Direct Yaw Control Research Articles

Coordinated torque vectoring and direct yaw control based on Kalman filter control allocation for a four in-wheel electric vehicle

This paper presents a novel hierarchical control architecture designed for a four in-wheel electric vehicle (EV), integrating longitudinal and lateral control strategies to optimize performance and lateral stability. On one hand, a high-level longitudinal controller is synthesized to track a desired speed profile and generate a total torque. On the other hand, a direct yaw control (DYC) strategy is employed for steerability and stability control to generate a yaw moment. To manage the total torque as well as the yaw moment, a control allocation layer is employed. Subsequently, the formulation of the control allocation problem is presented, incorporating a gain-scheduling Daisy chaining Kalman filter (DCKF) control allocation to deal with actuator dynamics and fault tolerance. Results demonstrate enhanced performance in normal and emergency driving. Quick adaptability in case of emergency is achieved without overshooting or undershooting, with a smooth response.

Negative effect and optimal control of in-wheel motor with inclined eccentricity on driving safety for electric vehicle

During the driving process of the in-wheel drive electric vehicle, the air gap eccentricity of the motor caused by external disturbance cannot be avoided, resulting in a negative effect on vehicle dynamics. In this paper, the negative effect of vehicle lateral dynamics caused by motor-inclined eccentricity and the corresponding control method are studied. According to the Maxwell stress tensor and the air gap permeance correction coefficient, the unbalanced radial force under the inclined eccentricity of the in-wheel motor is characterized, and the corresponding vehicle dynamics model is established. Based on the vehicle dynamics model, different steering conditions are set to explore the influence of inclined eccentricity on the negative effect of vehicle lateral dynamics. It is found that the inclined eccentricity affects the handling stability of the vehicle under normal conditions and affects the rollover stability under extreme conditions. In order to solve these problems, the integrated control strategy is formulated by using the steering and driving system of an electric vehicle, and the particle swarm optimization algorithm is introduced to optimize it. The simulation results show that the proposed integrated optimal control of active rear-wheel steering and direct yaw control can effectively alleviate the negative effect of vehicle lateral dynamics caused by the inclined eccentricity of the in-wheel motor under different working conditions.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

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

Coordination Control of Active Steering and Direct Yaw Control for the Articulated Steering Vehicle

Lateral stability is vital to vehicle handling stability and traffic safety. It is also a crucial factor for the path-tracking ability of the vehicle in the intelligent transportation system (ITS). Most of the research focuses on vehicles with an Ackerman steering system. The ASV (articulated steering vehicle) has a lower steering radius. Thus, it is widely used in some special vehicles, such as mining and construction vehicles. The ASV has weaker lateral stability than the vehicle with an Ackerman steering system. To improve the stability of an ASV, the nonlinear lateral dynamic model is established and validated by field test. With the lateral dynamic model, the steering characteristic of the ASV is analyzed. Based on the stability criteria analysis, the vehicle sideslip angle and angular velocity phase portrait are chosen as the stability indicator. An integrated AASS (active articulated steering system) DYC (direct yaw control) controller based on the adaptive MPC (model predictive control) method is designed according to the track on the phase plane. The double lane-change and 0.7 Hz sine with dwell maneuver are initiated based on the integrated vehicle dynamic model. The results suggest that the provided controller has a better stability performance than the current antiwindup PID control algorithm. It lays a good foundation for the vehicle safety and path tracking of ASV in the ITS.

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.

Advanced Vertical and Lateral Coupling Dynamics Control for a Four In-wheel Motor Drive Electric Vehicle

This paper presents an advanced vertical and lateral coupling dynamics control for stabilizing the vertical and lateral motion of a four in-wheel motor drive electric vehicle. The novelty is to investigate the possibilities of enhancing the lateral stability of electric vehicle through the correct control of their suspension system, and further improve the suspension system performances using direct yaw control (DYC) without having a contradictory effect between the vertical and lateral controllers. There are two main objectives; the first is to improve ride comfort, road holding and vehicle lateral handling performance simultaneously by using a new combined full vehicle suspension system, and the second is to strengthen electric vehicle lateral handling performance by using a sliding mode technique for controlling the direct yaw moment. For vehicle suspension system control, we are seeking to design a skyhook control to control the suspension damper and a sliding mode control to stabilize the position of the sprung masses of the in-wheel damper vibration absorber (DVA). To solve the problems caused by the chattering phenomena and ensure the exponential stability of the sprung mass, the continuous singular terminal sliding-mode controller is selected to design the sliding mode control used as a DVA controller. Simulation results show that the coordinated control between DYC and combined full vehicle suspension system under several turns' maneuvers and under random road excitations, can stabilize electric vehicle lateral motion, enhance the vehicle ride comfort, road holding, enhance the rollover resistance performance and satisfy the main suspension performances simultaneously.

Multi-Objective Control Architecture for an Autonomous In-wheel Driven Electric Vehicle

This paper investigates the high-level control of an in-wheel-motor-drive autonomous electric vehicle. Four distinct objectives are achieved, including lateral and longitudinal control, as well as stability and maneuverability control. Actuators designated at the low level are active front steering specified for the lateral control and the 4 independent in-wheel motors for the remnant objectives. Stability and maneuverability are realized using the direct yaw control by distributing driving and braking torques among the motors, along with the longitudinal control within a torque allocation unit. In contrast to critical situations, maneuverability is promoted while relaxing the stability objective during normal driving situations. Hence, a decision layer is developed to coordinate the stabilizing and maneuvering objectives on the same actuator, thus a multi-layer Global Chassis Control (GCC) architecture is established. The control architecture is tested and validated within a MatLab/Simulink environment. Simulation results carried out on a full nonlinear vehicle model emphasize the objectives’ achievement and demonstrate the superior performance of such system.

A Centralized Multilayer LPV/H∞ vs Decentralized Sliding Mode Control Architectures, for the Lane Keeping and Stability of an Intelligent Vehicle

This paper deals with the development of two Advanced Driving Assistance systems (ADASs) architectures involving the Active Front Steering (AFS) and the Direct Yaw Control (DYC) for the Lane Keeping and the lateral stability enhancement in Semi-Autonomous Vehicles. The objective of these systems is to assist and help the driver to keep the lane and to ensure lateral stability. To do that, a new Centralized and Decentralized architectures of ADAS system are developed. The different layers of each architecture are detailed including the control layer where the LPV/H∞ and the Super-Twisting Sliding Mode (STSM) control techniques are used for the development of ADAS controllers respectively. Then, a decision making layer monitors the driver's behavior and the lateral stability to adjust the different controllers. Both ADAS system architectures are validated on Matlab/Simulink for a defined scenario with a complete nonlinear model of the vehicle validated on “SCANeR Studio” (OKtal) professional simulator. Finally, a comparison is done between them to show the difference in performance and the effectiveness of both strategies of control on the assistance objective and guaranteeing vehicle's stability.

Integrated Direct Yaw Control and Antislip Regulation Mixed Control of Distributed Drive Electric Vehicle Using Cosimulation Methodology

The improvement of handling and stability performance of Distributed Drive Electric Vehicle (DDEV) is analyzed, visualized, and designed by proposing and deploying the mixed control strategies in this paper including Direct Yaw Control (DYC), Antislip Regulation (ASR) and a novel Dual-mode Switching Control (DMSC). First, by drawing the phase trajectory stability domain, the vehicle stability limit boundary can be determined, which provides the basis for the lateral stability constraint of the vehicle. Then, the practicability and real time visualization of driving efficiency and timeliness of DDEV is achieved to reduce the margin of error for the desired torque value by employing the DYC strategy which uses a fuzzy PID algorithm. Furthermore, the ASR strategy which adopts the optimal slip rate algorithm to determine the requirement of desired torque value based on the different road conditions is used to reduce slip phenomenon effectively and to maintain handling control of DDEV. In response to different scenes especially conflict and coexistence between DYC and ASR, the DMSC strategy is applied to find a more suitable slip rate range by using the root mean square error method (REME). Finally, the cosimulation platform of ADAMS/Car and MATLAB/Simulink is built to simulate the mixed control strategies by integrating DYC, ASR, and DMSC. The simulation results show that the DMSC can effectively prevent DDEV from entering the dangerous limit driving state when turning and driving. The strategy has a more significant control effect needed to meet the requirements of the driving safety of the vehicle and handling stability. The DMSC is adopted and downloaded into the electronic control unit of our student type formula vehicle called Flash V6 which was designed and developed by a team of students, the ZUST ATTACKER Team.

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.

Investigation of Vehicle Stability by Integration of Active Suspension, Torque Vectoring, and Direct Yaw Control

Investigation of Vehicle Stability by Integration of Active Suspension, Torque Vectoring, and Direct Yaw Control

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.

Evaluating Coordinated Cooperative Control of Three Active Car Systems Using Fuzzy-Logic

Vehicle systems automation is an inevitable process as a result of the growing safety requirements and the trend of increasing car operation autonomy by reducing driver’s influence. The paper covers the coordinated control operation of three active systems to assess the improvement of vehicle response. Direct yaw control through selective wheel braking (DYC), active front steering control (AFS) and active suspension normal force control (NFC) are included. The control method is fuzzy logic. The modelling is done in Matlab/Simulink, where 14-DOF nonlinear full vehicle model and 3-DOF reference model were introduced. Simulations were done for three manoeuvres: cornering event, single lane change and fishhook manoeuvre, at a speed of 130 km/h and dry road surface with adhesion coefficient of 0.9 was assumed. Coordination was done with adjusting scaling factors for each control. Results show improvements in the transient response behaviour of the vehicle compared with four vehicles: vehicle without any active system, vehicle with coordinated DYC and AFS, vehicle with coordinated DYC and NFC and vehicle with coordinated AFS and NFC. The biggest impact can be seen in results for sideslip angle and roll angle, coordinated cooperative control here gives lowest values in the transient period during the manoeuvre. Although it gives better results for the other variables as well. The goal of the coordinated cooperative control is fulfilled by improving the stability and handling of the vehicle, which helps the driver to maintain the feeling of manoeuvrability and the sense for the direction of movement of the vehicle. The system for direct yaw control through selective wheel braking has major impact but the coordination with the other two systems lowers the braking force per wheel. The system for active front steering control has lower impact but at lower speeds helps for maintaining better direction of vehicle movement. Considering these three active systems act simultaneously during high speeds and critical manoeuvres next step would be to define sequence of activation depending on the currently measured vehicle behaviour variables.

Model of Integrated of Car Brake-Active Suspension of Quarter Car Model By Using LQR Control Under Periodic Road Excitation

The most important technology in a vehicle is the stability of the direction of the vehicle and the comfort of the passengers. For stability, direct yaw control is used via ABS / TCS / DSC, while for convenience, use the active suspension. These two solutions also contribute to one another. In this research paper is produced and explained about the use of LQR control to control the response of the sprung mass to achieve high damping ratios in isolating the excitation force from nonlinear road surfaces. The simulation results prove that the damping ratio controlled by LQR was better than compared by using PID.

Game Theory-Based Control Strategy For Trajectory Following of Four-Wheel Independently Actuated Autonomous Vehicles

Trajectory Following Control (TFC) and lateral stability control are always significant for Autonomous Ground Vehicles (AGVs). However, sometimes it is quite a challenge to achieve excellent tracking performance with less stability loss. In this paper, a novel two-agent non-cooperative game framework is built to explore the coordination approach of the integrated TFC and Direct Yaw Control (DYC) issue. Then the game theory-based control strategies are proposed and two information patterns are investigated. In the open-loop case, the Nash equilibrium of each control cycle is determined by solving discrete-time Riccati equations. When it comes to the closed-loop pattern, the game complexity increases because each player can revise his own game strategy according to the other players’ strategy evolution, and thus the dynamic programming method based on the Bellman's principle is applied to derive the control strategies. Besides, a Linear Parameter-Varying (LPV) method is adopted to facilitate the process of discretizing the state-space model in every control cycle. Finally, simulation tests based on Carsim-Simulink joint platform and experimental tests are carried out to verify the effectiveness of the proposed approach.

A comparison between a centralised multilayer LPV/ℋ∞ and a decentralised multilayer sliding mode control architectures for vehicle's global chassis control

This paper investigates new achievements in global chassis control, involving Active Front steering (AFS) and Direct Yaw Control (DYC), to improve the overall vehicle performance, i.e. the vehicle manoeuvrability, lateral stability and rollover avoidance. Two multilayer control architectures, each formed by three hierarchical layers, are developed, validated and compared. The lower layer represents the actuators implemented into the vehicle which generate their control inputs based on the orders sent from the middle layer. The middle layer is the control layer which is responsible to generate the control inputs that minimise the errors between the desired and actual vehicle yaw rate, side-slip angle and roll angle, regardless of the driving situation. The control layer is the main difference of the proposed architectures, where a centralised and a decentralised controllers are developed. In the centralised architecture, the novelty is that one single Multi-Input-Multi-Output MIMO optimal controller generates the optimal additive steering angle provided by the AFS and the optimal differential braking provided by the DYC to minimise -at once- all the vehicle state errors . The optimal control technique based on offline Linear Matrix Inequality optimal solutions, in the framework of Linear-Parameter-Varying systems, is applied to synthesise the controller. In the decentralised architecture, a heuristic solution is proposed by decoupling the control problem where the Super-Twisting Sliding Mode (STSM) technique is applied to derive the AFS control input which minimises only the errors on the yaw rate, and the roll angle. Similarly, the DYC control input is privileged to minimise only the error on the side-slip angle. The higher layer of both architectures is the decision making layer which instantly monitors two criteria laying on lateral stability and rollover risks. Then, it generates two weighted parameters which adapt the controller(s) performance(s) according to the driving conditions in order to improve the vehicle's manoeuvrability, lateral stability and rollover avoidance. Both control architectures are tested and validated on the professional simulator ‘SCANeR Studio’. Simulation shows that both architectures are relevant to the global chassis control. The centralised one is optimal, complex and overall closed-loop stability is guaranteed, while the decentralised one does not guarantee the overall closed-loop stability, but it is intuitive, simple and robust.

An adaptive SMC controller for EVs with four IWMs handling and stability enhancement based on a stability index

In order to make full use of the direct yaw control (DYC) in electric vehicles with four in-wheel motors (IWMs), this paper presents an adaptive SMC control scheme to improve the handling and stability based on a novel stability index proposed to quantitatively represent stability level. Firstly, a vehicle stability evaluation method is designed based on the front and rear tire slip angles phase plane and the quantitative stability index is derived according to the area where the states stay and the changing trend of the states. Secondly, a sliding mode controller (SMC) is designed to track the reference obtained by a two degrees of freedom (DoF) vehicle model as the desired vehicle motion. To enhance both vehicle handling and stability, the weight of the sliding surface is adaptively scheduled according to this stability index. The torque of each wheel is then distributed by solving the optimal adhesion rate consumption problem. Finally, the effectiveness of the proposed method is verified by CarSim–Simulink co-simulation. The simulation results illustrate that the proposed method can effectively improve vehicle handling and stability and has good robustness for the uncertainties of vehicle sideslip angle and road friction coefficient.

New Integrated Vehicle Dynamics Control System Based on the Coordination of Active Front Steering, Direct Yaw Control, and Electric Differential for Improvements in Vehicle Handling and Stability

New Integrated Vehicle Dynamics Control System Based on the Coordination of Active Front Steering, Direct Yaw Control, and Electric Differential for Improvements in Vehicle Handling and Stability

Optimised coordinated chassis stability control using fuzzy genetic algorithm

Owing to proficient constraints of distinct vehicle subsystems, numerous individual active control systems are optimised individually in specific handling areas. Consequently, there is no solitary system which can be operative over the entire range of vehicle stability and handling. In this study, an optimised coordinated control system comprising two controllers namely direct yaw control (DYC) and active front steering (AFS) has been developed based on fuzzy logic control to boost vehicle handling, cornering stability, and rollover anticipation. Moreover, genetic algorithm has been accustomed to optimise the offered coordinated control system in two phases; initially, optimising the fuzzy logic controller for both DYC and AFS by linear scaling of the inputs and outputs of each controller, and by the weight of each rule. Different benchmark test manoeuvres have been carried out at different driving conditions to scrutinise the offered coordinated control effectiveness. Simulation results shows a significant enhancement in vehicle stability.