This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: 1) To enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; 2) An improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; 3) The RBFNN and composite nonlinear feedback (CNF)-based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-Matlab simulations.

This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.

This paper presents a new method to address issues associated with vehicle system state estimation using an Unscented Kalman Filter (UKF) with considering full-car system and nonlinear tire force under various International Standards Organization (ISO) road conditions. Due to the fact that practical road information is complex and noise covariance cannot be treated as a constant, influence of varying vehicle system process noise variance and measurement noise covariance on the estimation accuracy of the UKF is first discussed. To precisely estimate road information, a novel road classification method using measured signals (vertical acceleration of sprung mass and unsprung mass) of vehicle system is proposed. According to road excitation levels, different road process variances are defined to tune the vehicle system’s variance for application of UKF. Then, road classification and UKF are combined to form an Adaptive Unscented Kalman Filter (AUKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under varying road conditions. Simulation results reveal that the proposed AUKF algorithm has higher accuracy for state estimation of a vehicle system under various ISO road excitation condition.

Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM^® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario.

If disturbances are not properly handled, autonomous driving in commercial vehicles may result in significant tracking errors or even deviate from the intended trajectory. This paper proposes a solution to these issues by introducing a compensatory lateral control technique for path tracking that employs Model Predictive Control (MPC) as its underlying control strategy. Additionally, to enhance the robustness of the MPC, the paper presents a control algorithm that combines MPC with Active Disturbance Rejection Control (ADRC) compensation. Also, this paper introduces the Radial Basis Function Neural Network (RBFNN) as a technique for optimizing the parameters of the extended state observer (ESO) within the framework of ADRC. The introduced control strategy is evaluated via a hardware-in-the-loop (HIL) experiment, where it is demonstrated that the approach can precisely track an upper reference locus while remaining resistant to external interference.

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.