The connected and automated vehicle (CAV) technique is a critical application of intelligent vehicles (IV) and is gaining widespread attention for its prospects of increasing driving efficiency. This paper investigates longitudinal control of vehicle platoons subject to both mismatched velocity disturbance and matched acceleration disturbance. The mismatched and matched disturbances are estimated by a finite-time disturbance observer (DO). Furthermore, two coupled sliding mode platoon controllers are proposed incorporating observed disturbances for longitudinal cooperation of platoons. By using and extending the coupled sliding mode technique, not only closed-loop stability but also string stability is finally proved under bidirectional (BD) and predecessor-following (PF) topologies, respectively. Simulations and an experiment are conducted to validate not only the theoretical effectiveness but also the practical performance of the proposed controllers.

Communication packet loss is omnipresent and can disrupt state convergence and weaken system stability for connected and automated vehicles (CAVs). Research into communication packet loss has primarily focused ON state feedback, often leaving various constraints and objectives unaddressed. This study introduces a novel distributed model predictive control (DMPC) method for cooperative control of a CAV platoon subject to Markov communication packet loss. First, the vehicle platoon system is modeled, within which the state of each communication link is described by a Markov process. Second, a DMPC controller is designed by formulating an online open-loop optimization problem. Specifically, a self-deviation constraint is adopted to enhance robustness against packet loss, and a terminal state constraint with a consensus protocol-based update law is designed to achieve terminal mean-square consensus. Third, the terminal consensus, recursive feasibility, and internal stability are analyzed, and a sufficient condition on the asymptotic mean-square stability is deduced. A modified string stable DMPC is further designed with leader information. Finally, numerical simulations and an experiment are carried out to validate the proposed methods, revealing that the proposed DMPC controller outperforms the benchmark controller on tracking performance and fuel economy.

Piezoelectric actuators exhibit creep behavior in open-loop operation, which may lead to unaffordable errors in high precision static positioning systems. An inversion-based compensation strategy by open-loop control is presented for reducing creep effect. The approach utilizes a nonlinear viscoelastic model to portray creep phenomenon, which consists of a linear spring, a nonlinear dashpot and a series of nonlinear Voigt elements. It is shown that for the presented creep model the step responses are very similar to the piezoelectric actuators. In order to compensate creep effect, a concept of voltage relaxation in piezoelectric actuators is proposed. And the voltage relaxation model tantamount to the inverse creep model is derived using a PID closed-loop control system. Experimental results prove that, by insertion of voltage relaxation model in open-loop operation, creep effect is attenuated markedly in piezoelectric actuators.

The era of mixed autonomy when connected automated vehicles and human-driven vehicles co-exist will last a long time. Communication time delays are inevitable and may highly deteriorate the safety and stability of mixed vehicle platoons in dynamic tracking scenarios. To address this challenge, this paper proposes a model predictive control (MPC) method for cooperative control of mixed platoons with time-varying communication delays. The detriments of time delay to cooperative control are analyzed and two delay compensation mechanisms are established, based on which a dual-mode MPC controller is designed. The feasibility, closed-loop stability, and head-to-tail string stability are theoretically proved. Furthermore, numerical simulations are conducted to verify the effectiveness of the proposed MPC controller. Simulation results show that the proposed controller outperforms the state-of-the-art consensus-based controller with 4.5%-5.8% fuel consumption reduction and 28.1%-33.1% average velocity error reduction.

The two-speed transmission system significantly enhances the powertrain matching performance of heavy-duty electric military armored vehicles by optimizing high-torque output at low speed and energy efficiency at high speed. However, most existing electric vehicles do not incorporate driving styles or real-time driving condition prediction into their shift control strategies, resulting in suboptimal gear shift timing and smoothness that fail to align with driver expectations and operational requirements. To address these limitations, this study focuses on the two-speed automated manual transmission (AMT) system in heavy-duty electric military armored vehicles. Firstly, a comprehensive shift control model is established, integrating key components such as the drive motor and power battery. Furthermore, a shift control strategy based on vehicle speed prediction and driving style recognition is proposed. The operational logic of this strategy is systematically analyzed under various driving cycles. Simulation and hardware-in-the-loop (HIL) results confirm the performance gains. Simulation and hardware-in-the-loop (HIL) results indicate that the proposed approach improves vehicle power performance by 21.36%, increases energy efficiency by 3.94%, and reduces powertrain shock by 31.81% compared to the conventional vehicle-speed-based gear shifting method. Compared to the adaptive shift schedule design method, the proposed approach reduces shifting frequency by 21.43% and improves ride comfort by at least 19.17% while maintaining comparable dynamic performance and energy efficiency.