Adaptive Position Tracking System and Force Control Strategy for Mobile Robot Manipulators Using Fuzzy Wavelet Neural Networks

Abstract

In this paper, we propose an adaptive position tracking system and a force control strategy for nonholonomic mobile robot manipulators, which incorporate the merits of Fuzzy Wavelet Neural Networks (FWNNs). In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of mobile robot manipulators control system, such as unknown dynamics, disturbances and parameter variations. To solve this problem, an adaptive FWNNs control scheme with the online learning ability is utilized to approximate the unknown dynamics without the requirement of prior system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances, optimal parameters and higher order terms in Taylor series. According to adaptive position tracking control design, an adaptive robust control strategy is also considered for nonholonomic constraint force. The design of adaptive online learning algorithms is derived using Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability of mobile robot manipulators control system but also guarantee tracking performance. The effectiveness and robustness of the proposed method are demonstrated by comparing simulations and experimental results that are implemented in an indoor cleaning crawler-type mobile robot manipulators system.