Steering Torque Feedback Research Articles

카메라 각도 제어 및 조향 토크 피드백을 이용한 자율주행 모빌리티의 운영자 중심 관제 시스템 개발

카메라 각도 제어 및 조향 토크 피드백을 이용한 자율주행 모빌리티의 운영자 중심 관제 시스템 개발

Experimental identification of a linear parametric driver steering control model with torque feedback

Existing mathematical driver models with steering torque feedback are reviewed. It is argued that a new linear parametric driver-steering-vehicle model is required to provide greater insight to subjective assessment of steering torque feedback. The proposed new model incorporates: neuromuscular dynamics; proprioceptive (muscle stretch) and visual sensory dynamics; steering torque perception via efference copy; process and measurement noise; state estimator; and an internal predictive model of the plant. A series of path-following experiments with thirteen test subjects is performed on a fixed-base driving simulator. A Box-Jenkins model accounted for 90% of the variance of the measured handwheel angle time series averaged over the test subjects (VAF). The unknown parameter values of the model were identified from the driving simulator data. The prediction accuracy approached the upper bound given by the Box-Jenkins model. Process noise was found to be linearly dependent on muscle activation torque, and this signal-dependent behaviour was also present in the measurement noise. The model was re-identified using signal to noise ratios as parameters. The VAF was 86.6%, close to that of the Box-Jenkins model, and the identified parameter values are physically plausible, giving confidence that the model is suitable for further work on understanding steering torque feedback.

Steering Feedback Torque Prediction Based on Sequence-to-Sequence Network With Switcher-Assisted Training Algorithm

Steering Feedback Torque Prediction Based on Sequence-to-Sequence Network With Switcher-Assisted Training Algorithm

Optimizing Steering Feedback Torque with Reinforcement Learning guided by Human Reward Model

Optimizing Steering Feedback Torque with Reinforcement Learning guided by Human Reward Model

An innovative generalization method for data-driven models of steering feedback torque

Accurately modeling steering feedback torque (SFT) is crucial for the performance of both steer-by-wire systems and driving simulators of vehicles. Physics-based methods have poor model accuracy and real-time performance, due to the model complexity and unknown or inaccurate model parameters. Therefore data-driven methods have gained increasing attention in recent years for their advantages on SFT modeling. However, developing satisfactory data-driven models requires a large amount of data with sufficient coverage on various driving conditions, which can be time-consuming and expensive. To address this issue, this paper proposes an innovative generalization method that can transfer an SFT model trained for a specific vehicle to other target vehicles, dramatically reducing the dependence on large amounts of data and the coverage of various driving conditions, thus, saving time and cost. First, a pre-trained SFT model with high accuracy and strong generalization capability is built based on large, full-coverage data from source domain vehicles. Then, a network is designed to fine-tune the pre-trained model with a small amount of data from target domain vehicles with only a few driving conditions. To identify the most suitable driving conditions for training the fine-tuning network, the performance of five networks trained by different driving conditions is compared. Finally, a control network with the same structure as the fine-tuned network is trained based on the same target domain data, and the model accuracy of the transfer network and the controlled network under different conditions is compared and analyzed. The proposed transfer learning method along with the designed transfer network is proved to be effective on improving prediction accuracy of the target domain by 58%–74.7% compared to the pre-trained network.

An Improved Data-Driven Method for Steering Feedback Torque of Driving Simulator

Traditional modeling methods of steering feedback torque (SFT) widely used in driving simulator are mainly physics-based, but suffers from high model complexity and low model accuracy due to the inaccurate or unknown structure, dynamics, and related parameters of the steering system, such as backlash or dry friction. For these problems, data-driven methods were considered to have advantageous in modeling such a complex yet dynamic system. However, due to the limitation of the data collected for model training from real vehicle test, the commonly used artificial neural network (ANN) cannot fully reflect the steering dynamics, and does not work well in many cases where collected data do not cover, and thus lead to poor generalization performance. In this article, a long short-term memory (LSTM) model is proposed and proved to simulate well on the dynamics of SFT, and works well in most driving conditions. A driving simulator is developed to verify the performance of different models in terms of generalization of working conditions. Based on the driving simulator data, we found that ANN failure generally arises in the transition between conventional and unconventional operating conditions, which is recognized by the proposed driving pattern recognition method. The LSTM-based modeling method can effectively solve this problem. The comparative analysis reveals that the improved LSTM model can simulate the SFT with higher accuracy and broader adaptability to driving simulator conditions.

Modeling on Steering Feedback Torque Based on Data-Driven Method

The steering feedback torque (SFT) is a key part of driving simulator and steer-by-wire system, which provides driver with desired road feel and vehicle motion dynamics. Therefore, accurately modeling of SFT is of great significance for driver to get better steering feel. Since SFT can be affected by many linear or nonlinear factors, it is appropriate to model SFT using data-driven method. In this article, we adopt artificial neural network (ANN) and Gaussian process regression (GPR) to build the SFT model, and analyze the performance. Considering the fact that the contributing factors for SFT may vary under different driving conditions, we employ K-Means to precluster the training dataset to improve the model accuracy. The model training and validation processes are mainly data-driven, and the results show that GPR and ANN can achieve similar prediction accuracy with the mean square error to be around 0.10. Since the GPR model can be trained much faster than ANN model, it is more suitable for real-time application. It is further demonstrated that using preclustered data based on K-Means for model training can significantly improve its accuracy without sacrificing its computational efficiency.

Rider control identification in cycling taking into account steering torque feedback and sensory delays

Experiments and human rider models were used to investigate bicycle balance and steering using visuo/vestibular motion and proprioceptive feedback taking into account sensory delays. An instrumented steer-by-wire bicycle designed and built at the TU Delft bicycle laboratory was used to investigate rider responses with and with reduced steering torque feedback. Steering responses and bicycle motions were measured perturbing balance with impulsive forces at the seat post. The rider was commanded to follow a straight lane at unstable (2.6 and 3.7 ) and stable speeds (4.5 and 5.6 ). Bicycle speed was controlled with an electric drive and cruise control. Balance and steering responses could well be captured by linear impulse response functions which were consistent across participants. The impulse response functions were used to develop neuromuscular control models capturing rider–bicycle interaction. The Carvallo–Whipple bicycle model was extended with rider inertia and an additional degree of freedom for the steer-by-wire system. Rider behaviour was modelled as a balance and heading controller. This controller used visuo/vestibular motion feedback of roll angle and roll rate, heading angle and heading rate, and proprioceptive feedback of steering angle, velocity and torque. Results showed that the rider model followed the necessary stability condition of steer into the fall and was capable of stabilising the bicycle. Sensory delays had a negative effect on the model fit, which was resolved with an internal model and prediction algorithm. A model without steer angle and steer velocity feedback could not well capture the human response at the highest speeds and the absence of torque feedback had similar effects for all speeds, supporting the relevance of steer angle and torque feedback in bicycle control.

A new controller design method for an electric power steering system based on a target steering torque feedback controller

This paper describes a novel electric power steering (EPS) system controller that distinguishes the steering feel design problem from the system stability and control performance problem and provides a design methodology for each problem. The suggested controller, called the target steering torque feedback controller, consists of two modules: the steering feel generation logic module defines the target steering torque that the driver should feel under a given driving condition, and the steering torque feedback controller module controls the assist motor so that the driver actually feels the defined target torque. By disassociating all nonlinear elements related to steering feel tuning from the feedback controller, the inherent difficulties of the conventional EPS controller in terms of the coupling of system stability analysis and steering feel design are overcome. Rack-force-based steering feel generation logic that creates a familiar steering feel for drivers while transmitting the road condition information is suggested and design criteria of the steering torque feedback controller in the frequency domain are proposed to ensure robust stability, tracking performance and noise attenuation. In addition, a disturbance observer is applied to the feedback controller to compensate for the effect of disturbances and to improve tracking performance. The proposed controller design method is verified by conducting computer simulations and a vehicle test.

Haptic control of steer-by-wire systems for tracking of target steering feedback torque

This study proposes a haptic control of steer-by-wire systems for tracking a target steering feedback torque to achieve the conventional steering feedback torque. The haptic feedback control with a steer-by-wire steering-wheel system model was used to provide drivers with a conventional steering feedback torque. The steer-by-wire steering-wheel system model was developed, and a haptic control algorithm was designed for a desired steering feedback torque with a three-dimensional target steering torque map. In order to track the target steering torque to let the drivers feel the conventional steering efforts, an adaptive sliding-mode control was used to ensure robustness against parameter uncertainty. The angular velocity and angular acceleration used in the control algorithm were estimated using an infinite impulse response filter. The performance of the proposed controller was evaluated by computer simulation and hardware-in-the-loop simulation tests under various steering conditions. The proposed haptic controller successfully tracked the steering feedback torque for steer-by-wire systems.

Adaptive Steering Feedback Torque Design and Control for Driver–Vehicle System Considering Driver Handling Properties

Steering feedback torque system, as the joint between the driver and the vehicle, should be well designed to aid driver handling by considering the driver handling properties and the control accuracy and performance of the system. In this paper first, the compensation mechanism of the driver arm for the change of the steering feedback torque is researched. It is found that drivers try to maintain the dynamic properties of the overall driver-vehicle systems as constant as possible by adjusting their arm mechanical characteristics. Moreover, the desired dynamic properties of the overall driver-vehicle system, which include the information of the driver desired handling properties, can be defined as a system transfer function. Second, a novel adaptive steering feedback torque controller is proposed on the basis of the findings. In this controller, the consideration and realization of the desired driver-vehicle dynamic properties are decoupled in the outer and inner control loops. In the outer control loop, a desired steering feedback torque model is identified with consideration of the desired driver-vehicle dynamic properties. In the inner control loop, the identified desired steering feedback torque model is realized using an adaptive neural network algorithm with robust control. In this way, the controller can be easily used by different drivers and applied to different vehicles. The stability of the system with the controller is analyzed. In the end, simulation and experiments are carried out to prove the validity of the proposed controller.

Development of a neuromuscular driver model with an estimation of steering torque feedback in vehicle steer-by-wire systems

In this paper, a neuromuscular driver model for sensing torque feedback or haptic interaction between the vehicle equipped with steer-by-wire (SBW) system and the driver has been developed. The proposed driver model consists of a preview model and a neuromuscular model. The preview driver model calculates the desired angle of the steering-wheel to follow the path, and the neuromuscular driver model, with the ability of perceiving real-time torque feedback, determines the real angle of the steering-wheel angle according to muscular system transfer functions to follow the desired steering-wheel angle. In order to calculate torques on the steering-wheel, the lateral tyre-road forces are estimated by Kalman filter designed using a linear 2-DOF vehicle model. So, the design of the neuromuscular driver model combined with torque feedback estimation is the main contribution of this paper. The simulation results from TruckSim and Simulink software indicate that the novel designed driver model with torque feedback estimation has an important role in the controlling and steering vehicle to follow the desired paths.

Study on the stability of the steering torque feedback system considering the time delay and the system characteristics

The steering torque feedback system, as one of the most critical parts in a driving simulator or a steer-by-wire system, is used to generate an faithful or desired steering feel for drivers. This requires that the steering torque feedback system should stably simulate the steering feel for a sufficiently large range. There are many factors that can influence the stability region of the steering torque feedback system, such as the characteristics of the system, the time delay and other non-linear factors. In this paper, from the viewpoint of the system design, a stability criterion is derived using an energy-based approach. In particular, some influencing factors are quantitatively investigated and analysed in this paper, including the characteristics of the damping, friction and stiffness system, the time delay and other non-linear factors of the system. The stability criterion and the analysis results of these influencing factors can be used as guidelines for the design of a steering torque feedback system with the aim of achieving a better performance, such as appropriate parameters for the system damping, friction and stiffness, the maximum allowable time delay of the system and the design of the system control algorithms. As well as a finished steering torque feedback system, the stability region can also be calculated from the criterion using the system parameters. The derived stability criterion and analysis results of these influencing factors are all verified by both simulations and experiments.

The effect of torque feedback exerted to driver's hands on vehicle handling – a hardware-in-the-loop approach

In this paper, road forces on tire are exerted on driver's hands via an equivalent torque applied to the steering wheel using a hardware-in-the-loop method to analyze the effect of steering torque feedback in vehicle handling. An electrical torque-feedback steering system is used for experimental validation. A 14-degree-of-freedom vehicle dynamic model, including engine, tire, and steering system mechanism are simulated. The required inputs such as throttle angle, brake demand, and steering wheel angular position are transmitted to the computer via an I/O interface card. Tire forces and steering gear torque are solved. This torque is then sent via an I/O interface card to a DC motor connected to the steering shaft. All equations are solved in real time. To investigate the influence of torque feedback on vehicle handling, several experiments are executed on 25 users. For this purpose, an experimental protocol is defined. In the experiments, the users had to drive along a specific path with constant speed using the designed electrical torque-feedback steering system. During the tests, the driving pattern of each user was recorded and the simulator's instantaneous position was compared with its desirable value. The results show that the torque feedback improved the driver's perception from the surrounding environment and enables her/him to handle the vehicle satisfactorily.

Haptic Steering Support for Driving Near the Vehicle's Handling Limits: Test-Track Case

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle's motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this paper is to develop and evaluate a haptic steering support when driving near the vehicle's handling limits [Haptic Support near the Limits (HSNL)]. The support aims to promote the driver's perception of the vehicle's behavior and handling capacity (the vehicle's internal model) by providing haptic cues on the steering wheel. The HSNL has been evaluated in a test track where 17 test subjects drove around a narrow-twisting tarmac circuit, a vehicle (Opel Astra G/B) equipped with a steering system able to provide variable steering feedback torque. The drivers were instructed to achieve maximum velocity through corners while receiving haptic steering feedback cues related to the vehicle's cornering potentials. The test-track tests led to the conclusion that haptic support reduced drivers' mental and physical demand without affecting their driving performance.

Haptic steering support for driving near the vehicle’s handling limits; skid-pad case

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle’s motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this study is to develop and evaluate a haptic steering support when driving near the vehicle’s handling limits (Haptic Support Near the Limits; HSNL). The support aims to promote the driver’s perception of the vehicle’s behaviour and handling capacity (the vehicle’s internal model) by providing haptic (torque) cues on the steering wheel. The HSNL has been evaluated in (a) driving simulator tests and (b) tests with a vehicle (Opel Astra G/B) equipped with a variable steering feedback torque system. Drivers attempted to achieve maximum velocity while trying to retain control in a circular skid-pad. In the simulator (a) 25 subjects drove a vehicle model parameterised as the Astra on a dry skid-pad while in (b) 17 subjects drove the real Astra on a wet skid-pad. Both the driving simulator and the real vehicle tests led to the conclusion that the HSNL assisted subjects to drive closer to the designated path while achieving effectively the same speed. With the HSNL the drivers operated the tires in smaller slip angles and hence avoided saturation of the front wheels’ lateral forces and excessive understeer. Finally, the HSNL reduced their mental and physical demand.

A path-following driver–vehicle model with neuromuscular dynamics, including measured and simulated responses to a step in steering angle overlay

An existing driver–vehicle model with neuromuscular dynamics is improved in the areas of cognitive delay, intrinsic muscle dynamics and alpha–gamma co-activation. The model is used to investigate the influence of steering torque feedback and neuromuscular dynamics on the vehicle response to lateral force disturbances. When steering torque feedback is present, it is found that the longitudinal position of the lateral disturbance has a significant influence on whether the driver’s reflex response reinforces or attenuates the effect of the disturbance. The response to angle and torque overlay inputs to the steering system is also investigated. The presence of the steering torque feedback reduced the disturbing effect of torque overlay and angle overlay inputs. Reflex action reduced the disturbing effect of a torque overlay input, but increased the disturbing effect of an angle overlay input. Experiments on a driving simulator showed that measured handwheel angle response to an angle overlay input was consistent with the response predicted by the model with reflex action. However, there was significant intra- and inter-subject variability. The results highlight the significance of a driver’s neuromuscular dynamics in determining the vehicle response to disturbances.

A model of driver steering control incorporating the driver's sensing of steering torque

Steering feel, or steering torque feedback, is widely regarded as an important aspect of the handling quality of a vehicle. Despite this, there is little theoretical understanding of its role. This paper describes an initial attempt to model the role of steering torque feedback arising from lateral tyre forces. The path-following control of a nonlinear vehicle model is implemented using a time-varying model predictive controller. A series of Kalman filters are used to represent the driver's ability to generate estimates of the system states from noisy sensory measurements, including the steering torque. It is found that under constant road friction conditions, the steering torque feedback reduces path-following errors provided the friction is sufficiently high to prevent frequent saturation of the tyres. When the driver model is extended to allow identification of, and adaptation to, a varying friction condition, it is found that the steering torque assists in the accurate identification of the friction condition. The simulation results give insight into the role of steering torque feedback arising from lateral tyre forces. The paper concludes with recommendations for further work.

Experimental validation of steering torque feedback simulator through vehicle running test

This paper describes a force feedback system based on real-time multibody dynamic analysis. This system can provide the analyzed reactive force to the operator through the operational device. In this study, this system is used as a steering torque feedback simulator of an automobile. This simulator can provide the haptic sensation of the steering wheel to the operator. For the purpose of evaluating the validity of the developed simulator, we conducted some vehicle running tests with an experimental electric vehicle. The results of these tests were compared with the results simulated on the steering torque feedback simulator. It was shown that the developed simulator can provide a suitable steering torque to the operator.

A Mathematical Model of Driver Steering Control Including Neuromuscular Dynamics

A mathematical driver model is introduced in order to explain the driver steering behavior observed during successive double lane-change maneuvers. The model consists of a linear quadratic regulator path-following controller coupled to a neuromuscular system (NMS). The NMS generates the steering wheel angle demanded by the path-following controller. The model demonstrates that reflex action and muscle cocontraction improve the steer angle control and thus increase the path-following accuracy. Muscle cocontraction does not have the destabilizing effect of reflex action, but there is an energy cost. A cost function is used to calculate optimum values of cocontraction that are similar to those observed in the experiments. The observed reduction in cocontraction with experience of the vehicle is explained by the driver learning to predict the steering torque feedback. The observed robustness of the path-following control to unexpected changes in steering torque feedback arises from the reflex action and cocontraction stiffness of the NMS. The findings contribute to the understanding of driver-vehicle dynamic interaction. Further work is planned to improve the model; the aim is to enable the optimum design of steering feedback early in the vehicle development process.