Data-Driven Direct Adaptive Risk-Sensitive Control of Stochastic Systems

The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming (ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices. The authors use online data of the syste...

Full description

Saved in:
Bibliographic Details
Published in:Journal of systems science and complexity 2024, Vol.37 (4), p.1446-1469
Main Authors: Qiao, Nan, Li, Tao
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Complex Systems
Control
Control theory
Dynamic programming
Linear systems
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Optimal control
Riccati equation
Risk management
Statistics
Stochastic systems
System dynamics
Systems Theory
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming (ADP) algorithm for continuous-time stochastic linear systems with partially unknown system dynamics and infinite horizon quadratic risk-sensitive indices. The authors use online data of the system to iteratively solve the generalized algebraic Riccati equation (GARE) and to learn the optimal control law directly. For the case with measurable system noises, the authors show that the adaptive control law approximates the optimal control law as time goes on. For the case with unmeasurable system noises, the authors use the least-square solution calculated only from the measurable data instead of the real solution of the regression equation to iteratively solve the GARE. The authors also study the influences of the intensity of the system noises, the intensity of the exploration noises, the initial iterative matrix, and the sampling period on the convergence of the ADP algorithm. Finally, the authors present two numerical simulation examples to demonstrate the effectiveness of the proposed algorithms.
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-024-2421-z