Recurrent neural network non‐singular terminal sliding mode control for path following of autonomous ground vehicles with parametric uncertainties

Abstract Uncertain characteristics along with unknown exterior disturbances are fundamental issues in the path‐following control of autonomous ground vehicles (AGVs). Here, a novel robust non‐singular terminal sliding mode (NTSM) control method based on a recurrent neural network (RNN) structure is proposed to enhance the path‐following performance of AGVs. First, based on the dynamic model and path‐following model of AGVs, the robust NTSM steering controller is proposed to ensure that the lateral offset converges to zero within a limited time, and concurrently suppresses the chattering phenomenon that occurs in the regular sliding mode control (SMC) strategy. Then, the RNN is applied to approximate the unknown dynamics section of the system online. With an implied feedback loop, its study ability precedes the regular neural network to catch up with the dynamic response. In addition, the adaptive learning algorithms of the parameters in the RNN are derived using the Lyapunov stability theorem and Taylor linearisation technique to ensure the stability of the closed‐loop system. Finally, the robustness and effectiveness of the proposed control approach against parametric uncertainties and unknown external disturbances are verified in the Simulink‐CarSim simulation platform by comparison with selected reported control methods.