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With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel integrated path following control scheme for a 4-Wheel Independent Drive (4WID) autonomous vehicle that can adaptively change its mechanism according to the driving conditions. The proposed integrated system handles the lateral steering controller, longitudinal speed, and yaw moment controls considering tire force capacity of each corner. For the lateral controller, the cornering stiffness uncertainties and the transient performance are considered and combined into an H∞ robust controller based on linear matrix inequality (LMI) theory. A super-twisting sliding mode controller (STSMC) based longitudinal controller is designed to deal with disturbances and suppress chattering. When encountering extreme conditions, the active yaw moment controller with hierarchical structure is adaptively activated to prevent large deviation from the reference path and maintain the stability of vehicle. For the tire force allocation, an optimization algorithm is proposed, which has flexible equality constraints to coordinate the longitudinal and lateral motions according to the driving conditions. Simulations based on Carsim-Simulink co-simulation platform show that the proposed method is effective and has excellent performance in both normal and extreme driving conditions. [ABSTRACT FROM AUTHOR]

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.

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This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking. [ABSTRACT FROM AUTHOR]

Anti-lock brake system control by traction motor requires optimal utilization of ground adhesion coefficient during regenerative braking, and it is essential to maintain the stability of battery electric vehicle throughout the process. Further, the wheel slip ratio control of brake-electric system differs from that of brake-hydraulic system due to the large difference in the response time of brake torque. Therefore, it is particularly challenging to prevent the lock of all traction wheels through torque control of the traction motor. While much of the research on brake-electric system has focused on optimizing the energy regeneration efficiency in the barke process, comparatively little is known about the stability control of battery electric vehicle. Here this article discusses a series of studies on the nonlinear dynamics of wheel slip and model predictive controller, and a torque demand control approach was designed based on both of these. That is, how a nonlinear model predictive controller could be used for anti-lock brake control of traction wheels. In order to find the optimum value of the wheel slip ratio, an ideal slip ratio curve illustrated by ground adhesion coefficient and wheel slip ratio was developed, which is used as an optimal target boundary of control algorithm. The developed approach has been downloaded into a vehicle control unit, and tested in real-world conditions using a battery electric vehicle to fully realize practical application of anti-lock brake system control by traction motor torque. [ABSTRACT FROM AUTHOR]

The steady-state error problem of autonomous vehicle MPC-based motion control has not been effectively solved for a long time. This problem is more serious for lateral and longitudinal coupling control problems of vehicles with over-actuated configurations. Based on our newly designed general offset free MPC (OF-MPC) solver and the TMeasy tire model, a steady-state error free control strategy for simultaneous stability and path following control of four-wheel steering and four-wheel drive vehicles is proposed. OF-MPC uses the disturbances term to describe the model mismatch and external disturbances, then uses the Kalman filter to observe the disturbances, and finally considers the disturbances in the optimization stage to realize the control without steady-state error. Realtime simulation results show that OF-MPC can solve model mismatch and external disturbances problems, and the steady-state error free control is realized. The simulation results of the double lane change maneuver show that the OF-MPC dynamic control performance is also better than the traditional MPC (TRA-MPC), which is more obvious when the vehicle is at the stability boundary and under various constant or time-varying disturbances. Regardless of the dimensions and complex constraints of this problem, real-time performance is still guaranteed, thanks to the proposed OF-MPC. When the horizon length is 100, the average time consumption is only about 15 milliseconds. [ABSTRACT FROM AUTHOR]

This paper proposes a novel prioritisation model predictive control for improving the handling and stability of all-wheel-drive vehicles configured with an electric motor and an open differential per axle with differential braking capability. The configuration of the powertrain provides a significant handling improvement by optimising front/rear torque distribution. The controller gives a high priority to front/rear torque distribution and, if needed, activates the differential braking. A coupled force prediction model of the vehicle handling dynamics is developed to capture the interaction of longitudinal and lateral tyre forces. Since differential braking causes speed drop, energy waste, and noise, two control actuations are prioritised: (1) front/rear torque shifting, (2) differential braking. Appropriate stability constraints are defined for the vehicle yaw rate and sideslip, dividing the sideslip-yaw rate phase plane into three regions – stable, marginal, unstable – based on which three control objectives are defined. Then, the priorities of the control actuations and the control objectives are combined, and a model predictive control structure is developed for this multi-objective multi-actuation control problem. The predictive controller prioritises the objectives and actuations through the prediction horizon. The performance of the proposed controller is thoroughly evaluated through numerical and full vehicle experimental studies. [ABSTRACT FROM AUTHOR]

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time NMPC-based controller for four-wheel independent motor-drive electric vehicles to improve vehicle longitudinal and lateral stability under extreme driving conditions. First, considering the interactive and highly coupled longitudinal–lateral vehicle dynamics, a combined-slip tire model is applied to develop the stability controller on low friction coefficient surfaces. Second, the wheel slip ratios and slip angles are selected as the virtual control inputs of the NMPC controller to concurrently achieve three main control objectives: Slip control, lateral stability control, and handling performance improvement. Simultaneously, multiple safety constraints are contained. Then, based on the dynamic relationships between the longitudinal tire force and virtual control inputs, the wheel slip ratios and slip angles obtained from the NMPC controller are converted into additional torques acting directly on each wheel. Finally, the control performance is investigated by co-simulation with MATLAB/Simulink and CarSim, and a hardware-in-the-loop simulation system. The effect of uncertainties on control performance is also verified. The results show that the proposed controller can rapidly solve the optimization problem, and vehicle overall stability are efficiently enhanced under extreme conditions. The robustness of the controller is proved with uncertainties on the road adhesion coefficient and vehicle mass. • The predictive model considers highly coupled and nonlinearity for modeling accuracy. • The tire motion-based conversion to additional torque reduces the control complexity. • The multi-objective/constraint nonlinear optimization problem solves in real-time. [ABSTRACT FROM AUTHOR]

This study proposes an approach to the yaw and lateral stability of the vehicle that considers their steady-state trend. This study designs a new linear time-varying model predictive control (LTV-MPC) method based on the 2DOF vehicle model. The tire model curve of the magic formula is simplified into two straight lines to replace the nonlinear characteristic of tires and reduce the solution time of the controller under the premise of a good tracking effect. The lateral stiffness of tires is selected according to the predicted trend of stable state of the vehicle. Simulation results show that, under extreme conditions, the proposed method can effectively track the reference vehicle sideslip angle and yaw rate. The real-time performance of the controller is substantially improved compared with NMPC. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking.

Due to flexible drive-by-wire technology, vehicle stability control can improve handling and lateral stability under extreme conditions. However, this technology can also increase the probability of random transmission delay. This paper proposes a nonlinear model predictive control (NMPC) strategy to improve vehicle stability and compensate for the random time delay. First, by combining the nonlinear dynamic characteristics and driver behavior, we obtain a stable region of the yaw rate and the sideslip angle under complex driving conditions. Second, an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral stability. Finally, the actuator receives the optimized control sequence and compensates for the random time delay of the transmission channel. CarSim/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

This article presents an improved method of obtaining lateral stability regions for road vehicles, considering the influence of steering angle, center of gravity, longitudinal speed, and tire-road friction coefficient on the vehicle dynamics. Comprehensive stability regions are obtained for a wide range of such parameters. Moreover, conservative stability regions are proposed, in the case of control applications that demand robust or safety-critical control actions. Next, a steering-angle-dependent region is used to implement a safety-critical electronic stability control system with active front steering as actuation. The resulting control system extends safety-critical control based on control-dependent barrier functions, introducing a control-dependent Lyapunov function to improve its steady-state behavior. Finally, we identify and propose workarounds for problems that arise from the number of inputs being less than the dimension of the desired safe set. The conservative stability regions and the extended safety-critical control system are validated by means of simulation results based on a nonlinear lateral stability model.

Under extreme conditions, vehicle states change rapidly between stable and unstable, resulting in dynamic requirements for the vehicle’s overall safety stability. Simultaneously, the coupled nonlinear characteristics of vehicle dynamics cannot be ignored in controller design. To address the above problems and improve vehicle longitudinal and lateral stability integrally, an envelope-based model predictive control (MPC) strategy with dynamic objectives is proposed for four-wheel independent motor-drive electric vehicles (4WIMD EVs). First, according to the current driving behavior and the collected road information, the envelope control regions concerning vehicle side-slip angle and yaw rate are obtained online, and divided into stable, critically stable, and instable regions with different safety requirements. Then, the safety dynamic requirements are constructed in the designed MPC-based control structure. A nonlinear vehicle dynamics model with a combined-slip tire model, which integrates the longitudinal and lateral dynamics, is utilized to predict vehicle states. The switching of requirements is reflected in the variation of weighting factors and constraint values. Finally, CarSim and Matlab/Simulink co-simulation, and hardware-in-the-loop simulation test results show better satisfactory performance in improving overall vehicle stability under extreme driving conditions. [ABSTRACT FROM AUTHOR]

This paper presents a prioritization model predictive control for integrated lateral and roll stability control of all-wheel-drive vehicles. The vehicle’s powertrain includes an electric motor and an open differential per axle, which enables generating a corrective yaw moment by front/rear torque shifting. In addition, the brakes can be used in differential mode to further enhance the corrective yaw moment when needed. To ensure vehicle stability, safety limits are defined on the vehicle’s yaw rate, sideslip, and roll motions, considering road angle effects. The controller prioritizes the control actions and objectives based on, respectively, their advantages and their importance, and then combines the priorities such that differential braking, which is defined as the low priority actuation, will not kick in unless the stability limits are violated. The performance of the designed controller is thoroughly evaluated through numerical and full vehicle experimental studies in different driving scenarios on flat and non-flat roads. [ABSTRACT FROM AUTHOR]

In order to explore the nature of the human-vehicle game problem and analyze the torque-angle interaction between the two agents, i.e. the driver and advanced driver assistance system (ADAS), a human-vehicle game stability control strategy based on Nash negotiation principle is proposed. First a six-order vehicle dynamic model, a driver neuromuscular (NMS) model, etc., are set up to simulate drivers’ steering characteristics and vehicles’ response to the input of the two agents and external disturbance. Secondly, several significant parameters in NMS model are recognized, and an active rear steering (ARS) controller is designed using the sliding mode variable structure algorithm. Then, the Nash negotiation solution is worked out according to Nash negotiation principle, and a self-tuning method for the weight of ARS controller is put forward employing the fuzzy control theory. Finally simulations are carried out under the standard double-lane change maneuver. The results indicate that the stability control strategy proposed in this paper can effectively solve the game problem and achieve good vehicular stability control performance. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of improving vehicle stability using an electronic limited slip differential (ELSD). Unlike brake-based vehicle stability control systems, electronic limited slip differentials have the potential to control a vehicle's yaw stability without slowing down its speed. In this study, first, an ELSD model is designed to properly predict ELSD torque distribution. The model's accuracy is evaluated using experimental test data. Then, an intelligent ELSD control design is proposed using the developed ELSD model, capable of working in both off-throttle and on-throttle scenarios. The proposed design, which is based on a model predictive control approach, offers control of the ELSD differential by directly controlling the ELSD clutch pressure. This control design enables the controller to prevent unwanted oversteering yaw moments and avoid chattering in the ELSD clutch. The controller is implemented in a Cadillac CTS vehicle to evaluate its performance experimentally in various driving maneuvers. [ABSTRACT FROM AUTHOR]

This study discusses vehicle stability control based on explicit nonlinear model predictive control (NMPC) and investigates the influence of prediction model fidelity on controller performance. The explicit solutions are generated through an algorithm using multiparametric quadratic programming (mp-QP) approximations of the multiparametric nonlinear programming (mp-NLP) problems. Controllers with different prediction models are assessed through objective indicators in sine-with-dwell tests. The analysis considers the following prediction model features: 1) nonlinear lateral tire forces as functions of slip angles, which are essential for the operation of the stability controller at the limit of handling. Moreover, a simple nonlinear tire force model with saturation is shown to be an effective alternative to a more complex model based on a simplified version of the Magic Formula; 2) longitudinal and lateral load transfers, playing a crucial role in the accurate prediction of the lateral tire forces and their yaw moment contributions; 3) coupling between longitudinal and lateral tire forces, which has a significant influence on the front-to-rear distribution of the braking forces generated by the controller; and 4) nonlinear peak and stiffness factors of the tire model, with visible yet negligible effects on the results. [ABSTRACT FROM AUTHOR]

Vehicle stability control under extreme conditions is influenced by the coupled nonlinear characteristics of vehicle dynamics, corresponding safety constraints, and rapid response requirements. To address these problems, this brief proposes a real-time nonlinear predictive controller for a distributed drive electric vehicle. First, nonlinear lateral dynamics of the vehicle are applied to develop the stability controller on low friction coefficient surfaces. Second, the requirement for suppressing the sideslip angle is integrated into the objective function to prevent the vehicle from destabilizing due to excessive sideslip angles. Finally, a fast solution algorithm is proposed by solving the transformed two-point boundary value problem, making it possible to apply nonlinear predictive controller to experimental road tests. The experiments with a production vehicle are conducted on the snow-covered dynamic roads of the DongFeng’s winter test center. The test results on low friction coefficient roads show that the overall passing speed can be improved from 50–55 to 60–70 km/h with the proposed controller. [ABSTRACT FROM AUTHOR]

Autonomous vehicle functions in safety-critical situations show promise in reducing the risk and saving lives in accidents compared to existing safety systems. Consequently, it is from many perspectives advantageous to be able to quantify the potential benefits of new autonomous systems for vehicle maneuvers at-the-limit of tire friction. Here, to estimate the potential in terms of saved lives and reduced degree of injuries in accidents for new, not yet existing systems, a framework has been developed by combining available historic data, in the form of crash databases, and statistical methods with comparative calculations of vehicle behavior using numerical optimization rather than simulation. The framework performs effectively, it gives interesting insights into the relation between more traditional active yaw control and optimal autonomous lane-keeping control, and it clearly demonstrates the potential of saved lives by using autonomous vehicle maneuvers. [ABSTRACT FROM AUTHOR]

To address the problem of the mutual interference and control distribution in the integrated stability control of active front steering (AFS) and electronic stability control (ESC), this study proposes a new integrated stability controller of AFS and ESC based on holistic control framework. A distribution rule of longitudinal and lateral tire force based on equal reserve capacity of tire force is designed to distribute the control right of steering and braking. A UniTire model with combined slip condition is also designed for the integrated control of steering and braking. Model predictive control (MPC) is adopted to address the constrained multi-objective control problem. To reduce the complexity of the system and improve the real-time performance of the system, a linear time-varying MPC controller is designed by linearizing the nonlinear system, and then the constrained MPC control problem is transformed into a quadratic programming problem for solving. Finally, the proposed method is validated on the basis of Matlab/Simulink and CarSim co-simulation platform. Results show that the proposed method has obvious effects on solving the mutual interference and control right distribution of steering and braking and improving vehicle stability. [ABSTRACT FROM AUTHOR]

Featured Application: This work uses AFRBF-SMC to achieve the vehicle stability control after the estimation of the sideslip angle by ADUKF. This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles. [ABSTRACT FROM AUTHOR]

Active collision avoidance system has received more and more attraction, which has the capability to avoid potential accidents and reduce driver burden. This paper proposes an active collision avoidance system which consists of a path planner and a coordinated lateral controller. In the path planner, cubic B-spline is developed to obtain collision-free trajectories to bypass the obstacle by steering. Based on this, a coordinated lateral dynamic control of autonomous ground vehicles is presented to improve the accuracy and robustness of path following and simultaneously ensure vehicle stability via active front steering and direct yaw moment control. Then, second-order sliding mode control, based on super-twisting algorithm, is applied to reduce lateral offset and heading angle deviation as much as possible and avoid chattering phenomenon of tradition sliding mode control. Meanwhile, a new form of sliding mode control based on improved reaching law is devoted to forcing the vehicle state sideslip angle and yaw rate to stability envelope with less chattering in the case of low road friction coefficient. Eventually, the effectiveness and robustness of active collision avoidance system against external disturbance and parametric uncertainties are confirmed through different test cases in the MATLAB/Simulink simulation platform. [ABSTRACT FROM AUTHOR]

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions. [ABSTRACT FROM AUTHOR]

In order to improve the stability of an off-road vehicle with MEWs, a robust feedback control for electronic stability controller (ESC) was designed by considering uncertainty perturbation of MEW and traditional radial tire cornering characteristics based on Lyapunov stability theorem. Norm bounded model with the uncertainty of tire cornering stiffness was introduced herein, and the feedback matrix was solved by Schur complement lemma and LMI. Finally, fish hook tests with different vehicle speeds and road adhesion were carried out on CarSim/Simulink platform. Simulation results show that the ESC matched with MEW may ensure the stability of the vehicles, the yaw velocity and the center of mass sideslip angle tracking errors were maintained in the ranges of 0.03 -- 03 road/is and 0006 --0.1 road respectively, and the control algorithm may keep good robustness in the range of 0. 5 times lateral stiffness of ordinary inflatable wheels, which provides a theoretical reference for the whole vehicle active safety control matching with MEWs. [ABSTRACT FROM AUTHOR]

Recent electric vehicle studies in literature utilize electric motors within an anti-lock braking system, traction-control system, and/or vehicle-stability controller scheme. Electric motors are used as hub motors, on-board motors, or axle motors prior to the differential. This has led to the need for comparing these different drivetrain architectures with each other from a vehicle dynamics standpoint. With this background in place, using MATLAB simulations, these three drivetrain architectures are compared with each other in this study. In anti-lock braking system and vehicle-stability controller simulations, different control approaches are utilized to blend the electric motor torque with hydraulic brake torque; motor ABS, torque decomposition, and optimal slip-tracking control strategies. The results for the anti-lock braking system simulations can be summarized as follows: (1) Motor ABS strategy improves the stopping distance compared to the standard anti-lock braking system. (2) In case the motors are not solely capable of providing the required braking torque, torque decomposition strategy becomes a good solution. (3) Optimal slip-tracking control strategy improves the stopping distance remarkably compared to the standard anti-lock braking system, motor anti-lock braking system, and torque decomposition strategies for all architectures. The vehicle-stability controller simulation results can be summarized as follows: (1) higher affective wheel inertia of the on-board and hub motor architecture dictates a higher need of wheel torque in order to generate the tire force required for the desired yaw rate tracking. A higher level of torque causes a higher level of tire slip. (2) Optimal slip-tracking control strategy reduces the tire slip trends drastically and distributes the traction/braking action to each tire with the control-allocation algorithm specifying the reference slip values. This reduces reference tire slip-tracking error and reduces vehicle sideslip angle. (3) Tire slip trends are lower with the hub motor architecture, compared to the other architectures, due to more precise slip control. [ABSTRACT FROM AUTHOR]

Based on the traditional four-wheel steering system, this paper combines the steer-by-wire system and four-wheel-independent driving technology to develop a new steer-by-wire four-wheel-independent steering and four-wheel-independent driving electric vehicle, which can realize four-wheel-independent steering and four-wheel-independent driving. Aiming at the steering actuator redundancy characteristics of the four-wheel-independent steering and four-wheel-independent driving vehicle, a hierarchical control method is proposed in this paper. In view of parameters perturbation of vehicle speed and tire cornering stiffness and the model uncertainty problem, the structure singular value μ is used to study the stability control of four-wheel-independent steering and four-wheel-independent driving vehicle under multiple perturbations in the upper layer, and verifies the advantages of μ controller by comparing it with H∞ control and proportional-integral-derivative control. The lower layer is aimed at the redundancy of vehicle steering actuator, an on-line reconfigurable steering angle and driving force allocation control method based on tire force optimal allocation is proposed. This method can not only optimize the tire longitudinal force and lateral force distribution under normal conditions but also reconstruct the distribution control strategy on-line under fault conditions, realizing active fault tolerance. Finally, simulations by MATLAB/Simulink and hardware-in-the-loop experiments are conducted to verify the proposed control method. The simulation results show that the designed controller can maintain good stability under the conditions of model perturbation, separation pavement, and actuator failure. [ABSTRACT FROM AUTHOR]

In this paper, a general control allocation (CA) algorithm is proposed based on a lexicographic optimization (LO) strategy for a vehicle's longitudinal and lateral control. The primary objective of this CA is to distribute the torque adjustments of the lateral controller such that the error between the desired and actual forces and moments at the vehicle's center of gravity is minimized, while maintaining the longitudinal tire slip ratios close to a desired range. Presence of various constraints in practical systems can significantly increase the complexity and effort of properly adjusting the tuning parameters in the objective function. Hence, an LO method is used to prioritize the objectives. Lateral stability of the vehicle has the highest priority and is subject to the constraint of maintaining small tire slip ratios. The second priority of CA is assigned to minimize the adjustment torques. The proposed LO-based approach reduces exhaustive vehicle testing commonly required for tuning of the separately designed controllers and the cost and time of the implementation. In addition, it makes the algorithm easily transferable from one vehicle to another by reducing the number of tuning parameters. Simulation and experimental results are presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

[Display omitted] • A controller to prevent secondary crashes after an initial rear-end collision is proposed. • Rule-based switching control is embedded into reinforcement learning. • Pre-collision control and post-collision control are combined. • Results prove the superiority of the proposed controller. Rear-end collisions result in a large number of casualties and property losses, and the serious injury risk in multiple impact accidents is much higher than that in single impact accidents. In this paper, we propose a novel controller to facilitate the prevention of secondary crashes after an initial rear-end collision, which expands the operational horizon of conventional vehicle active safety systems from preventive measures to post-event mitigation measures. Considering the complexity of the problem with multi-object synthesis optimization and vehicle nonlinear dynamics, this study combines the pre-collision control and post-collision control to reduce the initial crash loss and the subsequent control difficulty. The rule-based switching control and drift manipulation are embedded into the reinforcement learning algorithm to improve the training efficiency and control performance. The bench test results validate the superiority of the proposed controller over other strategies and algorithms in different rear-end collision scenarios. [ABSTRACT FROM AUTHOR]

This study concerns the development and testing of three types of Anti-lock Brake Systems (ABS): a standard on-off wheel’s acceleration control; a wheel’s longitudinal slip controller based on a discrete Proportional-Integral-Derivative (PID) control; and a novel type of ABS that involves controlling the wheel’s speed through a discrete PID. This work was developed inside a wider project that will lead to the implementation of stability control systems in a prototype car. For this reason, the typologies of ABS must not require extra sensors compared to those in standard vehicles: Inertial Measurement Unit (IMU) and 4-wheel speed sensors. Furthermore, they must be easily integrated with other controls and electronic components in terms of sampling time and values. The standard ABS seems more appropriate than the others two because it uses only parameters defined by sensors and it has a simple architecture that does not have the problem of computational time. However, in recent years, cars have been equipped with Electro-Hydraulic-Braking (EHB) units that improve the performance of the system controls. In fact, it is possible to use a control that allows actuators to follow a continuous target and smooth out pressure actions. Even if the longitudinal Slip Controller has a simple architecture and uses a PID control, it is limited to using quantities estimated instead of measured: the tires’ friction coefficient, the tires’ longitudinal stiffness, and the car’s speed. Therefore, the use of a Wheel Speed Controller is the right compromise to link the advantages of both controllers by following the braking pressure continuously and not needing to know the condition and properties of the tires. The results of tests carried out in a Hardware-In-the-Loop (HiL) system are showed and involved a complex vehicle model implemented in real-time.

The sideslip angle is an important state parameter for vehicle stability control; in this study, a robust sideslip angle observer with regional stability constraint is presented. In order to achieve more accurate sideslip angle estimation, the vehicle heading angle and yaw angle are distinguished and the relational expression between vehicle heading angle, yaw angle and sideslip angle was applied to the singular vehicle modelling process. Considering the time‐varying tire cornering stiffness, the uncertain singular vehicle model is established by integrating the singular vehicle dynamic equation and model uncertainty. Then, a robust sideslip angle observer whose pole assignment has the regional stability constraint is designed. Simulation studies and experimental results illustrate the effectiveness of the proposed sideslip angle estimation method. [ABSTRACT FROM AUTHOR]

Abstract This study deals with the enhancement of directional stability of vehicle which turns with high speeds on various road conditions using integrated active steering and differential braking systems. In this respect, the minimum usage of intentional asymmetric braking force to compensate the drawbacks of active steering control with small reduction of vehicle longitudinal speed is desired. To this aim, a new optimal multivariable controller is analytically developed for integrated steering and braking systems based on the prediction of vehicle nonlinear responses. A fuzzy programming extracted from the nonlinear phase plane analysis is also used for managing the two control inputs in various driving conditions. With the proposed fuzzy programming, the weight factors of the control inputs are automatically tuned and softly changed. In order to simulate a real-world control system, some required information about the system states and parameters which cannot be directly measured, are estimated using the Unscented Kalman Filter (UKF). Finally, simulations studies are carried out using a validated vehicle model to show the effectiveness of the proposed integrated control system in the presence of model uncertainties and estimation errors. Highlights • Novel optimal integrated control for active steering and differential braking is proposed. • Controller weights are automatically tuned by fuzzy logic for various conditions. • Stability of integrated controller with uncertainty is proved in the sense of Lyapunov. • The proposed controller is evaluated in the presence of Unscented Kalman Filter. • Small intentional braking force is used to assist the steering control. [ABSTRACT FROM AUTHOR]

In this study, a hierarchical structured direct yaw-moment control (DYC) system, which consists of a main-loop controller and a servo-loop controller, is designed to enhance the handling and stability of an in-wheel motor driven driven electric vehicle (IEV). In the main loop, a Fractional Order PID (FO-PID) controller is proposed to generate desired external yaw moment. A modified Differential Evolution (M-DE) algorithm is adopted to optimize the controller parameters. In the servo-loop controller, the desired external yaw moment is optimally distributed to individual wheel torques by using sequential quadratic programming (SQP) approach, with the tire force boundaries estimated by Unscented Kalman Filter (UKF) based on a fitted empirical tire model. The IEV prototype is virtually modelled by using Adams/Car collaborating with SolidWorks, validated by track tests, and serves as the control plant for simulation. The feasibility and effectiveness of the designed control system are examined by simulations in typical handling maneuver scenarios. [ABSTRACT FROM AUTHOR]

Direct yaw moment controllers improve vehicle stability and handling in severe manoeuvres. In direct yaw moment control implementations based on Linear Quadratic Regulators (LQRs), the control system performance is limited by the unmodelled dynamics and parameter uncertainties. To guarantee robustness with respect to uncertainties, this paper proposes a gain scheduled Robust Linear Quadratic Regulator (RLQR), in which an extra control term is added to the feedback contribution of a conventional LQR to limit the closed-loop tracking error in a neighbourhood of the origin of its state-space, despite the uncertainties and disturbances acting on the plant. In addition, the intrinsic parameter-varying nature of the vehicle dynamics model with respect to the longitudinal vehicle velocity can compromise the closed-loop performance of fixed-gain controllers in varying driving conditions. Therefore, in this study the control gains optimally vary with velocity to adapt the closed-loop system to the variations of this parameter. The effectiveness of the proposed RLQR in improving the robustness of a classical LQR against model uncertainties and parameter variations is proven analytically, numerically and experimentally. The simulation and vehicle test results are consistent with the formal analysis proving that the RLQR reduces the ultimate bound of the error dynamics. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have advantages in the aspect of energy, environment, and vehicle motion control. However, it is still not competitive enough to conventional vehicles because of the limited driving range and the high cost of the battery. Therefore, the energy efficiency is of the most importance for the control of EVs. Existing range extension control systems on EVs mostly focus on longitudinal front and rear axle torque distribution or lower-level yaw moment allocation. It is a challenge to maintain the vehicle’s stability at the cost of the minimum energy when the vehicle is cornering, this paper proposes a phase plane-based controller for EVs, focusing on the energy-efficient upper-level yaw stability control. The phase plane-based controller is automatically adaptive to driving situations through the optimization of weights on the performance of the vehicle handling and stability. Firstly, a friction constrained desired model is presented for the model-following control. Secondly, β - β ̇ phase plane analysis is conducted based on a nonlinear vehicle model to graphically identify the vehicle lateral stability in real time. The self-stable region can be determined by the vehicle velocity, the road friction coefficient, and the wheel steering angle. Then, energy optimizing (i.e. gain scheduling of LQR controllers) rules are designed based on the vehicle lateral stability identification. Finally, the proposed phase plane-based controller is evaluated and the yaw moment costs are compared to other controllers’ in a realistic 7-DOF vehicle model. The results demonstrate that the proposed controller presents an excellent yaw stability control capability, and compared to the widely used Shino’s controller, the proposed controller reduces the energy consumption by 9.68% and 3% at the ‘light’ and ‘severe’ maneuver, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a vehicle dynamic stability controller for distributed-drive electric vehicles. A hierarchical control structure is adopted for the proposed controller. An upper controller is designed on the basis of integrated model-matching control. It consists of a feedforward component plus a feedback component to calculate the desired external yaw moment to achieve the desired vehicle motion. The feedforward control aims at compensating the effect caused by the variation in the linear cornering stiffnesses of the tyres during the life cycle of the tyres. It provides a rapid response under common driving conditions. The linear cornering stiffnesses of the tyres are estimated in real time by the adaptive forgetting-factor recursive least-squares method. Since many vehicle parameters have strongly non-linear and time-varying characteristics, adaptive sliding mode control is used as the feedback component to make the controller robust against systematic uncertainties. To combine the outputs of feedforward and feedback together and to avoid probable conflict, a weight gain coefficient is obtained. Additionally, a conventional sliding-mode controller is introduced as a comparative upper control strategy. The lower controller is utilized to allocate the required yaw moment and traction to the four independent motors, taking into account the tyre grip margins. Simulations for a low-g manoeuvre and a high-g manoeuvre are carried out to evaluate the proposed control algorithm. The results show that the proposed vehicle stability controller can significantly stabilize the vehicle motion and greatly reduce the driver's workload in comparison with the conventional sliding-mode controller. [ABSTRACT FROM AUTHOR]

The control of vehicle chassis dynamics requires a fast and complex process, with constantly changing tire forces and vehicle states. When the control system is completely rational, there are coupling relations and functional redundancy among subsystems, which makes it difficult to achieve optimal overall performance. This paper proposes a hierarchical extension coordinated controller, which considers the bounded rationality of the control system to address traditional stability control interference on driving speed and driver operation. The supervisory layer uses the extension phase plane to divide the operating state of vehicles. The coordinated decision-making layer considers active front-wheel steering (AFS) and direct yaw moment control (DYC) as game bodies, establishing an evolutionary game model to determine coordination weights. Results from the Carsim/Simulink platform comparison with a completely rational independent control system indicate the bounded rationality coordinated controller achieves better stability control with less intervention on driving speed. Vehicle testing has confirmed the effectiveness of the coordination control strategy. • Consider the bounded rationality of the chassis control system. • Evolutionary game model determines the coordination weights of AFS and DYC. • Coordinated controller solves the functional overlap among subsystems. • Simulation and vehicle tests verified the effectiveness and superiority. [ABSTRACT FROM AUTHOR]

This paper deals with vehicle sideslip angle estimation. The paper introduces an industrially amenable kinematic-based approach that does not need tire–road friction parameters or other dynamical properties of the vehicle. The convergence of the estimate is improved by the introduction of a heuristic based on readily available inertial measurements. The method is tested on a vast collection of tests performed in different conditions, showing a satisfactory behavior despite not using any information on the road friction. The extensive experimental validation confirms that the estimate is robust to a wide range of driving scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a design method of a preview controller for vehicle stability with vehicle-to-vehicle (V2V) communication. Using V2V communication technology, a driver's steering input of a preceding vehicle is obtained on a following vehicle for preview control. To cope with changes in speed and intervehicle distance in a real situation, a position-based sampling is used. With the position-based sampling data, the time-based preview data are resampled by interpolation. For the controller design, linear vehicle modes describing yaw and roll motions are used. To design a preview controller on the following vehicle with the previewed steering input, a linear optimal preview control methodology is adopted. To validate the preview control with V2V communication in terms of vehicle stability, a simulation is conducted using a vehicle simulation package. [ABSTRACT FROM PUBLISHER]

In this paper, a robust controller based on quantitative feedback theory is designed to improve the lateral dynamic of a four-wheel vehicle using direct yaw moment controller. The essential yaw moment is calculated by this robust and applied to the vehicle dynamics model using a differential brake system, which is allocated by a rule-based controller. Quantitative feedback theory controller design is based on bicycle model which is assumed as a simple linear handling model. Herein, simulations are carried out based on nonlinear handling dynamics. To examine the controller in an almost real environment, CARSIM software is used to face a challenging maneuver. The results show that the robust controller could overcome the system uncertainties and control the vehicle in various handling maneuvers. Meanwhile, the braking torque allocated by differential brake systems is accessible and reliable for a real vehicle. [ABSTRACT FROM AUTHOR]

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.

A model-based vehicle stability controller is proposed in this paper. Since a model-based controller highly depends on the model accuracy, the lateral forces of the tyres and the instantaneous cornering stiffnesses of the predictive vehicle model are estimated online in order to deal with the model mismatch. The model-based controller calculates a desired yaw moment according to the prevailing errors in the the yaw rate and the side-slip angles of the vehicle. Then, by using linearization, an optimal braking force allocation algorithm is proposed to allocate the yaw moments to the target slip ratios of each wheel. Simulations are carried out with a full-vehicle model in CarSim software. The effectiveness and the robustness of the proposed control algorithm are evaluated through simulation cases of open-loop and closed-loop manoeuvres. [ABSTRACT FROM AUTHOR]