Dynamic structure neural-fuzzy networks for robust adaptive control of robot manipulators

This paper proposes a novel dynamic structure neural-fuzzy network (DSNFN) via a robust adaptive sliding-mode approach to address trajectory-tracking control of an n-link robot manipulator. In the DSNFN, a five-layer neural-fuzzy network (NFN) is used to model complex processes and compensate for structured and unstructured uncertainties. However, it is difficult to find a suitable-sized NFN to achieve the required approximation error. To deal with the mentioned problem, the number of rule nodes in the DSNFN can be either increased or decreased over time based on the tracking errors, and the adaptation laws in the sense of a projection algorithm are derived for tuning all parameters of the parameterized NFN. Using DSNFN, good tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. The global stability and the robustness of the overall control scheme are guaranteed, and the tracking errors converge to the required precision by the Lyapunov synthesis approach. Experiments performed on a two-link robot manipulator demonstrate the effectiveness of our scheme.