4WS Intelligent Fire-Fighting Robot Trajectory Tracking Control Based on Adaptive Cornering Stiffness

Abstract

To address the challenges posed by nonlinear tire characteristics in trajectory tracking control of active four-wheel steering (4WS) vehicle or fire-fighting robot, a novel control method is proposed. This method incorporates time-varying corrected cornering stiffness to enhance adaptability. Initially, the impact of friction coefficient and vertical load on tire lateral force nonlinearity is analyzed. Key input features are identified, and a cornering stiffness estimation model is developed using a Back Propagation (BP) neural network. Subsequently, the trajectory tracking state equation is established based on the 2-degree-of-freedom (2-DOF) dynamic model and trajectory tracking error model. An active front-wheel steering (AFS) trajectory tracking sliding mode controller (SMC) is designed, augmented by a Radial Basis Function (RBF) neural network, forming an RBF-SMC AFS controller capable of real-time correction of cornering stiffness. Additionally, an active rear-wheel steering (ARS) controller is designed based on fuzzy control theory, considering the influence of center of gravity lateral deviation on vehicle stability. Finally, Simulation tests under varied speed conditions on icy and dry asphalt surfaces, using the Matlab/Simulink-Carsim co-simulation platform, demonstrate that the proposed method dynamically adjusts front and rear axle cornering stiffness, enhancing trajectory tracking accuracy and maneuvering stability, particularly in high-speed or low-friction road conditions.

Keywords

• 4WS
• trajectory tracking
• cornering stiffness estimation
• neural network
• sliding mode control
• fuzzy control