A Unified Practical Predefined-Time Interval Type-2 Fuzzy NN-Based Fault-Tolerant Control for Robotic Manipulators

Abstract

Fast response and safety operation are essential requirements for the tracking control of robotic manipulators. In this article, a unified predefined-time self-organizing interval type-2 fuzzy neural network control (SOIT2FNNC) framework is presented for robotic manipulators subject to actuator failures and uncertainties. Such a framework operates in a parallel structure where the model-free predefined-time controller guarantees the transient performance while the proposed network controller provides appropriate torques to handle failures and uncertainties, which leads to a solution for both normal and faulty conditions. Significant features of this study are that the control design does not depend on any information about system dynamics, and theoretically, the predefined-time convergence is accomplished by means of the online parameter learning algorithm. Moreover, a hierarchical self-organizing algorithm is embedded in the proposed network controller to overcome the network structure complexity and the input partition problem. Both numerical simulation and experiment results utilizing artificial faults are implemented to demonstrate the superiority of the proposed control scheme.