Adaptive Optimal Control of Linear Periodic Systems: An Off-Policy Value Iteration Approach

This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control 2021-02, Vol.66 (2), p.888-894
Main Authors: Pang, Bo, Jiang, Zhong-Ping
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive control
Adaptive dynamic programming
Adaptive systems
Algorithms
Approximation algorithms
Dynamic programming
Heuristic algorithms
Iterative methods
linear periodic systems
Optimal control
Pendulums
Stability analysis
System dynamics
Time-varying systems
value iteration
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article studies the infinite-horizon adaptive optimal control of continuous-time linear periodic (CTLP) systems. A novel value iteration (VI) based off-policy adaptive dynamic programming (ADP) algorithm is proposed for a general class of CTLP systems, so that approximate optimal solutions can be obtained directly from the collected data, without the exact knowledge of system dynamics. Under mild conditions, the proofs on uniform convergence of the proposed algorithm to the optimal solutions are given for both the model-based and model-free cases. The VI-based ADP algorithm is able to find suboptimal controllers without assuming the knowledge of an initial stabilizing controller. Application to the optimal control of a triple inverted pendulum subjected to a periodically varying load demonstrates the feasibility and effectiveness of the proposed method.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2020.2987313