Direct Yaw Moment Research Articles

Design of an omnidirectional, multi-axle, distributed-drive electric truck with Mecanum wheels and direct yaw moment controller

Design of an omnidirectional, multi-axle, distributed-drive electric truck with Mecanum wheels and direct yaw moment controller

Stability and Maneuverability Guaranteed Torque Distribution Strategy of DDEV in Handling Limit: A Novel LSTM-LMI Approach

Direct yaw moment (DYC) does increase the maneuverability and controllability of distributed driving electric vehicle. However, the additional DYC could also lead to the instability of vehicle in low friction road. Extensive literatures have proposed various methods to tackle this problem. Nevertheless, the conflict between maneuverability and stability in handling limit is difficult to solve. To this end, a novel multimode torque distribution algorithm is developed in this article. The conception of drive stability region is proposed to describe the feasible operations of the driver. Based on the different regions, we define multitorque distribution modes to handle the varying conditions. A linear matrix inequality based mode decision theorem is developed to estimate the boundaries of the drive stability region as well as determine the optimal mode. Considering that the lateral tire force is important for mode decision, a long short-term memory neural network is adopted to make a precise estimation. Finally, we develop multitorque distribution strategies to meet the requirements of each mode. Processor-in-the-loop test is also implemented to verify the real-time performance of the algorithm. Simulation and experiment indicate that, the multimodes torque distribution strategy show a better performance compared with the single mode, which mitigates the conflict of maneuverability and stability as well as ensures the safety of vehicle in handling limit.

Handling and Stability Integrated Control of AFS and DYC for Distributed Drive Electric Vehicles Based on Risk Assessment and Prediction

How to improve the trajectory following ability and lateral stability under extreme conditions is an important research problem for distributed drive electric vehicles (DDEVs). This paper proposes a novel integrated control architecture of active front steering control (AFS) system and direct yaw moment control (DYC) system for DDEVs. First, to deal with the future instability problem caused by driver’s misoperation or delayed control, online risk assessment and prediction models, including self-regulating phase plane stability judgment and future driving state prediction of vehicles, is designed to provide decision commands for actuators in advance under extreme conditions. Then, on the basis of comprehensive consideration of system chattering, robustness and control constraint index requirements, an integrated control method based on robust sliding mode predictive control (SMPC) to put forward to solve the multi-objective and multi-constraint optimization problem for multi-subsystem integration. Finally, the simulation and experimental results show that the proposed control architecture can effectively assist drivers improve the trajectory following ability and handling stability of DDEVs, as to ensure the maneuverability and safety of emergency obstacle avoidance under extreme conditions.

Analysis and Roll Prevention Control for Distributed Drive Electric Vehicles

This work presents an approach to improve the roll stability of distributed drive electric vehicles (DDEV). The effect of the reaction torque from the in-wheel motor exerts additional roll moment, which is different from traditional vehicles. The additional roll moment can be achieved by active control of the wheel torque adjustment, which achieves a control effect similar to the active suspension. The anti-roll control strategy of decoupling control of roll motion and yaw motion are proposed. The direct yaw moment is calculated by the linear quadratic regulator (LQR) algorithm while the additional rolling moment is calculated by the sliding mode variable structure. For maneuvering rollover caused by excessive lateral acceleration, an anti-rollover control strategy is designed based on differential braking. A fuzzy control theory is used to decide the yaw moment to be compensated. The distribution method of the braking torque applied to the outer wheel alone, and the lateral load transfer rate is the main evaluation index for simulation verification of typical working conditions. The simulation results show that the proposed control strategy for DDEV is effective.

Real-Time Predictive Control of Path Following to Stabilize Autonomous Electric Vehicles Under Extreme Drive Conditions

A novel real-time predictive control strategy is proposed for path following (PF) and vehicle stability of autonomous electric vehicles under extreme drive conditions. The investigated vehicle configuration is a distributed drive electric vehicle, which allows to independently control the torques of each in-wheel motor (IWM) for superior stability, but bringing control complexities. The control-oriented model is established by the Magic Formula tire function and the single-track vehicle model. For PF and direct yaw moment control, the nonlinear model predictive control (NMPC) strategy is developed to minimize PF tracking error and stabilize vehicle, outputting front tires’ lateral force and external yaw moment. To mitigate the calculation burdens, the continuation/general minimal residual algorithm is proposed for real-time optimization in NMPC. The relaxation function method is adopted to handle the inequality constraints. To prevent vehicle instability and improve steering capacity, the lateral velocity differential of the vehicle is considered in phase plane analysis, and the novel stable bounds of lateral forces are developed and online applied in the proposed NMPC controller. Additionally, the Lyapunov-based constraint is proposed to guarantee the closed-loop stability for the PF issue, and sufficient conditions regarding recursive feasibility and closed-loop stability are provided analytically. The target lateral force is transformed as front steering angle command by the inversive tire model, and the external yaw moment and total traction torque are distributed as the torque commands of IWMs by optimization. The validations prove the effectiveness of the proposed strategy in improved steering capacity, desirable PF effects, vehicle stabilization, and real-time applicability.

Coordinated Vehicle Lateral Stability Control Based on Direct Yaw Moment and Engine Torque Regulation under Complicated Road Conditions

This paper proposed a control method of vehicle lateral stability control system. In order to obtain the best driving performance under the complicated friction conditions track, the direct yaw moment (DYC) and the engine torque regulation (ETC) value need to be adjusted to avoid penalisation of each other. Firstly, the intervention timing of direct yaw moment control and engine torque regulation control is studied, which is judged by the phase plane method and longitudinal speed threshold method, respectively. Then, a direct yaw moment control algorithm based on sliding mode control algorithm is designed, and its target value calculation depends on the linear two-degree-of-freedom model. The engine torque regulation is calculated by fuzzy algorithm. The input values are mainly the vehicle state parameters and the driver’s input to the steering wheel. Matlab/Simulink, AMESim, and CarSim software programs are used to simulate sine with dwell maneuver. The simulation results show the effectiveness of the cooperation control algorithm proposed in this paper.

Cascade Direct Yaw Moment Control for an Independent Eight In-Wheel Motor-Driven Autonomous Vehicle

Unstructured off-road environments with complex terrain obstacles and pavement properties bring obvious challenges for special purpose autonomous vehicle control. A cascade direct yaw moment control strategy (CDYC), which contains a main loop and a servo loop, is proposed to enhance the accuracy and stability of an independent eight in-wheel motor-driven autonomous vehicle with rear-wheel steering (8WD/RWS). In the main loop, double PID controllers are designed to generate the desired drive moment and yaw rate. In the servo loop, the quadratic programming (QP) algorithm with the tire force boundaries optimally allocates the demanded yaw moment to individual wheel torques. The 8WD/RWS prototype is virtually established using TruckSim and serves as the control object for co-simulation. The proposed cascade controller is verified by simulations in customized off-road driving scenarios. The simulation results show that the proposed control architecture can effectively enhance the path-tracking ability and handling stability of the 8WD/RWS, as to ensure the maneuverability and control stability under extreme off-road conditions.

Direct yaw moment control and state observer design for four in-wheel motor drive electric vehicles based on super-twisting algorithm

In order to improve the handling stability for four in-wheel motor drive electric vehicles (4IWMD EVs), a novel direct yaw moment control (DYC) strategy is proposed in this paper. To begin with, the problem formulation is stated by considering modeling errors and external disturbance. To obtain direct yaw moment output, a direct yaw moment controller is given by using conventional sliding mode theory at first. In order to deal with large gains of sign function, which brings severe chattering problem in conventional first-order sling mode (FOSM) control, an improved adaptive second-order sliding mode control strategy is proposed based on super-twisting algorithm (STA) to estimate vehicle model errors and external lumped disturbance. In addition, taking the cost problem in practice into account, an integral sliding mode observer (ISMO) is constructed to estimate and calculate the actual values of sideslip angle indirectly on the basis of STA. Furthermore, a composite control strategy is proposed according to stability analysis, combining adaptive second-order sliding mode controller with ISMO. Eventually, comparative simulations are conducted in Carsim and Simulink. The results show that proposed control strategy can achieve superior tracking performance under the premise of tackling chattering phenomenon, and the response curves are smoother. Meanwhile, the influence of uncertain disturbance proves to be weakened significantly with STA-based scheme while performances of other control strategies are seriously affected, which verifies the robustness and effectiveness of proposed control strategy.

Research on Direct Yaw Moment Control of Electric Vehicles Based on Electrohydraulic Joint Action

To solve the problem of lateral instability of the vehicle caused by insufficient lateral force of the tires due to the insufficient torque provided by the motor to the tire when the vehicle turns sharply or avoids obstacles in an emergency, a layered control method is used to design a lateral stability control system. The upper decision layer selects the yaw rate and the sideslip angle of the center of mass as the control variables and uses the joint state deviation of the yaw rate and the sideslip angle of the center of mass and the rate of change of the deviation as the input of the sliding mode variable structure controller to calculate the additional yaw moment required to maintain vehicle stability. The lower torque distribution layer realizes the distribution of torque through the electro-hydraulic coordinated control method: the torque distribution rule based on real-time load transfer calculates the torque corresponding to the control wheel and generates the torque through the hub motor and transmits it to the wheel. When the torque output from the motor cannot provide sufficient torque for the vehicle, hydraulic braking is used as a compensating control, and the difference between the required yaw torque and the motor-generated yaw torque is used as the required torque for hydraulic control to calculate the wheel cylinder pressure required to brake the wheels. Based on the joint simulation model of MATLAB/Simulink and Carsim, the sine and double shift line working condition are selected for stability simulation experiments. From the simulation results, it can be seen that the yaw rate and sideslip angle of the center of mass of the vehicle with sliding mode control and electro-hydraulic coordinated control almost coincide with the ideal value curve, which are both smaller than the output parameters of the uncontrolled vehicle. From the perspective of the motor output torque, compared with pure motor control, the effect of electro-hydraulic coordinated control is better, and the hydraulic system can compensate for the braking torque in time and enhance the lateral stability of the vehicle. The designed control strategy can make the yaw rate and the sideslip angle of the center of mass of the vehicle follow the reference value better, which can effectively avoid the vehicle sideslip and instability and improve the vehicle yaw stability and driving safety. However, due to the limitations of experimental equipment, the proposed method could not be applied to the real vehicle test. The real vehicle test can better test the control effect of the proposed method.

On nonlinear model predictive direct yaw moment control for trailer sway mitigation

In car–trailer combinations, the hitch angle is the relative yaw angle between towing car and trailer. The literature has shown that the inclusion of the hitch angle measurement for the feedback control of trailer oscillations can bring safety benefits, compared with conventional trailer sway mitigation algorithms based on the yaw rate of the car. Given the nonlinearity of the vehicle system in the typical conditions requiring the hitch angle control function intervention, nonlinear model-based controllers could be an effective solution. This paper presents four real-time implementable nonlinear model predictive control (NMPC) formulations, using the hitch angle measurement for the torque-vectoring (TV) control of an electric front-wheel drive car towing a trailer. The simulation results show that: (i) the active safety is enhanced by the proposed NMPC TV formulations, with respect to a benchmarking NMPC TV controller only based on the control of the towing car; (ii) the NMPC formulations that directly constrain the hitch angle error, or perform continuous hitch angle tracking, outperform those that modify the reference yaw rate or yaw rate error based on the hitch angle error; and (iii) the NMPC approaches including a dynamic model of the trailer are robust with respect to variations of trailer parameters.

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 Path Tracking and Lateral Stability Control with Four-Wheel Independent Steering for Autonomous Electric Vehicles on Low Friction Roads

This paper presents a method to design an integrated path tracking and lateral stability controller for an autonomous electric vehicle with four-wheel independent steering (4WIS) on low friction roads. Recent advances in autonomous driving have led to extensive studies on path tracking control. However, path tracking is difficult on low friction roads. In this paper, path tracking control was converted to the yaw rate tracking one to cope with problems caused by low friction roads. To generate a reference yaw rate for path tracking, we present several methods using a driver model and a target path. For yaw rate tracking, we designed a controller with a two-layer control hierarchy, i.e., upper and lower layers. The control yaw moment was calculated using a direct yaw moment controller in the upper layer. In the low layer, a control allocation method was adopted to allocate the control yaw moment into steering angles of 4WIS. To verify the performance of the proposed controller, we conducted a simulation on vehicle simulation software. From the simulation results, it is shown that the proposed controller is effective for path tracking and lateral stability on low friction roads. To analyze path tracking and lateral stability performance of the proposed controller on low friction roads, the effects of the steady-state yaw rate gain are investigated from the simulation results.

Fuzzy Yaw Rate and Sideslip Angle Direct Yaw Moment Control for Student Electric Racing Vehicle with Independent Motors

In this paper, a new concurrent yaw rate, sideslip angle, and longitudinal-velocity direct yaw moment control (DYC) strategy is proposed to improve the handling and stability of a rear-wheel drive student electric racing vehicle (EV) equipped with two independent motors. In order to control these three parameters concurrently, three control schemes are developed: three fuzzy controllers, three optimized PID controllers, and two fuzzy controllers for the yaw rate and sideslip angle with a PID for longitudinal velocity. The EV dynamic behavior for the different control schemes is compared by using a nonlinear model of the EV. This model consists of three main parts: vehicle dynamics, wheel dynamics, and tire dynamics. Simulations under a circular-path driving scenario show that the proposed fuzzy controllers can effectively reduce the consumed energy by 10%, track the desired speed and path, and enhance the vehicle’s behavior and stability while maneuvering by decreasing both the yaw rate and sideslip angle deviation.

Path tracking and stability integrated control of intelligent vehicles under steering collision avoidance

In order to improve the path tracking performance and stability of intelligent vehicles during steering and collision avoidance working condition, an integrated control method combining four-wheel steering, active suspension and direct yaw-moment is proposed. Firstly, a unified chassis dynamics model with 18 degrees-of-freedom (DOF) is established, which includes steering, braking and suspension subsystems. Phase plane analysis results for both traditional 2-DOF bicycle model and the 18-DOF model are compared. The results show that although the uneven road has a certain influence on the phase portraits of sideslip angle and sideslip angular velocity of the 18-DOF model, the overall trend is consistent with those of the 2-DOF model. Secondly, a general control framework for steering and collision avoidance of autonomous vehicle is put forward, consisting of the steering control for path tracking, and active suspension and direct yaw-moment control for stability. The collision avoidance reference path is planned by the fifth-degree polynomial, and three types of steering control methods including optimal control front wheel steering, optimal control four-wheel steering and front wheel steering combined with feedforward and feedback control, are adopted for comparison. The main contribution of this paper is that the role of active suspension is considered in the path tracking and stability integrated control of intelligent vehicles under steering collision avoidance. The active suspension subsystem uses optimal control method by two different kinds of weight coefficients, which can be switched between “comfort mode” and “sport mode” based on the vehicle lateral acceleration. According to the phase portraits of vehicle center of gravity sideslip angle and sideslip angular velocity, the direct-yaw moment control system adopts proportional differential control method based on the distance from the state point to the stability boundary. Finally, simulations and comparative analysis are compared among four different control systems on high and low adhesion roads. The research results indicate that the integrated control system can improve the vehicle path tracking performance and stability significantly.

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.

Integration sliding mode control for vehicle yaw and rollover stability based on nonlinear observation

An active rear steering and direct yaw moment (ARS-DYC) coordination control is conducted on the basis of nonlinear fuzzy observation to improve yaw and roll stability control of vehicles under extreme conditions. First, a Takagi–Sugeno (T-S) model of yaw and roll motion is established, and tire nonlinearity at emergency steering is approximated with sector domain. A nonlinear fuzzy observation model is established on the basis of the extended Kalman filter (EKF) to observe the sideslip angle, yaw rate, and roll state accurately under extreme conditions in real time. Second, an improved ideal yaw reference model under the T-S framework is constructed in accordance with the effect of yaw rate on roll stability, and the feedforward control of roll stability is achieved through yaw motion tracking. A fuzzy sliding mode controller is designed on the basis of nonlinear fuzzy observation considering tire sideslip stiffness and actuator constraints, and the coordinated control strategy of layered sliding mode surface for yaw and roll is constructed. Finally, the parameters of sub-sliding mode surface are adjusted with the 4-wire phase plane stability domain, the main sliding mode surface parameters are adjusted following the roll stability index, and the stability of the fuzzy sliding mode controller is proven in the Lyapunov framework. A hardware-in-loop simulation test is established with Carsim-Labview software, and results show that the proposed controller significantly enhances the yaw and roll stability of vehicles under the extreme steering process, due to the accuracy of nonlinear dynamic observation and the flexibility of layered sliding surface coordinated control.

Research on Steering Control of Modern Trackless Train with Multi-Articulated Distributed Drive

Modern trackless train has the characteristics of long body and many articulated modules, and the steering control is extremely difficult. This paper first established its Adams dynamic model, and then designed a fuzzy PID controller to generate the yaw moment and rear wheel angle. So that the vehicle can track the trajectory of the first axle when turning. Finally, the control model was established in Matlab and simulated jointly with Adams model. The simulation experiments under step input and sinusoidal input were carried out. The results show that the active steering control strategy based on the combination of direct yaw moment and rear wheel angle can meet the requirements of vehicle steering stability and trajectory following.

Vehicle Lateral Stability Control Based on Stability Category Recognition With Improved Brain Emotional Learning Network

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified.

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.

Coordinated control strategy of roll-yaw stability and path tracking for centralized drive electric vehicles

Previous anti-rollover studies only focused on reducing the risk of vehicle rollover, which could not guarantee yaw stability and ignored path tracking problems. This paper presents an integrated control scheme for centralized drive electric vehicles to prevent rollover, enhance the yaw stability, and ensure path tracking capability. The integration of stability control is implemented by switching among several control modes which include four control modes: yaw stability control, roll stability control, roll-yaw stability control, and rollover prevention. A differential drive based on direct yaw moment control (DYC) is employed to improve yaw stability, and the roll stability is improved via longitudinal speed control. Then, the DYC-based single-wheel braking is utilized to prevent rollover. Moreover, a model predictive control (MPC) algorithm is adopted to design the path tracking controller, and then the coordinated control scheme is proposed to realize a trade-off between stability control and path following. Simulation results show that the proposed scheme can realize the rollover prevention function while satisfying the path tracking target and yaw stability.