PSO算法在多模型自校正动态矩阵控制中的应用

为解决多模型控制中固定模型获取问题,将粒子群优化(PSO)算法应用于多模型自校正动态矩阵控制.对一类含跳变参数的单输入单输出离散时间系统,当模型参数突然跳变时,通过PSO算法在线优化自适应模型参数,并根据模型相似度实现固定模型的动态管理,以有效控制模型数量和减轻系统负担.模型切换策略用于选择当前与实际被控对象最接近的控制器.仿真结果表明,该方法能够较大地改善系统的瞬态响应,优于常规的自校正动态矩阵控制算法;并说明了其有效性和可行性.

Application of PSO to Multimodel Self-Tuning Dynamic Matrix Control

To obtain the fixed models in multimodel control problem,the PSO(particle swarm optimization) algorithm is applied to multimodel self-tuning dynamic matrix control.For a class of SISO discrete time systems with jumping model parameters, the PSO algorithm is used to optimize the self-tuning model parameters online and manage dynamically the fixed models in accordance to the similarity between models,so that the number of models may be handled and system burden can be reduced effectively.Model switching tactics is used to select the controller best to describe the actual controlled system.Simulation results show that the method proposed can improve the transient response greatly in comparison with conventional control algorithms and demonstrate its efficiency and feasibility.