Backing Up a Truck on Gaussian and Non-Gaussian Impulsive Noise with Extended Kalman Filter and Fuzzy Controller

Abstract

Truck backing-up problem is a typical test bed for fuzzy control system. The control performance affects the safety of the truck well, but has not been studied when location of the truck is given by GPS which introduces sensing noises into the system. In this paper, we study the impact of noise on control performance of the system, and we propose an extended Kalman filter which claims to adapt to only Gaussian noise for improving control performance in Gaussian and non-Gaussian impulsive noise situation. To implement the filter, we propose screening the input to get the output of the fuzzy controller such that the partial derivative of the input–output function of the controller required by the extended Kalman filter is computationally available. Our simulation results of the truck system with and without noise, the noise being Gaussian and non-Gaussian impulsive, and the system with and without the extended Kalman filter, indicate that the average performance of the system with the filter is much better than that without the filter no matter the noise is Gaussian or impulsive, the great power of the extended Kalman filter in dealing with even non-Gaussian impulsive noises for fuzzy truck control, while the great deviation from the average performance makes an urgent call for non-Gaussian version of the extended Kalman filter to adapt to more general non-Gaussian impulsive noise situation.