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Adaptive Neural Network Conformable Fractional-Order Nonsingular Terminal Sliding Mode Control for a Class of Second-Order Nonlinear Systems

The paper introduces a novel adaptive neural network fractional-order nonsingular terminal sliding mode controller using conformable fractional-order (CFO) derivative for a class of uncertain nonlinear systems. For this purpose, a new conformable fractional-order nonlinear sliding surface is proposed and the corresponding control law is designed using Lyapunov stability theorem in order to satisfy the sliding condition in finite time. To deal with uncertainties, the lumped uncertainty is approximated by neural networks and adaptation laws are designed using Lyapunov stability concept. As adaptive neural network uses small switching control gain in the presence of large time varying uncertainties the chattering phenomenon is omitted. The proposed adaptive neural network conformable fractional-order nonsingular terminal sliding mode controller (ANN-CFONTSMC) exhibits better control performance, guaranties finite-time convergence and robust stability of the closed-loop control system. Finally, effectiveness of the proposed controller is illustrated through numerical simulations.