Discrete-Time Neural Inverse Optimal Control for Nonlinear Systems via Passivation

被引：31
|
作者：
Ornelas-Tellez, Fernando [1 ]
Sanchez, Edgar N. [2 ]
Loukianov, Alexander G. [2 ]
机构：
[1] Univ Michoacana, Div Estudios Posgrad, Fac Ingn Elect, Morelia 58030, Michoacan, Mexico
[2] IPN, Ctr Invest & Estudios Avanzados, Unidad Guadalajara, Guadalajara 45019, Jalisco, Mexico
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2012年 / 23卷 / 08期
关键词：
Control Lyapunov function; inverse optimal control; passivity; recurrent neural network; trajectory tracking; STABILIZATION; DESIGN; CONVERGENCE; DRIVEN; NOISE;
D O I：
10.1109/TNNLS.2012.2200501
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.
引用
收藏
页码：1327 / 1339
页数：13
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