基于Hammerstein-Wiener模型的广义预测控制

提出了一种新型的基于Hammerstein-Wiener模型的广义预测控制策略。采用基于最小二乘支持向量机的Hammerstein-Wiener模型描述非线性系统动态特性,作为被控对象预测模型。同时,针对现有遗传算法和混沌粒子群优化算法收敛速度慢和精度低等缺点,给出一种拟牛顿信赖域混沌粒子群混合优化算法,作为预测控制的滚动优化策略,函数测试和非线性对象的广义预测控制的滚动优化表明该算法的优越性。最后,对设计的预测控制器进行实例仿真,结果表明它能满足系统实时稳定运行的需求,取得了良好的控制效果。

Generalized predictive control based on Hammerstein-Wiener model

A novel generalized predictive control (GPC) strategy based on the Hammerstein-Wiener model is proposed. The dynamic characteristics of the nonlinear system are described by the Hammerstein-Wiener model based on the support vector machine, so a prediction model of the controlled object is obtained. Furthermore, an optimization algorithm of chaotic particle swarm combined with quasi Newton trust region (QN-TR) is proposed in order to avoid the deficiency of slow convergence speed and low accuracy of the genetic algorithm and the chaotic particle swarm optimization （CPSO） algorithm, so a rolling optimization strategy of the predictive control is obtained. Function tests and rolling optimization of the GPC to the nonlinear object reflect the superiority of the algorithm. Finally, the resultsof the simulation example for the generalized predictive controller show that it can meet the demand of real-time and stable operation of the system, and a good control effect is obtained.