Comparative Study of Non Linear System Modeling Using Artificial Intelligent Techniques

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 models. It is known that artificial neural networks and fuzzy logics are powerful tools for handling problems of large dimension. Many studies have been reported on the ability of neural networks and fuzzy logics for approximating nonlinear functions. The tasks of our paper are to model the truck backer upper control problem using different neural networks and fuzzy logic. This paper proposes a comparative study of neural networks (FFN, RBFN and RNN) and fuzzy logic for modeling the truck backer upper control problem. The body angle ∅, x position and the steering angle θ of the truck are used as training data for neural network and fuzzy logic. The results showed the performance of RBFN, better than other neural networks and fuzzy logic with lesser number of iterations, training period and minimum mean square error (MSE).