Control of Mobile Robot Using Prediction-based FNN

Abstract

A prediction model-based fuzzy neural network (PFNN) approach is proposed, in which a basic FNN is created at first to predict the relative position of the trajectory. Then a FNN is used independently to get the control values of the variables for motor motion according to those variables including trajectory position both from those measured and predicted values, and those speed variables. At last membership functions and network weights of the second FNN are also trained with a BP algorithm. Meanwhile, the measured values of the trajectory are memorized so as to compare them with the memorized values to confirm if the motion is moving in cycles. If it is moving in cycles, a decision making unit would cease the prediction unit. The emulated experiments show that the performance of the proposed approach is higher, the process to train the network is relatively easy, and the control strategy is simple.