Robust receding horizon control of uncertain fuzzy systems

被引：1
|
作者：
Song, Chonghui [1 ]
Ye, Jinchun [2 ]
机构：
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[2] CTC Holdings, Chicago, IL 60604 USA
来源：
NEURAL COMPUTING & APPLICATIONS | 2013年 / 22卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Differential game; Dynamic programming; Uncertain fuzzy systems; Numerical methods; Robust receding horizon control; MODEL-PREDICTIVE CONTROL; DESIGN; APPROXIMATION;
D O I：
10.1007/s00521-011-0710-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The optimal control of uncertain fuzzy systems with constraints is still an open problem. One candidate to deal with this problem is robust receding horizon control (RRHC) schemes, which can be formulated as a differential game. Our focus concerns numerically solving Hamilton-Jacobi-Issac (HJI) equations derived from RRHC schemes for uncertain fuzzy systems. The developed finite difference approximation scheme with sigmoidal transformation is a stable and convergent algorithm for HJI equations. Accelerated procedures with boundary value iteration are developed to increase the calculation accuracy with less time consumption. Then, the state-feedback RRHC controller is designed for some class of uncertain fuzzy systems with constraints. The value function calculated by numerical methods acts as the design parameter. The closed-loop system is proven to be asymptotically stable. An engineering implementation of the controller is discussed.
引用
收藏
页码：237 / 247
页数：11
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