Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults

Qiu Jun-hao; Cheng Zhi-jian; Lin Guo-huai; Ren Hong-ru; Lu Ren-quan

doi:10.12052/gdutxb.210110

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults

更新时间：2026-05-27

- Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults
- Vol. 40, Issue 2, Pages: 55-63(2023)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210110
  CLC： TP13
- Online First：25 March 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
邱俊豪, 程志键, 林国怀, 任鸿儒, 鲁仁全. 具有执行器故障的非线性系统指定性能控制[J]. 广东工业大学学报, 2023, 40(2): 55-63.

Qiu Jun-hao, Cheng Zhi-jian, Lin Guo-huai, et al. Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults[J]. 2023, 40(2): 55-63.
邱俊豪, 程志键, 林国怀, 任鸿儒, 鲁仁全. 具有执行器故障的非线性系统指定性能控制[J]. 广东工业大学学报, 2023, 40(2): 55-63. DOI： 10.12052/gdutxb.210110.

Qiu Jun-hao, Cheng Zhi-jian, Lin Guo-huai, et al. Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults[J]. 2023, 40(2): 55-63. DOI： 10.12052/gdutxb.210110.

摘要

针对一类带有全状态约束和执行器故障的非线性系统，提出一种具有指定性能的自适应神经网络输出反馈容错控制方案。首先，建立状态观测器估计系统中的不可测状态，利用径向基神经网络(RBF NNs) 逼近系统中的未知非线性函数。其次，引入非线性映射将状态约束系统转化为一个没有约束的新系统。然后，采用新的性能函数，不仅能使跟踪误差在预先设定的时间内收敛，还可以利用设计参数改变误差的收敛速度。最后证明，该控制方法能够保证闭环系统中所有信号都是半全局一致最终有界的，并通过一个数值仿真验证该方法的有效性。

Abstract

In this paper

considering a class of nonlinear systems with full state constraints and actuator faults

an adaptive neural network output feedback fault-tolerant control algorithm with prescribed performance is proposed. A state observer is constructed to solve the unmeasurable states problem. Unknown nonlinear functions in the systems are approximated by radial basis function neural networks (RBF NNs) . By introducing the nonlinear mapping

the systems with state constraints are transformed into novel systems without state constraints. Moreover

a novel performance function is utilized to guarantee that the tracking error converges within a preset time. Meanwhile

the convergence speed can be adjusted through the parameter design. Finally

it is proved that the control algorithm ensures that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the algorithm is verified by a numerical simulation.

关键词

Keywords

references

LI H Y, WU Y, CHEN M. Adaptive fault-tolerant tracking control fordiscrete-time multiagent systems via reinforcement learning algorithm [J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1163-1174.

TIAN W G, WANG Y H, LI Y J. Fuzzy adaptive output tracking control for a class of uncertain nonlinear systems [J]. Journal of Guangdong University of Technology, 2015, 32(1): 91-97.

LIN G H, LI H Y, MA H, et al . Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults [J ] . IEEE/CAA Journal of Automatica Sinica, 2022, 9(1): 111-122.

MA H, LI H Y, LIANG H J, et al . Adaptive fuzzy event-triggeredcontrol for stochastic nonlinear systems with full state constraints andactuator faults [J ] . IEEE Transactions on Fuzzy Systems, 2019, 27(11): 2242-2254.

ZHENG X H, DONG G W, ZHOU Q, et al . Fault-tolerantcontrol for stochastic multi-agent systems with output constraints [J ] . Control Theory Applications, 2020, 37(5): 961-968.

REN H R, KARIMI H R, LU R Q, et al . Synchronization of network systems via aperiodic sampled-data control with constant delay andapplication to unmanned ground vehicles [J ] . IEEE Transactions on Industrial Electronics, 2020, 67(6): 4980-4990.

ZHOU Q, CHEN G D, LU R Q, et al . Disturbance-observer-based event-triggered control for multi-agent systems withinput saturation [J ] . Science China Information Sciences, 2019, 49: 1502-1516.

MA H, LI H Y, LU R Q, et al . Adaptive event-triggered control for aclass of nonlinear systems with periodic disturbances [J ] . Science ChinaInformation Sciences, 2020, 63(5): 150212.

ZENG G, SUN B D, LIANG H B. A direct adaptive controller based on neural network [J]. Journal of Guangdong University of Technology, 2000, 17(1): 20-23.

ZHOU Q, ZHAO S Y, LI H Y, et al . Adaptive neural network tracking control for robotic manipulators with dead zone [J ] . IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(12): 3611 -3620.

LI Y M, TONG S C. Adaptive neural networks prescribed performance control design for switched interconnected uncertain nonlinear systems [J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(7): 3059-3068.

REN B B, GE S S, TEE K P, et al . Adaptive neural control for outputfeedback nonlinear systems using a barrier Lyapunov function [J ] . IEEE Transactions on Neural Networks, 2010, 21(8): 1339-1345.

ZHANG J X, YANG G H. Prescribed performance fault-tolerant control of uncertain nonlinear systems with unknown control directions [J]. IEEE Transactions on Automatic Control, 2017, 62(12): 6529-6535.

WANG C C, YANG G H. Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown controldirection and input saturation [J]. Neurocomputing, 2018, 284: 17-26.

KOSTARIGKA A K, ROVITHAKIS G A. Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinearsystems with prescribed performance [J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(1): 138-149.

NI J K, AHN C K, LIU L, et al . Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems [J ] . Neurocomputing, 2019, 363: 351-365.

YANG B, ZHOU Q, CAO L, et al . Event-triggered control formulti-agent systems with prescribed performance and full state constraints [J ] . Acta Automatica Sinica, 2019, 45(8): 1527-1535.

ZHAO G L, GAO R S, CHEN J N. Adaptive prescribedperformance control of quadrotor with unknown actuator fault [J]. Control and Decision, 2021, 36(9): 2103-2112.

WANG F, CHEN B, LIN C, et al . Adaptive neural network finite-time output feedback control of quantized nonlinear systems [J ] . IEEE Transactions on Cybernetics, 2018, 48(6): 1839-1848.

WANG H H, CHEN B, LIN C, et al . Observer-based adaptive neuralcontrol for a class of nonlinear pure-feedback systems [J ] . Neurocomputing, 2016, 171: 1517-1523.

ZHOU Q, SHI P, XU S Y, et al . Observer-based adaptive neural network control for nonlinear stochastic systems with time delay [J ] . IEEE Transactionson Neural Networks and Learning Systems, 2013, 24(1): 71-80.

YANG H J, SHI P, ZHAO X D, et al . Adaptive output-feedback neuraltracking control for a class of nonstrict-feedback nonlinear systems [J ] . Information Sciences, 2016, 334: 205-218.

AREFIM M, ZAREI J, KARIMI H R. Adaptiveoutput feedback neural network control of uncertain non-affine systems with unknowncontrol direction [J]. Journal of the Franklin Institute, 2014, 351: 4302-4316.

MAO J, ZHANG T P, XIA X N, et al . Adaptive outputfeedback control for multi-input single-output systems with actuatorfailures [J ] . Control Theory Applications, 2016, 33(4): 512-522.

LI Y M, TONG S C. Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults [J]. IEEE Transactions on Neural Networksand Learning Systems, 2017, 28(11): 2541-2554.

LI D P, LIU Y J, TONG S C, et al . Neural networks-based adaptivecontrol for nonlinear state constrained systems with input delay [J ] . IEEE Transactions on Cybernetics, 2019, 49(4): 1249-1258.

BOULKROUNE A. A fuzzy adaptive control approach for nonlinear systems with unknown control gain sign [J]. Neurocomputing, 2016, 179: 318-325.

ZHANG T P, XIA M Z, YI Y. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraintsand unmodeled dynamics [J]. Automatica, 2017, 81: 232-239.

LIU C G, WANG H Q, LIU X P, et al . Adaptive finite-time fuzzy funnel control for nonaffine nonlinear systems [J ] . IEEE Transactions on Systems, Man, And Cybernetics:Systems, 2021, 51(5): 2894-2903.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Output Feedback Stabilization of a Class of Nonlinear Uncertain Time-delay Systems

Bimodal Iterative Cross-attention Fusion Ensemble Framework

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

SCA-YOLO: Real-time Target Detection Based on Deformable Convolution and Context-aware Attention

Research on Foreign Object Detection in Power Transmission Lines Based on the AAGV-YOLOX Model

Related Author

Luo Liang

Jin Chao-yong

Chen De-yin

Hu Nan-hui

Related Institution

No data

AI问答

⁰