Quasi-Sliding Mode Control With Orthogonal Endocrine Neural Network-Based Estimator Applied in Anti-Lock Braking System

This paper presents a new control method for nonlinear discrete-time systems, described by an input-output model which is based on a combination of quasi-sliding mode and neural networks. First, an input-output discrete-time quasi-sliding mode control with inserted digital integrator, which additionally reduces chattering, is described. Due to the presence of various nonlinearities and uncertainties, the model of the controlled object cannot be described adequately enough. These imperfections in modeling cause a modeling error, resulting in rather poor system performances. In order to increase the steady-state accuracy, an estimated value of the modeling error in the next sampling period is implemented into the control law. For this purpose, we propose two improved structures of the neural networks by implementing the generalized quasi-orthogonal functions of Legendre type. These functions have already been proven as an effective tool for the signal approximation, as well as for modeling, identification, analysis, synthesis, and simulation of dynamical systems. Finally, the proposed method is verified through digital simulations and real-time experiments on an anti-lock braking system as a representative of the considered class of mechatronic systems, in a laboratory environment. A detailed analysis of the obtained results confirms the effectiveness of the proposed approach in terms of better steady-state performances.