基于模型集动态优化的具有有界扰动系统多模型自适应控制

Multiple model adaptive control of system with bounded disturbance based on dynamic optimization of model set

针对一类具有有界扰动和跳变参数的离散时间被控对象,提出基于模型集动态优化的切换式多模型自适应控制算法.该算法采用多个固定模型来提高系统暂态性能,采用2个自适应模型消除系统稳态误差,通过指标切换函数实现模型间的切换.为克服传统多模型自适应控制方法中固定模型数目过多的问题,提出基于最优可行子集定位、动态覆盖的优化策略动态建立被控对象的固定模型集,在不损失控制精度的前提下,显著减少了固定模型数.最后证明了该算法能够保证闭环系统输入输出稳定,且保证被控对象输出可在一定范围内.

A switching multiple models adaptive control strategy based on dynamic optimization of model set is designed for discrete time systems with bounded disturbance and jumping parameters. Multiple fixed models are adopted to improve the transient response,and two adaptive models are used to eliminate the steady state error. By using a switching index function,the best model is selected. At the same time,an optimal feasible set orientation and dynamic bestrow based optimization strategy is proposed to on-line create fixed model set. Consequently,the number of fixed models and the computation burden are reduced greatly. It is proved that the closed-loop system is input-output stable,and the output of the system can track any bounded reference input with bounded error. The simulation results confirm the efficacy of the proposed method.