The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter varying lateral model is established through utilizing a polytope with finite vertices to describe vehicle longitudinal velocity. Then, the MPC-based vehicle path tracking controller, which is robust against parameter uncertainties, is designed; the proposed controller can be solved via a set of linear matrix inequalities (LMI), which are derived from Lyapunov asymptotic stability and the minimization of the worst case infinite horizon quadratic objective function. The proposed control system is evaluated by both cosimulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed controller, and it can ensure the control accuracy and strong robustness.

The over-actuated characteristics of distributed drive electric vehicles (DDEVs) provide a flexible platform to pursue higher holistic performance. This paper proposes a dual-model predictive control (MPC)-based hierarchical framework to realize the energy saving while improving the handling stability for DDEVs. The upper layer allocates the torque vector through the front/rear axles, which can provide a high-efficiency zone for the in-wheel motors and reduce the energy consumption. The lower layer generates a direct-yaw-moment control input by differential longitudinal forces of the left/right wheels to ensure the vehicle handling stability. Considering the time-varying state variables, a linear-time-varying model-predictive-control (LTV-MPC) method is adopted to guarantee the accuracy of the model. The combined magic formula tire model is used to modify the tire parameters, including tire longitudinal stiffness and cornering stiffness. The soft constraint constructed by β-γ phase plane is introduced in the LTV-MPC to ensure the vehicle stability, based on which, a relaxation factor is designed to reduce the energy consumption due to the excessive direct-yaw-moment control inputs. The simulation and hardware-in-the-loop (HIL) test results show that the proposed control framework can effectively reduce the energy consumption for DDEVs while ensuring the vehicle handling stability.

Intelligent driving system requires more precise and reliable steering control, and steering-by-wire (SBW) technology can better adapt to the development of autonomous driving. The permanent magnet synchronous motor (PMSM) servo system is the actuator of the SBW. Aiming at the uncertainties of the PMSM servo system, a new adaptive control method for the PMSM servo system is proposed. Parameter adaptive laws are designed for each slowly varying parameter and unknown parameter, and robust terms are introduced to the adaptive laws for the parameter uncertainties. The convergence speed of the parameter adaptive law proposed can be improved by adjusting the parameters related to the convergence rate. To reduce the scope of disturbance uncertainties, the steering load disturbances are divided into a bounded unknown time-varying part and a predictable time-varying disturbance. Based on the Luenberger observer, the prediction term of the load torque is estimated by the current of PMSM in real time. By introducing boundary estimation and robust term of unknown disturbance into the control law and parameter adaptive law, respectively, the uncertainties of parameters and unknown external disturbance can be compensated. Simulation and hardware-in-the-loop tests are carried out on the SBW platform. Experiments show that the control strategy of the PMSM servo system designed in this article has good tracking accuracy and stability.

In intelligent vehicle cooperative systems, the mismatch in driving characteristics between a human and a machine and the driver misoperation caused by this mismatch result in human–machine conflicts, which significantly affect driving safety. Therefore, an intelligent vehicle human–machine cooperative steering torque control method is proposed herein. To adapt the intelligent system to the varying previewing characteristics of a human, a time-varying previewing driver model is constructed, and a penalty factor for human–machine intervention is designed based on fuzzy rules to assign driving control rights by assessing the driver’s state. Consequently, a human–vehicle–road model with driver preview time and penalty factor as varying parameters is established. Based on gain-scheduling control, a human–machine cooperative steering torque controller is designed to adapt to the varying previewing characteristics of a human and the change in human–machine intervention. The stability and robustness of the entire parameter space are guaranteed by constraining the poles in a certain region. Finally, the proposed human–machine cooperative control scheme demonstrates the effective alleviation of conflicts between the driver and the intelligent driving system.

It is a difficult and important project to coordinate active front steering (AFS) and direct yaw moment control (DYC), which has great potential to improve vehicle dynamic stability. Moreover, the balance between driver’s operation and advanced technologies’ intervention is a critical problem. This paper proposes a human-machine-cooperative-driving controller (HMCDC) with a hierarchical structure for vehicle dynamic stability and it consists of a supervisor, an upper coordination layer, and two lower layers (AFS and DYC). The range of AFS additional angle is constrained, with consideration of the influence of AFS on drivers’ feeling. First, in the supervisor, a nonlinear vehicle model was utilized to predict vehicle states, and the reference yaw rate, and side slip angle values were calculated. Then, the upper coordination layer decides the control object and control mode. At last, DYC and AFS calculate brake pressures and the range of active steering angle, respectively. The proposed HMCDC is evaluated by co-simulation of CarSim and MATLAB. Results show that the proposed controller could improve vehicle dynamic stability effectively for the premise of ensuring the driver’s intention.

Human–machine cooperation (HMC) driving can realize the sharing of vehicle control rights between drivers and autonomous driving systems. However, the system disturbances and parameter uncertainties introduced by HMC driving are also inevitable. To solve the above problems, this study proposes an HMC steering torque control method based on the model reference adaptive control (MRAC). First, to describe the driver’s operating behavior and the transient characteristics of the steering system and the vehicle, an HMC vehicle-road model with steering torque input is established. Then, the exponential decay function is taken as a reference model to increase the decay rate of road lateral deviation. Besides, for the problem of model parameter uncertainties, the parameter adaptive laws are designed in the control process, and the robust term is considered into the parameter adaption law to improve the robustness of the closed-loop system. Robust stability under bounded disturbances is guaranteed by boundary estimation of unknown disturbances between driver input and steering load. Finally, simulations and experiments are carried out to verify the proposed control strategy. The results demonstrate that the proposed control strategy can converge rapidly under the disturbances and parameter uncertainties introduced by HMC.