高阶非线性控制系统的适应性仿真研究  

Control of High-order Nonlinear Systems and Its Adaptability Simulations

作　　者：王军[1] 李东海[1] 宋跃进[2] 

机构地区：[1]清华大学热力系统仿真与控制研究所,热科学与动力工程教育部重点实验室,北京100084 [2]中国兵器工业集团二○七研究所,太原030006

出　　处：《系统仿真学报》2007年第8期1749-1753,共5页Journal of System Simulation

基　　金：国家自然科学基金项目(50376029)。

摘　　要：针对带有外部扰动和参数摄动的不确定高阶非线性系统,利用积分行为补偿系统的各种未知因素,设计适应性非线性控制器(ANLC),并提出了全面的控制器适应性评价方法。首先结合典型信号扰动试验和模型参数摄动试验,检验系统的抗扰性和鲁棒稳定性;然后引入神经网络和Taylor级数展开理论构造非线性函数,改变高阶非线性系统的模型结构,利用Monte-Carlo随机试验方法,进行模型摄动的性能鲁棒性分析;并与精确反馈线性化(EFL)方法进行了定量比较。结果表明,适应性非线性控制器(ANLC)具有很强的适应性能,是解决不确定高阶非线性系统控制的有效途径。A nonlinear controller（ANLC） for uncertain high-order nonlinear plant with perturbation and disturbance was designed. A method for comprehensive adaptability evaluation was also presented. The controller includes integral actions for compensation of the entire dynamics of system. System disturbances and model parameter uncertainty tests were carried to validate auto-disturbance ability and robustness. In order to verify the performances robustness and adaptability of controller, the Neural Network and the Taylor series were applied to construct nonlinear function, change the weights and biases of the network or the coefficients of Taylor series to remodel the plant. Monte-Carlo stochastic methods were also used to analyze the characteristics of controller. Simulation results compared with exact feedback linearization show that ANLC has good adaptability and it is an effective approach for uncertain high-order nonlinear systems.

关 键 词：高阶非线性系统 神经网络 TAYLOR级数 Monte-Carlo随机试验方法 

分 类 号：TP271[自动化与计算机技术—检测技术与自动化装置]