An Observer-Based Reinforcement Learning Solution for Model-Following Problems

被引：0

作者：

Abouheaf, Mohammed I. ^{[1
]}

Vamvoudakis, Kyriakos G. ^{[2
]}

Mayyas, Mohammad A. ^{[1
]}

Hashim, Hashim A. ^{[3
]}

机构：

[1] Bowling Green State Univ, Robot Engn, Bowling Green, OH 43403 USA

[2] Georgia Inst Technol, Daniel Guggenheim Sch Aerosp Engn, Atlanta, GA 30332 USA

[3] Carleton Univ, Dept Mech & Aerosp Engn, Ottawa, ON K1S 5B6, Canada

来源：

2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC | 2023年

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

TIME; TRACKING; GAMES;

D O I：

10.1109/CDC49753.2023.10384059

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces a novel model-free solution for a multi-objective model-following control problem, utilizing an observer-based adaptive learning approach. The goal is to regulate model-following error dynamics and optimize process variables simultaneously. Integral reinforcement learning is employed to adapt three key strategies, including observation, closed-loop stabilization, and reference trajectory tracking. Implementation uses an approximate projection estimation method under mild conditions on learning parameters.

引用

页码：7976 / 7981

页数：6

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