Fault Tolerant-Based Broad Fuzzy Neural Control for a Flexible Manipulator With Constraints

In this study, a novel fault-tolerant broad fuzzy learning control scheme is proposed for a flexible single-link manipulator with input delay and output constraints. The broad fuzzy neural network is utilized to effectively approximate the time delay of the controller and compensate for the unknown nonlinear uncertainties. By applying the Lyapunov direct method and barrier Lyapunov function, the semi-global uniformly ultimately boundedness (SGUUB) of the system is demonstrated, which guarantees that all system states converge to zero within the specified limitation. Finally, the simulation and experiment results, compared with those of different neural networks, manifest the validity of the proposed control method.