A comparative study of fuzzy logic and neural network control of the truck backer-upper system

In this paper a simulated comparison of fuzzy logic and neural network control of the truck backer-upper system is presented. The aim of the controller is to back a truck to a loading dock which is a difficult task. It is a nonlinear control problem for which no traditional control system design method exists. We assumed that there were no linguistic rules available, and therefore the controllers were designed from the available numerical data only. We provided the same desired input-output pairs to both the neural and the fuzzy approaches, and compared the final control performance of both controllers. The results showed the superiority of the neural controller over the fuzzy one, when the later was influenced by the amount of overlapping between its sets and the missing rules from its rule base.