Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators

This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) e...

Full description

Saved in:
Bibliographic Details
Published in:Actuators Vol. 14; no. 10; p. 506
Main Authors: Liang, Yuling, Xie, Mengjia, Zhang, Juan, Ming, Zhongyang, Gao, Zhiyun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2025
Subjects:
Actuators
adaptive dynamic programming
Aircraft
Algorithms
Analysis
asymmetric saturation actuators
Asymmetry
Collocation methods
Communication
Cost control
Design
Dynamic programming
Economic aspects
Methods
multivariate probabilistic collocation method
Optimal control
Ordinary differential equations
Random variables
Simulation
Stability
stochastic systems
Systems stability
Time varying control systems
ISSN:2076-0825, 2076-0825
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then the worst-case disturbance policy and the asymmetric saturation optimal control signal can be obtained. Secondly, the multivariate probabilistic collocation method (MPCM) is used to evaluate the value function at designated sampling points. The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods. Furthermore, the DET mode is utilized to solve the SDP problem to reduce the computational burden on communication resources. Finally, the Lyapunov stability theorem is applied to analyze the stability of time-varying systems, and the simulation shows the feasibility of the designed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-0825
2076-0825
DOI:10.3390/act14100506