Self-evolving fuzzy model-based controller with online structure and parameter learning for hypersonic vehicle

This paper presents a self-evolving fuzzy model-based controller for a generic hypersonic vehicle (HV). The self-evolving fuzzy model can dynamically evolve its rule base through evaluating the influence of a rule and then is introduced to reconstruct the unknown dynamics so that the changing dynamics and unknown uncertainties of the HV are integrated. The control law is designed using the fuzzy identified model instead of the actual HV one. The stability analysis of the whole control system is presented from the Lyapunov function and shows that the tracking errors converge to zero. The bounds of the control inputs are considered in this study and ensured by means of the stable projection-type parameter adaptation laws. To reduce the computation complexity, only the parameters of a 'winner' rule closest to the current state are adjusted while those of other rules maintain unvaried. This differs from the existing studies in which the parameters of all rules need to be adjusted. The simulation results under the nominal conditions and parameter uncertainties demonstrate the superior performance of the proposed controller.