Robust adaptive type-2 neural fuzzy sliding mode control of a class of nonlinear systems

This study aims to develop an adaptive control scheme for the control of a class of flexible multi-body nonlinear systems with infinite dimensions and extremely coupled dynamics. The finite-dimensional model is obtained by the assumed modes method (AMM), but there are uncertainties in the truncated model, which makes the system a challenging control problem. The proposed adaptive control scheme is the hybrid of the sliding mode control (SMC) and the type-2 neural fuzzy system (NFS). A newly modified conjugate gradient (CG) algorithm is used to optimize the NFS parameters, thus enhancing its self-adapting capabilities. The control law of the proposed control scheme requires the estimation of the unknown system functions, which is provided by the adaptive NFS. The stability of the control scheme is guaranteed by the Lyapunov stability theorem. Several other intelligent control schemes have also been tested to provide a comparison with the proposed control scheme. The simulation results clearly show that the proposed control scheme improves tracking efficiency while managing the inherent deflections of the system, making it an appropriate adaptive control technique for the class of flexible multi-body nonlinear systems.