Modified λ-Policy Iteration Based Adaptive Dynamic Programming for Unknown Discrete-Time Linear Systems

被引：6
|
作者：
Jiang, Huaiyuan [1 ]
Zhou, Bin [1 ]
Duan, Guang-Ren [1 ]
机构：
[1] Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150001, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2024年 / 35卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Adaptive dynamic programming (ADP); data-driven control; discrete-time systems; modified 1-policy iteration (1-PI); policy iteration; unknown systems; STABILIZATION;
D O I：
10.1109/TNNLS.2023.3244934
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
this article, the 1-policy iteration (1-PI) method for the optimal control problem of discrete-time linear systems is reconsidered and restated from a novel aspect. First, the traditional 1-PI method is recalled, and some new properties of the traditional 1-PI are proposed. Based on these new properties, a modified 1-PI algorithm is introduced with its convergence proven. Compared with the existing results, the initial con-dition is further relaxed. The data-driven implementation is then constructed with a new matrix rank condition for veri-fying the feasibility of the proposed data-driven implementation. A simulation example verifies the effectiveness of the proposed method.
引用
收藏
页码：3291 / 3301
页数：11
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