基于PSO的连续系统直接辨识及在带弹性负载电机的应用

针对连续方程误差模型辨识须引入数值滤波器而造成有偏估计,提出一种基于粒子群优化(PSO)算法和连续输出误差法的连续系统直接辨识方法.该方法用简化四阶龙格库塔法进行系统输出的数值逼近,并采用PSO进行数值优化以避免有偏估计.对所提出方法的估计性质进行分析,进而得出辨识问题的全局解在开环下参数估计的一致性.仿真案例表明,所提出方法对案例的辨识精度高于简化修正辅助变量法等几种连续系统辨识方法.将所提出的方法应用于带弹性负载的电机模型辨识,获得了良好的估计,从而表明了所提出方法在应用上的有效性.

Direct identification of continuous-time models based on PSO and its application

To avoid the bias of continuous-time equation model''s estimation by numerical filters, the direct identification of the continuous-time model based on the PSO and output error method is proposed, which uses a simplified fourth Runge-Kutta method to the approximate system''s output, and used PSO for the numerical optimization to aviod the estimation bias. The consistence of the proposed method is analysed, which shows that the parameters can be estimated asymptotically consistent in an open loop. Simulations show that the proposed method has a good estimation in this case than several continuous-time identification methods such as SRIVC. Finally, the proposed method is used for a good dynamic model of machine with elastic load, which verifies its effectiveness.