The wheel slip tracking control is the basis of automatic braking control systems, and the accurate tracking for the desired wheel slip in the presence of lumped uncertainty is a vital guarantee of automatic braking control systems reliable operation. Therefore, an adaptive backstepping sliding mode control approach with radial basis function neural network is proposed to design the nonlinear robust wheel slip controller based on a quarter-vehicle model with lumped uncertainty. The radial basis function neural network as the uncertainty observer can effectively reduce the chattering of sliding mode by estimating the lumped uncertainty, and the adaptive law for the unknown weight vector of radial basis function neural network is derived by Lyapunov-based method. The influence of changes in tire sideslip angle and camber angle on the tire -road friction coefficient acts as an unknown scaling factor, and the adaptive law for the unknown scaling factor is derived via Lyapunov-based method. Then, the performance of the proposed controller is verified through simulations of various maneuvers on a full-vehicle dynamics simulation software.

This paper presents an integrated nonlinear robust adaptive controller with uncertainty observer for active front wheel steering system and direct yaw moment control system. First, an integrated vehicle chassis control model is established as the nominal model with the additive and multiplicative uncertainties of the system. Secondly, an integrated nonlinear robust adaptive control law with the additive uncertainty observer is designed via Lyapunov stability theory to calculate the corrective yaw moment, and an adaptive law is designed based on projection correction method to online estimate and compensate the multiplicative uncertainty of the system. Then, the constrained optimal allocation problem of the corrective yaw moment is transformed into the nonlinear optimization problem, and the sequential quadratic programming method is used to solve the nonlinear optimization problem to coordinate active front wheel steering system and direct yaw moment control system. Finally, the performance of the proposed integrated nonlinear robust adaptive controller is verified via vehicle dynamics simulation software.

This article proposes a novel robust adaptive wheel slip rate tracking control method with state observer. First, a modified tracking differentiator is proposed based on a combination of tangent sigmoid function with terminal attraction factor and linear function to improve convergence speed and avoid chattering phenomenon, and then, the modified tracking differentiator is used as state observer to smooth and estimate the states of the system. Second, a robust adaptive wheel slip rate tracking control law with fuzzy uncertainty observer and modified adaptive laws is derived based on Lyapunov-based method. The fuzzy uncertainty observer is used for estimating and compensating the additive uncertainty, and the modified adaptive laws are used for estimating the unknown optimal weight vector of the fuzzy uncertainty observer and the multiplicative uncertainty. Finally, the performance of the robust adaptive wheel slip rate tracking control method is verified based on the model-in-the-loop simulation system.