A Supervised Adaptive Learning-based Fuzzy Controller for a non-linear vehicle system using Neural Network Identification

In this paper, a Supervised Adaptive Learning-based Fuzzy Controller (ALFC) with Neural Network Identification and Convex Parameterization is designed to identify and control the unmanned vehicle in an autonomous parking system. The objective is to achieve robust learning and control while maintaining a low implementation cost. The proposed algorithm design incorporates the following learning and control theorems - non-linear system identification using neural network, fuzzy logic, supervised adaptive learning as well as multiple model based convex parameterization. To demonstrate the algorithm in a more straight forward manner, we are using a real nonlinear unmanned autonomous driving system as an example to apply the algorithm and showing the superior performance of controller. In the autonomous driving system, the proposed method can be used for both estimating and further controlling a desired vehicle speed and steering wheel turning. With a supervised adaptive learning-based method, robustness can be also assured under various operating environments regardless of unpredictable disturbances. The convex parameterization further improves the speed of convergence of the adaptive learning process for the Fuzzy controller by using the multiple models concept. Last but not least, comparative experiments have also demonstrated that systems equipped with the new algorithm are able to achieve faster and smoother convergence.