摘要
针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号,将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wie-ner模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性.
An improved nonlinear predictive control algorithm is proposed for a class of nonlinear systems which are described by Wiener model. In the algorithm, Laguerre functions are used to describe the control signals from the dynamic linear section of Wiener model, and the optimization solutions of the future control input sequences in predic- tive control are converted into the optimization of a set of immemorial Laguerre coefficients within prediction horizon in order to reduce the computation burden in optimization. A static fuzzy model is used to approximate the nonlinear sec- tion of Wiener model, and the optimization of nonlinear predictive control is converted into the optimization problem of linear predictive control. Consequently the difficulty in solving the nonlinear equations are overcome for obtaining con- trol input. Analysis solution of predictive control input is further deduced. Simulation results of CSTR （ Continuous Stirred Tank Reactor） process show that the proposed algorithm is valid and feasible.
出处
《信息与控制》
CSCD
北大核心
2007年第1期86-92,共7页
Information and Control
基金
国家自然科学基金资助项目(60421002)
