Active Front Steering Research Articles

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 lateral trajectory tracking control method for intelligent commercial vehicles considering active anti‐roll decision based on Stackelberg equilibrium

Existing research on path following strategies generally does not consider the interference effect of active anti-roll (AAR) action. This seriously affects the driving safety under extreme obstacle avoidance and other conditions. Therefore, based on the non-cooperative Stackelberg equilibrium theory, this research proposes a lateral trajectory tracking strategy that considers AAR decision. First, a yaw-roll coupled dynamic model of commercial vehicle with front wheel angle and AAR moment as input is established to describe the coupling effect of vehicle lateral and roll motions. Then, based on the Stackelberg equilibrium theory, active front steering (AFS) system and AAR bar system are modelled as the leader and follower in the leader–follower collaborative control strategy, respectively. Finally, simulation and experiments show that the leader–follower collaborative control strategy can effectively improve the control conflict between the two systems and improve the anti-roll performance during trajectory tracking.

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

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

Real-time lateral stability and steering characteristic control using non-linear model predictive control

This paper presents a non-linear integrated control strategy that primarily focuses maintaining vehicle lateral stability using active front steering and differential braking. The proposed control strategy utilises a non-linear model predictive controller to improve lateral stability. A stable linear reference model is used for reference generation. By including the understeer gradient in the reference model, different kinematic responses are obtained from the controlled vehicle. The prediction model utilises the road friction estimate to create dynamic stability constraints that include rollover and sliding of the vehicle. The design of the model predictive controller allows easy activation of different control actuators and dynamic modification to the control behaviour. The control methodology is validated using MATLAB/Simulink and a validated MSC ADAMS model. A sensitivity analysis is conducted to identify the susceptibility of the control strategy to various parameters and states.

A Decentralized Cooperative Control Framework for Active Steering and Active Suspension: Multi-Agent Approach

A modular electric vehicle (EV) platform enables ability to integrate different control systems in a flexible way. In this article, a decentralized cooperative control framework is proposed to achieve the integration of active front steering (AFS) system and active-suspension system (ASS) by applying a multi-constrained distributed model predictive control (MDMPC) approach, which aims to improve the vehicle lateral stability, ride comfort, and roll safety during path tracking. First, a partly decoupled 6-degree-of-freedom (DoF) half-vehicle dynamics model is constructed. Then, a multi-agent-system (MAS)-based framework is introduced to coordinate AFS and ASS, which allows for the multi-constraints within the information exchange between agents. Through minimizing linear convex combination of objective functions, the cooperative control strategy is ultimately solved by the Pareto-optimality theory. Moreover, vehicle lateral and roll motion stability region described by the phase plane is employed to bound the controllable limits and achieve the dynamic cooperation between AFS and ASS. The simulation and hardware-in-the-loop (HIL) test results show that the proposed framework is effective for coordinating AFS and ASS, thereby enhancing the vehicle lateral and vertical stability during path tracking.

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.

Static Output Feedback Control of Vehicle Active Front Steering Considering Multiple Performance Constraints

Static Output Feedback Control of Vehicle Active Front Steering Considering Multiple Performance Constraints

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.

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

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

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>

Vehicle Lateral Stability Regions for Control Applications

This article presents an improved method of obtaining lateral stability regions for road vehicles, considering the influence of steering angle, center of gravity, longitudinal speed, and tire-road friction coefficient on the vehicle dynamics. Comprehensive stability regions are obtained for a wide range of such parameters. Moreover, conservative stability regions are proposed, in the case of control applications that demand robust or safety-critical control actions. Next, a steering-angle-dependent region is used to implement a safety-critical electronic stability control system with active front steering as actuation. The resulting control system extends safety-critical control based on control-dependent barrier functions, introducing a control-dependent Lyapunov function to improve its steady-state behavior. Finally, we identify and propose workarounds for problems that arise from the number of inputs being less than the dimension of the desired safe set. The conservative stability regions and the extended safety-critical control system are validated by means of simulation results based on a nonlinear lateral stability model.

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.

Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim® Validation

This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim® full vehicle model running through a double–lane change maneuver.

GRNN inverse system based decoupling control strategy for active front steering and hydro-pneumatic suspension systems of emergency rescue vehicle

Aiming at eliminating the mutual interference and coupling between the active front steering (AFS) and hydro-pneumatic suspension (HPS) subsystems of the emergency rescue vehicle, this paper presents a neural network inverse system based decoupling control strategy, which consists of the neural network inverse system and PID feedback control strategy. At first, a three-degrees of freedom dynamics model of the vehicle is constructed, and the reversibility of the vehicle chassis system is analyzed by the Interactor algorithm. Then, the generalized regression neural network (GRNN) model is used to identify the vehicle chassis inverse system, which decouples the multivariable vehicle chassis system into two independent single input and single output systems. Next, this paper proposes a closed-loop compound controller for the single input and single output systems, which is designed based on the PID control strategy and GRNN inverse system to realize the feedback control of the vehicle chassis system, so that the vehicle can track the desired yaw rate and roll angle well. Simulations and experiments are conducted on the self-developed fire rescue prototype vehicle, and the results prove that the proposed decoupling control strategy can not only eliminate coupling between AFS and HPS subsystems of the vehicle chassis, but also make the vehicle have better handling stability than controlled by the integrated control.

Slip-aware driver assistance path tracking and stability control

This paper presents a novel integrated control framework that includes simultaneous vehicle lateral stabilization and path tracking by considering the combined-slip effect, wheel dynamics, and tire force nonlinearities for driver-assistance systems. Active front steering and brakes are the actuators of the developed slip-aware receding horizon control system, in which the loss of cornering forces caused by longitudinal slip dynamics is considered in the prediction model. The controller monitors tire capacities and normal forces, corrects driver’s input, and adjusts wheel torques and steering to provide safe performance in path tracking (e.g., lane-keeping) while stabilizing the vehicle within its handling limits. The main advantage of the developed slip-aware driver-assistance controller is the ability in handling multi-actuation vehicle systems through its more accurate prediction model. The performance and computational efficiency of the integrated strategy is evaluated in hardware-in-the-loop real-time experiments, in various pure- and combined-slip maneuvers, under different road friction conditions. Stability of the controller and longitudinal tire force observer is also investigated. The real-time experiments and simulations reveal that the proposed control framework outperforms the existing algorithms in dealing with reduced tire capacities in harsh maneuvers as a consequence of simultaneous vehicle and wheel stabilization, and path tracking.

Robust Takagi–Sugeno Fuzzy Fault Tolerant Control for Vehicle Lateral Dynamics Stabilization With Integrated Actuator Fault and Time Delay

Abstract This paper investigates the lateral dynamics stabilization problem for autonomous electric vehicles (AEVs) through the active front steering (AFS) system. A fault-estimation-observer-based robust fuzzy fault tolerant controller is proposed to tackle actuator faults, time delay, modeling nonlinearities, and external disturbances. First, to establish a more accurate dynamics model, the Takagi–Sugeno (T–S) fuzzy modeling strategy is utilized to handle velocity change and parameter uncertainties. Second, to further improve the lateral stability and driving active safety of the AEV, an integrated actuator fault model comprising efficiency loss fault and additional bias fault is proposed. Meanwhile, in order to diagnose actuator additional bias fault, a fuzzy fault estimation observer (FFEO) is designed to acquire fault information online. Third, to eliminate the influence caused by integrated fault and actuator time delay, a fuzzy fault tolerant controller is constructed to improve the handling performance and driving active safety of the AEV. Finally, the effectiveness of the proposed control scheme is demonstrated via a full-car model based on the joint simulation of carsim and matlab/simulink.

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.

Digital sliding mode controllers for active control of ground vehicles

AbstractThis paper aims at designing, in the discrete‐time setting and using sliding mode techniques, some controllers ensuring the tracking of desired references of lateral and yaw velocities for a ground vehicle with active front steering and rear torque vectoring actuators. The vehicle is described by an enhanced discrete‐time model, recently presented in the literature. These three controllers have been designed considering the modified equivalent control method for perturbation attenuation, the discrete‐time‐like super‐twisting algorithm, and the discrete‐time reaching law. They have been tested with a simulation study using CarSim, where the three controllers are compared under parametric variations, external perturbations, and different sampling periods.

Composite chattering-free discrete-time sliding mode controller design for active front steering system of electric vehicles

Composite chattering-free discrete-time sliding mode controller design for active front steering system of electric vehicles