Multiple Incremental Fuzzy Neuro-Adaptive Control of Robot Manipulators

Abstract

An adaptive control using multiple incremental fuzzy neural networks (FNNs) is proposed for robot manipulators. The structure and parameters of the FNNs are determined dynamically by using an incremental FNN. By incorporating incremental learning and adaptive control with multiple models, the proposed method not only reduces complexity and computation induced by the use of multiple models, but also provides favorable transient and tracking performance. The multiple FNNs are switched or blended to improve the transient response when manipulating objects are changed. The parameters are refined adaptively to compensate for system uncertainties. The resulting closed-loop system with a switching or blending law is proven to be asymptotically stable. The proposed scheme is applied to control a two-link robot manipulator in conjunction with varying payloads.