Jian Gong

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.

This paper presents a hierarchical secure control framework for resilient vehicular platooning under hybrid cyber-physical threats, including coupled false data injection (FDI) and denial-of-service (DoS) attacks, as well as actuator faults. A two-layer architecture is adopted to decouple cyber-layer disruptions from physical-layer execution, thereby enhancing system modularity and fault isolation. At the upper layer, a virtual platoon system is constructed, where a distributed resilient controller integrated with an event-triggered mechanism (ETM) is developed to ensure coordinated behavior while reducing communication overhead. At the lower layer, an adaptive fault-tolerant tracking controller is designed to compensate for actuator degradation and external disturbances, enabling each physical vehicle to follow its virtual reference independently. A layer-wise Lyapunov-based analysis is conducted to guarantee the practical exponential stability of the hierarchical control framework, where tractable LMI conditions are derived for both the cyber coordination and physical tracking components. Simulation results demonstrate that the proposed architecture effectively mitigates fault propagation, maintains robust performance under concurrent cyber and physical threats, and outperforms non-hierarchical benchmarks in terms of system stability.

Navigating restricted waterways is widely considered one of the most stressful phases for ship operators due to the confined navigable areas and the uncertainty of encounter situations. Most existing ship collision avoidance methods are developed for open-water navigation and do not adequately address the unique constraints and uncertainties associated with restricted environments. This paper proposes PROMPTER, a probabilistic motion planning framework designed to assist pilots in navigating restricted waterways. PROMPTER transforms motion planning into an inference task by integrating ship maneuverability, situational awareness, and path planning within a Bayesian factor graph, allowing optimal policies to be derived through posterior inference over control. Within the graph, operational restrictions are encoded as prior knowledge to constrain and guide the control inference process. Furthermore, we theoretically prove the closed-form solution and convergence of the constrained control inference. To ensure that PROMPTER is applicable in diverse operational conditions, we consider scenarios with both reliable and unreliable communication, leading to the development of cooperative and non-cooperative collision avoidance strategies. Experiments conducted in both synthetic and real-world restricted waterways demonstrate that PROMPTER outperforms existing methods by generating collision-free and kinematically feasible paths.

This paper proposes a vehicular platoon control approach under Denial-of-Service (DoS) attacks and external disturbances. DoS attacks increase the service time on the communication network and cause additional transmission delays, which consequently increase the risk of rear-end collisions of vehicles in the platoon. To counter DoS attacks, we propose a resilient control scheme that exploits polytopic overapproximations of the closed-loop dynamics under DoS attacks. This scheme allows synthesizing robust controllers that guarantee tracking of both the desired spacing policy and spatially varying reference velocity under all space-varying DoS attacks within a hard upper bound on the attack duration. In addition, ${\mathcal {L}}_{2}$ string stability conditions are derived to ensure that external perturbations do not grow as they propagate through the platoon, thus ensuring the string stability. Numerical simulations illustrate the effectiveness of the proposed control method.

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 proposes a distributed coordination scheme for connected vehicles, including automated vehicles (AVs) and manual vehicles (MVs), at signal-free intersections. The cooperation issue of vehicles at an intersection is formulated into a multi-objective optimization problem that aims to eliminate conflicts and improve traffic mobility and fuel economy. For this purpose, the future trajectories of AVs and MVs are predicted by the respective car-following models, and are shared with neighboring vehicles in conflict relationships. The proposed scheme optimizes the sum of the performance of AVs within the cooperative zone in a prediction horizon. A distributed optimization algorithm in the receding horizon is presented to obtain the local optimal solutions, and is tested in simulations with different demand levels and penetration rates of AVs. The results show that the proposed scheme reduces travel time by 29.7–45.5% and 34.5–49.2%, and decreases fuel consumption by 27.6–35.3% and 21.6–29.9% under 70–100% penetration rates of AVs, compared to the no-control operation and fixed-time signal control strategy. In addition, a comparison simulation with the strategy of jointly optimizing the vehicle trajectory and signal timing is conducted to evaluate the relative merits of the proposed scheme.

Emerging technologies of connected and automated vehicles (CAVs) applied at intersections have great potential to improve traffic efficiency, driving safety, and fuel economy. This paper proposes a virtual spring strategy for coordinating CAVs to pass through signal-free intersections. A virtual spring coordination system (VSCS) is established to force the movements of conflicting CAVs in terms of spring characteristics. Inspired by the properties of springs with dampers, a distributed control protocol is designed to regulate CAVs to reach a desired state of motion when crossing intersections. For ensuring the convergence of the VSCS, i.e., the total elastic potential energy approaches to zero, sufficient conditions for the internal stability are derived subject to the input saturation. To further improve the anti-disturbance capability of the VSCS, we ensure the upper bound of the disturbance propagation, which is characterized by an $H_{\infty}$ performance index. The proposed strategy is developed in a receding horizon framework and tested under the two scenarios in simulations. The simulation results show the effectiveness and superiority of the proposed strategy.