多变量非线性系统的有约束模糊预测控制

Constrained fuzzy predictive control for MIMO nonlinear systems

针对多变量非线性系统提出了一种带约束输入的广义预测控制(GPC)算法.首先对多变量非线性系统建立T-S模糊模型,利用模糊聚类算法和正交最小二乘算法对输入变量的模糊划分及后件部分的参数分别进行辨识,然后在每个采样点对系统进行局部动态线性化.根据得到的系统线性化模型设计GPC算法,该算法充分考虑了控制输入及其增量受约束的情况,而且不必求D iophantine方程,大大减小了计算量.仿真结果表明该算法能保证系统输出有效跟踪设定值,而且控制输入和控制增量均在其约束范围之内.

A generalized predictive control （GPC） with input constraints is presented for MIMO nonlinear systems. First, a T- S fuzzy model is constructed for MIMO nonlinear system. This fuzzy model is identified by the fuzzy cluster algorithm and the orthogonal least square method. And then the local dynamic linearization is applied to the system at each sampling point. For the linearizing model, the GPC algorithm is presented. This algorithm takes into account all the constraints of the control signals and their increments. It does not require calculating the Diophantine equation, and needs only smaller computer memory. The simulation results show that this algorithm can ensure outputs of the system are tracking set-points, and inputs and their increments move in their allowed regions.