Active Steering Control Research Articles

Active steering control using a monocular camera mounted on railway vehicle

Active steering control using a monocular camera mounted on railway vehicle

Active steering control research using closed-loop dynamic simulation for Semi-Trailer Trains

Active steering control research using closed-loop dynamic simulation for Semi-Trailer Trains

Active steering control research using closed-loop dynamic simulation for semi-trailer trains

Active steering control research using closed-loop dynamic simulation for semi-trailer trains

A Randomized Filtering Strategy Against Inference Attacks on Active Steering Control Systems

In this paper, we develop a framework against inference attacks aimed at inferring the values of the controller gains of an active steering control system (ASCS). We first show that an adversary with access to the shared information by a vehicle, via a vehicular ad hoc network (VANET), can reliably infer the values of the controller gains of an ASCS. This vulnerability may expose the driver as well as the manufacturer of the ASCS to severe financial and safety risks. To protect controller gains of an ASCS against inference attacks, we propose a randomized filtering framework wherein the lateral velocity and yaw rate states of a vehicle are processed by a filter consisting of two components: a nonlinear mapping and a randomizer. The randomizer randomly generates a pair of pseudo gains which are different from the true gains of the ASCS. The nonlinear mapping performs a nonlinear transformation on the lateral velocity and yaw rate states. The nonlinear transformation is in the form of a dynamical system with a feedforward-feedback structure which allows real-time and causal implementation of the proposed privacy filter. The output of the filter is then shared via the VANET. The optimal design of randomizer is studied under a privacy constraint that determines the protection level of controller gains against inference attacks, and is in terms of mutual information. It is shown that the optimal randomizer is the solution of a convex optimization problem. By characterizing the distribution of the output of the filter, it is shown that the statistical distribution of the filter’s output depends on the pseudo gains rather than the true gains. Using information-theoretic inequalities, we analyze the inference ability of an adversary in estimating the control gains based on the output of the filter. Our analysis shows that the performance of any estimator in recovering the controller gains of an ASCS based on the output of the filter is limited by the privacy constraint. The performance of the proposed privacy filter is compared with that of an additive noise privacy mechanism. Our numerical results show that the proposed privacy filter significantly outperforms the additive noise mechanism, especially in the low distortion regime.

Variable transmission ratio and active steering control for steer-by-wire steering

Variable transmission ratio and active steering control for steer-by-wire steering

Variable transmission ratio and active steering control for steer-by-wire steering

Variable transmission ratio and active steering control for steer-by-wire steering

Actively steering a wheeled tractor against potential rollover using a sliding-mode control algorithm: Scaled physical test

Tractor rollover is a severe safety problem in crop production. Active steering (AS) is an effective active control technology that can be used to enhance the lateral stability of the tractor. The purpose of this study is to implement AS system that converts tractor roll and pitch motion to restore the tractor attitude in a potential rollover. Based on the Lagrange method, a nonlinear time-varying attitude dynamic model is established according to different rollover statuses. A sliding mode control (SMC) algorithm for the tractor is constructed based on variable structure theory. A lateral stability indicator depending on tractor dynamic attitude is further proposed. The effectiveness of this SMC-AS control strategy was validated in simulation and scale-model experiments under various test conditions. The results showed that by adopting SMC-AS control, the roll-stable time of tractor was reduced by 46.07%, which demonstrates that the proposed SMC-AS control strategy was robust and effective. The developed approach is expected to supply technical support to help drivers to prevent tractor rollover accidents. • A 7-DOFs nonlinear dynamic tractor model is established. • The sliding mode algorithm is applied to active steering control strategy. • Simulations and scale-model experiments are conducted for verification.

Optimal path tracking control for intelligent four-wheel steering vehicles based on MPC and state estimation

Four-wheel steering (4WS) vehicles have better stability control and path following performance than front-wheel steering (FWS) vehicles. Aiming at this characteristic, a new four-wheel active steering control strategy is proposed. For intelligent vehicle path tracking and nonlinear vehicle system state estimation, a path tracking control algorithm based on the MPC algorithm is designed to analyze the stability of the vehicle and set up constraints to achieve accurate tracking of the reference path. Automotive dynamic control systems require information on system variables. For example, the sideslip angle of electric vehicles cannot be measured directly. Based on UKF theory and the information input of low-cost sensors on the vehicle, an estimator is designed to estimate vehicle sideslip angle and yaw rate. According to the difference between the estimated value and the ideal value of the vehicle state, the LQR optimal controller is designed to realize the optimal control of the front and rear steering. And compared with the dynamic simulation results of front-wheel steering, proportional control four-wheel steering and yaw rate feedback four-wheel steering. The experimental results of the simulation platform show that the path tracking 4WS state feedback optimal control method has good lateral control stability and path tracking accuracy.

An adaptive tracking control method for the all-wheel-driving and active-steering articulated vehicle with n-units

To achieve the running control of the all-wheel-driving and active-steering articulated vehicles (AWDASAVs) with n-units, an adaptive tracking control method is proposed in this paper, which includes a real-time target trajectory generation and an adaptive tracking control system. Firstly, the AWDASAV kinematics model is derived, and then the front-axle trace as the target trajectory is computed for all rear-axle steering by using data compressing and filtering, coordinate transformation, and local spline differences, which has small data storage and high computational efficiency and makes it easier to use in AWDASAV. Secondly, an adaptive tracking control system composed of an adaptive active steering controller and a differential distribution controller is designed to achieve accurate trajectory tracking and coordinated movement for AWDASAV. Finally, the AWDASAV simulation model with five-units was built in ADAMS by code development for cross-validation simulation, and the simulations with two cases at various speeds are carried out to verify the simulation model and control method. To further investigate the proposed method, the influence of three parameters on the tracking control performance and comparison with different control methods are carried out. The results exhibit excellent tracking control 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.

Active steering control based on preview theory for articulated heavy vehicles.

This paper investigates the active steering control of the tractor and the trailer for the articulated heavy vehicle (AHV) to improve its high-speed lateral stability and low-speed path following. The four-degree-of-freedom (4-DOF) single track dynamic model of the AHV with a front-wheel steered trailer is established. Considering that the road information at the driver’s focus is the most clear and those away from the focus blurred, a new kind controller based on the fractional calculus, i.e., a focus preview controller is designed to provide the steering input for the tractor to make it travel along the desired path. In addition, the active steering controllers based on the linear quadratic regulator (LQR) and single-point preview controller respectively are also proposed for the trailer. However, the latter is designed on the basis of the articulation angle between the tractor and trailer, inspired by the idea of the driver’s single-point preview controller. Finally, the single lane change maneuver and 90o turn maneuver are carried out. And the simulation results show that compared with the single-point preview controller, the new kind preview controller for the tractor can have good high speed maneuvering stability and low speed path tracking ability by adjusting the fractional order of the controller. On this basis, three different AHVs with the same tractor are simulated and the simulation results show that the AHV whose trailer adopts the single-point preview controller has better high-speed lateral stability and low-speed path tracking than the AHV whose trailer adopts the LQR controller.

Modular estimation of lateral vehicle dynamics and application in optimal AFS control

The paper introduces a novel modular estimation approach for lateral vehicle and tire dynamics using a simplified vehicle model and a non-linear estimation algorithm. A dynamics-oriented representation of lateral tire forces with a single track lateral vehicle model (STVM) has been introduced. Subsequently, extended Kalman filter (EKF) based distributed observer modules for each dynamical parameter has been designed and combined into a Unified Estimation Scheme (UES). Finally, a linear quadratic regulator (LQR) based Active Front Steering (AFS) control system has been designed using the estimated parameters. The accuracy and computational efficiency of the designed scheme has been analyzed and compared to non-modular UKF, EKF, and Particle Filter (PF) algorithms, through Monte-Carlo Simulations using the CarSim dataset for both high and low [Formula: see text] surfaces, followed by further validation using real-time dataset. The results show that the proposed system significantly improve the accuracy and speed of estimation, as well as stable performance in closed loop control.

Trajectory Tracking Control in Real-Time of Dual-Motor-Driven Driverless Racing Car Based on Optimal Control Theory and Fuzzy Logic Method

To improve the accuracy and timeliness of the trajectory tracking control of the driverless racing car during the race, this paper proposes a track tracking control method that integrates the rear wheel differential drive and the front wheel active steering based on optimal control theory and fuzzy logic method. The model of the lateral track tracking error of the racing car is established. The model is linearized and discretized, and the quadratic optimal steering control problem is constructed. Taking advantage of the differential drive of dual-motor-driven racing car, the dual motors differential drive fuzzy controller is designed and integrated driving with active steering control. Simulation analysis and actual car verification show that this integrated control method can ensure that the car tracks different race tracks well and improve the track tracking control accuracy by nearly 30%.

A new effective nonlinear strategy for lateral stability increment of an articulated vehicle rigid cargo

In this paper, the effects of the most important parameters on directional dynamics of a tractor-semitrailer vehicle are examined. Initially, a three DOF dynamic model of a tractor-semitrailer vehicle is proposed. Then, the developed model is validated by means of TruckSim software during a standard maneuver. In order to analyze the system stability, the Lyapunov method has been used and the stability conditions have been extracted based on Routh criterion. The most important parameters are selected based on the articulation angle gain. Among the studied parameters, the semitrailer mass, the distance of the tractor unit center of mass and its front axle, and the tires cornering stiffness exhibited more effective behavior on the vehicle’s stability. The simulation results show that as the tractor center of mass moves toward its rear axle, the probability of the jackknifing increases. Moreover, an increment in the semitrailer mass leads to a turn of the semitrailer with respect to the tractor. Also, the understeer specification of the vehicle strengthens due to the tire cornering stiffness increment. Moreover, in order to increase the maneuverability of the articulated vehicle a new active steering controller is proposed using two different control methods. The controller is developed using the simplified dynamic model and the basis of feedback linearization method using dynamic sliding mode control method. In this system, the yaw rate and the lateral velocity of the tractor unit as well as articulation angle are studied as state variables which are targeted to track their desired references. Then, the vehicle dynamic performance is investigated during standard maneuvers. A more investigation shows that the track of the desired values of the vehicle state variables leads to eliminate off-tracking path.

Driver assistant yaw stability control via integration of AFS and DYC

This paper aims to develop an integrated adaptive control strategy to control vehicle lateral dynamics. A hierarchical control scheme consisted of two control levels is designed to keep the vehicle handling and improve yaw stability control using the coordination of active front steering (AFS) and direct yaw moment control (DYC). At the upper control level, the desired yaw moment and additive steering angle are obtained through proper adaptive laws in the first level adaptation (FLA) control structure. A constraint optimisation algorithm is utilised in the lower control level to adjust the impact of the steering and braking actuators. The desired yaw moment is converted into the brake torques and effectively distributed to the wheels. Improvement in the yaw stability control is demonstrated through simulation studies for a nonlinear 8 degree-of-freedom (DOF) vehicle model. Furthermore, the performance of the proposed control strategy is compared with that of the adaptive controller without integration. Simulation results of two different maneuvers point to the superiority of the proposed approach, highlighting some of its appealing features, like better tracking performance with lower control efforts in terms of brake torque and steering angle, as compared to the conventional adaptive method.

Robust model referenced control for vehicle rollover prevention with time-varying speed

In this paper, an active steering control strategy is proposed to prevent rollover of a vehicle with a time-varying forward speed. The controller is designed with the aim of reducing the rollover index (RI) from an initial dangerous status to an absolutely safe status by active steering. The controller consists of two parts. The nominal control is firstly designed on the basis of the fundamental equation of constrained motion (FECM) of the vehicle, which will guarantee the nominal vehicle to track the desired states and thus rollover is prevented. To handle system uncertainties, compensatory sliding mode control (SMC) is proposed, by which the actual states are forced to track the desired ones. In the controller, the chattering problem can be alleviated by selecting a suitable performance function. To indicate the merits of the designed controller, simulation is conducted. Simulation results demonstrate that compared to conventional controllers, the proposed controller can prevent the vehicle from rollover with stronger robustness and without system chattering.

Design and development of sensor based Automatic steering control system for automobiles

Drowsy driving is the major problem seen throughout the world which usually happens when the driver has not slept enough, and this state of mind may lead to accidents. Above being the case, the project helps driver to keep him alert by giving an alarm and to minimize accidents due to driver error, distractions and drowsiness. The research derives, implements, tunes and compares selected path tracking methods for controlling a car-like robot along a predetermined path. The model mainly aims on using active steering control to increase safety and to reduce both accidents and drivers work load. The objective of the project is to design and develop a steering control system for the path tracking of autonomous vehicle & to understand the stability of the vehicles on curved road. The steering controller controls the steering actuator to follow the desired steering angle. A servo motor is installed to control the steering handle, and it can transmit the steering force using a belt and pulley. The developed mechanism is a steering controller that is applied to a micro controller. However, because of a dead band, the path tracking performance and stability of autonomous vehicles are reduced. To overcome this, steering system and programme is developed. As a result of the steering system and programme, the performance and stability are improved. GPS shows the geographical coordinates which specifies any given location on the earth surface as latitude and longitude. Ultrasonic sensor used for detecting the obstacles. A magnetometer compass shows the quadrants. Arduino UNO board is used like a mother board in the project. Finally, the project augments the literature with a comprehensive collection of important path tracking ideas, a guide to their implementations and, most importantly, an independent and realistic comparison of the performance of these various approaches.

SUV 차량용 능동전륜조향장치(AFS)의 제어기 개발

SUV 차량용 능동전륜조향장치(AFS)의 제어기 개발

Active steering control for vehicle rollover risk reduction based on slip angle estimation

In this study, a robust controller is proposed for rollover risk suppression in automatically-driven vehicles by reducing the lateral acceleration through a steer-by-wire system equipped on the vehicles. First, since the slip angle is difficult to measure directly, a sliding mode observer combining an add-on switching term with a linear observer is developed to provide more robust estimation of slip angle in the presence of system uncertainties. Second, an adaptive sliding mode control method is proposed, in which the control gain is adaptively tuned to compensate for the system uncertainties. Lastly, simulation is conducted and the results verify that the proposed estimator and controller can reduce the vehicle rollover risk efficiently and robustly.

Lane Departure Assistance Coordinated Control System for In-Wheel Motor Drive Vehicles Based on Dynamic Extension Boundary Decision

Aiming at the advantages in active safety control of in-wheel motor drive vehicles, the classified decision-control-execution is proposed to the lane departure assistance system under the premise of ensuring the stability of the vehicle. The dynamic extension boundaries which vary with vehicle speed, road curvature and road adhesion coefficient are designed in the decision layer by using the neural network algorithm, which divides different dangerous degrees into different decision regions. The active differential steering controller, the electronic differential controller and their coordinated control strategy are designed in the control layer, and the corresponding control is carried out among different decision regions. In order to avoid the dangerous situation of secondary deviation caused by the existence of drift angle when the vehicle is rectified to the lane centerline, the heading angle controller is then designed in the control layer. The different torque distribution modes are divided according to the current vehicle speed, road adhesion coefficient and lateral acceleration in the execution layer, and the economic distribution, stability distribution, joint distribution are correspondingly executed among different torque distribution modes. Finally, Carsim and Matlab/Simulink are used for simulation verification.