Active Front Wheel Steering System Research Articles

Research on the Control Strategy for Handling Stability of Electric Power Steering System with Active Front Wheel Steering Function

<div>Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system. Finally, experiments are conducted in the CarSim/Simulink joint simulation environment. The results indicate that compared to traditional PI/PID controllers and advanced ADRC controllers, DR-PI/DR-PID controllers can effectively suppress step disturbances, sinusoidal disturbances, and white noise disturbances while ensuring the dynamic response characteristics of the system. Under double-lane conditions, the root mean square values of the yaw rate and sideslip angle decrease by 4.35% to 12.3% and 3.21% to 7.72%, respectively. Therefore, the designed control strategy can improve handling stability while ensuring the robustness of the system.</div>

A Robust Dynamic Game-Based Control Framework for Integrated Torque Vectoring and Active Front-Wheel Steering System

Distributed drive electric vehicles (DDEVs) eliminate the complex drivetrain. The independently driven in-wheel motors also endow the vehicle with more ability for improving maneuverability. To this end, this paper proposes an integrated control framework of torque vectoring (TV) and active front-wheel steering system (AFS) to ensure the vehicle lateral motion stability performance. First, the polytope method with finite vertices is employed to deal with the system uncertainties and simplify the modeling structure, based on which a distributed model predictive control is adopted to construct a dynamic interactive model between agents. Then, through introducing the game theory, a distributed parallel control scheme is developed to obtain the cooperative strategy of agents. Such a design can also satisfy the modular and scalable requirement for integrated chassis control. To ensure the system asymptotic stability, the terminal input combined with the terminal cost function are treated as the constraints in the game paradigm and then transformed as the linear matrix inequalities. Furthermore, a robust H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $</tex-math> </inline-formula> compensation method is used to suppress the system disturbance. Finally, the hardware-in-the-loop (HIL) tests are conducted to assess the control performance. The results verify the proposed integrated control scheme is effective to enhance the vehicle handling stability.

Active Front Wheel Steering System using Yaw Rate Estimation based Fuzzy Logic Due to Various Lateral Wind Disturbance

The paper devised and compared the performances of PID, fuzzy-tuned PID and fuzzy logic controller in an Active Front Wheel Steering system to stabilize a 9-DOF nonlinear passenger vehicle when subjected to lateral wind disturbance. The vehicle model was derived mathematically and verified with data from IPG CarMaker at a longitudinal speed of 80 km/h. Initially, the disturbance test was conducted using three lateral wind disturbance profiles to test for controller resiliency with zero steering input. Then, a simple but effective yaw rate observer was derived without compromising the linearity of the vehicle model to simulate the disturbance test with a double lane change (DLC) steering input. A more extreme disturbance magnitude was evaluated in the latter test using the developed control designs. The three controllers showed good performances in both disturbance tests, with fuzzy logic having the lowest error out of the three, which is less than 5% for using the estimated yaw rate observer.

Robust Control Design of Active Front-Wheel Steering on Low-Adhesion Road Surfaces

In order to improve the stability of vehicle steering on low-adhesion road surfaces, this paper designed a hybrid robust control strategy, H2/H∞, for active front-wheel steering (AFS) based on robust control theory. Firstly, we analyzed the influence of the sidewall stiffness and road adhesion coefficient of the tires on vehicle stability, through which we can study the wheel deflection characteristics of low-adhesion roads. Secondly, the reference yaw velocity of the vehicle was calculated using the three-degrees-of-freedom model as the reference model, through which, taking the norm H∞ as the objective function and the norm H2 as the limit to control the output, the hybrid robust control strategy H2/H∞ of the AFS system on a low-adhesion road surface was developed. Finally, the simulation experiment was carried out by the Simulink/CarSim co-simulation platform and a hardware-in-the-loop (HIL) experiment. In this paper, the results show that the AFS control strategy can improve the vehicle handling stability on low-adhesion road surfaces, and the controller has good path tracking performance and robustness.

Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

This paper presents an integrated nonlinear robust adaptive controller with uncertainty observer for active front wheel steering system and direct yaw moment control system. First, an integrated vehicle chassis control model is established as the nominal model with the additive and multiplicative uncertainties of the system. Secondly, an integrated nonlinear robust adaptive control law with the additive uncertainty observer is designed via Lyapunov stability theory to calculate the corrective yaw moment, and an adaptive law is designed based on projection correction method to online estimate and compensate the multiplicative uncertainty of the system. Then, the constrained optimal allocation problem of the corrective yaw moment is transformed into the nonlinear optimization problem, and the sequential quadratic programming method is used to solve the nonlinear optimization problem to coordinate active front wheel steering system and direct yaw moment control system. Finally, the performance of the proposed integrated nonlinear robust adaptive controller is verified via vehicle dynamics simulation software.

Identification of an optimum control algorithm to reject unwanted yaw effect on wheeled armored vehicle due to the recoil force

This article presents an active safety system for a wheeled armored vehicle to encounter the effect of the firing force. The firing force which acts as an external disturbance causes unwanted yaw moment occurred at the center of gravity of the wheeled armored vehicle. This effect causes the wheeled armored vehicle lose its handling stability and the traveling path after the firing condition. In order to overcome the stability problem, a Firing-On-the-Move assisted by an Active Front Wheel Steering system is proposed in this study. This system is developed based on two established systems, namely, Firing-On-the-Move and Active Front Wheel Steering systems. The proposed system is designed to improve the handling and directional stability performances of the armored vehicle while fires in dynamic condition. Four types of control strategies are designed and investigated in this study to identify the most optimum control strategy as the Firing-On-the-Move assisted by an Active Front Wheel Steering system using optimization tool, genetic algorithm. The control strategies for the Firing-On-the-Move assisted by an Active Front Wheel Steering are evaluated using various types of vehicle speeds and firing angle in order to obtain an appropriate control structure as the Firing-On-the-Move assisted by an Active Front Wheel Steering system for the wheeled armored vehicle.

Modelling and optimisation of active front wheel steering system control for armoured vehicle for firing disturbance rejection

While firing on the move, the handling performance of an armoured vehicle is affected, thus causing it to lose its directional stability. This is due to an impulse force generated at the centre of the gun turret, which can produce an unwanted yaw moment at the centre of gravity of the armoured vehicle. In order to reject the unwanted yaw moment, a new hybrid control strategy known as Neural-PI controller had been introduced by combining neural network system and conventional PI controller. This paper developed 14 DOF of armoured vehicle and 2 DOF of Pitman arm steering system. Other than that, determination of the most suitable activation function to be implemented in the Neural-PI controller has been carried out and optimised by using the Genetic Algorithm (GA) method. The performance of the controller was evaluated by comparing the conventional PI controller with the Neural-PI controller implemented with different activation functions.

Modelling and optimisation of active front wheel steering system control for armoured vehicle for firing disturbance rejection

While firing on the move, the handling performance of an armoured vehicle is affected, thus causing it to lose its directional stability. This is due to an impulse force generated at the centre of the gun turret, which can produce an unwanted yaw moment at the centre of gravity of the armoured vehicle. In order to reject the unwanted yaw moment, a new hybrid control strategy known as Neural-PI controller had been introduced by combining neural network system and conventional PI controller. This paper developed 14 DOF of armoured vehicle and 2 DOF of Pitman arm steering system. Other than that, determination of the most suitable activation function to be implemented in the Neural-PI controller has been carried out and optimised by using the Genetic Algorithm (GA) method. The performance of the controller was evaluated by comparing the conventional PI controller with the Neural-PI controller implemented with different activation functions.

Development of estimated disturbance rejection feedback for an armoured vehicle using active front wheel steering

An unwanted yaw motion occurred at the centre of gravity (CG) of the wheeled armoured vehicle caused by the impulse force generated during gun turret firing. The recoil force from the gun fire tends to create instability conditions for the armoured vehicle during firing condition and affects the dynamic performance of armoured vehicle in lateral direction. In this paper, an active safety system, active front wheel steering (AFWS) system using estimated disturbance rejection feedback (EDRF) embodiment is proposed to reject the unwanted yaw disturbance and stabilise the armoured vehicle. Besides, the proposed control strategy is also used to re-position the armoured vehicle back to its initial position. Therefore, a summation moment reference input is used to counter back the unwanted firing moment occurred due to gun firing impulse at CG of the armoured vehicle. The armoured vehicle is evaluated via simulation analysis in term of yaw rate, yaw angle, vehicle sideslip angle, lateral acceleration and lateral displacement. Significant improvements up to 75% have been achieved by using the proposed control strategy of AFWS system to reject the external disturbance due to the firing force.

Robust Composite Nonlinear Feedback Path-Following Control for Independently Actuated Autonomous Vehicles With Differential Steering

This paper investigates utilizing the front-wheel differential drive-assisted steering (DDAS) to achieve the path-following control for independently actuated (IA) electric autonomous ground vehicles (AGVs), in the case of the complete failure of the active front-wheel steering system. DDAS, which is generated by the differential torque between the left and right wheels of IA electric vehicles, can be utilized to actuate the front wheels as the sole steering power when the regular steering system fails, and thus avoids dangerous consequences for AGVs. As an inherent emergency measure and an active safety control method for the steering system of electric vehicles, DDAS strategy is a valuable fault-tolerant control approach against active steering system failure. To improve the transient performance of the fault-tolerant control with the DDAS, a novel multiple-disturbance observer-based composite nonlinear feedback (CNF) approach is proposed to realize the path-following control for IA AGVs considering the tire force saturations. The disturbance observer is designed to estimate the external multiple disturbances with unknown bounds. CarSim–Simulink joint simulation results indicate that the proposed controller can effectively achieve the fault-tolerant control and improve the transient performance for path following in the faulted-steering situation.

Active front wheel steering model and controller for integrated dynamics control systems

We report a model and controller for an active front-wheel steering (AFS) system. Two integrated dynamics control (IDC) systems are designed to investigate the performance of the AFS system when integrated with braking and steering systems. An 8-degrees-of-freedom vehicle model was employed to test the controllers. The controllers were inspected and compared under different driving and road conditions, with and without braking input, and with and without steering input. The results show that the AFS system performs kinematic steering assistance function and kinematic stabilisation function very well. Three controllers allowed the yaw rate to accurately follow a reference yaw rate, improving the lateral stability. The two IDC systems improved the lateral stability and vehicle control and were effective in reducing the sideslip angle.

Active Steering Actuator Fault Detection for An Automatically-steered Electric Ground Vehicle

In this paper, we investigate the actuator fault detector design problem for an electric ground vehicle (EGV) that is equipped with an active front-wheel steering system. Since the EGV can be steered by a motor automatically, it is desired to design a fault detector for the steering actuator for safety reasons. A two degree of freedom lateral nonlinear vehicle model is established. The nonlinear vehicle model is converted to a linear-parameter-varying (LPV) form and the scheduling vector is related to the vehicle longitudinal velocity. Since it is not easy to measure the longitudinal velocity precisely, the uncertain measurement on the longitudinal velocity is considered and the weighting factors of LPV submodels are subject to uncertainties. Based on the uncertain LPV model, a gain-scheduling fault detector is proposed and an augmented system is obtained. The desired steering angle and the faulty steering angle are both involved in the augmented system. As the steering angle generally has a low-frequency working range, the steering angle amplitude spectrums of three different maneuvers are studied, and the frequency working range is determined. The stability, the $\mathcal {H}{\_}$ performance, and the $\mathcal {H}_{\infty }$ performance of the augmented system are all exploited. Based on the analysis results, the mixed $\mathcal {H}{\_}$ / $\mathcal {H}_{\infty }$ fault detector design method is developed. An experimental test is used to show the performance of the designed fault detector.

Modeling, validation and firing-on-the-move control of armored vehicles using active front-wheel steering

It is a well-known fact that an armored vehicle will lose its directional stability when firing a large-caliber gun while moving. The instability is caused by the impulse force from firing, acting at the center of a weapons platform that produces a yaw moment at the center of gravity of the armored vehicle. In order to improve the stability, this paper introduces a firing-on-the-move technology for armored vehicles using an active front-wheel steering (AFS) system. The AFS system is proposed to maintain the directional stability of the armored vehicle by providing an electronically controlled correction to the steering mechanism. The steering correction is designed to reject the unwanted yaw motion and bring the vehicle back to its intended direction of travel after firing. The proposed control strategy of the AFS system in this study consists of yaw rate feedback with lateral force rejection control. The AFS system controller is developed on a validated 10-degrees-of-freedom armored vehicle. The results indicate that the developed control strategy can effectively maintain the directional stability, in terms of yaw and lateral motions, of the armored vehicle after firing. The superiority of the proposed AFS system controller is also evaluated by comparing its performance to an AFS system without lateral force rejection control as well as to a conventional armored vehicle without AFS.

Design and Evaluation of Active Front Wheel Steering System Model and Controller

Design and Evaluation of Active Front Wheel Steering System Model and Controller

Robust Active Steering System Taking Account of Nonlinear Dynamics

The objective of this study is to design an active front wheel steering system in which the steering characteristics do not vary for changes of environment. It is based on the Robust Model Matching (RMM) algorithm which realizes desired frequency characteristics between the driver's steering angle and vehicle yaw rate. This makes the frequency characteristics robust for parameter errors and non linear dynamics. The effectiveness of the proposed system is verified by computer simulation of vehicle behavior running under various velocities and road surface friction conditions.