Active Front Steering Control Research Articles

Coordination of Active Front Steering and Direct Yaw Control Systems Using MIMO Sliding Mode Control

Coordination of Active Front Steering and Direct Yaw Control Systems Using MIMO Sliding Mode Control

Fault diagnosis and fault tolerant control for distributed drive electric vehicles through integration of active front steering and direct yaw moment control

For the purpose of improving the lateral stability in the path following process for the distributed drive electric vehicles subject to multiple actuator/sensor faults, a new fault diagnosis and fault tolerant control method is proposed in this paper through the integration of the active front steering (AFS) and direct yaw-moment control (DYC). Firstly, considering the actuator/sensor faults and parameter uncertainties together, a Takagi–Sugeno fuzzy model is established to describe the integrated AFS/DYC system with nonlinear dynamics. Secondly, because the fault information plays a key role in the fault tolerant control, a fuzzy estimation observer is presented to diagnose actuator/sensor faults online. Thirdly, considering the high cost of the measurement sensors for lateral speed, it is hard to measure the complete state of the vehicle. Thus, in the frame of dynamic output feedback structure, a fuzzy fault tolerant control method is proposed to improve the path following performance and guarantee the dynamic stability. Finally, the hardware-in-the-loop experimental results of two typical driving maneuvers show that the proposed control method has a superior performance in the improvement of the driving stability and active safety.

AFS control system research of distributed drive electric vehicles by adaptive super-twisting sliding mode control

To solve the problems of serious buffeting in traditional sliding mode control and difficulty in obtaining the derivative information of the system sliding mode surface, a distributed drive electric vehicles active front steering (AFS) control method based on adaptive super-twisting sliding mode control (ASTSMC) is proposed. Taking the yaw rate deviation as the state quantity, the stable and convergent sliding mode surface is designed to obtain the equivalent control input of the front wheel angle. The sliding mode function information is substituted into the parameters of the super-twisting algorithm; the discontinuous term is kept in the integrand function to keep the control signal continuous and weaken the system chattering; the adaptive control law is added to design the AFS controller. The co-simulation results of Matlab/Simulink and Carsim show that ASTSMC can reduce the yaw rate by 40.59% compared with no control under step steering condition. Compared with the sliding mode controller, ASTSMC has optimized the sideslip angle by 5.41% under the double line shifting condition.

Coordinated control of AFS and DYC for electric vehicles with mechanical elastic electric wheels considering the roll effects and uncertain parameters

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

Chassis Coordinated Control for Full X-by-Wire Four-Wheel-Independent-Drive Electric Vehicles

In this paper, a full X-by-wire chassis coordination control scheme is proposed by synthetically utilizing the Direct Yaw-moment Control, Active Front Steering, Anti-Slip Regulation and Active Roll Control to improve the longitudinal, yaw and roll stability. First, a vehicle state prediction module is established to predict vehicle yaw and roll stability and to generate three reference vehicle states, i.e., the desired roll angle, yaw rate and longitudinal velocity. Then, a decentralized event-triggered discrete sliding mode control scheme is developed to track these reference states by coordinating the X-by-wire subsystems based on their respective effective working areas. For yaw rate control, dynamic torque regulation factors are introduced to prevent wheel slip and lock-up based on real-time slip ratio feedback. Under longitudinal driving conditions, a robust sliding mode control and a hybrid control-based method are used under emergency acceleration or braking conditions. The hardware-in-the-loop (HIL) tests under the slalom, double lane change and fish hook tests show that the proposed chassis coordinated control scheme can comprehensively improve vehicle ride comfort, handling performance, longitudinal and lateral stability, and rollover prevention ability.

Coordinated Lateral Stability Control of Autonomous Vehicles Based on State Estimation and Path Tracking

This paper proposes a coordinated control system of autonomous vehicles’ lateral stability based on state estimation and path tracking, which contributes to enhancing path-tracking capability and at the same time maintaining the stability of autonomous vehicles driving in complex and dynamic road conditions. First, an extended Kalman filter observer is designed to estimate the vehicle parameters. Then, based on the single-point preview theory, an adaptive preview time model is built for the accuracy of path tracking. When designing the direct yaw-moment controller, sideslip angle and yaw rate, two critical parameters for lateral dynamic stability control are considered, and the single-wheel braking method is utilized to reasonably distribute the additional yaw moment to a certain wheel. To make full use of active front steering and direct yaw-moment control, the critical steering angle of the front wheel is determined, and a switching function is formulated. Co-simulation shows that the suggested coordinated control system can effectively strengthen maneuvering stability and simultaneously satisfy tracking accuracy.

Coordinated control of ESC with active front steering and active suspension normal force control for better vehicle’s dynamic response

Advances in vehicle technologies brought changes into architectures of integration of chassis control systems. Alongside other improvements, integration can provide better vehicle handling, stability and safety. Different chassis control systems can use different control methods. Considering while in motion vehicle has movements in longitudinal, lateral and vertical direction, different control systems target different motion. If there is no coordination among active control systems, interaction and performance conflict can arise. This paper presents coordination of three control systems: electronic stability control (ESC), active front steering control (AFSC) and active suspension normal force control (ASNF). ESC is actually direct yaw and anti-roll control using selective wheel braking. ASNF is considered only on front axle. All systems use fuzzy-logic as a control method. Vehicle is presented as 14-DOF nonlinear full vehicle model and for the control purposes 3-DOF reference model was introduced. Emphasis was given on vehicle parameters characterizing dynamic behavior in longitudinal and lateral direction. Benefits of coordinated action of the three systems can be seen from the results gained through simulation of cornering event and single lane change in Matlab/Simulink. Coordinated control adds to the action of ESC itself, resulting in improved stability and handling, and thus safety of the vehicle.

Gain-Scheduled Steering and Braking Coordinated Control in Path Tracking of Intelligent Heavy Vehicles

Abstract In the context of intelligent transportation system and advanced vehicle control system, higher requirements for intelligent control and coordinated control of heavy vehicles (HVs) have been proposed. Automatic path tracking control is essential for automatic driving of heavy vehicles. However, uncertain lateral disturbances and time-varying characteristics of system parameters make it difficult to guarantee roll stability and path tracking accuracy during automatic path tracking. This paper investigates the coordination of active front steering (AFS) and direct yaw moment control (DYC) in the automatic path tracking system for intelligent heavy vehicles. The main idea is to adjust the braking action according to the rollover risk evaluated by a specific rollover index (RI). The coordination of steering and braking systems and the robustness of the system against time-varying parameters are achieved by a gain-scheduled linear parameter varying (LPV) controller. Based on the linear matrix inequality (LMI) framework, the LPV controller is synthesized to ensure the robust H∞ performance against external disturbances. Simulation results show that the proposed gain-scheduled LPV/H∞ control strategy can enhance roll stability and path tracking accuracy in the path tracking process of intelligent heavy vehicles.

Integrated stability control for a vehicle in the vehicle-to-grid system on low adhesion coefficient road

For the vehicle-to-grid system, the dynamic performance of the vehicle in the transportation system is quite crucial. The stability of the vehicle is the basis of the whole system. Compared to the traditional vehicle, the vehicle with a torque distribution system allows the vehicle to have a better dynamic performance. The torque distribution method has attracted a lot of attention from researchers. Most of the current work focuses on the vehicle on the concrete road. To improve the vehicle lateral stability in the critical work condition, the nonlinear reference model and vehicle dynamic model with 8 degrees of freedom are established based on the vehicle dynamic theory. An integrated active front steering (AFS) and direct yaw control (DYC) controller are designed based on LQR (Linear quadratic regulator) and vehicle stability phase portrait. To evaluate the performance of the vehicle on the road with a relatively low adhesion coefficient. The double lane-change and fishhook maneuver are chosen as the work condition. The steering angle, wheel torque, vehicle routine, phase portrait track, and yaw rate are calculated and compared. The simulation result validates the effectiveness of the proposed integrated AFS and DYC control method. The stability of the vehicle on the low adhesion coefficient road can lay a good foundation of the vehicle-to-grid system.

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

To improve safety and stability of four-wheel independent drive electric vehicles (4WIDEVs) in extreme path tracking maneuvers, a hierarchical yaw stability control scheme based on predictive control is proposed in this work. The upper-level stability control strategy coordinates active front steering (AFS) and direct yaw moment control (DYC), yielding desired front steering angle and longitudinal force of each tire. The control task is formulated into a finite horizon optimal control problem, which is solved by hybrid model predictive control (hMPC) algorithm. The lower-level slip ratio control strategy calculates and tracks the optimal longitudinal slip ratio of each tire for implementation of the longitudinal tire force distributions in a wide range of road friction coefficient. Based on a qualified piecewise affine (PWA) approximation of lateral tire force models, the stable regions of vehicle states under multiple varying inputs are obtained by analyzing trajectories of fixed points. Furthermore, the control modes of the upper-level control strategy with different control inputs and state constraints are determined by judging bifurcation point of system. Finally, the proposed control approach is verified and compared through the dSPACE-based hardware-in-the-loop (HIL) experiments. The results indicate that the proposed yaw stability control strategy can improve yaw stability performance and reduce the tracking error significantly in extreme situations.

Disturbance Observer Based Active Independent Front Steering Control for Improving Vehicle Yaw Stability and Tire Utilization

Disturbance Observer Based Active Independent Front Steering Control for Improving Vehicle Yaw Stability and Tire Utilization

A Post Impact Stability Control for Four Hub-Motor Independent-Drive Electric Vehicles

This article presents a post-impact lateral stability control strategy integrated for Four Hub-Motor Independent-Drive Electric Vehicles (4MIDEV) to address the issue of loss of vehicle stability control due to vehicle skidding and saturated tire forces upon light vehicle collision using active front steering (AFS) and direct yaw moment control (DYC). Upon a light vehicle collision, vehicle experiences lateral and yaw motions due to collision impact-induced yaw moment. A multiple-objective hierarchical control strategy to attenuate vehicle yaw moment and regain stability control is proposed. Two potential sets of control reference states for the control strategy motion control are considered: desired DYC states and the drift equilibrium. A direct yaw moment controller based on sliding mode control (SMC) theory is designed to track the desired yaw rate. For the AFS, the tracking of desired DYC sideslip angle is performed using a SMC based controller, whereas the tracking of drift equilibrium state values employs a state feedback controller. An SMC longitudinal controller is designed for deceleration control of the vehicle after collision. Upon compensation of the yaw moment, a multiple sliding surface control theory-based lane controller is employed for lateral path deviation and heading angle control. The effectiveness of the proposed control strategy is validated on Carsim and Matlab/Simulink joint simulation platform. Simulation results showed that the proposed control strategy is effective in improving the post-impact stability control of the 4MIDEV on a high-velocity condition, and the control strategy that tracks the drift equilibrium showed better control performance on low adhesion coefficient surface.

Integrated control of electric vehicles based on active front steering and model predictive control

Energy efficiency and handling stability are of the most importance for the control of electric vehicles. It is a challenge to maintain the stability and mobility of the vehicle at the cost of minimum energy under various driving conditions. A novel integrated control strategy of yaw stability and energy efficiency based on model predictive control and active steering system is presented. To improve the energy efficiency of the in-wheel motors, a global search algorithm of the instantaneous optimal efficiency of the motor is used to obtain the optimal torque distribution of the front and rear axles. Meanwhile, a terminal sliding mode controller is presented to maintain the lateral stability of the vehicle under general conditions. To reduce the large additional yaw moment of the vehicle under high-speed cornering condition, the model predictive controller is used to coordinate the active steering system and the direct yaw moment control system. Then, to balance the lateral stability and energy efficiency of the vehicle under extreme conditions, a stability judgment controller is proposed, which based on the theory of phase plane and yaw rate threshold method. The simulation results show that the integrated control strategy can improve the lateral stability of the vehicle under different road friction conditions, and compared with the control strategy without motor efficiency optimization , the integrated control strategy reduces the energy consumption by 21.66% and 11.18% in the ‘sine steering’ and ‘single lane change’ maneuver, respectively. In the slalom maneuver, the energy consumption of the integrated control strategy with energy saving optimization function is reduced by 10.13%.

Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle.

Coordination of Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) has been widely used for non-autonomous vehicle lateral stability control. Recently, some researchers used it (AFS/DYC) for path-following of autonomous vehicles. However, current controllers are not robust enough with respect to uncertainties and different road conditions to guarantee lateral stability of Autonomous Four In-wheel Motor Drive Electric Vehicles. Thus, a coordinated control is proposed to address this issue. In this paper, a two-layer hierarchical control strategy is utilized. In the upper-layer, a self-tunable super-twisting sliding mode control is utilized to deal with parametric uncertainties, and a Model Predictive Control (MPC) is used in order to allocate the control action to each AFS and DYC. Parametric uncertainties of tires’ cornering stiffness, vehicle mass and moment of inertia are considered. Simulations with different road conditions for path-following scenario have been conducted in MATLAB/Simulink. An autonomous vehicle equipped with Four In-wheel Motor and two degrees of freedom vehicle dynamics model is used in this study. In the end, the performance of the proposed controller is compared with the MPC controller. Simulation results reveal that the proposed controller provides better path-following in comparison with the MPC controller.

Research on intervention criterion and stability coordinated control of AFS and DYC

To improve vehicle handling stability, an intervention criterion and a coordinated control method for active front steering (AFS) and direct yaw moment control (DYC) are proposed. First, the intervention criterion of AFS/DYC stability control based on the working state of lateral tyre force is proposed. Next, a 7-DOF (degree-of-freedom) dynamics model is established, and a method for identifying nonlinear pure lateral force model based on onboard sensors and vehicle handling stability experiment is developed. Then, the mapping relationship between lateral tyre force saturation point and wheel angle is constructed to rapidly judge the working state of lateral tire force. Subsequently, a UKF-based state observer, a hierarchical control algorithm and the yaw moment distribution coefficient are designed for the proposed AFS/DYC control system. Finally, a fishhook test and a step input test are carried out on low adhesion coefficient roads to verify the effectiveness of the proposed AFS/DYC coordinated control method.

Design of the integrated AFS and DYC scheme for vehicles via FTSM and SOSM techniques

<p style='text-indent:20px;'>Improving the stability and safety is of great significance for the in-wheel electric vehicle. There are many studies only concentrating on active front steering (AFS) control or direct yaw-moment control (DYC). However, When the in-wheel electric vehicle is under extreme conditions, AFS or DYC alone is not effective. In this paper, an integrated controller of AFS and DYC is proposed. Firstly, the ideal values of yaw rate and sideslip angle can be calculated based on the two-degree-of-freedom vehicle model. Secondly, the AFS controller is obtained according to the backstepping-based fast terminal sliding mode (FTSM). Then, the DYC controller which consists of the upper controller and the lower controller is constructed. The upper controller is developed via the integral-based second-order sliding mode (SOSM). The appropriate torque is outputted to each wheel by the lower controller. Finally, the simulation results show that the actual yaw rate and sideslip angle can approach the ideal ones as closely as possible under the proposed integrated controller.</p>

An adaptive hierarchical control approach of vehicle handling stability improvement based on Steer-by-Wire Systems

This paper proposes a novel adaptive hierarchical control approach for Steer-by-Wire (SbW) vehicles to improve the handling stability. The high-level stability control scheme contains a variable steering ratio (VSR) strategy based on the adaptive-network-based fuzzy inference system (ANFIS) and an active front steering (AFS) controller designed with the integral sliding mode method by tracking the expected yaw rate, in which the desired front wheel angle is generated to enhance the cornering stability performance. Besides, an adaptive tracking controller (ATC) for the SbW system is designed by using the adaptive sliding mode control method to achieve desired steering performance in the lower level. The proposed adaptive control strategy is validated with different driving circles from ISO standards in simulation tests and hardware-in-the-loop (HiL) experiments. The results demonstrate that the designed control approach improve the vehicle handling stability significantly, even in some extreme driving conditions.

A Distributed Integrated Control Architecture of AFS and DYC Based on MAS for Distributed Drive Electric Vehicles

Reconstitution of control architecture creates a great challenge for distributed drive electric vehicles (DDEV), due to the emergence of a new distributed driving strategy. To this end, a novel distributed control architecture is proposed in this paper for integrated control of active front steering (AFS) system and direct yaw moment (DYC) system. First, a multi-agent system (MAS) is employed to construct a general framework, where AFS and DYC act as agents that work together to improve vehicle lateral stability and simultaneously reduce workloads of drivers during path tracking. The cooperative control strategies of two agents are obtained through Pareto-optimality theory to ensure optimal control performance of AFS and DYC. Then, on the basis of dynamic interaction between agents, terminal constraints, including terminal cost function and terminal input with local static feedback, are designed to guarantee the asymptotic stability of the close-loop system. Finally, virtual simulations are conducted to evaluate the proposed controller. The results indicate that the proposed control architecture can effectively preserve vehicle stability and reduce workloads of drivers, especially for the inexperienced driver. Furthermore, the hardware-in-loop (HIL) test results also demonstrate the feasibility of the proposed controller.