Article
Automation & Control Systems
Hui Ma, Hongru Ren, Qi Zhou, Renquan Lu, Hongyi Li
Summary: This article investigates Nussbaum gain adaptive control for a type of nonlinear systems, tackling challenges such as periodic disturbances and unknown control direction by utilizing Fourier series expansion and radial basis function neural network for function approximation. The control algorithm is designed with a Nussbaum-type function to handle dead zone output and unknown control direction, ensuring bounded closed-loop signals and tracking error convergence. Simulation results validate the effectiveness and applicability of the proposed analysis approach.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yang Yang, Didi Chen, Qidong Liu, Tengfei Zhang, Aaron Liu, Wenbin Yue
Summary: In this paper, a predictor-based neural dynamic surface control strategy is proposed for a class of nontriangular nonlinear systems in the presence of unknown disturbances. The strategy utilizes a predictor and neural networks to approximate unknown dynamics and compensates for approximation errors. A predictor-based neural network disturbance observer is constructed to compensate for external disturbances and approximation errors. The effectiveness of the proposed control strategy is demonstrated through numerical examples.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Engineering, Mechanical
Jianfeng Wang, Ping Zhang, Yan Wang, Zhicheng Ji
Summary: This paper investigates the problem of adaptive optimal tracking control for full-state constrained strict-feedback nonlinear systems with input delay. A novel control approach is developed by combining the backstepping design technique and adaptive dynamic programming (ADP) theory. The approach utilizes Pade approximation to handle input delay and barrier Lyapunov functions for state constraints. Neural networks are employed to approximate unknown functions. An adaptive backstepping feedforward controller is developed to convert the tracking task into an equivalent regulation problem. A critic network is constructed within the ADP framework to obtain the optimal control. The resulting controller consists of feedforward and feedback parts, while ensuring that all signals are uniformly ultimately bounded in the closed-loop system.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Jiaxi Chen, Junmin Li, Sunyang Liu, Ailiang Zhao
Summary: This article addresses the consensus problem of nonlinearly parameterized multi-agent systems with periodic disturbances by employing matrix theory, adaptive control, neural networks, and Fourier series expansion. It proposes novel distributed control protocols to achieve stability and validates them through simulation examples.
Article
Computer Science, Artificial Intelligence
Junchang Zhai, Huanqing Wang, Jiaqing Tao
Summary: This paper proposes a disturbance-observer-based adaptive neural control approach for addressing the issues of long input delay and dead-zone in nonlinear systems. By introducing an auxiliary system and dynamic surface control, the unknown time-varying external disturbance and approximation error are successfully estimated, improving the disturbance rejection performance of the closed-loop system.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zhiyong Zhou, Dongbing Tong, Qiaoyu Chen, Wuneng Zhou, Yuhua Xu
Summary: This paper discusses the use of radial basis function-neural networks for approximation in nonlinear systems and dynamic surface control method to address complexity issues, ensuring global asymptotic stability through Lyapunov stability theory. The effectiveness of the proposed control technique is validated through simulation examples.
Article
Engineering, Electrical & Electronic
Fabin Cheng, Ben Niu, Liang Zhang, Zhongyu Chen
Summary: This paper studies the problem of tracking control for uncertain nonlinear systems with periodic disturbances. By introducing two different error transformation functions and prescribed performance technology, the proposed low-computation adaptive control method ensures the boundedness of the signals of the closed-loop system and demonstrates accurate tracking through the counter evidence method. Compared with existing results, this method avoids the complexity explosion problem caused by introducing a filter, making it more applicable and less complex. For the time-dependent periodic disturbances considered in the system, a function approximator based on Fourier series expansion achieves accurate approximation with the least number of Gaussian basis functions.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Computer Science, Artificial Intelligence
Wei Liu, Jianhang Zhao, Huanyu Zhao, Qian Ma, Shengyuan Xu, Ju H. Park
Summary: This article studies a finite-time fuzzy adaptive dynamic surface control (DSC) method based on a nonlinear disturbance observer (NDO) for nonlinear systems with external disturbances and preassigned performance indices. By constructing a finite-time preassigned-performance function (FTPF), the tracking error is confined within its boundaries, satisfying performance metrics. The proposed composite NDO (CNDO) scheme incorporates fuzzy adaptive control to estimate unknown composite disturbances.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Shuai Song, Baoyong Zhang, Xiaona Song, Zhengqiang Zhang
Summary: This article investigates the application of neuro-fuzzy-based adaptive dynamic surface control in uncertain fractional-order nonlinear systems. The proposed method handles unknown nonlinear terms in the systems, solves the complexity issue caused by traditional design, and ensures system stability using a neuro-fuzzy-based controller. The effectiveness and superiority of the control scheme are verified through three examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yongming Li, Jiaxin Zhang, Wei Liu, Shaocheng Tong
Summary: This work investigates an adaptive neural network optimized output-feedback control problem for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. It proposes an optimized control strategy based on the backstepping technique and actor-critic architecture to prevent system violations of state constraints and ensure bounded signals in the closed-loop system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Kun Jiang, Xuxi Zhang
Summary: This article investigates the event-triggered adaptive neural networks tracking control problem for nonlinear systems with prescribed performance and actuator fault. The study takes into account bias faults and the loss of effectiveness in actuators, while the effectiveness factor remains unknown. Neural networks are utilized to model the unknown terms of the systems during controller design using the backstepping technology framework, and an error transformation is employed to confine the tracking error within a predefined boundary. An adaptive neural networks event-triggered control strategy is developed to economize communication resources, ensuring that all closed-loop signals remain bounded and the tracking error asymptotically converges to zero. Two simulation examples are presented to validate the effectiveness of the proposed control strategy.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Kexin Ding, Qiang Chen, Yurong Nan, Xiaoye Luo
Summary: This paper presents an adaptive fixed-time neural control scheme for a class of nonlinear uncertain systems with full-state constraints. A novel asymmetric hyperbolic barrier Lyapunov function (AHBLF) is introduced to handle the time-varying constraints of all the system states. An adaptive controller is designed to ensure that the tracking errors converge to the equilibrium point within a fixed time, while the system states remain within predefined time-varying boundaries. The proposed control scheme avoids the singularity problem and does not require prior knowledge of the gain function bounds.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Yuxiao Lian, Jianwei Xia, Ju H. Park, Wei Sun, Hao Shen
Summary: This article focuses on the output feedback control of a nonlinear system with unknown control directions, unknown Bouc-Wen hysteresis, and unknown disturbances. The design obstacles caused by these unknown factors are eliminated through the use of linear state and coordinate transformations, avoiding the need for high-frequency oscillating Nussbaum function. A novel nonlinear disturbance observer is designed to handle unknown disturbances, which has a simple structure, low coupling, and easy implementation. An output feedback controller is devised using neural networks and backstepping technology, ensuring bounded closed-loop signals and convergence of system output, state observation error, and disturbance observation error. Simulation verification using numerical examples and a Nomoto ship model illustrates the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yifei Xing, Yantao Wang
Summary: This article proposes a finite-time adaptive dynamic surface control (DSC) approach for nonstrict fractional-order nonlinear systems (FONSs) with input delay. An auxiliary compensation function is used to handle the input delay problem. To address the computational complexity, a fractional-order filter is utilized to approximate the virtual controller and its fractional-order derivative in each step of the backstepping procedure. The developed finite-time adaptive DSC approach incorporates backstepping technology and neural network (NN), and introduces finite-time stability criteria based on fractional-order Lyapunov method to ensure the convergence of the tracking error within a small region around the origin. The effectiveness of the proposed control scheme is demonstrated through two examples.
Article
Computer Science, Artificial Intelligence
Jiaming Zhu, Yuequan Yang, Tianping Zhang, Zhiqiang Cao
Summary: In this paper, a self-limiting control term is defined to guarantee the boundedness of variables, and it is applied to a finite-time stability control problem. By adding self-limiting terms to the controller and virtual control laws, the boundedness of the overall system state is guaranteed. Unknown continuous functions are estimated using neural networks. Simulation examples illustrate the theoretical results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
