Indirect Adaptive Fuzzy-Neural Control of Robot Manipulator

The paper presents an indirect adaptive fuzzyneural control scheme for an n-link robot manipulator. In the proposed scheme a fuzzy-neural controller is constructed based on the fuzzy neural networks for approximating the unknown nonlinearities of dynamic systems, and also a sliding mode controller is incorporated to compensate for the modelling errors of fuzzy neural networks. The parameters of the fuzzy neural network approximators are modified using the recently proposed fuzzy-neural algorithm named Online Sequential Fuzzy Extreme Learning Machine (OS-Fuzzy-ELM), where the parameters of the membership functions characterizing the linguistic terms in the if-then rules are assigned by random values independent from the training data. Different from the original OS-Fuzzy-ELM algorithm, the consequent parameters of if-then rules are updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. Finally the proposed adaptive fuzzy-neural controller is applied to control a two-link robot manipulator and the simulation results verify the effectiveness of the proposed control scheme.