With the popularity and increasing demand of new energy automotive, four-wheel independent drive electric vehicles (4WID-EVs) and its distributed control mode is promising to be a better chassis architecture for its flexibility and maneuverability. However, due to the uncertainty of the vehicle internal time-varying parameters and imprecise fault detection method, a novel robust fault estimator-based stochastic model predictive control (SMPC) is proposed in this paper to ensure the vehicle lateral motion control and track the desired longitudinal velocity considering the estimation error of motor failure degree. Firstly, a robust H-infinity fault estimator with a linear parameter-varying (LPV) model is established to estimate the real-time fault signal considering the external disturbance and parameter perturbation (i.g. longitudinal and lateral vehicle speed). Then, a SMPC controller framework is developed to alleviate the effect of inevitable evaluated estimation error while the vehicle lateral safety and vehicle speed tracking can be ensured. Finally, the hardware-in-loop (HIL) tests are conducted to prove the feasibility of SMPC controller. The experiment results indicate that the proposed controller framework can effectively preserve vehicle stability and keep vehicle in a healthy state with faster response compared with the traditional fault-tolerant controller.

Active steering control is the key technology to improving vehicle safety and driver comfort, and its performance is largely affected by the uncertainties of human-machine interaction. To this end, this paper proposes a robust cooperative game based active steering control strategy considering the human-machine interaction characteristic to ensure the steering stability of the vehicle. The driver-vehicle system is modeled based on the T-S fuzzy structure to describe the uncertainty of the vehicle’s longitudinal velocity. Then the human-machine shared steering control is represented by a dynamic game process, in which the driver and the active steering control system are regarded as the participants. The cooperative game theory is introduced to decide the interaction control level. Such a design can achieve a better overall performance of agents through modeling the human-machine interaction process. Finally, a robust H ∞ compensation control method is proposed to improve the anti-interference performance of the system, thus ensuring vehicle stability. The MATLAB/Carsim joint simulation platform is established to verify the effectiveness of the proposed controller. The results show that the proposed human-machine cooperative control framework can guarantee the dynamic interactive performance while improving the vehicle handling performance. Furthermore, the hardware-in-the-loop (HIL) tests based on the LabVIEW-RT system also prove the feasibility.

This article proposes event-triggered cruise control in platoons of connected automated vehicles (CAVs) with heterogeneous input limitations. A distributed control protocol is developed to ensure the stability and performance of the platoon, explicitly addressing varying levels of input saturation among vehicles. To further enhance communication efficiency, a centralized event-triggered mechanism is introduced, activating control updates only when necessary, effectively preventing Zeno behaviors through a predefined threshold. The proposed approach not only achieves global asymptotic stability but also significantly reduces communication demands, making it suitable for real-world driving conditions characterized by input constraints. Simulation results validate the effectiveness and robustness of the proposed control strategy, demonstrating its potential for practical implementation in intelligent transportation systems.

This paper presents an LMI-based discrete PI–P cascade control method for active quarter-car suspension systems. The quarter-car dynamics are reformulated into a modified cascade sampled-data model that retains the body–wheel coupling between the sprung-mass and unsprung-mass dynamics. Based on this structure, a finite-memory discrete PI–P controller is developed, where the primary PI loop regulates the body-side response and the secondary proportional loop shapes the wheel–actuator-side dynamics. A Lyapunov stability condition and a tractable LMI synthesis method are derived for controller gain co-design. Numerical simulations show that the proposed controller improves sprung-mass acceleration attenuation while keeping the suspension deflection, tire-load-related response, and actuator effort bounded. The study is positioned for vertical ride comfort control rather than full-vehicle handling evaluation.

Complex traffic scenes greatly challenge the road safety of automated vehicles (AVs). This paper integrates the decision-making, path-planning, and motion-control modules to ensure the autonomous driving performance in the high-speed cruising scenario. First, to guarantee deep exploration of the reinforcement learning method, a Bootstrapped deep-Q-Network (BDQN) is proposed to address the adaptive decision-making of AVs. The artificial potential field (APF) is introduced in the reward function to improve the autonomous obstacle-avoidance ability. Then, quantifying the multi-performance requirements of AVs under high-speed cruising can be complex. We employ an inverse reinforcement learning (IRL) approach to learn path-planning ability from skilled drivers, generating a reference path for executing lane changes. An embedded linear-quadratic regulator (LQR) tracking controller is further developed to output the trajectory points. These points effectively satisfy the vehicle safety constraints and ensure reference feasibility. By introducing the IRL and LQR into the training process of BDQN, the path-planning, motion-control modules are coupled with decision-making. Finally, simulation results demonstrate the proposed framework can guarantee the high-speed cruising performance.