State Following (StaF) Kernel Functions for Function Approximation Part II: Adaptive Dynamic Programming

被引：0
作者：
Kamalapurkar, Rushikesh [1 ]
Rosenfeld, Joel A. [1 ]
Dixon, Warren E. [1 ]
机构：
[1] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL USA
来源：
2015 AMERICAN CONTROL CONFERENCE (ACC) | 2015年
基金：
美国国家科学基金会;
关键词：
CONTINUOUS-TIME;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods.
引用
收藏
页码：521 / 526
页数：6
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