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


This paper presents a brand new control technique for nonlinear discrete-time systems, described by an input-output model which is predicated on a mix of quasi-sliding mode and neural networks. 1st, an input-output discrete-time quasi-sliding mode control with inserted digital integrator, which additionally reduces chattering, is described. Because of the presence of numerous nonlinearities and uncertainties, the model of the controlled object can't be described adequately enough. These imperfections in modeling cause a modeling error, resulting in rather poor system performances. So as to extend the steady-state accuracy, an estimated price of the modeling error in the next sampling amount is implemented into the management law. For this purpose, we have a tendency to propose 2 improved structures of the neural networks by implementing the generalized quasi-orthogonal functions of Legendre kind. These functions have already been proven as an efficient tool for the signal approximation, and for modeling, identification, analysis, synthesis, and simulation of dynamical systems. Finally, the proposed methodology is verified through digital simulations and real-time experiments on an anti-lock braking system as a representative of the thought of category of mechatronic systems, in a laboratory atmosphere. An in depth analysis of the obtained results confirms the effectiveness of the proposed approach in terms of better steady-state performances.

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