In this paper, a hierarchical control scheme, including a decision-making layer and a control layer, is proposed for vehicle platoons executing vehicle-following, cut-in, and cut-out maneuvers. Based on a finite-state machine, the decision-making layer is designed to ensure collision avoidance for vehicle platoons. In the control layer, a distributed model predictive control strategy is employed in the outer-loop to generate a reference sequence, where a linear parameter-varying kinematic model is established to account for the coupling of the vehicle platoon. Incremental constraints are designed to ensure bumpless transfer control during phase switching. By selecting the sum of local cost functions as a Lyapunov candidate, the asymptotic consensus of the vehicle platoon is proven. Furthermore, considering the coupled longitudinal and lateral dynamics of vehicles, in the inner-loop, a nonlinear model predictive control strategy is proposed to track the reference sequence. The effectiveness of the hierarchical control scheme is validated through co-simulation using MATLAB and TruckSim.

This paper proposes a novel hierarchical controller for connected truck platoons. To this end, the predecessor following topology is used to characterize the communication connectivity between connected trucks. Then, a longitudinal efficient controller consisting of upper-level and lower-level controllers is proposed. In particular, the upper-level controller is designed based on the kinematic model to handle the car-following interactions between connected trucks and delays in communication and input. The lower-level controller comprises a feedforward and a feedback control law. The feedforward control law converts the desired acceleration from the upper-level controller into the vehicle throttle or braking pressure using the inverse dynamic model, while the feedback control law compensates for the control error caused by unknown vehicle parameters. In addition, in the linear region, the internal stability is analyzed based on the second-order kinematic model using s-domain analysis and linearization method, respectively. Then, the string stability is proved. The influence of parameters on the stability performance is extensively discussed using the stability diagram. Finally, the feasibility of the proposed controller is verified via co-simulations in PreScan and TruckSim, in terms of acceleration, velocity, and spacing error profiles.

In high-temperature environments, integrating dynamic traffic information with an efficient thermal management optimization strategy has proven effective in rapidly reducing battery temperature, keeping it within the optimal operational range. This approach ensures electric vehicles (EVs) maintain optimal power output and range performance. However, due to the prolonged battery heat accumulation, effective optimization requires a long predictive horizon. The real-time implementation of centralized model predictive control (MPC) faces challenges in computational complexity. To address this, the proposed bipartite integration optimization strategy combines two levels for cabin and battery thermal management, enhancing computational efficiency through a segmented, hierarchical optimization approach. A comprehensive thermal model for the cooling system is developed, employing a combination of liquid cooling and active air cooling. In the upper level, vehicle-to-cloud (V2C) communication is utilized to obtain the average vehicle velocity over a long horizon, enabling more accurate forecasts of power demand and thermal load, thereby optimizing the thermal trajectory. In the lower level, the extreme learning machine (ELM) method is used to predict future vehicle velocity over a short horizon, facilitating precise temperature tracking and minimizing variations for optimal control. Simulation results under real driving conditions indicate that the proposed strategy reduces energy consumption by 18.90%, improves computational efficiency by 83.53%, and results in a 0.055% improvement in battery state of health (SOH) over extended cycles, without compromising the cooling requirements of the passenger cabin.

Battery capacity and safety are closely related to the battery temperature. The battery thermal management (BTM) system consumes considerable energy to maintain the temperature of the battery in the safe range. This energy consumption significantly decreases the driving range of the electric vehicle (EV). This article investigates the optimal control strategy of the BTM system based on model predictive control (MPC) for the connected and automated EV (CAEV), which minimizes energy consumption of the BTM system under the constraint of power and thermal at the same time. The slow thermodynamics of the battery requires a long prediction horizon to achieve optimal temperature and energy consumption of the BTM system. However, long preview information, such as vehicle speed, has large uncertainties, which significantly affects the energy efficiency performance and constraint enforcement robustness. In this study, the effects of the different prediction horizon lengths and the information within the prediction horizon on the MPC performance are first analyzed. Then, the MPC optimization strategy based on adaptive optimization horizon and multitimescale (AOH-MT) is proposed to reduce the temperature constraint violations and computation time. Finally, to improve the robustness under the real driving condition where there are large uncertainties in the speed preview information, a chance-constrained stochastic MPC (C-SMPC) is proposed and the AOH-MT framework is integrated into its prediction horizon to reduce time cost. The simulation results under real-world traffic data show that the proposed approach reduces the constraint violation by 84.88% and the energy cost by 2.44%, which improves robustness against uncertainty in the speed preview information.

In this paper, a hierarchical control strategy of vehicle platoons is presented, in which the longitudinal and lateral coupling property of vehicles is taken into account. A three-degree-of-freedom dynamic model of vehicles is approximated to a “global” linear model by the Koopman operator theory. A synchronous distributed predictive control scheme of vehicle platoons is proposed as an upper-level controller, where both the linear vehicle model and a linear parametric-varying lane-keeping model are adopted to predict the dynamic of vehicles, and keep vehicles in the designated lane. Thus, it can avoid the solution of nonlinear optimization problems and reduce the computational burden accordingly. A lower-level controller is designed, where the desired longitudinal control force determined by the upper-level controller is transformed into the desired throttle angle and brake pressure through an inverse longitudinal dynamics model of vehicles. The joint simulation results by PreScan, CarSim and MATLAB/Simulink show that when the leader vehicle accelerates or decelerates, the following vehicles in the platoon can keep the same velocity as the leader vehicle, and maintain the desired safety distance between the front and rear vehicles. In addition, joint simulation in the curved road scenario show that the performance of lane keeping can be guaranteed for vehicle platoons with the proposed control strategy.

Benefiting from the advancement of intelligent transportation systems (ITSs), intelligent connected vehicles (ICVs) are ushering in a once-in-a-generation development opportunity. Considering the widespread presence of heterogeneous vehicles with disturbances and uncertain dynamics in actual platoon scenarios as well as the multimodel switching produced by unavoidable interruptions in the communication process, this paper proposes a data–mechanism adaptive switched predictive (DASP) control strategy. The characteristics of the mechanism model are mapped based on state data to more accurately describe the system’s dynamic characteristics and improve the interpretability of variables. The introduction of Givens rotations and switching criteria enables online adaptive switching of the controller. A robustness analysis of heterogeneous platoon switching control under bounded disturbance is presented, and sufficient conditions for$\mathcal {L}_{2}$string stability are provided. Finally, CarSim simulations and real-time bench experiments are reported to demonstrate the effectiveness of the DASP algorithm for heterogeneous multivehicle regulation with communication interruptions.

So far, many control algorithms have been developed for singularly perturbed systems. However, in many industrial processes, enforcing closed-loop fast-slow dynamics for peculiarly nonseparable ones is a prior request and a crucial issue to be resolved. Aiming at the above problem, this article presents two dual-level model predictive control (MPC) algorithms for multitimescale dynamical systems with unknown bounded disturbances and input constraints. The proposed algorithms, each one composed of two regulators working in slow and fast time scales, are designed to generate closed-loop separable dynamics at high and low levels. As a prominent feature, the proposed algorithms are not only suitable for singularly perturbed systems but also capable of imposing separable closed-loop performance for dynamics that are nonseparable and strongly coupled. The recursive feasibility and convergence properties are proven under suitable assumptions. The simulation results on controlling a boiler turbine (BT) system, including the comparisons with other classic controllers, are demonstrated, which show the effectiveness of the proposed algorithms.

To reduce the impact of human–machine conflict on vehicle safety, this study proposes a novel human–machine cooperative steering control approach from the perspective of driver trust in the machine. The relationship between driver trust in the machine and driving skill is analyzed by the chi-square test method, and an online cooperative algorithm is designed using fuzzy control for different conditions, which assigns control authority based on driver trust under safe conditions and gives most of the authority to the machine to ensure safety under dangerous conditions. The machine is designed using model predictive control as an alternative controller parallel to the driver. To implement the proposed approach, a simulation platform that includes drivers and a test vehicle is established. Based on the driving data of human drivers collected in field tests, a two-point visual driver model is established to simulate steering behaviors and reflect physical workload. The parameters of the driver model are identified by a particle swarm optimization method to represent different drivers. The effectiveness of the approach, such as guaranteeing vehicle safety and reducing physical workload and human–machine conflict, is verified by simulations under typical conditions and obstacle avoidance conditions based on veDYNA vehicle dynamics software.

This article develops a new platoon control strategy for heterogeneous connected vehicles (CVs) subject to time delays and external disturbances. Specifically, based on the third-order vehicle model, a novel platoon controller is developed by embedding the variable time headway (VTH) spacing policy and the nonlinear motion coupling interactions between CVs. Simultaneously, an integral sliding mode (ISM) controller is developed to resist the disturbances. Then, the condition of asymptotic stability for the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability is proved by using the infinity-norm method. Finally, extensive simulations and co-simulations are provided to show the validity of the developed controller. Moreover, experiments with intelligent micro vehicles are conducted further to validate the practical feasibility of the developed controller.

Robust model predictive control (MPC) is a well-known control technique for model-based control with constraints and uncertainties. In classic robust tube-based MPC approaches, an open-loop control sequence is computed via periodically solving an online nominal MPC problem, which requires prior model information and frequent access to onboard computational resources. In this article, we propose an efficient robust MPC solution based on receding horizon reinforcement learning, called r-LPC, for unknown nonlinear systems with state constraints and disturbances. The proposed r-LPC utilizes a Koopman operator-based prediction model obtained offline from precollected input–output datasets. Unlike classic tube-based MPC, in each prediction time interval of r-LPC, we use an actor–critic structure to learn a near-optimal feedback control policy rather than a control sequence. The resulting closed-loop control policy can be learned offline and deployed online or learned online in an asynchronous way. In the latter case, online learning can be activated whenever necessary; for instance, the safety constraint is violated with the deployed policy. The closed-loop recursive feasibility, robustness, and asymptotic stability are proven under function approximation errors of the actor–critic networks. Simulation and experimental results on two nonlinear systems with unknown dynamics and disturbances have demonstrated that our approach has better or comparable performance when compared with tube-based MPC and linear quadratic regulator, and outperforms a recently developed actor–critic learning approach.