Path Tracking Control Using a Wavelet Based Fuzzy Neural Network for Mobile Robots

Motion control of mobile robots is a typical nonlinear tracking control issue and has been discussed with different control schemes such as PID, GPC, sliding mode, predictive control etc[1]-[3]. Intelligent control techniques, based on neural networks and fuzzy logic, have also been developed for path tracking control of mobile robots[4][5]. While conventional neural networks have good ability of self-learning, they also have some limitations such as slow convergence, the difficulty in reaching the global minima in the parameter space, and sometimes even instability as well. In the case of fuzzy logic, it is a human-imitating logic, but lacks the ability of self-learning and self-tuning. Therefore, in the research area of intelligent control, fuzzy neural networks(FNNs) are devised to overcome these limitations and to combine the advantages of both neural networks and fuzzy logic[6][7]. This provides a strong motivation for using FNNs in the modeling and control of nonlinear systems. And the wavelet fuzzy model(WFM) has the advantage of wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. The conventional fuzzy model can not give the satisfactory result for the transient signal. On the contrary, in the case of WFM, the accurate fuzzy model can be obtained because the energy compaction by the unconditional basis and the description of a transient signal by wavelet basis functions are distinguished[8]. Therefore, we design a FNN structure based on wavelet, which merges these advantages of neural network, fuzzy model and wavelet. The basic idea of wavelet based fuzzy neural network(WFNN) is to realize the process of fuzzy reasoning of WFM by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. In this paper, we design the direct adaptive control system using the WFNN structure. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method and compare the control performance of the WFNN controller with those of the FNN, the WFM and the wavelet neural network(WNN) controllers. 2. WAVELET BASED FUZZY NEURAL NETWORK Stable Path Tracking Control Using a Wavelet Based Fuzzy Neural Network for Mobile Robots °Joon Seop Oh*, Jin Bae Park, and Yoon Ho Choi