Adaptive Output Feedback Optimal Tracking Control for Nonlinear Systems

doi:10.7652/xjtuxb201701020

您当前的位置：

首页 >

文章列表页 >

Adaptive Output Feedback Optimal Tracking Control for Nonlinear Systems

更新时间：2025-07-09

- Adaptive Output Feedback Optimal Tracking Control for Nonlinear Systems
- Vol. 51, Issue 1, Pages: 128-134(2017)
- 作者机构：
  
  空军工程大学航空航天工程学院,西安,710038
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201701020
  CLC： TP273
- Online First：10 January 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
Adaptive Output Feedback Optimal Tracking Control for Nonlinear Systems[J]. 2017, 51(1): 128-134.
DOI：

Adaptive Output Feedback Optimal Tracking Control for Nonlinear Systems[J]. 2017, 51(1): 128-134. DOI： 10.7652/xjtuxb201701020.

摘要

针对只有输出信号可测且不可测状态与未知非线性函数相关

同时还受到外部扰动的非线性系统

提出了一种自适应神经网络动态面优化控制方法

旨在解决该类非线性系统的输出反馈跟踪问题。首先

构建了基于径向基函数神经网络的状态观测器

保证其观测误差可渐进收敛至0; 其次

利用动态面技术设计了自适应输出反馈控制律; 最后

利用最速下降法选取了最优控制参数

优化了控制输入和跟踪性能

减轻了控制参数整定的工作量。该方法能够保证闭环系统所有信号半全局一致有界

而且通过引入性能函数和跟踪误差转换

保证系统输出具有指定的跟踪性能

改善了控制系统的过渡性质。仿真结果表明:设计的状态观测器较准确地实现了对系统信号的估计

跟踪误差信号幅值始终不超过0.02

控制参数优化后的输入信号幅值明显减小

验证了该控制方法的有效性。

Abstract

Aiming at the nonlinear systems where only the output signals can be measured and their unpredictable states are associated with unknown nonlinear functions and affected by external disturbances

this paper proposes an adaptive neural network dynamic surface control method to solve the problem of output feedback tracking of nonlinear systems. First

the state observer based on RBF neural network is built to ensure the observation error can be gradually converged to zero; second

the adaptive output feedback control rule is designed by using dynamic surface technology; finally

using the steepest descent method and selecting the optimal control parameters

the control input and tracking performance are optimized

and hence the workload of control parameter setting is reduced. This method can ensure the semi-global uniform boundedness of all closed-loop signals

and guarantee system output with specified tracking performance through introducing the performance function and tracking error transformation

thus the transition quality of the control system is improved. Simulation results show that the designed state observer can realize accurate estimation of the system signals

the tracking error signal amplitude is controlled less than 0.02

and the input signal amplitude with optimized control parameters is decreased

verifying the effectiveness of the proposed control method.

关键词

Keywords

references

朱芳来, 蒋鹏, 李晓航. 基于T-S模糊模型的观测器和动态输出反馈容错控制器设计 [J]. 西安交通大学学报, 2016, 50(9): 91-96.

ZHU Fanglai, JIANG Peng, LI Xiaohang. Design of observer and dynamic output feedback fault tolerant controller based on T-S fuzzy model [J]. Journal of Xi'an Jiaotong University, 2016, 50(9): 91-96.

杨俊起, 张良, 朱芳来, 等. Lipschitz非线性系统未知输入观测器设计 [J]. 西安交通大学学报, 2013, 47(8): 87-92.

YANG Junqi, ZHANG Liang, ZHU Fanglai, et al. Design of observer with unknown input for Lipschitz nonlinear systems [J]. Journal of Xi'an Jiaotong University, 2013, 47(8): 87-92.

CHEN M, GE S S. Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer [J]. IEEE Transactions on Cybernetics, 2013, 43(4): 1213-1225.

XU B, YANG C, SHI Z. Reinforcement learning output feedback NN control using deterministic learning technique [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(3): 635-641.

HUANG Y S, WU M. Robust decentralized direct adaptive output feedback fuzzy control for a class of large-scale non-affine nonlinear systems [J]. Information Sciences, 2011, 181(11): 2392-2404.

TONG S C, LI Y M, JING X J. Adaptive fuzzy decentralized dynamics surface control for nonlinear large-scale systems based on high-gain observer [J]. Information Sciences, 2013, 235: 287-307.

TONG S C, LI Y M, ZHANG H G. Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays [J]. IEEE Transactions on Neural Networks, 2011, 22(7): 1073-1086.

WANG M, WANG C, LIU X P. Dynamic learning from adaptive neural control with predefined performance for a class of nonlinear systems [J]. Information Sciences, 2014, 279: 874-888.

HAN S I, LEE J M. Adaptive fuzzy backstepping dynamics surface control for output-constrained non-smooth nonlinear dynamic system [J]. International Journal of Control Automation Systems, 2012, 10(4): 684-696.

SANNER R M, SLOTINE J E. Gaussian networks for direct adaptive control [J]. IEEE Transaction on Neural Networks, 1992, 3(6): 837-863.

TONG S C, LI Y M. Observer-based fuzzy adaptive control for strict-feedback nonlinear systems [J]. Fuzzy Sets and Systems, 2009, 160(12): 1749-1764.

VIDYASAGAR M. Nonlinear systems analysis: 2nd edition [M]. Philadelphia, PA, USA: SIAM, 1993: 6-19.

KIM Y H, LEWIS F L, ABDALLAH C T. A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems [J]. Automatica, 1997, 33(8): 1539-1543.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Reduced-Order Prediction Method for the Dynamic Thermal Response of Phase-Change Cooling Heat Exchangers

Dynamic Flow Field Prediction Method for Compressed Air Storage Tanks Using Dynamic Mode Decomposition and Long Short-Term Memory Network

Collaborative Optimization Strategy for Thermal Management System of New Energy Vehicles

Disturbance Compensation and Decoupling Optimal Backstepping Control Strategy for Hypersonic Vehicle

Numerical Simulation of Heat-Moisture Barrier Performance of a Horizontal Air Curtain in Cold Storage

Related Author

HAN Zeran

TANG Zicheng

KE Hanbing

WANG Junrong

WEI Zhiguo

WANG Qiuwang

MA Ting

SU Hao

Related Institution

Wuhan Second Ship Design and Research Institute

School of Energy and Power Engineering，Xi’an Jiaotong University

School of Future Technology, Xi’an Jiaotong University, Xi’an 710049, China

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

School of Civil Engineering and Architecture, Henan University of Science and Technology

⁰