Neurodynamics-Based Robust Pole Assignment for High-Order Descriptor Systems

被引:29
|
作者
Le, Xinyi [1 ]
Wang, Jun [1 ,2 ]
机构
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China
[2] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
Descriptor systems; high-order systems; neurodynamic optimization; pseudoconvexity; robust pole assignment; RECURRENT NEURAL-NETWORK; GRADIENT FLOW APPROACH; EIGENSTRUCTURE ASSIGNMENT; PSEUDOCONVEX OPTIMIZATION; VARIATIONAL-INEQUALITIES; LINEAR-SYSTEMS; EIGENVALUE; STATE; CONTROLLABILITY; OBSERVABILITY;
D O I
10.1109/TNNLS.2015.2461553
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a neurodynamic optimization approach is proposed for synthesizing high-order descriptor linear systems with state feedback control via robust pole assignment. With a new robustness measure serving as the objective function, the robust eigenstructure assignment problem is formulated as a pseudoconvex optimization problem. A neurodynamic optimization approach is applied and shown to be capable of maximizing the robust stability margin for high-order singular systems with guaranteed optimality and exact pole assignment. Two numerical examples and vehicle vibration control application are discussed to substantiate the efficacy of the proposed approach.
引用
收藏
页码:2962 / 2971
页数:10
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