Path tracking is one of the most crucial issues of autonomous vehicles, but uncertain factors including road roughness and wind gusts could cause lateral deviation and even divergence of the control system. Therefore, this paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire (SBW) system to improve tracking accuracy and dynamic stability for autonomous vehicles. The proposed architecture is robust strongly against different types of disturbances in the path tracking process by means of hierarchical decoupling. Firstly, in the upper-layer path tracking module, a robust model predictive control (MPC) method with a feedback correction term calculates the expected front-wheel steering angle. Secondly, in the lower-layer SBW module, a nonlinear high gain rack force observer is proposed to compensate for the generalized rack force disturbance. Then, an improved non-singular fast terminal sliding mode auxiliary control law based on the super-twisting algorithm is presented to attenuate the norm-bounded rack disturbance effectively. Simulation and experiment results show that the proposed control architecture can improve the path tracking accuracy and has strong interference resistance and robustness against both modeled and unmodeled disturbances.

Safety at high speed is the most important performance of autonomous vehicle. Wherein, yaw stability control (YSC) is a key technology to improve vehicle safety. However, the high-speed vehicles are easy to be out of control in nonlinear region, which will result in serious accidents. This study proposes a model predictive control (MPC) method based on the Takagi-Sugeno (T-S) fuzzy model to realize YSC in the nonlinear region. Firstly, a T-S fuzzy model of vehicle is established based on a two-degree-of-freedom model to describe the vehicle dynamic characteristics in the nonlinear region. Then, a robust MPC is designed and its solution is given in the linear matrix inequalities (LMIs) form. Next, a sliding mode controller (SMC) is proposed to prevent wheel lock in the YSC process. Finally, the proposed strategy is verified in simulations and experiments. The results show that the proposed strategy has similar performance in vehicle stable region with linear MPC, and shows the ability to suppress the instability of the vehicle in the nonlinear region, and its computation burden is acceptable.

The tire pressure state directly affects the safety and economy of the vehicle. Its monitoring is gradually becoming a necessary function for all types of vehicles. However, the existing sensor-based monitoring method is costly and without any redundancy when the sensor fails, which hinders its wide applications. This article proposed a machine learning-based framework without any additional sensors. First, the rigid tire model is introduced for feature extraction. Then, the manufacturing error in speed gear is calculated and removed by the recursive least square method. Next, the features in time- and frequency-domain of speed signals are extracted. Finally, based on the suitable signals filtered by the decision tree, the normal and pressure-loss judgment are given out by support vector machine and final synthesis. The results show that the proposed framework has 96.18% report accuracy and has the potential for further estimation of the characteristics in tires and roads.

To achieve precise lateral dynamics control for electric and autonomous vehicles under system model uncertainties and unknown external disturbances, this article introduces a novel barrier function (BF)-based neural network (NN) adaptive integral sliding mode control (ISMC) for multiaxle independent steering vehicles (MISVs). Initially, NNs are designed to approximate the unknown parameters associated with uncertainties in the MISV dynamics model. Subsequently, an adaptive ISMC based on BF is developed to ensure robust vehicle state tracking. Unlike traditional adaptive sliding mode controllers, the proposed approach guarantees rapid convergence of variables within a finite time, even without prior knowledge of the upper bounds of unknown external disturbances, thereby significantly reducing vibration and oscillation phenomena. Finally, the Lyapunov stability theory is applied to rigorously demonstrate that the closed-loop system of the MISV remains stable within finite time. The efficacy of the proposed controller is validated through hardware-in-the-loop (HIL) experiments, with results indicating a reduction in maximum tracking errors for the desired yaw rate and sideslip angle by 8.24% and 40.44%, respectively, compared to the conventional control method.

The precise wheel pressure estimation is a necessary input for sensor cross-checking and diagnosis, and is the basis for subsequent vehicle dynamics control. Pressure estimation algorithms based on hydraulic models have been widely used in dynamics control, while stability and convergence are not well guaranteed. To mitigate the hydraulic model error, the Kalman Filter is adopted to fuse the hydraulic model and the additional signal from the vehicle. However, the change of inputs will lead to the change of pressure circuit dynamics which makes the Kalman Filter not well suited for all pressure estimation conditions. This paper presents two kinds of hydraulic models depending on the valve dynamics, and an interacting multiple model (IMM) filter is presented to integrate different hydraulic models to suit various control inputs. The filter developed in this paper is verified in typical driving conditions. Experimental results show that the proposed filter presents better pressure estimation compared to Kalman Filter based on a single model.