A hybrid dynamical system with robust switching control by action dependent heuristic dynamic programming

被引：0
|
作者：
Hanselmann, T [1 ]
Zaknich, A [1 ]
Noakes, L [1 ]
Savkin, A [1 ]
机构：
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia
来源：
2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年
关键词：
D O I：
暂无
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 hybrid dynamical system with linear plant characteristics but unknown state, disturbance and observation inputs is considered and controlled by switching between fixed linear output feedback controllers. Using state estimation based on Kalman filtering and solving a Riccati equation, a dynamic programming solution based on the estimated state can be obtained and a switching sequence for the output feedback controllers can be deduced. However, solving the dynamic programming equation is difficult in practice due to the 'curse of dimensionality'. Action Dependent Heuristic Dynamic Programming (ADHDP), also known as Q-learning, is applied to achieve an approximate dynamic programming solution based on piecewise quadratic interpolation and explicit determination of extremal values.
引用
收藏
页码：1799 / 1804
页数：6
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