设计基于零和博弈的 H∞$$ {H}_\{infty }有外部干扰和输入延迟的 $$ 最佳预览重复控制系统

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Da Liu, Yong-Hong Lan
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引用次数: 0

摘要

本文提出了一种最优预览重复控制(OPRC)方案,用于处理具有外部未知干扰和输入延迟的连续时间线性系统的干扰衰减问题。通过引入性能折扣函数,将一般有界增益应用于 OPRC 跟踪控制问题。首先,构建一个包含系统状态方程、跟踪误差动态和修正重复控制输出方程的增强系统,然后通过状态变换将其转化为非延迟系统。接着,给出 OPRC 控制器,并通过将跟踪问题转化为双人零和博弈问题,得出博弈代数里卡提方程(GARE),从而给出相关最小-最大优化问题的纳什均衡解。此外,还引入了一种值迭代(VI)算法来优化连续时间 GARE 的解,并确保其收敛性。此外,通过给出贴现因子的上限,得到了闭环系统的有界输入有界输出稳定性。最后,通过数值模拟实例说明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of zero-sum game-based H ∞ $$ {H}_{\infty } $$ optimal preview repetitive control systems with external disturbance and input delay

In this article, an H $$ {H}_{\infty } $$ optimal preview repetitive control (OPRC) scheme is proposed to deal with the disturbance attenuation problem for continuous-time linear systems with external unknown disturbance and input delay. A general bounded L 2 $$ {L}_2 $$ gain is applied to the H $$ {H}_{\infty } $$ OPRC tracking control problem by introducing a function with discounted performance. First, an augmented system containing system state equation, tracking error dynamics, and modified repetitive control output equation is constructed, which is then transformed into a non-delayed one by state transformation. Next, the OPRC controller is given and the game algebraic Riccati equation (GARE) is derived by transforming the H $$ {H}_{\infty } $$ tracking problem into a 2-player zero-sum game problem to give a Nash equilibrium solution of the associated min–max optimization problem. Besides, a value iteration (VI) algorithm is introduced to optimize the solution of continuous time GARE and ensure its convergence. Furthermore, the bounded-input bounded-output stability of the closed-loop system is obtained by giving an upper bound on the discount factor. Finally, the numerical simulation example is provided to illustrate the effectiveness of the proposed method.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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