Active Rear Steering Research Articles

Investigating off-road vehicle lateral stability with integrated chassis control

This study investigates the improvement of off-road vehicle lateral stability by integrated control of active rear steering (ARS) and rear differential braking (RDB) and how the performance of such systems compares on smooth and rough roads. The ARS and RDB controllers comprise a sliding mode controller (SMC) for which critical design choices are the SMC reference model, SMC gain and integration rule. Findings include that the kinematic model reference error is preferred over the phase plane location error on both terrains, the SMC gain is terrain dependent, and the rear axle slip angle is the preferred integration rule over the stability index (SI) on both terrains. The study also found that RDB, and to a lesser degree ARS, tends to improve on the baseline vehicle path-following ability for a double lane change (DLC) manoeuvre on both terrains, but RDB has a larger loss of speed compared to ARS. The Rear axle slip angle was found to be a terrain-dependent tuneable integration rule to combine ARS and RDB, and this resulted in a control system that has the good path-following ability of RDB but the low loss of speed associated with ARS after tuning.

Multi input multi output lateral dynamics control of an electric vehicle

Full electric vehicles with multiple and independently controlled powertrains allow for an improvement of vehicle handling capabilities both in steady state and in transient manoeuvres. This paper focuses on active lateral dynamics control of an electric vehicle equipped with 4 in-wheel motors and active rear steering. An integral terminal sliding mode controller (ITSMC) is derived starting from the linearized single track model with the addition of the rear wheel steering angle. The controller has a multi-input multi-output structure and is designed to track vehicle yaw rate and sideslip angle reference quantities through torque vectoring and active rear steering actuation. A novel approach for calculating reference sideslip angle and yaw rate using a logistic function is also presented in this paper. The ITSMC relies on real time knowledge of sideslip angle which cannot be measured in the real vehicle, thus it is estimated through the addition of an extended Kalman filter to the control loop. The performance of the controller is tested with VI-CarRealTime 14 degrees of freedom nonlinear model both for steady state and transient manoeuvres. Simulation results show a good tracking of the reference value with no chattering issues and with an improved behaviour if compared to a sliding mode controller from literature.

Coordinated Control for the Trajectory Tracking of Four-Wheel Independent Drive–Four-Wheel Independent Steering Electric Vehicles Based on the Extension Dynamic Stability Domain

In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. Firstly, expert PID and model predictive control (MPC) are used to achieve longitudinal speed tracking and lateral path tracking, respectively. Then, a sliding mode controller is designed to calculate the expected yaw moment based on the desired vehicle states. The extension theory is applied to construct the extension dynamic stability domain, taking into account the linear response characteristics of the vehicle. Different coordinated allocation strategies are devised within various extension domains, providing control targets for direct yaw moment control (DYC) and active rear steering (ARS). Additionally, a compound torque distribution strategy is formulated to optimize driving efficiency and tire adhesion rate, considering the vehicle’s economy and stability requirements. The optimal wheel torque is calculated based on this strategy. Simulation tests using the CarSim/Simulink co-simulation platform are conducted under slalom test and double-lane change to validate the control strategy. The test results demonstrate that the proposed control strategy not only achieves good trajectory tracking performance but also enhances handling stability and economy during driving.

Personalized Collision Avoidance Control for Intelligent Vehicles Based on Driving Characteristics

Collision avoidance has been widely researched in the field of intelligent vehicles (IV). However, the majority of research neglects the individual driver differences. This paper introduced a novel personalized collision avoidance control (PCAC) strategy for IV based on driving characteristics (DC), which can better satisfy various scenarios and improve drivers’ acceptance. First, the driver’s DC is initially classified into four types using K-means clustering, followed by the application of the analytic hierarchy process (AHP) method to construct the DC identification model for the PCAC design. Then, a novel PCAC is integrated with a preview-follower control (PFC) module, an active rear steering (ARS) module, and a forward collision control (FCC) module to ensure individual requirements and driving stability. Moreover, simulations verified the validity of the developed PCAC in terms of path tracking, lateral acceleration, and yaw rate. The research results indicate that DC can be identified effectively through APH, and PCAC based on DC can facilitate the development of intelligent driving vehicles with superior human acceptance performance.

The Effect of Nonlinear Feature of Tire on the Control of Active Rear Steering at High Speed

The two-degree-of-freedom model of the vehicle is more accurate with considering of the nonlinear feature of tire. Based on the model, the active rear steering control strategy for achieving zero sideslip angle is yielded. Simulation results show that both of the two cases, constant and variable cornering stiffness, are useful in the control of sideslip motion, and using the variable stiffness due to the effect of load transfer in control strategy is more effective.

Emergency collision avoidance strategy for autonomous vehicles based on steering and differential braking

This paper develops a novel integrated collision avoidance strategy for autonomous vehicles in an emergency based on steering and braking. Specifically, the framework of the collision avoidance strategy is composed of two parts: an up-level decision-making layer and a low-level controller layer. The purpose of the up-level is to select the appropriate control strategy based on the vehicle information, and the low-level is to drive the vehicle according to the instructions generated by the up-level. More concretely, a novel control strategy is proposed by integrating four-wheel steering, active rear steering, and differential braking with guaranteed path-tracking accuracy and driving stability by adaptive model predictive control (AMPC). Finally, extensive co-simulations in MATLAB/Simulink and CarSim are conducted to verify the effectiveness of the proposed collision avoidance strategy in terms of tracking error, yaw rate, and roll angle.

Towards Active Safety Driving: Controller Design of an Active Rear Steering System for Intelligent Vehicles

To advance the active safety performance for vehicles, especially in extreme conditions, an active rear steering (ARS) control system is designed in this paper. A driver model is established to simulate the driving behaviour of a human driver who is in charge of the front steering control. In the ARS control system, the sliding mode predictive control (SMPC) approach is applied to the ARS controller design based on a 3 degrees of freedom (DoF) nonlinear vehicle model. In the ARS controller design, four kinds of active safety performances are considered, namely, path-tracking performance, handling performance, lateral stability, and rollover prevention. Furthermore, the priority of the four kinds of active safety performance is defined. According to the control priority, an event-triggered mechanism (ETM) is designed to adjust the SMPC controller of the ARS system to address different driving conditions. Finally, two simulation cases are conducted to evaluate the performance of the proposed ARS system. The results show that the ARS system is in favour of the active safety performance advancement for human drivers. Additionally, the comparative simulation indicates that the SMPC algorithm is superior to the fast terminal sliding mode control (FTSMC) algorithm.

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.

Active Obstacle Avoidance Control Strategy Based on Four-Wheel Steering

Active obstacle avoidance control strategy is the main question of intelligent vehicles, and active four-wheel steering is gradually used in the intelligent control system. Considering both tracking performance and driving characters, an active controller with four-wheel steering (4WS) and active rear steering (ARS) based on adaptive model predictive theory (AMPC) is designed. The control architecture is composed a supervisor and an AMPC controller. The supervisor is used to select the appropriate control mode and the AMPC is used to calculate the expected steering angle when the stability indexes over the safety threshold. Finally, the proposed control strategy is simulated via Carsim-Simulink co-simulation. The results show that the integrated controller can track the obstacle avoidance path and has good driving performance.

Path-following enhancement of an 8×8 combat vehicle using active rear axles steering strategies

Active rear steering has been used in many research work to enhance ground vehicles’ lateral stability. However, there is a shortage in the published research studies that consider the incorporation of active rear steering for autonomous vehicles applications, especially in case of multi-axle combat vehicles. In this paper, various H∞ controllers are developed to actively steer rear axles of a multi-axle combat vehicle using a linearized bicycle model. The proposed controllers are incorporated with a 22 degrees of Freedom nonlinear Trucksim full vehicle model to study and compare the developed controllers’ performance on a hard surface. Moreover, a frequency-domain analysis is conducted to investigate the influence of the active rear steering on the path-following controllers’ robustness in terms of stability and performance. Three path-following controllers are designed, where the first controller is applied on the front two axles of the vehicle, while the rear two axles are fixed. The second is applied to all-wheel steering vehicle. The third controller is an integration between the designed front steering path-following controller and a developed lateral stability active rear steering controller. Eventually, a series of virtual maneuvers are performed to evaluate the effectiveness of the intended controllers to present the advantages and limitations of each controller at different driving conditions.

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

A cooperative control strategy for yaw rate and sideslip angle control combining torque vectoring with rear wheel steering

ABSTRACT Automobiles are becoming more and more complex as multiple control systems are integrated into the vehicle platform. This paper investigates the coordination of active rear steering (RWS) and torque vectoring (TV) – which is enabled by independent electric motors at the rear axle – in controlling vehicle lateral dynamics. The proposed controller aims at enhancing vehicle handling performance and stability while cornering. The coordination of the two actuators is achieved by weighting their contribution based on their impact on vehicle dynamics according to the working condition. The impact of each control system is assessed by means of phase portraits. These plots are a very powerful tool for analysing vehicle nonlinear dynamics as they readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and control inputs. Based on phase portrait analysis, a performance index is thus proposed, which weights more the control action (TV or RWS) capable of leading the vehicle at the nearest equilibrium point with the fastest rate. The controller performance is assessed through numerical simulations carried out using a nonlinear 14 dofs vehicle model. Results are compared with ones of the two controllers alone (RWS and TV) in different manoeuvers and adherence conditions.

Enhancement of vehicle stability based on coordinated active rear steering and additional yaw moment

To improve the handling and manoeuvrability of four-wheel steer (4WS) and in-wheel motor-driven electric vehicle (EV) under critical conditions, a coordinated control method combining active rear-wheel steering (ARS) and additional yaw moment control in the form of additional drive torque distribution is proposed. Considering the nonlinear properties of tyre force, a nonlinear model predictive controller (NMPC) is designed to track the desired yaw rate and constrain the sideslip angle. Meanwhile, the actuator and security constraints are satisfied effectively. Finally, the control performance is investigated by co-simulation with MATLAB/Simulink and CarSim. The results show that the vehicle longitudinal and lateral stability are both efficiently enhanced under extreme conditions.

Enhancement of vehicle stability based on coordinated active rear steering and additional yaw moment

Enhancement of vehicle stability based on coordinated active rear steering and additional yaw moment

Cooperative Control Framework for Human Driver and Active Rear Steering System to Advance Active Safety

Driving safety is quite significant for human drivers, especially in the emergencies and extreme conditions. To advance the active safety of vehicles, a cooperative control framework is proposed for human driver and the active rear steering system (ARS). Human driver model is established considering different driving characteristics to simulate different drivers. Moreover, vehicle-road model is built for ARS controller design. Then, the performance indexes of active safety, i.e., lateral stability, handling performance, rollover prevention and path tracking performance, are defined. Meanwhile, constraints for ARS controller design are proposed. Furthermore, linear-time-varying (LTV) model predictive control (MPC) is applied to ARS controller design with the consideration of multi-constraints. To evaluate the effectiveness and feasibility of the cooperative control framework, three maneuvers on the low adhesion road are conducted based on the co-simulation platform of CarSim and Simulink. Testing results indicate that the cooperative control framework can assist human driver to enhance vehicle's active safety. With the application of ARS control, the performance indexes of active safety are reduced more than 50%. In addition, it also shows that the ARS system has good adaptability and effectiveness to deal with different driving characteristics.

A Human-Vehicle Game Stability Control Strategy Considering Drivers’ Steering Characteristics

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

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads.

Integrated Vehicle Dynamics System through Coordinating Active Aerodynamics Control, Active Rear Steering, Torque Vectoring and Hydraulically Interconnected Suspension

This paper investigates integrated vehicle dynamics control through coordinating active aerodynamics control, active rear steering, torque vectoring and hydraulically interconnected suspension for improving the overall vehicle performance including handling, stability, and comfort. After developing each chassis control system, it is tested by various manoeuvres in order to assess each subsystem. Then, a rule-based coordinate system is proposed for integrated control of the four chassis control systems. Simulation investigation is performed to display the effectiveness of the proposed integrated vehicle dynamics system. Results demonstrate that the proposed control scheme is able to enhance the multiple performance indices of the vehicle including both the ride comfort, and the lateral stability, compared to the non-integrated control system.

Optimization of control allocation with ESC, AFS, ARS and TVD in integrated chassis control

This paper presents an optimization of control allocation in integrated chassis control with active front steering, active rear steering, electronic stability control and torque-vectoring device under the saturation of lateral tire forces on front wheels. After a control yaw moment is calculated in the upper-level controller, a weighted pseudo-inverse based control allocation is used for yaw moment generation in the lower-level controller. Variable coefficients of the weighted pseudo-inverse based control allocation are used to represent various actuator combinations and are optimized for each actuator combination to enhance control performances using simulation on vehicle simulation package, CarSim. Due to severe cornering on low friction road, the front lateral tire forces can be easily saturated. Under the condition, the active front steering has little effect on control performance and, consequently, the desired control yaw moment cannot be generated. So, the lateral force generated by AFS should be restricted to its maximum, and a constrained weighted pseudoinverse based control allocation with electronic stability control, active rear steering and torque-vectoring device is applied to compensate the loss of the control yaw moment. Variable coefficients of the constrained weighted pseudo-inverse based control allocation with electronic stability control, active rear steering and torque-vectoring device are also optimized using simulated-based tuning. To validate the proposed method, simulation was done on CarSim. From simulation, it was verified which actuator combination is effective for integrated chassis control if the lateral forces on front wheels are saturated.

Integrated chassis control with AFS, ARS and ESC under lateral force constraint on AFS

This paper presents an integrated chassis control with active front steering (AFS), active rear steering (ARS) and electronic stability control (ESC) under lateral force constraint on front wheels. The control yaw moment is calculated using sliding mode control. A weighted pseudo-inverse-based control allocation (WPCA) is used for yaw moment distribution. On low-friction road, AFS has little effect on control performance since the lateral tire forces of front wheels are easily saturated. To overcome the problem, the lateral force generated by AFS is limited to its maximum, and the braking force of ESC and the angle of ARS, obtained from WPCA, are applied. To check the effectiveness of the proposed method, simulation was performed on the vehicle simulation package, CarSim. From simulation, it was verified that the proposed method can enhance the maneuverability and lateral stability, if the lateral forces on front wheels are saturated.