Adaptive Linear Quadratic Control for Stochastic Discrete-Time Linear Systems With Unmeasurable Multiplicative and Additive Noises

This note investigates the adaptive linear quadratic control problem (ALQCP) for stochastic discrete-time (DT) linear systems with unmeasurable multiplicative and additive noises. A data-driven value iteration algorithm is developed to solve the stochastic algebraic Riccati equation (SARE) that resu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 69; no. 11; pp. 7808 - 7815
Main Authors: Jiang, Yi, Liu, Lu, Feng, Gang
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
Adaptive linear quadratic control
Adaptive systems
Additive noise
Classification algorithms
Discrete time systems
Heuristic algorithms
Iterative algorithms
Linear systems
Riccati equation
stochastic algebraic Riccati equation (SARE)
Stochastic processes
Symmetric matrices
System dynamics
unmeasurable noises
value iteration (VI)
ISSN:0018-9286, 1558-2523
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This note investigates the adaptive linear quadratic control problem (ALQCP) for stochastic discrete-time (DT) linear systems with unmeasurable multiplicative and additive noises. A data-driven value iteration algorithm is developed to solve the stochastic algebraic Riccati equation (SARE) that results from the concerned problem and to simultaneously obtain the optimal feedback policy. The proposed algorithm directly uses online data to solve the ALQCP based on an unbiased estimator and an initial stabilizing controller with unknown system dynamics and unmeasurable multiplicative and additive noises. It is shown that the proposed algorithm under a finite length of online data converges to a neighborhood of the solution to the SARE with a probability and the input-to-state stability, and the neighborhood can be arbitrarily small while the probability can be arbitrarily close to one as the length of online data increases. The simulation results demonstrate the efficacy of the proposed algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2024.3399630