Neural-Network-based Near-optimal Control for a Class of Nonlinear Descriptor Systems with Control Constraint

被引：0

作者：

Luo, Yanhong ^{[1
]}

Zhang, Huaguang ^{[1
]}

Lun, Shuxian ^{[2
]}

Wang, Yingchun ^{[1
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China

[2] Bohai Univ, Sch Informat Sci & Engn, Jinzhou 121013, Liaoning, Peoples R China

来源：

2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年

基金：

中国国家自然科学基金;

关键词：

Constraint; Descriptor system; Nonquadratic functional; GI-DHP; Neural network;

D O I：

10.1109/CCDC.2008.4597779

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The near-optimal control problem for nonlinear constrained descriptor systems is solved by greedy iterative DHP(GI-DHP) algorithm. The descriptor system is first conceptually reduced to a state space form and then a nonquadratic functional is developed in order to deal with the control constraint problem. Then the GI-DHP algorithm is proposed to solve the optimal control problem of the state space system. For facilitating the implementation of the iterative algorithm, two neural networks are utilized to approximate the costate function and compute the optimal control policy respectively. An example is given to demonstrate the validity and feasibility of the proposed optimal control scheme.

引用

页码：2521 / 2526

页数：6

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