Steering Torque Control Research Articles

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

Research on the Steering Torque Control for Intelligent Vehicles Co-Driving With the Penalty Factor of Human–Machine Intervention

In intelligent vehicle cooperative systems, the mismatch in driving characteristics between a human and a machine and the driver misoperation caused by this mismatch result in human–machine conflicts, which significantly affect driving safety. Therefore, an intelligent vehicle human–machine cooperative steering torque control method is proposed herein. To adapt the intelligent system to the varying previewing characteristics of a human, a time-varying previewing driver model is constructed, and a penalty factor for human–machine intervention is designed based on fuzzy rules to assign driving control rights by assessing the driver’s state. Consequently, a human–vehicle–road model with driver preview time and penalty factor as varying parameters is established. Based on gain-scheduling control, a human–machine cooperative steering torque controller is designed to adapt to the varying previewing characteristics of a human and the change in human–machine intervention. The stability and robustness of the entire parameter space are guaranteed by constraining the poles in a certain region. Finally, the proposed human–machine cooperative control scheme demonstrates the effective alleviation of conflicts between the driver and the intelligent driving system.

A Novel Adaptive Steering Torque Control Approach for Human–Machine Cooperation Autonomous Vehicles

Human–machine cooperation (HMC) driving can realize the sharing of vehicle control rights between drivers and autonomous driving systems. However, the system disturbances and parameter uncertainties introduced by HMC driving are also inevitable. To solve the above problems, this study proposes an HMC steering torque control method based on the model reference adaptive control (MRAC). First, to describe the driver’s operating behavior and the transient characteristics of the steering system and the vehicle, an HMC vehicle-road model with steering torque input is established. Then, the exponential decay function is taken as a reference model to increase the decay rate of road lateral deviation. Besides, for the problem of model parameter uncertainties, the parameter adaptive laws are designed in the control process, and the robust term is considered into the parameter adaption law to improve the robustness of the closed-loop system. Robust stability under bounded disturbances is guaranteed by boundary estimation of unknown disturbances between driver input and steering load. Finally, simulations and experiments are carried out to verify the proposed control strategy. The results demonstrate that the proposed control strategy can converge rapidly under the disturbances and parameter uncertainties introduced by HMC.

A robust lateral tracking control strategy for autonomous driving vehicles

A robust steering torque control strategy for lateral tracking functionality of autonomous driving vehicles that perform active steering, active accelerating and active braking is researched in this paper. The main target of this research is to track references of lateral position and heading angle, which are provided by upstream motion planning module, via torque that robotic arm applies to steering hand wheel. Firstly, a system with tracking errors is generated and analyzed. To keep control system’s robustness against time varying parameters, such as speed, center of mass, etc., gain-scheduling approach is utilized to obtain proper feedback gain. To achieve performance robustness, methods of linear matrix inequalities are used in design to maintain a performance function. Control performance is validated in Hardware in the loop environment built by ETAS labcar and Matlab/Simulink for a lane changing scenario and two typical parking scenarios. Results show that the proposed control strategy can regulate vehicle to follow target trajectory precisely even when speed varies. In lane changing scenario, steady state error is close to zero and maximum lateral position error is about ±30 cm. In parking scenarios, tracking error of lateral position is about ±3 cm, and of heading angle is about ±0.1 rad at end points. In addition, in comparison with linear quadratic regulator (LQR) and model predictive control (MPC), the proposed control outperformances in the three test scenarios.

Model predictive control of steering torque in shared driving of autonomous vehicles.

The current path tracking control method is usually based on the steering wheel angle loop, which often makes the driver lose control of the automatic driving control loop. In order to involve the driver in the automatic driving control loop, and to solve the vehicle path tracking control problem with system robustness and model uncertainty, this paper puts forward a steering torque control method based on model predictive control algorithm. Based on the vehicle model, this method introduces the steering system model and the steering resistance torque model, and calculates the optimal control torque of the vehicle through the real-time vehicle status, so as to make up for the model mismatch, interference and other uncertainties, and ensure the real-time participation of the driver in the automatic driving control loop. To combine the nonlinear vehicle dynamics model with the steering column model, and to take the vehicle state parameters as the feedback variables of the model predictive controller model, then input the solution of the steering superposition control rate into the vehicle model, the design of the steering controller is realized. Finally, to carry out the simulation of lane keeping based on CarSim software and Simulink control model, and the hardware in-the-loop test on the hardware in-the-loop experimental platform of CarSim/LabVIEW-RT. The simulation and test results indicate that the designed torque loop path tracking control method based on model predictive control can help the driver track the target path better.

Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach

The challenging issue of “human–machine copilot” opens up a new frontier to enhancing driving safety. However, driver–machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system's path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the steering torque interaction between the driver and the intelligent electric power steering (IEPS) system. A six-order driver–vehicle dynamic system, including driver/external uncertainty, is established for path-tracking. Then, the affine linear-quadratic-based path-tracking problem is proposed to model the maneuvers of the driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming. Two cases of copilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between the stochastic Nash and Stackelberg strategies is investigated based on the results. And the steering-in-the-loop experiment reveals the potential of the proposed shared control framework in handling driver–IEPS conflicts and uncertain driver/external turbulence. Finally, the copiloting experiments with a human driver further demonstrate the rationality of the game-based pattern between both the agents.

Emergency Steering Evasion Assistance Control Based on Driving Behavior Analysis

Research on collision avoidance has received significant attention in academia, but few attempts have been made regarding the impact of the real driver. Focusing on the hybrid vehicle controlled by a real driver, this paper proposes a combined yaw moment and steering torque control method based on driving behaviors during the emergency steering evasion (ESE). First, a CarSim vehicle model and a vision-based driving simulator are built to acquire and analyze ESE driving behaviors, and the trigger condition of the assistance is defined. Second, a preview distance adaptive driver steering model is created and followed by the calculation of the desired value of the steering angle and yaw rate from the planned collision avoidance path. Third, an ESE assistance controller is designed on the basis of the electric power steering (EPS) torque assistance law during normal driving and driving behaviors for the steering evasion lane change. Controllers used for this paper include a yaw rate tracking controller and a steering torque assistance fuzzy controller. The actuators are EPS, an electric stability program, and two hub motors in the rear axle. The preview distance is optimized by the particle swarm optimization method and the driving behavior-based ESE controller is tested with the driving simulator. The result shows that the optimized preview distance is credible, and the path tracking accuracy and the vehicle stability are improved with the help of the proposed ESE assistance controller by giving full consideration to driving behaviors in ESE.

GA‐BPNN Based Hybrid Steering Control Approach for Unmanned Driving Electric Vehicle with In‐Wheel Motors

The steering system is a key component of the unmanned driving electric vehicle with in‐wheel motors (IWM‐EV), which is closely related to the operating safety of the vehicle. To characterize the complex nonlinear structure of the steering system of unmanned driving IWM‐EV, a hierarchical modeling and hybrid steering control approach are presented. Firstly the 2‐DOF model is introduced for the entire vehicle system, and then the models of the steering system and the in‐wheel drive system are analyzed sequentially. The steering torque control system based on electronic differential (ED) and differential assist steering (DAS) is studied. The back propagation neural network (BPNN) is used to optimize the network structure, parameters, and the weight coefficient of the hybrid steering system. The genetic algorithm (GA) is employed to optimize the initial weight of BPNN and search within a large range. The GA‐BPNN model is established with the yaw moment and differential torque as the input of BPNN. Simulation and experimental results show that the proposed GA‐BPNN‐based hybrid steering control approach not only accelerates the convergence speed of steering torque weight adjustment but also improves the response speed and flexibility of the steering system. Through optimizing and distributing the steering torque dynamically, the proposed GA‐BPNN‐based control approach has inherited the advantages of both vehicle stability under ED and the steering assistance under DAS, which further guarantees the safety and stability of unmanned driving IWM‐EV.

Steering torque control using variable impedance models for a steer-by-wire system

This paper presents a novel sensor-less steering torque control method for applications to the steer-by-wire system. A steer-by-wire system has not any mechanical link to connect a steering wheel and a rack and pinion gear module. Instead of mechanical devices, two electric motors are used on each side. A one motor is attached to the steering wheel and the other is set on rack and pinion. The motor on the steering wheel works as a deliverer between a steering torque and load torque from the road. In this paper, we focus on motion control related to the steering feel based on impedance control. Therefore, the model of rack and pinion is not considered in this work. In most power steering systems, a torque sensor is used to set impedance effect on driver’s steering feel. In this paper, we proposed a novel steering control method without using any torque sensors. The effectiveness of a proposed method is confirmed from experimental results.

Experimental Verification of Active Front Steering Based on Driver‐Friendly Reaction Torque Control

SUMMARYIn recent years the demand for safer vehicles has increased. Active front steering (AFS) is an effective technology for stabilizing motion control after the detection of dangerous motion in a vehicle such as slip. However, if AFS intervenes excessively during the steering process, it becomes difficult to prevent interference between driver steering and AFS automatic steering. Therefore, at present vehicles are rarely equipped with AFS. This paper proposes a steering torque control method based on reaction torque estimation and variable damping control for use in a human‐friendly AFS system and discusses how an experimental device was used to verify the effectiveness of this control method.

双対制御論的運転支援システム

This paper proposes a driver assistance system with a dual control theoretic scheme, which tries to perform safety control of a vehicle as well as identification of the driver's cognitive state simultaneously. Suppose that a vehicle is going to deviate from the driving lane. When the system predicted that the vehicle's lane departure occurs in td seconds, the system implements a partial control (α%) as the control of the first stage to prevent the lane departure from occurring. The control of the first stage is powerful enough to keep the vehicle in the lane but not to bring the vehicle back to the center of the lane. If the driver does not implement himself/herself the remaining control within the time period of ti seconds, the system determines that the driver is in an off-normal cognitive state, and thus implements the remaining control (100-α%) as the control of the second stage. This paper shows a design of the dual control theoretic driver assistance with a steering torque control based on optimum control using a linear quadratic regulator, and validates the control model of the proposed system by a numerical simulation. This paper discusses the functions and characteristics of the safety control and the identification of the driver's cognitive state.

H∞-control of a rack-assisted electric power steering system

Electric power steering (EPS) is more and more in use for passenger cars. Compared with hydraulic steering systems there are many advantages, such as reduced CO2 emissions and the possibility to use the EPS motor torque for advanced driver assistance systems. One task of the steering system is to give the driver an adequate steering feel. This includes providing road feedback and the right level of assistance torque. This article describes the steering torque control of a rack-assisted EPS. The controller's task is to follow a reference steering torque quickly and accurately. First, a mechanical model of the EPS is shown. Then, an H∞-controller is designed, implemented and compared with other steering torque controllers. As steering torque discontinuities are a topic when looking at new control algorithms, the phenomenon and its cause are analysed using a detailed mechanical model. The results of this analysis are considered in the controller design.

Experimental Verification of Active Front Steering Based on Driver-Friendly Reaction Torque Control

SUMMARY In recent years the demand for safer vehicles has increased. Active front steering (AFS) is an effective technology for stabilizing motion control after the detection of dangerous motion in a vehicle such as slip. However, if AFS intervenes excessively during the steering process, it becomes difficult to prevent interference between driver steering and AFS automatic steering. Therefore, at present vehicles are rarely equipped with AFS. This paper proposes a steering torque control method based on reaction torque estimation and variable damping control for use in a human-friendly AFS system and discusses how an experimental device was used to verify the effectiveness of this control method.

Vehicle rollover recovery using active steering/wheel torque control

It is well understood that the driver's steering input strongly affects lateral vehicle dynamics, and excessive steering command may result in unstable vehicle motion. In a certain driving condition, it is possible for a skilled driver to prevent vehicle rollover with better perceptive capability of judging conditions and responding faster with smooth compensatory actions. This paper investigates the possibility of using active steering and wheel torque control to assist drivers in avoiding vehicle rollovers in emergency situations. The effectiveness of steering control alone and the combination of steering/wheel torque control in recovery from an unstable roll condition were demonstrated through simulation of both low and high vehicle speeds.

Motorcycle Steering Control by Road Preview

The main objectives of the work described are to devise an effective path-based motorcycle simulation capability and to add to understanding of how riders control motorcycles. Optimal linear preview control theory was previously applied to the tracking of a roadway by a car, using a simple car model operating in fixed control. Similar theory is applied to path control of motorcycles. The simple car previously employed is replaced by a much more elaborate motorcycle. The steering angle control used previously is changed into steering torque control. Rider upper body lean torque is also allowed as a control input. The machine speed is considered constant but is a parameter of the motion. The objective of the optimal control is to minimize a weighted sum of tracking errors, rider lean angle and control power. The time-invariant optimal control corresponding to a white noise disturbance and to an infinite optimization horizon is found for many situations, involving variations in machine speed and performance priorities. Tight controls, corresponding to high weightings on performance, and loose controls, corresponding to high weightings on control power, are identified. Results show the expected pattern for preview control, that information well into the future is of limited value in determining the present control inputs. Full system performance is achievable with only finite preview. The extent of the preview necessary for full performance is determined as a function of machine speed and performance priorities. This necessary preview is found to be in accord with conventional wisdom of motorcycle riding and rider training. Optimal path tracking preview controls are shown to represent the inverse dynamics of the motorcycle. New light is shed on the relative effectiveness of steering torque and body lean torque controls. Simulations of an optimally controlled motorcycle and rider combination are conducted. A typical lane change path and an S-shaped path from the literature are used. For a chosen speed, optimal controls are installed on the machine for which they were derived and simulation results showing tracking performance, control inputs, and other responses are included. Transformation of the problem from a global description, in which the optimal control is found, to a local description corresponding to the rider’s view, is described. It is concluded that a motorcycle rider model representing a useful combination of steering control capability and computational economy has been established. The model yields new insights into rider and motorcycle behavior.

Improvement in the Straight-Running Stability of Two-wheeled Vehicles by Means of the Energy Flow Method (Steering Torque Control by Rolling Acceleration Feedback)

This study proposes an improved method for stabilizing straight running of two-wheeled vehicles. An analysis of the two-wheeled vehicle's vibration modes was completed using the Energy Flow Method, which calculates energy flow of external forces in each motion. Based on these results, we examined the control using steering torque as the control input. Controlling steering torque with rolling acceleration feedback increases wobble and weave mode stabilities. Wobble mode stability is increased by the external force concerning rolling motion. Weave mode stability is also increased by the phase lead of front tire side force in yawing motion. Using another variable as a feedback control signal, for example controlling steering torque with yaw rate feedback, increases only one mode's stability and destabilizes the other mode.

Application of Analytic Hierarchy Process to Stochastic Robustness Synthesis of Integrated Vehicle Controllers

This paper deals with the robust design procedure of integrated vehicle dynamics controller based on Stochastic Robustness Synthesis with use of a rational decision making process of the controller parameters. The basic control structure that integrates four-wheel steering and four-wheel torque control is determined using a nonlinear predictive control theory. The Analytic Hierarchy Process, a basic approach to decision making, is applied to determine the weight coefficients of robustness evaluation function of the controller. The desired vehicle dynamic performance is described as four-layer hierarchy structure and the design priority is determined with respect to several design criteria. The proposed design process produced a control system with excellent stability and performance robustness to vehicle parameter variations.

Vehicle Lane-Tracking Control with Steering Torque Input

SUMMARYAlternative methods of automatic steering system are investigated by using a vehicle-mounted camera that detects lane markers ahead of the vehicle. The control input of such a steering system can be classified into steering angle and steering torque. This paper investigates the physical characteristics of automatic lane-tracking controller using steering angle and steering torque as the control input, and compares their differences. Generally, most of researches about lane-tracking control manoeuvre deal with steering angle control method due to its excellent robustness. In contrast, the steering torque control provides some degree of freedom in permitting the driver to steer the vehicle, thus it has potential for development as a steering assist mechanism. In this paper, the controller design is Linear Quadratic (LQ) controller based on the simplified dynamics of a 2 degree-of-freedom bicycle model and simplified steering model.

3101 運転状態図を用いたアンダーステア時における操舵反力制御とその効果(OS7 人間・機械システム,ヒューマンインターフェース)

3101 運転状態図を用いたアンダーステア時における操舵反力制御とその効果(OS7 人間・機械システム,ヒューマンインターフェース)

Effects of Integrated Control of Active Four Wheel Steering and Individual Wheel Torque on Vehicle Handling and Stability - A Comparison of Alternative Control Strategies -

SUMMARYThis paper investigates the effectiveness of several different combinations of control inputs for improvement of vehicle handling and stability. Three combinations of control inputs are considered in this paper; namely, (1) rear wheel steering control with conventional front wheel steering and standard brake system, (2) four wheel torque control plus conventional front wheel steering, (3) front and rear wheel steering control plus four wheel torque control. The focus of this investigation is to clarify the relative merit of the different control strategies to accomplish a given task. The theory for nonlinear predictive control was extended to take into account partially given input and applied to the design of the control system. Simulations of braking combined with steering and braking under μ-split condition indicate that the integrated control of four wheel steering and individual wheel torque control results in a vehicle that can maintain directional control and stability.