自动化学报Issue(9)：1998-2004,7.DOI:10.3724/SP.J.1004.2014.01998
输入饱和非线性系统的周期自适应补偿学习控制
Periodic Adaptive Compensating Learning Control of Nonlinear Systems with Saturated Input
摘要
Abstract
A periodic adaptive tracking compensating learning algorithm is proposed for a class of Brunovsky standard nonlinear uncertain systems with time delay and input saturation. By restructuring the system according to signal replace-ment theory and functional transformation with the lowest common cycle, the delay and other time-varying parameters are combined into an auxiliary time-varying parameter. Then a periodic adaptive learning algorithm is designed to es-timate the auxiliary parameter for approximating and compensating the section which exceeds the saturated limit by a compensator. Finally a comprehensive controller is constituted so that the system state can track the bounded expected value and the repeated iterative learning control problem based on periodic system with input saturation is solved. It is proved that the track error is convergent and all the closed-loop signals are bounded by the difference calculation of Lyapunov-Krasovskii composite energy function. The torque control simulation of common coupled nonlinear manipulator further confirms the effectiveness of the algorithm.关键词
输入饱和/非线性时滞系统/周期自适应/补偿学习Key words
Saturated input/nonlinear time-delay systems/cyclical adaptive/compensating learning引用本文复制引用
陶洪峰,霰学会,杨慧中..输入饱和非线性系统的周期自适应补偿学习控制[J].自动化学报,2014,(9):1998-2004,7.基金项目
国家自然科学基金(61273070,61203092),江苏省高校自然科学研究项目(11KJB510007),高等学校学科创新引智计划(B12018),江苏高校优势学科建设工程项目资助Supported by National Natural Science Foundation of China (61273070,61203092), the Higher Education Natural Science Basic Research of Jiangsu Province (11KJB510007), the 111 Project (B12018), and the Priority Academic Program Devel-opment of Higher Education Institutions of Jiangsu Province本文责任编委刘德荣 （61273070,61203092）
