动态优化双估计器的多模型自适应混合控制

针对参数子集个数较多导致计算量较大和由于系统参数发生跳变造成系统暂态性能差的问题,本文提出了基于动态优化双估计器的多模型自适应混合控制方法.首先对多个参数子集进行动态优化得到最优参数子集,减少了需要计算的模型数量,提高了系统收敛速度;其次对被控对象设置一个固定初值的估计器和一个可重新赋值的估计器,固定估计器用于初始时刻对参数的估计,可赋值估计器动态调整估计初值用于减小估计误差,提高系统暂态性能.最后的仿真结果表明了该方法的有效性,并给出了系统的稳定性及收敛性分析.

Dynamically optimized multiple model adaptive mixing control of dual estimators

Aiming at the large number of parameter subset and the large amount of computation and transient performance due to the jump of system parameters, a multiple model adaptive mixing control method based on dynamic optimization and dual estimator is proposed in this paper. First, parameters of dynamic optimization to obtain the optimal subset of parameters, reduce the number of models of computation, improve the convergence rate of the system; secondly, set a fixed initial estimator and a reassignment estimator for the plant, with fixed estimator to the initial estimation of the parameters, you can assign the estimator of dynamic adjustment initial estimates for reducing the estimation error, improve the system transient performance. The final simulation results show the effectiveness of the proposed method, and the stability and convergence analysis of the system are given.