Comparison of fuzzy and neural truck backer-upper control systems

Abstract

A simple fuzzy control system and a simple neural control system for backing up a truck in an open parking lot are developed. The choice of control problem was prompted by the recent, successful, neural network truck backer-upper simulation of Nguyen and Widrow (Proc. Int. Joint Conference on Neural Networks, vol.2, p.357-363, June, 1989). The authors were unable to exactly replicate the neural network they used. Instead the authors built the best backpropagation network they could with essentially the same kinematics and compared it to the best fuzzy controller they could develop. The fuzzy controller compares favorably with the neural controller in terms of black-box computation load, smoothness of truck trajectories, and robustness. Robustness of the fuzzy controller is studied by deliberately adding confusing FAM (fuzzy associative memory), rules-sabotage rules-to the system and by randomly removing different subsets of FAM rules. Robustness of the neural controller is studied by randomly removing different portions of the training data. It is concluded that fuzzy control shows optimal truck backing-up performance