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.

The direct yaw moment control system of distributed drive electric vehicles provides a flexible control scheme to enhance the vehicle stability through four independently driven in-wheel motors. However, the vehicle nonlinearities may lead to the invalidation of control. To this end, a Takagi–Sugeno (T-S) fuzzy-based robust H∞ control method is proposed to ensure the vehicle performance while addressing the nonlinear challenge. First, thanks to the T-S fuzzy modeling technology, the tire nonlinear characteristics are described by fuzzy rules, based on which the vehicle lateral dynamics model is established. Next, the safety region represented by the tire slip angles phase plane is presented to evaluate the vehicle stability performance. Considering the control priority of vehicle handling performance and stability control with different stability margins, a multiobjective optimization function is transformed into a standard robust performance optimization problem with dynamic weight coefficients. A T-S fuzzy-based robust H∞ state feedback controller is then designed to ensure the system stability and H∞ performance. Finally, the hardware-in-the-loop tests are conducted to validate the proposed controller. Comparative results show the effectiveness to improve the vehicle handling performance while ensuring the stability.

The rapid development of the active front-wheel steering technique makes it possible to pursue precise, fast, and reliable path-tracking control for autonomous vehicles. To mitigate the impact of time-varying model parameters and system disturbances on the control performance, this paper proposes a continuous-time solving scheme for the autonomous path- tracking problem, which combines the robust model predictive control (RMPC) technique, non-parallel distributed compensation (non-PDC) method, and polytopic model. First, considering the time-varying longitudinal vehicle velocity, a polytopic model is utilized to construct a vehicle path-tracking system with a simpler structure. Then, a state-feedback controller for the continuous system model is developed based on the non- PDC method. By using the Lyapunov stability proof to transform the optimal control problem into the linear matrix inequalities (LMIs) solving form, an RMPC method is used to ensure path-tracking accuracy. Moreover, a robust compensation control is introduced to improve the anti-disturbance and transient response of the system. The virtual simulation by Carsim/ Simulink and field vehicle tests are conducted to assess the proposed controller. Both the simulation and field vehicle tests prove the effectiveness of the proposed controller in improving the path-tracking performance.

Complex traffic scenarios pose a challenge to the cooperation control of connected and automated vehicles (CAVs). Conventional platoon control or advanced driver-assistance systems only provide an isolated solution due to their inadequacies in systematic perspective. To this end, a multi-agent system (MAS) based distributed control architecture together with a hierarchical controller is proposed for the CAVs cooperation control system. The upper layer considers the potential states of a CAV in the platoon evolution (i.e. vehicle-cruising maneuver and vehicle-following maneuver) and elaborates the transition rules for discrete maneuvering behaviors. The lower layer includes the corresponding motion controller for each CAV. Artificial potential field (APF) combined with the distributed model predictive control algorithm is adopted to realize the integrated longitudinal, lateral and yaw motion control of CAVs. Next, inspired by the Pareto-optimal method for tackling the MAS-cooperation system, an optimal solution strategy is introduced to solve the CAVs cooperation problem from the viewpoint of the control theory. Furthermore, multi-constraints are designed to ensure a safety inter-vehicle spacing between CAVs, while the vehicle stability can also be guaranteed. The simulation results demonstrate that the MAS-based hierarchical control architecture can effectively handle the cooperation control of CAVs in the typical traffic scenarios. The real-time implementation of the proposed controller also proves the feasibility.

Independently driven electric vehicles (IDEVs) with typical redundancy characteristics provide more potential to improve global performance. However, the model uncertainties would cause extra energy consumption and even destroy vehicle safety. This study presents a hierarchical control scheme to simultaneously guarantee the vehicle energy conservation and handling stability. In the upper layer, the tire nonlinear characteristics are reconstructed by the polytopic method to facilitate the controller design. Then a robust controller is developed to address the model uncertainties and ensure the vehicle handling stability by integrating the direct yaw moment control (DYC) system and the active front wheel steering (AFS) system. Furthermore, the stability performance of independently driven electric vehicles is analyzed and quantified with different regions in the tire slip angles phase plane. Considering the uncertain longitudinal velocity, the lower layer utilizes a linear time-varying model predictive controller (LTV-MPC) to develop the torque allocation strategy and realize energy conservation. The control authority for stability performance and energy conservation is dynamically adjusted by assessing the vehicle safety state. Hardware-in-the loop (HIL) tests are carried out to verify the effectiveness. The test results demonstrate the proposed method is feasible to balance different control objectives.

Realistic uncertainties and disturbances cannot be ignored when striving towards high-performance autonomous driving. Meanwhile, advanced vehicular sensing technology enables more and more self-driving techniques to come out. Against this backdrop, this paper aims to holistically address the system uncertainties and disturbances in the path tracking control of autonomous vehicles (AVs) by the preview control theory to achieve superior control performance. First, unmeasureable multi-uncertainties and the external disturbance are considered simultaneously into the robust gain-scheduling framework with variable longitudinal velocity. Then, by the equivalent delay approach, ineluctable time-varying system delays are effectively handled. Next, the physical constraints of the steering system and direct yaw moment control (DYC) system are considered for practical implementation through robust set invariance theory. The parallel distributed compensation (PDC) technique and auxiliary feedback matrices approach are introduced for reducing design conservatism. After that, by incorporating the road curvature into the augmented system state, the path tracking preview control problem can be solved without online optimization. Thereafter, the closed-loop system stability and guaranteed performance have been proved through the Lyapunov- Krasovski approach. The proposed controller design is finally reformulated as the optimization problem based on linear matrix inequalities (LMIs) via specific convexification techniques. Finally, to verify the validity of the proposed controller, Software in Loop (SIL) tests with Carsim full-vehicle model under different dynamic conditions have been implemented.