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

The objective of this study is to design an optimal control scheme for the control of a class of nonlinear flexible multi-body systems with extremely coupled dynamics and infinite dimensions. The assumed mode method (AMM) acquires a finite-dimensional model, but there are uncertainties in the truncated model that make the system a difficult control problem. The proposed control scheme is a hybrid of the sliding mode control (SMC) and the type-2 neural fuzzy system (NFS). A new modified conjugate gradient (CG) algorithm is used to optimize the NFS parameters. The control law of the proposed control scheme requires the estimation of the uncertain system functions, which is provided by the NFS. Moreover, NFS plays an essential role in avoiding the chattering phenomenon commonly observed in conventional SMC. The control scheme stability is assured by the Lyapunov stability theorem. Various other intelligent control schemes have also been tested to make a comparison with the proposed control scheme. The simulation results evidently indicate that the proposed control scheme has enhanced tracking efficiency while managing the inherent deflections of the system and is therefore concluded as a reliable control technique for the nonlinear, flexible multi-body class of systems.