摘要
针对广义预测控制算法需要在线递推求解Diophantine方程及矩阵求逆等计算量大的缺陷,对参数未知多变量非线性系统提出一种径向基函数神经网络的直接广义预测控制算法。该算法将多变量非线性系统转化为多变量时变线性系统,用三次样条基函数逼近系统广义误差向量中的时变系数,然后利用径向基神经网络来逼近控制增量表达式,并基于广义误差估计值对控制器参数向量即网络权值向量Φu和广义误差估计值中的未知向量进行自适应调整。仿真结果验证了此算法的有效性。
Direct generalized predictive control （GPC） based on radial basis function （RBF） neural network method for a class of multiple-input multiple-output （MIMO） nonlinear system with unknown parameters is presentedto overcome the high load of computing of traditional GPC as on-line recursion of Diophantine, matrix inversion etc. In this method, the MIMO nonlinear system is turned into a MIMO time-varying linear system, then a group of cubic spline functions are used to approach the MIMO time-varying coefficients of generalized error, and a RBF neural network is used to approximate the function of control increment, and both the controller parameters vectors θu and the unknown vectors θe in the estimation of generalized error are adjusted adaptively. It is proved that the proposed method can make the estimation of generalized error converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of this method.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第1期138-141,共4页
Computer Engineering and Design
基金
国家科技部高新技术计划基金项目(2005EJ000017)
河北省科技研究与发展计划基金项目(02547015D)
关键词
人工智能
径向基函数神经网络
广义预测控制
多变量非线性系统
时变线性系统
artificial intelligence
RBF neural network
generalized predictive control
MIMO nnonlinear system
time-varying linear system
