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The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

ABSTRACT This article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle's 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system's stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov's theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity. [ABSTRACT FROM AUTHOR]

Lane-keeping assistance (LKA) systems aim to address the vehicle deviation problem from normal lanes due to driver distraction, and their performance is affected by factors such as the stability of the steering system and external interference. This study proposed a vehicle LKA strategy based on the electric power steering system (EPS) to reveal the relationship between the control strategy and the performance of LKA systems. Firstly, an EPS system, a vehicle three-degree-offreedom dynamics model, and a driver model were established for the nonlinear electromechanically coupled vehicle EPS system. Both the serial lane-keeping control strategy for the EPS system controller based on robust sliding mode observers and the model prediction controller based on vehicle trajectory were devised. Then, based on various driving conditions, the vehicle's intervention coefficient outputs were dynamically adjusted in accordance with the lateral safety deviation and the driver's manual force. Finally, the intervention coefficients of the lane-keeping system were designed, and the accuracy of the control strategy was verified through experiments. Results show that after adding lane-keeping auxiliary control, the wheel turning angle is reduced by a maximum of 9°, the traverse angular velocity is decreased by a maximum of 0.52 rad, and the vehicle lateral offset is reduced by a maximum of 3.4 m. This study provides a significant reference for constructing vehicle models and evaluating the performance of LKA control strategies. [ABSTRACT FROM AUTHOR]

Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the lateral dynamic control of a vehicle in scenarios where both tracks and wheels are in contact with the ground. A dynamic model of a vehicle is first established based on the tire brush model and linearized general track model. Based on the dynamic model, a novel adaptive model predictive control (AMPC) method is designed considering the coupling and nonlinearity of the wheels and tracks to simultaneously regulate both mechanisms. Compared with traditional model predictive control approaches, the AMPC controller takes the side-slip angle and slip ratio as constraints to prevent the vehicle from reaching unstable states. Simulations are conducted to validate the effectiveness of the controller, and the results indicate that the controller has the capacity to optimize the objective's yaw-rate response while maintaining lateral vehicle stability and preventing slip by imposing constraints. [ABSTRACT FROM AUTHOR]

Nonlinear Model Predictive Control (NMPC) is effective for local planning of automated vehicles, especially when there exist dynamical objects and multipe requirements. But it requires many computation resources for numerical optimization, which limits its practical application becase of the limited power of onboard unit. To extend the application range of the NMPC based local planner, the coupled nonlinear vehicle dynamics model is adopted based on the numerical analysis, which conversely requires much more discretization poits for acceptable accuracy. For better computation efficiency, Lagrange polynomials are used to discretize the vehicle dynamics model and objective function with less points and fine numerical accuracy. Furthermore, an adaptive strategy is designed to determine the order of Lagrange polynomials according to running state by numerical analysis of discretization error. Both acceleration effect and performance of the local planner designed by NMPC are validated by experimental tests under scenarios with multiple dynamical obstacles. The test results show that compared with the original one the accuracy and efficiency are improved by 74% and 60%, respectively. [ABSTRACT FROM AUTHOR]

Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy. Therefore, a trajectory tracking method considering road curvature based on MPC is proposed in this paper. In this method, the controller can automatically switch between MPC types. Linear model predictive control (LMPC) is selected for small road curvatures, while nonlinear model predictive control (NMPC) is employed for large road curvatures. In addition, the NMPC algorithm in this work considers the effect of road curvature on tracking accuracy, making it suitable for tracking time-varying curvature roads. To verify the feasibility of the algorithm, simulation comparisons with the basic MPC model were carried out at different testing roads and vehicle longitudinal speeds. The results indicate that the method significantly improves trajectory tracking accuracy, all while ensuring real-time calculations. The intelligent switching capability of control models based on road curvature allows its application to track trajectories on arbitrarily complex roads. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups. [ABSTRACT FROM AUTHOR]

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

The development of intelligent transportation technology has provided a significant impetus for autonomous driving technology. Currently, autonomous vehicles based on Model Predictive Control (MPC) employ motion control strategies based on sampling time, which fail to fully utilize the spatial information of obstacles. To address this issue, this paper proposes a dual-layer MPC vehicle collision-free trajectory tracking control strategy that integrates spatial kinematics and vehicle dynamics. To fully utilize the spatial information of obstacles, we designed a vehicle model based on spatial kinematics, enabling the upper-layer MPC to plan collision avoidance trajectories based on distance sampling. To improve the accuracy and safety of trajectory tracking, we designed an 8-degree-of-freedom vehicle dynamic model. This allows the lower-layer MPC to consider lateral stability and roll stability during trajectory tracking. In collision avoidance trajectory tracking experiments using three scenarios, compared to two advanced time-based algorithms, the trajectories planned by the proposed algorithm in this paper exhibited predictability. The proposed algorithm can initiate collision avoidance at predetermined positions and can avoid collisions in predetermined directions, with all state variables within safe ranges. In terms of time efficiency, it also outperformed the comparative algorithms. [ABSTRACT FROM AUTHOR]

This work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [−0.25, 0.30] (m=s) and the space error at [−0.13, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%. [ABSTRACT FROM AUTHOR]

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles' past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed. [ABSTRACT FROM AUTHOR]

Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historical driving data to approximate optimal control law, but a concern is that the NNC lacks security guarantees when encountering new scenarios that it has never been trained on. Inspired by the prediction process of MPC, the deviation sequence neural network control (DS-NNC) separates the vehicle dynamic model from the approximation process and rebuilds the input of the neural network (NN). Taking full use of the deviation sequence architecture and the real-time vehicle dynamic model, the DS-NNC is expected to enhance the adaptability and the training efficiency of NN. Finally, the effectiveness of the proposed controller is verified through simulations in Matlab/Simulink. The simulation results indicate that the proposed path tracking NN controller possesses adaptability and learning capabilities, enabling it to generate optimal control variables within a shorter computation time and handle variations in vehicle models and driving scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits. [ABSTRACT FROM AUTHOR]

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader's state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle's dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon. [ABSTRACT FROM AUTHOR]

This study demonstrates the applicability of an explicit model predictive controller (ExMPC) to the air mass flow control in a fuel cell electric vehicle. The two control tasks comprise the supply of air to the fuel cell stack for the electrochemical reaction as well as the dilution of hydrogen in the outlet gas flow of the cathode subsystem. With the aim of achieving zero control deviation for steady states, the linear model in the state observer is extended with two perturbations. During the experimental validation of the proposed controller, fast dynamics are achieved with the ExMPC, which generally exploits the limits of the control variables. After the calibration of the ExMPC, the final comparison to a set of reference PI controllers reveals a significant improvement in the closed-loop behavior of the cathode subsystem during step changes in the controller setpoints. • An explicit model predictive controller is applied to a fuel cell electric vehicle. • Delays for control and controlled variables are considered. • The operating limits of the control variables are exploited during dynamic operation. • Improved closed-loop behavior is achieved in comparison to reference PI controllers. [ABSTRACT FROM AUTHOR]

Aiming to promote the control performance of traditional fixed-parameter model predictive control (MPC), a new longitudinal and lateral coupling path tracking control method for the autonomous vehicle is proposed. First, a dynamic model of the autonomous vehicle, which can describe the longitudinal and lateral motion states, is established. Secondly, both the tire side-slip angle and centroid side-slip angle constraints are introduced in the path tracking controller design, which improves the accuracy of the prediction model at high speed. Furthermore, the key parameters of the proposed path tracking controller are optimized. The simulation and experimental results show that compared with the traditional MPC tracking control method, the lateral error and directional error of the proposed optimized path tracking control method under high speed decrease by 19.17 % and 11.89 %, respectively, which improves the path tracking performance significantly. [ABSTRACT FROM AUTHOR]

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink. [ABSTRACT FROM AUTHOR]

The present work deals with the design of a traction system for a hybrid vehicle using a PMSM-type motor, including an energy regeneration stage, using a Neutral Point Piloted (NPP) inverter. Optimal operation allows the motor to be supplied in the traction stage with the correct energy in such a way that it operates properly; on the other hand, during braking, it facilitates the transfer of energy in regenerative mode to the DC bus, storing the greatest amount of energy in the storage elements, which, in this case, refers to a battery bank. It also includes a DC-DC bidirectional boost interleaved converter to regulate the DC voltage levels both in traction and in braking. Its fundamental characteristic is that with a reduced number of switching devices, it allows for the reduction in DC voltage of the DC bus with adequate characteristics, constant and stable, regardless of abrupt changes in the output voltage reference value. Also, other features include oscillation control, that is, reducing or increasing oscillations according to operating conditions. Its transient operation is excellent, since the settling time is considerably reduced, which implies that the voltage of the DC bus does not cause mishaps in the operation of the motor. In its operation as a boost converter, in the event of any voltage value of the DC bus, the converter raises the voltage value according to the reference conditions. Similarly, control allows the voltage to be held at a stable value regardless of changes in the set point. This work presents a novel energy management scheme for permanent magnet electric drive based on a Model Predictive Control strategy, thus contributing to the effective energy management of a standard hybrid electric vehicle driving cycle. Results obtained using a real-time Hardware-in-the-Loop (HIL) platform showed the effectiveness of Model Predictive Control in dealing with power flow management while ensuring control of electric drive in all the required speed–torque profiles. [ABSTRACT FROM AUTHOR]

In recent years, with the indepth research on driverless technology, model predictive control theory was extensively applied in the field of vehicle control. In order to improve the accurate tracking of reference trajectories by driverless vehicles, a model predictive control trajectory tracking controller for driverless vehicles optimized by an improved sparrow search algorithm is proposed. Firstly, an objective function with constraints is added to the model predictive control trajectory tracking controller by establishing the vehicle dynamics model; Secondly, the improved sparrow search algorithm is enhanced to speed up convergence and expand the program's search capabilities; Then, in order to discover the best value, the model predictive control trajectory tracking controller's prediction time domain and control time domain are optimized using the improved sparrow search algorithm; Finally, to confirm the method's viability, collaborative simulations in Simulink/Carsim were completed. The simulation results show that the lateral errors generated by the improved sparrow search algorithm-based optimized model predictive control trajectory tracking controller are reduced by 53.53% and 65.44%, respectively, when the vehicle speed is 36 km/h, compared with the traditional model predictive control trajectory tracking controller. When the vehicle speed is 54 km/h, the lateral deviations are reduced by 81.08% and 86.76%, respectively. In addition, the optimized model predictive control trajectory tracking controller improves the accuracy and at the same time, the driving stability of the control vehicle is significantly improved. [ABSTRACT FROM AUTHOR]

• Flame radiation heat transfer and heat flux of vehicle fires beneath a tunnel arced ceiling are studied. • The transverse flame extension length of an arched ceiling is less than that of a horizontal ceiling. • The flame extension length beneath the arced ceiling decreases as the burner aspect ratio increases. • A correlation is developed to characterize the flame extension length beneath an arced ceiling. • A cuboid-arc plate flame model is proposed to predict near-field radiative heat flux upon below surface of rectangular fires. As a major form of heat transfer, flame thermal radiation has an important effect on the ignition of adjacent energy, fire spread and environmental damage. Forecasting the flame radiation heat flux can provide theoretical basis for fire hazard assessment, fire extinguishing and salvation. Meanwhile, the distinctive structure of the arc tunnel can result in significant variations in the local heat and mass transfer processes during tunnel fires compared to those in rectangular tunnels, leading to different fire characteristics parameters. In this study, experiments and theoretical analyses were conducted to investigate the flame length scale and near-field radiation heat flux on the lower surface of an arc-shaped ceiling induced by vehicle fires in a tunnel. The findings reveal that the flame length scale in the transverse direction beneath the arc-shaped ceiling decreases with an increasing burner aspect ratio and increases with the increasing heat release rate. A dimensionless model of the flame length scale under the arced ceiling with various burner aspect ratios was proposed based on the amount of remaining unburned fuel after flame impingement and the buoyancy component along the arc-shaped ceiling. Simultaneously, radiative heat fluxes on the lower surface also increase with rising heat release rate. The flame geometry beneath the arc-shaped ceiling is assumed to be a combination of a cuboid and an arc plate model based on the actual flame length scale. Subsequently, the view factor, radiative fraction, and flame surface area are analyzed to predict the radiative heat fluxes on the lower surface. The predictions of the cuboid and arc plate flame model show good agreement with the current experimental data. This work presents a dimensionless correlation of the flame length scale and calculates the view factor between the target and the arc plate to predict the near-field radiative heat flux more accurately. This approach also facilitates the understanding of flame spreading behavior and thermal hazards impacted by arced ceiling. [Display omitted] [ABSTRACT FROM AUTHOR]

Maintaining the curved path-tracking accuracy and stability of autonomous vehicles under various road conditions is a significant challenge in the field of vehicle control. To address this limitation, a curved path-tracking strategy under variable velocity based on the adaptive model predictive control (MPC) is proposed for autonomous vehicles. Through the analysis of the vehicle dynamics model, the theoretical basis is presented to improve the performance of the curved path tracking by changing the vehicle velocity, and the adaptive velocity (AV) planner is designed to generate variable velocities depending on the path curvature and road friction coefficients. In addition, the adaptive model predictive controller, which adopts the fuzzy inference system with vehicle velocity and path curvature as inputs to obtain the adaptive prediction horizon (APH), is employed to realize curved path-tracking and velocity control with actuator constraints by manipulating the steering angle of the front wheels and the longitudinal tire forces of the vehicle. In comparison with the control strategy with three other control strategies based on MPC algorithm via simulation experiments on the Simulink/CarSim platform, the curved path-tracking control strategy with AV and APH proposed in this paper exhibits satisfactory performance in terms of path-tracking accuracy and stability. [ABSTRACT FROM AUTHOR]

This article presents an experimental validation of a model predictive path-following control algorithm (PF-MPC) applied to a truck–trailer system, encompassing both forward and backward motions. The proposed controller is designed to precisely follow a predefined path generated by a path planner, with a designated guidance point positioned on either the truck or the trailer. The algorithm's performance is assessed through implementation and validation on a model-scaled truck–trailer system, where MPC, state estimation, and low-level control are executed on a microcontroller (MCU). The experimental results demonstrate the effectiveness of the proposed control approach in achieving highly accurate path-following performance, even when operating in the challenging context of unstable backward motion, and with the involvement of up to two trailers. Moreover, the successful implementation of the algorithm on a microcontroller underscores its suitability for real-time control applications. The results of this study collectively highlight the promising potential of the proposed control algorithm for practical utilization in autonomous driving systems. • Online MPC on microcontroller. • Stabilizing reverse drive of real truck–trailer model vehicle. • Path-following with specific point (Guidance Point) on the truck–trailer combination. [ABSTRACT FROM AUTHOR]

The rising presence of autonomous vehicles (AVs) on public roads necessitates the development of advanced control strategies that account for the unpredictable nature of human-driven vehicles (HVs). This study introduces a learning-based method for modeling HV behavior, combining a traditional first-principles approach with a Gaussian process (GP) learning component. This hybrid model enhances the accuracy of velocity predictions and provides measurable uncertainty estimates. We leverage this model to develop a GP-based model predictive control (GP-MPC) strategy to improve safety in mixed vehicle platoons by integrating uncertainty assessments into distance constraints. Comparative simulations between our GP-MPC approach and a conventional model predictive control (MPC) strategy reveal that the GP-MPC ensures safer distancing and more efficient travel within the mixed platoon. By incorporating sparse GP modeling for HVs and a dynamic GP prediction in MPC, we significantly reduce the computation time of GP-MPC, making it only marginally longer than standard MPC and approximately 100 times faster than previous models not employing these techniques. Our findings underscore the effectiveness of learning-based HV modeling in enhancing safety and efficiency in mixed-traffic environments involving AV and HV interactions. • A Gaussian process (GP) based model corrects predictions and estimates uncertainty. • An uncertainty-aware controller uses GP models to enhance vehicle interaction safety. • The controller is 100× faster than previous methods, ensuring real-time control. [ABSTRACT FROM AUTHOR]

This paper investigates the event-triggered model predictive control (MPC) for the series–series (SS) resonant inductive coupling power transfer (ICPT) system in electric vehicles (EVs). Different from most existing literature in ICPT systems, a data-driven modeling approach based on input–output data is proposed to describe the system dynamics and achieve constant voltage output in the presence of load variations. In the traditional MPC control strategies, the optimal control input should be calculated at each time instant to achieve the desired output voltage, which causes great computational burden. To tackle this issue, an event-triggered MPC mechanism is therefore developed to effectively alleviate the computational burden, which will generate the optimal control input only when the norm of the state error exceeds a predefined threshold. The effectiveness and reliability of the proposed event-triggered MPC control strategy are successfully verified by the experimental results. [ABSTRACT FROM AUTHOR]