is globally fixed-time stable.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yingkang Xie, Qian Ma, Zhen Wang
Summary: This article introduces an adaptive fuzzy event-triggered controller design for nonstrict nonlinear systems, utilizing a high-gain fuzzy state observer and backstepping technique. A new switching threshold event-triggered mechanism is proposed to avoid Zeno behavior.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ming Chen, Huanqing Wang, Xiaoping Liu
Summary: This article proposes an adaptive practical fixed-time control strategy for the output tracking control of a class of strict feedback nonlinear systems. By utilizing backstepping algorithm, finite-time Lyapunov stable theory, and fuzzy logic control, a novel controller is constructed. Theoretical analysis shows that the closed-loop system is practically fixed-time stable under the presented control strategy, and all signals are bounded while the tracking error converges to a small neighborhood of the origin within a fixed-time interval.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Aerospace
Jing Chang, Roeland De Breuker, Xuerui Wang
Summary: This article reveals that existing sensor-based nonlinear fault-tolerant control methods, including incremental nonlinear dynamic inversion control, cannot guarantee closed-loop stability when the control direction is unknown, regardless of which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially or completely unknown control effectiveness. Its effectiveness is proven in the Lyapunov sense and verified through numerical simulations of an aircraft attitude tracking problem in the presence of various uncertainties and faults.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
