基于MIT规则的自适应扩展集员估计方法

A MIT-based Nonlinear Adaptive Set-membership Filter for Ellipsoidal Estimation

用于非线性椭球估计的自适应扩展集员(Adaptive extended set-membership filter,AESMF)算法在实际应用中存在着过程噪声设定椭球与真实噪声椭球失配的问题,导致滤波器的估计出现偏差甚至发散.本文提出了一种基于MIT规则过程噪声椭球最优化的自适应扩展集员估计算法(MIT-AESMF),用于解决非线性系统时变状态和参数的联合估计和定界中过程噪声无法精确建模问题的新算法.本算法通过MIT优化规则,在线计算使一步预测偏差包络椭球最小化的过程噪声包络椭球,以此保证滤波器健康指标满足有效条件;最后,采用地面移动机器人状态和动力学参数联合估计验证了所提出方法的有效性.

The adaptive extended set-membership filter （AESMF） for nonlinear ellipsoidal estimation suffers the mis- match between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter for optimization of the set boundaries of process noise is developed and applied to nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT, the estimation stability and boundaries accuracy of conventional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.