Adaptive locally-linear-models-based fault tolerant control for humanoid robot with unknown faults

Today the problem of fault tolerant control (FTC) is considered as an important and essential counterpart of control engineering systems. Because of importance and existence of faults that don´t have a known structure in control system, i.e., fault occurred because of tangle of complex factors, in this paper a Humanoid Robot with unknown faults is considered and a novel FTC architecture is presented. A neuro/fuzzy model consisting of a few locally linear models (LLMs) with on-line updated centers and width vectors is used to approximate the fault model. A linear estimator is employed to estimate the states of the system that are inputs to LLMs. The stability analysis of system is accomplished via Lyapunov theory, from which the parameter updating rules are derived. At the end of this paper some numerical simulations are given to show the effectiveness of the proposed method.