Improved set-membership identification algorithm with adaptive noise bounding
自适应噪声定界的改进集员辨识算法

In the set-membership identification (SMI), it is difficult to precisely determine the bounds of the system noise in most real applications. The widely used over-estimated bounds will deteriorate the performance of the algorithm. We investigate this problem when the a priori knowledge of the noise bound is insufficient. Under the assumptions of bounded system inputs and measurement errors, we model the noise bound as a time-varying variable depending on some model parameters. We propose an enhanced optimal bounding ellipsoid (OBE) identification algorithm with adaptive bound-tuning to adjust the noise bound based on the estimated parameters, which prevents the increased conservation from the overestimated bound assumption and improves the convergence rate of the algorithm. Simulation results show higher effectiveness of the proposed algorithm than that of the conventional algorithm with fixed over-estimated noise bound.