Adaptive neural network-based sliding mode control of rotary inverted pendulum system

This paper introduces a new adaptive neural network-based sliding mode control of rotary inverted pendulum system (RIPS) subject to model uncertainties and disturbances. First, the sliding mode control is designed using the double power rate reaching law. Second, an adaptive neural network (ANN) is exploited to deal with model uncertainties and disturbances. The weight matrices of the neural network are adaptively tuned. In the proposed control method the exact dynamic model of the RIPS is not required due to the exploitation of the universal property of the neural network. Moreover, the implementation of a double power-reaching law can greatly suppress the undesirable chattering. The convergence and stability analysis is validated using Lyapunov stability theory. Finally, the robustness and excellence of the proposed control strategy are verified by comparing the system performance with the existing SMC method and improved super-twisting SMC method.