Search

In this paper, an observer-based adaptive neural network controller is developed for the Steer-by-Wire (SbW) system of automated vehicles with uncertain nonlinearity and unmeasured state. An observer is introduced to estimate the angular velocity of the front wheels, so the hardware cost and the complexity of mechanical structure and electronic circuits are reduced. Then, an observer-based adaptive neural network controller is proposed for the SbW system to achieve excellent steering precision. A radial basis function (RBF) neural network is used to model the uncertain nonlinearity, which mainly includes self-aligning torque and unknown friction torque with strong nonlinearity. The adaptive law of the RBF neural network designed by fuzzy basis functions rather than by its filtering is derived by Lyapunov stability theory and the Strictly Positive Real (SPR) condition. The tracking error and the observation error can be guaranteed to converge asymptotically to zero. Simulation and experimental results for two paths highlight the effectiveness of the proposed control algorithm.

The process of modelling vehicle motion in a road traffic environment requires the integration of trajectory generation with vehicle control. The steps involved here are generating a feasible trajectory based on the existing traffic and tracking the trajectory to control it with a steering angle input. Since the parameters of a physical system vary with changes in operating conditions, it is important to consider robustness when designing controllers. This article aims at developing a trajectory-following model with robust steering control strategies to accurately follow a generated trajectory. In this study, performance-based proportional, robust proportional and sliding mode control strategies are designed for trajectory following. The robustness of the proportional controller is established using Kharitonov’s theorem, which is compared with a proportional controller tuned for performance. Sliding mode control is designed for robustness and chattering elimination using two kinds of reaching laws – a constant reaching law and a novel power rate exponential reaching law. The controllers are designed using a dynamic bicycle model considering the error with respect to the trajectory. The controllers are then evaluated in IPG CarMaker®. The resulting trajectories and control inputs are compared for the considered control methodologies using the ISO double lane change and the Slalom tests. Sliding mode control with power rate exponential reaching law is concluded to be more robust as compared to the other controllers, with lower response times, up to 84% lower heading angle deviations from the trajectory and an overshoot of only 3.2% in lane changing.

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.

This paper proposes a control strategy for the design of an automated steering control for an autonomous electric vehicle. The proposed Active Disturbance Rejection Control (ADRC) with Model Predective Control (MPC) is not only capable of alleviating the disturbance but also shows robustness against structured uncertainties which may arise due to models that represent the vehicle dynamics. Simulations have been carried out to assess the effectiveness of the proposed control strategy. Simulation results show that the proposed scheme is better in terms of tracking performance than MPC and ADRC. The steering control system, with the proposed strategy, can achieve faster response, higher tracking accuracy, and improved robustness performance in dealing with model uncertainties and external disturbances.

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

The lateral position control of the vehicle is analyzed in the presence of time delay. To compensate the negative effects of dead time, the predictor control approach called finite spectrum assignment is applied. This controller includes a linear model of the plant and uses the solution of this model over the delay interval to predict the current system states. The focus of the article is whether to include tire dynamics in the predictive model of the controller. Although the more detailed model should improve control performance, the additional parameters (e.g., tire stiffnesses and yaw moment of inertia) are difficult to determine accurately. The effects of parameter mismatches are analyzed in detail, and recommendations are given to ensure safe control of the vehicle. It is shown that the inclusion of tire dynamics in the predictive model vastly improves control performance even in the presence of large parameter errors, but in certain cases, the inaccuracies may lead to instability.

Path tracking is an essential task for autonomous vehicles (AV), for which controllers are designed to issue commands so that the AV will follow the planned path properly to ensure operational safety, comfort, and efficiency. While solving the time-varying nonlinear vehicle dynamic problem is still challenging today, deep neural network (NN) methods, with their capability to deal with nonlinear systems, provide an alternative approach to tackle the difficulties. This study explores the potential of using deep reinforcement learning (DRL) for vehicle control and applies it to the path tracking task. In this study, proximal policy optimization (PPO) is selected as the DRL algorithm and is combined with the conventional pure pursuit (PP) method to structure the vehicle controller architecture. The PP method is used to generate a baseline steering control command, and the PPO is used to derive a correction command to mitigate the inaccuracy associated with the baseline from PP. The blend of the two controllers makes the overall operation more robust and adaptive and attains the optimality to improve tracking performance. In this paper, the structure, settings and training process of the PPO are described. Simulation experiments are carried out based on the proposed methodology, and the results show that the path tracking capability in a low-speed driving condition is significantly enhanced.

In an era of automated driving, steer-by-wire system seems to be an indispensable component for an autonomous vehicle. Among the many problems concerned, steering control of the system may be arguably the most indispensable part that lay the foundation for the system and is essentially a position servo control problem. Traditionally, the most commonly used control loop structures of the position servo system can be classified into two categories, both considering the current control loop as an essential part. However, this is not necessarily true in practical applications. In view of this, control systems with two different types of control loops based on proportional–integral–derivative controllers have been designed and compared in this paper for trajectory tracking of the steer-by-wire system with Coulomb friction. The aim is to explore the essential differences between the two types of control loops and study the effect of current loop on performance of the system. When properly designed, it can be seen that the asymptotic stability of the control system can be guaranteed. However, the system with type I control loop has a faster response speed, whereas the system with type II control loop has a smoother response. Theoretical as well as simulation and experimental results have been provided in detail, which can contribute to the understanding of the two types of control loops and can be used as guidance not only for researchers but also for engineering practice.

Motorcycles are a primary mode of daily transportation in many developing countries, especially in towns and cities. The increased traffic congestion constrains the average speed of the motorcycle, causing stability and safety concerns for the riders. A controller that assists the riders can improve this scenario. This paper presents a new controller developed using an experimental study that improves the low-speed stability of a motorcycle. The experiments were conducted on a motorcycle with the riders of three experience levels: beginner, intermediate and expert. The input parameters: steering angle and steering torque; the output parameters: roll angle, yaw angle, roll rate and yaw rate were measured. Critical input and output parameters were identified statistically from the experimental measurements and used for the controller modelled in Simulink. The controller model was co-simulated with a multi-body dynamics model of the motorcycle. The co-simulation results showed the controller developed herein stabilises the motorcycle model at low speeds.

In this paper, a steering robust control method based on professional driver behavior with modeling uncertainties and external disturbance is proposed for an unmanned driving robotic vehicle, to realize the accurate and stable steering control like a professional human driver. An unmanned driving robotic vehicle nonlinear dynamics model considering modeling uncertainties and external disturbance is established. A driver behavior model composed of an adaptive preview model, a driver path planning model, and a driver desired yaw rate model is established, with the influences on preview time and driver path planning strategy, respectively. On the basis of this, a robust steering controller based on professional driver behavior is presented, by taking road information, driver reaction delay time, and vehicle driving status as inputs and the servo motor rotation angle of the steering mechanical arm as output. The stability of the control system with modeling uncertainties and external disturbance is proved. A comparison of the analysis results of simulation and experiment among the proposed control method, other existing control methods, and professional human driver demonstrates the effectiveness of the proposed method.

As demands increase for goods transportation services, long articulated vehicles are introduced as a viable alternative to conventional heavy-duty vehicles. Nowadays, steering control systems are commonly used for enhancing the stability and handling of articulated vehicles. As situations become more difficult for the movement of a vehicle, the ability of the steering actuators decreases and it will not be possible to use this controller alone in critical maneuvers. Another effective way to adjust the directional dynamics of a long articulated vehicle is the simultaneous application of the braking and steering systems. In a situation where the vehicle is close to the ultimate steering limit, it is desirable to reduce the speed, and the steering system can be strengthened through the intervention of the braking system. In this article, a 23 degree of freedom dynamic model of the long articulated vehicle has been developed in MATLAB software. After determining the reference control variables, we will design a sliding mode controller to steer the tractor’s front axle and the semi-trailer’s rear axles. After defining and setting the weight coefficients using a performance indicator, we will design an integrated controller in a way that if maneuvers become more difficult to perform and the efficiency of the steering actuators decreases, the braking forces exerted on the tractor’s rear axle and the semi-trailer’s rear axles will take a share in regulating the vehicle’s movement. The main achievement of this article is the introduction of a new method to integrate braking and steering control systems in long articulated vehicles. The paper aims to prove that only if manoeuvres become more difficult to perform and the performance of steering actuators decreases, then braking forces can take part in regulating the vehicle’s movement.

This paper presents a framework of shared steering control for vehicle obstacle avoidance, which is combined with the driver’s driving intention and risk assessment. The driving intention is recognized by a support vector machine model through the steering wheel angle, the relative lateral offset, and lateral velocity to the road center line. The elastic band theory is adopted to plan the obstacle avoidance path, which takes the driving intention and environment information into consideration. A linear model predictive control algorithm based on a bicycle vehicle model is used to realize path following. The risk assessment of current traffic situation is calculated by time to collision and the time required from the starting position of lane changing to the obstacle to collide. The shared steering control strategy is designed by the risk assessment and driving intention, simultaneously, so that the cooperative coefficient and the driving mode would be determined. In order to validate the framework proposed in this paper, the shared steering control system is tested on four obstacle avoidance scenarios, including the static and moving obstacles on a straight road and a curvy road. The results show that the shared steering control system could help the human driver avoid obstacles safely. Besides, both the path and steering wheel angle curve of the shared steering control system are smooth, and the vehicle stability performance also maintains well through a suitable shared strategy.

In this article, a new automated steering control method is presented for vehicle lane keeping. This method is a combination between the linear active disturbance rejection control and the quantitative feedback theory. The structure of the steering controller is first determined based on the linear active disturbance rejection control, then the controller is tuned in the framework of the quantitative feedback theory to meet the prescribed design specifications on sensitivity and closed-loop stability. The parameter uncertainties of the vehicle system are considered at the tuning stage. The proposed steering controller is simulated and tested on a scale vehicle. Both the simulation and experimental results demonstrate that the scale vehicle controlled by the proposed controller is able to perform the lane keeping. In the experiments, the lateral offset between the scale vehicle and the road centerline is regulated within the acceptable ranges of ±0.03 m during straight lane keeping and ±0.15 m during curved lane keeping. The proposed controller is easy to be implemented and is simple without requiring complex calculations and measurements of vehicle states. Simulations also show that the control method can be implemented on a full-scale vehicle.

Objective: The aim of this study was to investigate steering control in a low-cost driving simulator with and without a virtual vehicle cab. Background: In low-cost simulators, the lack of a vehicle cab denies driver access to vehicle width, which could affect steering control, insofar as locomotor adjustments are known to be based on action-scaled visual judgments of the environment. Method: Two experiments were conducted in which steering control with and without a virtual vehicle cab was investigated in a within-subject design, using cornering and straight-lane-keeping tasks. Results: Driving around curves without vehicle cab information made drivers deviate more from the lane center toward the inner edge in right (virtual cab = 4 ± 19 cm; no cab = 42 ± 28 cm; at the apex of the curve, p < .001) but not in left curves. More lateral deviation from the lane center toward the edge line was also found in driving without the virtual cab on straight roads (virtual cab = 21 ± 28 cm; no cab = 36 ± 27 cm; p < .001), whereas driving stability and presence ratings were not affected. In both experiments, the greater lateral deviation in the no-cab condition led to significantly more time driving off the lane. Conclusion: The findings strongly suggest that without cab information, participants underestimate the distance to the right edge of the car (in contrast to the left edge) and thus vehicle width. This produces considerable differences in the steering trajectory. Application: Providing a virtual vehicle cab must be encouraged for more effectively capturing drivers’ steering control in low-cost simulators.

This research deals with assist wheel control to improve the steering efficiency and dynamic stability of the multi-axle crane based on model predictive control. Since multi-axle crane has relatively high inertia and long distance between the axles, it has slow dynamic response, and thus, different steering strategies according to driving speed intervals are required. Specifically, the steering strategy is that the number of wheels fixed mechanically increases to secure dynamic stability as the driving speed increases. However, although this strategy enables to secure stability by slowing down the dynamic response, it also has a weakness to decrease the steering efficiency. If the steering efficiency is decreased, it may result in augmenting an accident rate due to an increase in driver’s fatigue. Therefore, this study suggests a new steering strategy to improve the steering efficiency by simultaneously guaranteeing dynamic stability. The suggested steering control algorithm can enhance both steering efficiency and stability by deriving an assist wheel control input based on model predictive control using a variable weighting factor derived from the driver’s steering input (steering angle on the first axle). Development and performance evaluation of the suggested algorithm were conducted in the MATLAB/Simulink environment, and evaluation results confirmed that the developed algorithm could both enhance the driver’s steering efficiency and secure dynamic stability.

The steering-by-wire (SbW) system is one of the main subsystems of automatic vehicles, realizing the steering control of autonomous vehicles. This paper proposes an event-triggered adaptive sliding mode control for the SbW system subject to the uncertain nonlinearity, time-varying disturbance, and limited communication resources. Firstly, an event-triggered nested adaptive sliding mode control is proposed for SbW systems. The uncertain nonlinearity is approximated by the interval type-2 fuzzy logic system (IT2 FLS). The time-varying disturbance, modeling error, and event-triggering error can be offset by robust terms of sliding mode control. The key advantage is that the high-frequency switching of sliding mode control only appears on the time derivate of control input without increasing the input-output relative degree of closed-loop SbW systems, such that the chattering phenomenon can be eliminated. Finally, theoretical analysis shows that the practical finite-time stability of the closed-loop SbW system can be achieved, and communication resources in the controller-to-actuator channels can be saved while avoiding the Zeno-behavior. Numerical simulations and experiments are given to evaluate the effectiveness of the proposed method.

In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the runway exit manoeuvre with a lower track error. To investigate the optimality of PSC, its solution is further optimised using a numerical optimal controller Generalized Optimal Control (GOC). PSC is shown to be close to the final optimal solution. To study robustness, PSC is tested with various aircraft configurations, road conditions and disturbances. The simulation results show that PSC is robust to disturbances within a normal range of operational parameters.

Optic flow can be used by humans to determine their direction of heading as well as controlling steering. Dot-flow displays have been widely used to investigate these abilities but it is unclear whether photorealistic textures would provide better information for controlling high-speed steering. Here, we examine the accuracy of heading judgements from dot-flow displays of different densities and luminance and then compare to a scene containing a textured ground. We then examine steering behaviour using these same displays to determine whether accurate heading conditions necessarily equate to successful steering. Our findings suggest that the bright dense dot-flow displays led to equivalent performance as the ground texture when judging heading, and this was also true when steering. The intermediate dot-flow conditions (with fewer and faded dots) revealed that some conditions that led to accurate heading judgements were insufficient for accurate steering. It seems, therefore, that heading perception should not be considered synonymous with successful steering control, and displays that support one ability will not necessarily support the other.

UCL Interaction Centre, University College London London WC1E 6BT, UK c.janssen@ucl.ac.uk, d.brumby@ucl.ac.uk, raegarnett@gmail.com We investigate how people utilize motor preparation time under varying task objectives as a cue to switch between tasks when dialing and driving. Previous research has shown that people tend to switch between tasks at positions where a chunk of digits is retrieved from memory. If the number of chunks is minimized, do people use motor preparation time as a cue to switch between tasks instead? A study was conducted in which participants drove a simulated vehicle while also dialing two phone numbers that contained sets of repeating digits. Participants tended to switch between tasks after typing in a complete set of repeating digits. This effect took precedence over cognitive cues, and was robust when different relative priorities for the two tasks were adhered to (focus on driving, or on dialing). However, when participants prioritized driving they invested more in steering control. Limitations and implications of the work are discussed.

The goal of the present study was to explore how simulator motion cuing affects the driver's control performance of a car. Steering behavior was used as a measure of control performance. The experimental task was a slalom maneuver in which the velocity of the car was limited to 70 km/h. Subjective and objective variables were measured. The paper describes the objective steering behavior. The slalom task was driven under four conditions in which the lateral motion scale factors were 1 (one-to-one lateral motion), 0.7, 0.4, and 0 (no-motion), respectively. In total, 16 participants completed the experiment. The study showed that the motion condition affects the steering wheel behavior. The general tendency is that less steering correction took place when the magnitude of the motion cues was increased, which was quantified by two performance indicators. First, the number of steering wheel reversals was reduced when the motion cue magnitude was increased. Second, the amount of relatively high-frequency correction was reduced with increasing motion cue magnitude. It is concluded that motion feedback can improve the driver's control performance in an extreme scenario such as a slalom maneuver. Therefore, the effect of motion on control performance must be considered when a driving simulator study addressing control performance is designed.

This study focuses on driver eye movements and vehicle control behaviour, factors implicated in the human errors that are considered to be important factors that may cause traffic accidents. Some curved sections of urban motorways have a comparatively high frequency of accident occurrence. The authors attempted to link three factors, namely eye movement, roadway alignment, and driver speed and steering control. In order to analyse the factors that lead to human error, an in-car experiment was carried out on some routes with curved sections of the Tokyo Metropolitan Expressway. This study focuses on single-car collisions occurring primarily in free-flow regions. Using the results of the experiment, the authors were able to analyse the factors that produce human error in relation to motorway alignment, eye movement, and centrifugal acceleration in the curved sections. As a result, the authors proposed a model that evaluates the risk of curved sections in relation to the driver's eye movement and driving behaviour.

This paper presents the use of a feedforward-plus-proportional-integral-derivative (FPID) controller for improving the control performance of the electrohydraulic steering system on an offroad vehicle. The FPID controller used an inverse valve transform in the feedforward loop to compensate for an electrohydraulic steering system deadband and used a conventional PID feedback loop to minimize the tracking error in steering control. On-simulator evaluation tests verified that the FPID resulted in a superior steering rate tracking performance over both a feedforward controller and a PID controller. On-vehicle evaluation tests verified that this FPID controller could achieve prompt and accurate steering angle tracking for agricultural vehicle automated guidance applications.

Experimental results relating to the steering control system of an experimental autonomous vehicle operating in a pre-defined and structured environment are presented. A method of steering control is formulated which relates second-order criteria, such as rise time and damping factor, to the parameters of the control system. With the control system described, the vehicle can track a path of constant heading with very little steady-state error and, if the control parameters are chosen in the correct ratio, it will respond in a critically damped fashion to the step disturbances which are generated by position corrections. For a curved path, demand curvature is treated as an input to the system in order to eliminate steady-state offset errors in tracking. In order to make the vehicle's performance independent of velocity, control parameters are derived as simple functions of velocity. This has the effect of allowing the vehicle to manoeuvre more tightly at lower speeds, and makes use of the increased availability of drive-system torque at such speeds. In order to prevent stalling of the stepper-motor drive system at large changes in demand curvature, a method of limiting the rate of steering is described.

A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Driver describing functions were calculated. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

The dynamic control properties of drivers and driver/vehicle systems in steering operations have been widely investigated. This paper presents a short review of the combined compensatory, pursuit, and precognitive features needed to describe the total properties of the driver as a controller. Specific combinations of these features are associated with particular driving maneuvers. Some recent results are presented to confirm previous hypotheses and more completely quantify the models. The driver-organized system structure for regulation control is reviewed with emphasis on the loops closed and adjustments made by the driver in compensating for vehicle dynamic changes. Pursuit structures are given which describe steering control with preview and as one explanation for lane change maneuvers. Precognitive behavior is then presented as the most skilled mode utilized in rapid lane changes and other well-practiced maneuvers including obstacle avoidance. For all three categories of control, full-scale or simulator data are presented as indications of model verification.

An optimal control procedure is presented for steering an Auto-Guided- Vehicle (AGV) which has two motored wheels on its left and right side. Steering, running and stopping are enabled by controlling the motor speed independently. An optimal proportional-plus-integral control is employed to eliminate steady-state error which is sustained by a simple proportional control for tracking a circular path. A simple and readily-implemented suboptimal control is also examined. The suboptimal control gives comparable performance and therefore provides an effective approach for industrial application of the AGV. Effects of design parameters of the AGV such as forward velocity, wheel radius and position of sensor are investigated. It is shown that within the practicable values the controlled performance improves rapidly but with an increase of those parameters then the improvement becomes negligible, which suggests base values for the parameters.

A review is made of past work in which steering reversal rates were used as a measure of driver performance. The data from two previously reported experiments carried out in a controlled situation, are used to compare steering reversal rates with other performance measures. It is shown that, while steering reversal rates correlate with other measures of control frequency, they do not necessarily correlate with measures of absolute steering performance. This result is consistent with the view that frequency characteristics provide a measure of steering task difficulty rather than steering performance. It is shown that, when considering steering task difficulty, care must be taken to differentiate between the difficulty imposed by the task constraints and the factors which affect the driver's ability to maintain a level of performance commensurate with those constraints.

Classical mechanics is applied to the automobile in order to study the lateral rigid-body motions produced by steering control. This research in the field of automotive lateral dynamics has included (1) the development of a mathematical model for a pneumatic-tyred vehicle and (2) its subsequent substantiation by means of full-scale response tests made with an instrumented vehicle. A review of the theoretical work is presented together with a description of the methods used to measure the response of the test car. The resulting agreement between theory and experiment is examined, and is followed by an extensive discussion of the nature of the lateral response of the automobile to steering control.