Optimized Super-Twisting Sliding Mode Control with Parameter Estimation for Autonomous Vehicle Longitudinal Motion on Downhill Road - Kbrom Lbsu Gdey; Woo Young Choi (Applied Sciences)
Longitudinal motion control is a critical aspect of autonomous vehicle area, requiring a well-designed controller to ensure optimal system performance, safety, and comfort under varying driving conditions. Previous control methods often face challenges such as chattering effects, and model uncertainties. To address such challenges, in this paper, we propose the application of an optimized super-twisting sliding mode control (OST-SMC) for the longitudinal motion control of autonomous vehicles. The motivation is to enhance the robustness and efficiency of the control system while minimizing the chattering problem. The proposed system’s mathematical modeling and control design are presented in detail with stability analyzed using Lyapunov theory. To enhance the controller’s performance, uncertain parameters are optimized using the gradient descent method, a linear regression-based technique. The OST-SMC algorithm shows enhanced robustness against disturbances and parameter uncertainties compared to conventional sliding mode controllers. Simulations in MATLAB/Simulink and CarMaker validate the proposed method, demonstrating strong performance even on downhill roads. The OST-SMC reduces chattering more effectively than traditional SMCs, achieving smooth tracking and consistent robustness under varying road conditions.