基于单边SPSA的动态偏差数据驱动控制算法  

One-Sided SPSA-Based Data-Driven Control Algorithm with Dynamic Deviations

作　　者：哀微[1] 朱学峰[1] 

机构地区：[1]华南理工大学自动化科学与工程学院,广东广州510640

出　　处：《华南理工大学学报（自然科学版）》2012年第9期81-86,92,共7页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(60704012);华南理工大学中央高校基本科研业务费专项资金资助项目(2011ZM0120)

摘　　要：当受控系统的数学模型完全未知时,应用同时扰动随机逼近算法(SPSA)建立数据驱动控制方法是比较可行的.文中对SPSA及其改进算法的原理和有效性进行了研究,分析了其在面向控制中的梯度逼近效率不高等不足,提出了一种针对控制问题的单边SPSA算法,该算法在实际控制中节约1/3的数据,使得其收敛速度和精度能够适用于控制问题.然后将单边SPSA与数据驱动控制方法相结合,提出了基于动态历史偏差的数据利用方案,对神经网络结构的函数逼近器进行直接自适应权值调整.仿真结果表明,该数据驱动方法完全不依赖于模型,对非线性系统进行了良好的控制.Using simultaneous-perturbation stochastic approximation （SPSA）algorithm to establish a data-driven control method is feasible when the mathematical models of controlled systems are totally unknown. In this paper, the principle and effectiveness of both the SPSA algorithm and its improved algorithms are investigated, and the shortcomings of the SPSA algorithm in the control-oriented situation, such as lower gradient approximation efficiency, are analyzed. Then, a one-sided SPSA algorithm is proposed for the control problem, which saves one-third data compared with the SPSA algorithm in the actual control, the optimization procedure for the control problem. Then, thus obtaining enough convergence rate and accuracy of by combining the one-sided SPSA algorithm and the datadriven control method, a data utilization scheme is designed based on dynamic deviations, and the connection weights in a neural network function approximator are adjusted directly and adaptively. Simulatied results show that the proposed data-driven control method secures excellent control over nonlinear systems without the help of any model.

关 键 词：同时扰动随机逼近 数据驱动控制 梯度逼近 单边SPSA 数据利用 非线性系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]