Active Front Wheel Steering Research Articles

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

Nonlinear model predictive control for trajectory-planning and tracking based on tilting technology to achieve vehicle obstacle avoidance

ABSTRACT High-speed autonomous vehicles can effectively improve the performance of obstacle-avoidance trajectory-planning and tracking control through integration with existing electronic control systems. However, relatively little research has been conducted in this field. This paper proposes a novel methodology of obstacle-avoidance trajectory-planning and tracking control based on active front-wheel steering integrated with tilting technology that can tilt the vehicle body toward the inside of the curve via active suspension when turning. The controller is designed using hierarchical control, in which the upper layer uses the point-mass vehicle model to design the trajectory-planning algorithm based on model predictive control. The lower layer uses the nonlinear vehicle model to design obstacle-avoidance tracking nonlinear model predictive control based on active steering integrated with tilt control. Then, the constrained nonlinear model predictive control problem is transformed into a constrained nonlinear programming problem, which is solved by sequential quadratic programming. Finally, simulations were performed using the CarSim/Simulink co-simulation platform. Two other hierarchical obstacle-avoidance tracking nonlinear model predictive controllers were designed as comparison objects. The simulation results show that the planning trajectory of the proposed integrated controller is closest to the obstacle. This controller effectively improves the vehicle obstacle-avoidance trajectory-tracking performance, handling stability, and maneuverability.

Coordinated Longitudinal and Lateral Motion Control for Distributed Drive Electric Vehicles With Mechanical Elastic Wheels

The distributed drive electric vehicle (DDEV) with active front wheel steering is an over-actuated system. For such a system, more optimization targets should be considered to make the system better in addition to meeting the driving command of drivers and keeping the vehicle stable. The slip energy generated by excessive tire slipping reduces the utilization of the output energy of the motor, resulting in a reduction of electric vehicle mileage. In order to improve the vehicle performance, a coordinated longitudinal and lateral controller is designed for DDEV with mechanical elastic wheels (MEWs). The motion control layer of the hierarchical controller adopts the integral sliding mode control (ISMC) to determine the desired longitudinal force, lateral force and yaw moment as virtual inputs of the vehicle. Then, a novel tire force distribution strategy is proposed to dynamically assign the virtual inputs among front wheels and four motors with the goal of optimizing the vehicle stability and longitudinal tire slip energy dissipation. To characterize tire slip energy more accurately, the brush model matching MEW is deduced in the combined slip conditions. The proposed controller is evaluated on the hardware-in-the-loop (HIL) test platform under different driving conditions. Simulation results demonstrate that this controller not only ensures the vehicle handling stability performance, but also improves the utilization of motor output energy and reduces tire wear.

Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.

Research on the Control Strategy for Handling Stability of Electric Power Steering System with Active Front Wheel Steering Function

<div>Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system. Finally, experiments are conducted in the CarSim/Simulink joint simulation environment. The results indicate that compared to traditional PI/PID controllers and advanced ADRC controllers, DR-PI/DR-PID controllers can effectively suppress step disturbances, sinusoidal disturbances, and white noise disturbances while ensuring the dynamic response characteristics of the system. Under double-lane conditions, the root mean square values of the yaw rate and sideslip angle decrease by 4.35% to 12.3% and 3.21% to 7.72%, respectively. Therefore, the designed control strategy can improve handling stability while ensuring the robustness of the system.</div>

Improved sliding mode extension control of vehicle active front wheel steering based on extended state observer

In order to improve the handling stability of electric vehicles, a new active front-wheel steering (AFS) control method was proposed. Firstly, parametric uncertainty and external interference in vehicle dynamics are summarized as a nonlinear interference term in vehicle model. An extended state observer (ESO) is designed to observe and compensate the nonlinear interference terms in real time, so as to improve the accuracy and control effect of the model. Secondly, in order to further increase the convergence speed and effectively suppress the chattering phenomenon in sliding mode control without affecting its robustness and reaching speed, an integral exponential fast terminal sliding mode controller (IEFTSMC) based on fast exponential reaching law (FERL) is designed. The problem that the control effect of some regions becomes worse when a single control algorithm is used for global region control in traditional AFS control is addressed. By combining extension theory with sliding mode control method, an improved sliding mode extension control is designed to improve the effect of AFS global control. Finally, comparative simulation tests are carried out on the CarSim/Simulink co-simulation platform. The results show that compared with the traditional FTSMC, the improved sliding mode extension control method based on ESO can not only suppress chattering more effectively, but also smoother the response curve. It also has good control effect when there is external disturbance. The effectiveness and robustness of the control strategy are verified.

Analysis and control for ideal variable transmission ratio characteristics of active front wheel steering

Analysis and control for ideal variable transmission ratio characteristics of active front wheel steering

Analysis and control for ideal variable transmission ratio characteristics of active front wheel steering

Analysis and control for ideal variable transmission ratio characteristics of active front wheel steering

A Robust Dynamic Game-Based Control Framework for Integrated Torque Vectoring and Active Front-Wheel Steering System

Distributed drive electric vehicles (DDEVs) eliminate the complex drivetrain. The independently driven in-wheel motors also endow the vehicle with more ability for improving maneuverability. To this end, this paper proposes an integrated control framework of torque vectoring (TV) and active front-wheel steering system (AFS) to ensure the vehicle lateral motion stability performance. First, the polytope method with finite vertices is employed to deal with the system uncertainties and simplify the modeling structure, based on which a distributed model predictive control is adopted to construct a dynamic interactive model between agents. Then, through introducing the game theory, a distributed parallel control scheme is developed to obtain the cooperative strategy of agents. Such a design can also satisfy the modular and scalable requirement for integrated chassis control. To ensure the system asymptotic stability, the terminal input combined with the terminal cost function are treated as the constraints in the game paradigm and then transformed as the linear matrix inequalities. Furthermore, a robust H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $</tex-math> </inline-formula> compensation method is used to suppress the system disturbance. Finally, the hardware-in-the-loop (HIL) tests are conducted to assess the control performance. The results verify the proposed integrated control scheme is effective to enhance the vehicle handling stability.

Extension coordinated control of distributed-driven electric vehicles based on evolutionary game theory

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.

Improved Deep Reinforcement Learning Decision for Integrated Control of Distributed Drive Electric Vehicle Dynamics

To solve the problem of nonlinear coupling with longitudinal and lateral tire forces in the integrated control of distributed driving electric vehicles, an improved SAC reinforcement learning algorithm is proposed to coordinate the control method of active front steering (AFS) and torque distribution. The coordinated control adopts a layered structure, and the upper layer adopts the LQR controller to determine the additional front wheel rotation angle and yaw moment required for stability control. The three-level reward function is designed with the goal of vehicle stability and state tracking and is determined by the reinforcement learning algorithm. For the weight factors of the two optimization objectives, the SAC reinforcement learning algorithm is used to coordinate the decision-making of the active front wheel steering and the additional yaw moment; the lower controller uses the comprehensive utilization rate of the tire as the objective function to perform the optimal torque distribution. A co-simulation platform is established in MATLAB/Simulink and CarSim software. The results show that compared with AFS control, SAC coordinated control can not only ensure the stability of the vehicle and achieve accurate tracking of the reference state but also effectively reduces the comprehensive utilization rate of tires and the effectiveness of coordinated control.

Coordinated Optimal Control of AFS and DYC for Four-Wheel Independent Drive Electric Vehicles Based on MAS Model.

The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0-5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.

A layered roll stability control strategy for commercial vehicles based on adaptive model predictive control

Due to the high centre of mass, commercial vehicles are prone to ultimate rollover under extreme steering conditions. The rollover of commercial vehicles leads to serious traffic accidents and poses a threat to road traffic. In this paper, the mechanism of vehicle rollover is revealed by static equilibrium and phase plane analysis. Meanwhile, a robust roll angle observer is designed by combining Kalman filter and sliding mode observation. Considering the influence of wheel lifting on roll dynamics, a novel layered control strategy is proposed by employing engine torque limit, differential braking and active front-wheel steering. Moreover, the layered control of roll stability is realised by adaptive model predictive control with time-varying weights and constraints. To verify the validity and reliability of the proposed algorithm, the hardware-in-loop tests under typical driving conditions have been carried out. The results show that the proposed method can effectively restrain the divergence of roll state and prevent vehicle rollover.

Improving vehicle dynamics employing individual and coordinated sliding mode control in vehicle stability, active front wheel steering and active rear wheel steering systems in co-simulation environment

The goal for the future, safer and more sustainable transportation leads to implementation of many advanced systems in vehicles. In this paper, a vehicle model with stability control system that uses the braking system is regarded as a base active vehicle. Two alternative vehicle models are considered where this system is combined and integrated with active front wheel steering (AFS) and active rear wheel steering (ARS) system, separately. For the purpose of this research, 3D virtual vehicle models based on a B-segment vehicle were created in ADAMS/Car. As a research tool, a co-simulation approach between ADAMS/Car and MATLAB/Simulink was used. Several sliding mode controllers (SMC) have been proposed and implemented in MATLAB/Simulink in order to analyse the potential improvements of the vehicle dynamics due to the integration and coordination of these systems. In the same MATLAB/Simulink environment, a reference 2DOF nonlinear bicycle model was used. The vehicle models were simulated in driving scenarios based on standardized ISO 7401 test procedures. The scenarios include situations where the passive vehicle loses its stability or fails to complete the manoeuvre while the vehicles with stability control or integrated control systems successfully complete it.

Active Front Wheel Steering System using Yaw Rate Estimation based Fuzzy Logic Due to Various Lateral Wind Disturbance

The paper devised and compared the performances of PID, fuzzy-tuned PID and fuzzy logic controller in an Active Front Wheel Steering system to stabilize a 9-DOF nonlinear passenger vehicle when subjected to lateral wind disturbance. The vehicle model was derived mathematically and verified with data from IPG CarMaker at a longitudinal speed of 80 km/h. Initially, the disturbance test was conducted using three lateral wind disturbance profiles to test for controller resiliency with zero steering input. Then, a simple but effective yaw rate observer was derived without compromising the linearity of the vehicle model to simulate the disturbance test with a double lane change (DLC) steering input. A more extreme disturbance magnitude was evaluated in the latter test using the developed control designs. The three controllers showed good performances in both disturbance tests, with fuzzy logic having the lowest error out of the three, which is less than 5% for using the estimated yaw rate observer.

Gain-scheduling LPV synthesis H∞ robust lateral motion control for path following of autonomous vehicle via coordination of steering and braking

This paper proposes a robust gain-scheduling lateral motion control strategy via coordination of the active front-wheel steering (AFS) and direct yaw moment control (DYC) for path following of autonomous vehicle. Distinguishing from other relevant researches, a seamless application of the DYC is designed to reduce the influence of braking for the vehicle longitudinal dynamics. In order to achieve this objective, the weighting function with a self-scheduling parameter is designed. Based on the above, a novel LPV (Linear Parameter Varying) synthesis H∞ performances lateral motion controller is designed considering the vehicle longitudinal velocity as a time-varying parameter. The path following is realised through achieving a desired yaw rate, which is generated based on the path-following dynamics. The feedback gains can be derived by solving the LMI (Linear Matrix Inequalities). Furthermore, the physical limitations of the actuators (steer-by wire and brake-by-wire) are considered by weighting functions. Finally, experiments in the HIL system are carried out to verify the effectiveness and real-time performance of the proposed control strategy, and the results show that the proposed controller has good tracking capability under different manoeuvres and can reduce the possibility of actuator saturation and the influence of braking for the vehicle longitudinal dynamics.

Integrated Control of Active Front Wheel Steering and Active Suspension Based on Differential Flatness and Nonlinear Disturbance Observer

Mechanical elastic wheel (MEW) has the advantage of explosion-proof, which helps to guarantee the safety and maneuverability of the automobile. Aiming to improve the stability of vehicles matching with MEW, this paper proposes an integrated control strategy that combines the active front steering and the active suspension systems. The integrated controller consists of feed-forward and feedback controllers. Firstly, the differential flatness system of the vehicle is constructed concerning the coupling among yaw, lateral, and roll motion. And the flatness based feed-forward control inputs of the desired front wheel angle and active roll moment are derived. At the same time, the backstepping-adaptive feedback controllers for yaw rate and roll angle are designed. Then, the feed-forward and feedback controllers are coordinated and their weight coefficients are adjusted according to the sideslip angle. Furthermore, this paper also adopts a nonlinear disturbance observer to estimate the vertical load of MEW. The influence of the vertical load is fully considered when estimating the equivalent cornering stiffness of each MEW, so as to realize the online adjustment of the controller parameters. Finally, the maneuver tests of step input steering and double lane change are simulated, respectively. The results show that the proposed control strategy yields a better control effect than the fuzzy-PID controller, and the performance of the vehicle matching with MEW has been significantly improved.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

Switched model predictive controller for path tracking of autonomous vehicle considering rollover stability

Lateral stability and rollover stability are two most crucial concerns for path tracking of an autonomous vehicle under handling limit conditions. To improve path tracking accuracy and ensure vehicle stability under handling limit conditions, this paper proposes a force-driven switched MPC path tracking control strategy that coordinates active front-wheel steering with an external yaw moment. Firstly, a linear time-varying tire model is modelled using the affine approximate linearisation method to describe rear tire dymamics. And vehicle dynamics is modelled to predict vehicle states in the prediction horizon. Then, a force-driven switched MPC control strategy is proposed based on the system prediction model. The vehicle lateral stability and rollover stability are restricted using maximum tire lateral force and the zero-point moment method, respectively. Finally, according to the simulation results under normal speed and limiting speed conditions, a force-driven switched MPC control strategy can improve control accuracy and ensure vehicle stability.

Robust Control Design of Active Front-Wheel Steering on Low-Adhesion Road Surfaces

In order to improve the stability of vehicle steering on low-adhesion road surfaces, this paper designed a hybrid robust control strategy, H2/H∞, for active front-wheel steering (AFS) based on robust control theory. Firstly, we analyzed the influence of the sidewall stiffness and road adhesion coefficient of the tires on vehicle stability, through which we can study the wheel deflection characteristics of low-adhesion roads. Secondly, the reference yaw velocity of the vehicle was calculated using the three-degrees-of-freedom model as the reference model, through which, taking the norm H∞ as the objective function and the norm H2 as the limit to control the output, the hybrid robust control strategy H2/H∞ of the AFS system on a low-adhesion road surface was developed. Finally, the simulation experiment was carried out by the Simulink/CarSim co-simulation platform and a hardware-in-the-loop (HIL) experiment. In this paper, the results show that the AFS control strategy can improve the vehicle handling stability on low-adhesion road surfaces, and the controller has good path tracking performance and robustness.