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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.

Various endeavors for environmental protection are continuing in many fields of industry. The author has approached this problem by developing new technologies for minimizing fuel oil consumption to reduce exhaust gases over the navigation routes in marine transportation.In the preceeding studies, the relationship between speed drop caused by waves and wave resistance was made clear quantitatively in the case that the work of thrust to navigate straight over a definite distance is the same between calm sea and wave conditions. Moreover the amount of fuel oil consumption was analyzed to distribute the main engine outputs optimally on the basis of this relationship.In this paper, the above relationship was formulated more strictly in the case that ships navigate under wave conditions with the same main engine output as under calm sea conditions to analyze fuel oil consumption , and the decision equations for optimal steering and distribution of main engine outputs are modified.Finally, the effectiveness of the results of this study is evaluated in comparison with preceeding studies through several cases of numerical simulation.

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.

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 describes steering returnability by analyzing forces applied to kingpin axis in limit cornering conditions. The steering returnability is related to forces applied to kingpin axis. These forces are classified by its source: Tire force and steering force. Tire force is generated from tire such as longitudinal, lateral, vertical force, and residual self-aligning torque. Steering force consists of friction force and assist force. These forces are vectorized by suspension geometry and calculated with respect to kingpin axis. The simulation results are compared with measurement data and verifies that steering returnability can be formulated by tire forces and steering forces. Using these relations, this paper proposes new steering control logics at limit driving conditions. This logic is verified by simulation model to check performance and confirmed in various test conditions for robustness.

The Steer-by-Wire (SbW) system is probably the most significant innovation among drive-by-Wire technologies in the automotive industry. Without the mechanical link, the most challenging issue is to control the wheels to closely follow the driver’s command. To improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances in the steering control processes, a sliding mode predictive tracking control (SMPC) strategy for a SbW system with uncertain dynamics is proposed. The simulation and experimental results demonstrate that the performance of the proposed SMPC tracking controller is superior to both SMC and MPC methods for the steering angle tracking task.

The developments in Steer-by-Wire Systems allow for the re-thinking of the commonly known steering system meaning both the development of new controls replacing the steering wheel as well as the design of the feedback corresponding to inform the human driver about the vehicle state. By conducting a user study in a high fidelity driving simulator this paper investigates the quality of vehicle stabilization performed via Active Drivesticks as alternative controls in comparison to the conventional steering wheel in a high-speed scenario. On a subjective level, assessed driving pleasure, experienced comfort and usability were high. Still, the subjective evaluation of safety feeling, experienced stress as well as necessary effort, was in favor of the conventional steering wheel, which subjects have been using over many years of driving. In addition, objective driving state variables that were measured to assess the participants’ performance in controlling the target path show a slight advantage for the classical steering control. In conclusion, it could be shown that despite the difference in experience and training with the different controls, the vehicle stabilization performed via Drivesticks is almost as good as the one performed via the conventional steering wheel in a high-speed driving scenario.

Recent research efforts on automated driving reveal the need to address driver’s interaction with vehicle automation. This paper focuses on the design of an override mode for automated driving systems, a topic little studied in literature. In this paper, a shared control framework for driver’s override of automatic steering control is proposed. This framework formulates the control transfer between the driver and the system as a constrained optimization problem which is solved online by a model predictive controller. At a higher level in the framework, the system detects driver’s lane-change intention so that the low-level controller can actively assist the driver in a lane-change maneuver. The whole framework was evaluated in a user test based on a driving simulator. The test results show that the proposed shared control framework has the advantage of allowing the driver to regain control with ease while ensuring the smoothness of control transition.

In a fully autonomous driving situation, passengers hand over the steering control to a highly automated system. Autonomous driving behaviour may lead to confusion and negative user experience. When establishing such new technology, the user’s acceptance and understanding are crucial factors regarding success and failure. Using a driving simulator and a mobile application, we evaluated if system transparency during and after the interaction can increase the user experience and subjective feeling of safety and control. We contribute an initial guideline for autonomous driving experience design, bringing together the areas of user experience, explainable artificial intelligence and autonomous driving. The AVAM questionnaire, UEQ-S and interviews show that explanations during or after the ride help turn a negative user experience into a neutral one, which might be due to the increased feeling of control. However, we did not detect an effect for combining explanations during and after the ride.

In order to improve the vehicle handling performance and lateral stability, an integration of Active Front Steering Control (AFS) and Direct Yaw Moment Control (DYC) has been investegated. Model Predictive Controller (MPC) has been formulated for integrated AFS and DYC. The driver steer angle has been treated as a desired steering command and MPC controller is designed to calculate optimal steer angle and the amount of brakes by considering steering angle constraints and driver's steer angle. To design more effective control scheme, different road conditions like slippery, rough and normal, have been considered. In these situations vehicle's tires road adhesion coeficients (µ) varies. The proposed work treats variation of adhesion coefficients as parameter uncertainty and Optimal Guaranted Cost Controller (OGCC) used to handle this uncertainity. So in the proposed work both MPC and OGCC work in coordination to calculate the suitable steer angle and optimal brakes so that the best possible responce can be achieved. Different simulations have been performed in order to verify the effectivness of proposed controller. Simulation results of the proposed control scheme have also been compared with the results of different control stratigies.

In this paper, the authors suggest practical techniques and machine learning algorithms to develop an intelligent autopilot steering control system. The system consists of a module with the neural network models of sea-craft movement patterns, a module with a neural network classifier, and a module containing an optimizer that calculates the best fuzzy controller operating. The paper presents the findings of reviewing neural network paths for six types of marine vessels. It emphasizes that it is necessary to develop an original neural network model on different maritime navigation conditions. Therefore, one requires a neural network classifier that can select an optimal neural network model of a marine vessel movement patterns under certain waterway navigation conditions. The authors figure out the neural network models of sea vessel movement patterns using a certified IC-2005 signal simulator for an autopilot. The researchers apply machine learning algorithms to generate parameters of neural network models and classify ship movement patterns. The authors also carry out a computer simulation of sea vessels with different speed and on various navigation conditions to test if it is easy to use the neural network classifier reported. The paper covers the correspondence of the mean square root error of learning to navigation conditions for various sea vessels, including a trawler with a slow response.

Aiming at the problem of inflexible steering and motion of robot, this paper applies fuzzy adaptive PID control algorithm to the differential steering control system of mowing robot to improve its rapidity and accuracy. In this paper, according to the domestic green environment and the overall parameters of the mowing robot, the overall design of the mowing robot is made, the fuzzy adaptive PID control algorithm of the mowing robot differential steering control system is designed, the mathematical model of the mowing robot differential steering is constructed, and the MATLAB/Simulink is used to simulate the fuzzy adaptive algorithm. The simulation results show that the response speed and overshoot of fuzzy adaptive PID algorithm are 2.42 s and 16.4, respectively, The response speed and overshoot of PID are 7.18 s and 36.3% respectively, so the dynamic performance of the differential steering control system based on Fuzzy Adaptive PID algorithm is better than that based on ordinary PID control.

This paper proposes a particular type of nonlinear data-driven Model Predictive Control (NMPC) strategy, called Neural Network Model Predictive Control (NNMPC), applied to a simulated neuro-inspired quadruped robot. The locomotion control is realised using a central pattern generator (CPG) implemented through oscillators synchronized through environmental feedback. The NMPC provides a descending command to the robot for steering control. This is realized by regulating a parameter governing the dynamics of the CPG structure. In order to test the performance obtained applying the NMPC, the results are compared with those obtained using a linear MPC. Carrying out a comparative analysis, the differences between the two methods will be highlighted.

This paper presents the problem of steering control for autonomous underwater vehicles (AUVs). A six degrees-of-freedom (DOF) system of coupled equations of motion of an AUV is first decoupled as a lateral model to control steering angle of an AUV. Sliding Mode Control (SMC) technique is used to stabilize the system and track a constant reference yaw angle in presence of model perturbations and uncertainties. Using numerical simulations, it is shown that the closed-loop system under the proposed control scheme is stable and the tracking error converges to zero in presence of model perturbations and uncertainties.

A new type of walking mechanism is designed for the quadruped robot. Each mechanical leg consists of a steering leg, a height-adjusting leg, a thigh and a calf. The height of the legs and the distance between the fore legs and the rear legs can be adjusted through the rotation of the height-adjusting joints which makes the robot climb up or down a slope just like walking on flat ground with high stability. And the robot can cross a higher obstacle. A steering control strategy based on diagonal trotting gait is proposed. The walking direction of the mechanical legs can be adjusted through the rotation of the steering joints and the robot can steer quickly and continuously while diagonal trotting. The relevant parameters of steering are also analyzed which provides reference and basis for the application of robot steering.

This paper describes the ship’s steering process and the common practice of the crew during steering operation. It analyzes and finds the mathematical theoretical basis of the crew steering control. According to its theoretical basis and considering the wind and flow conditions from the steering timing, steering rudder angle and steering time. The steering of the ship was designed and verified by the MATLAB Simulink simulation environment. The verification results show the actual operability of the design.

In this paper, an effective solution for the steering control system which is indeed an important part to build the autonomous vehicle is presented. A Model Predictive Controller (MPC) is designed to control the steering angle. Vehicle is controlled by varied counting of the steering angles according to the obstacle's position in the lane. The system has obstacle swerving abilities which is supplemented by a LIDAR detector. The proposed setup analyses the space surrounding the vehicle's lane and tracks moving obstacles. The foremost maneuver is to initiate the motive force to quickly move the vehicle to an adjacent lane, drive past the obstacle and move to the initial lane. Other feature incorporated is the Lane Keeping Assist (LKA) system used to detect when the vehicle deviates from the lane and accordingly adjusts the steering angle mechanically. This performance of the controller is evaluated using MATLAB simulations and is implemented on vehicle prototype.

In this paper we implement and evaluate two ways of controlling the steering angle of an autonomous vehicle, PID control with manual tuning followed by gradient descent algorithm tuning-which is able to enhance the performance through self-adjusting the controller parameters-and using supervised machine learning through the end-to-end deep learning for self-driving car which implement Convolutional Neural Network (CNN) to predict the steering angle for a given instance of a track. The verification testing went through two phases: software simulation using python for first run testing and C++ for simulation followed by track testing with a vehicle prototype. The proposed PID steering control system exhibits more stable steering commands-less oscillations-which makes it better than CNN Behavioral cloning control model. However, CNN Behavioral Cloning model may show better results after many several hours of training.

Cooperative shared steering control opens up new perspectives for the driver steering assistance systems which can produce interactive feedback through the steering wheel. This paper addresses cooperative shared steering control method for lane change (LC) maneuver in highway traffic. The aim behind this concept is the challenging issue of driver machine cooperative method. In order to characterize the human driving behaviors, traffic comprehensive decision factor (TCDF) is designed by using the correlation of vehicles in contextual traffic. Based on the LC experiments on a driver-in-loop Platform, driver’s intent recognition method is proposed by a stacked-layer long short term memory (SL-LSTM) algorithm. To achieve a shared steering control during the LC maneuver, a cooperative authority allocation factor is designed to integrate the inputs from driver and shared controller. The reference input of shared controller is dynamically planned according to the driver’s intent and TCDF, and it satisfies the vehicle steering dynamics. Both of the driver’s torque and TCDF jointly determine the cooperative authority allocation factor. Finally, two traffic scenarios are designed to verify the proposed shared steering control, experimental results have shown that the proposed shared steering control algorithm can improve the LC performance with better comfort and safety.

The study deals with the design of control algorithms to enhance the steering performance of all-terrain cranes. For this, its hydraulic steering system was modeled using a multiphysics simulation tool and the self-tuning fuzzy PID controller was developed to control hydraulic steering actuators with nonlinearities. The designed control algorithm was validated through co-simulation using a real-time simulator and showed satisfactory performance.

One of the typical autonomous driving systems is a human-machine cooperative system that intervenes in the driver operation. The autonomous driving needs to make consideration of the driver individuality in addition to safety. This paper considers a human-machine cooperative system balancing safety with the driver individuality using the Human-In-The-Loop System (HITLS) for rear-wheel steering control. This paper assumes that it is safe for HITLS to follow the target side-slip angle and target angular velocity without conflicts between the controller and driver operations. We propose HITLS using the primal-dual algorithm and the internal model control (IMC) type I-PD controller. In HITLS, the signal expander delimits the human-selectable operating range and the controller cooperates stably the human operation and automated control in that range. The primal-dual algorithm realizes the driver and the signal expander. Our outcomes are the making of the rear-wheel steering system which converges to the target value while reflecting the driver individuality.

Understanding the relationship between driving behavior and visual information is an important issue in order to understand driving behavior holistically. In this study, we constructed a driver model that reproduces the driver’s steering behavior from visual information based on the Convolutional Neural Network (CNN) with human physical characteristics. We obtained the driving behavior in a simulator study to train the proposed CNN model. Which region in the visual field influencing drivers’ steering behavior was analyzed using the results of the feature maps generated by the trained CNN model and the driver’s gaze behavior. The results indicate that the drivers perform steering action using the information within 20 degrees from the gaze point.

This paper aims to investigate the drivetrain load response caused by induction and wake steering control on two floating wind turbines (FWTs) in a wind farm. In this study, two DTU 10 MW turbines, supported on the nautilus floater, are modelled using FAST.Farm. The downstream turbine is placed at the distance of seven rotor diameters (D) from the upstream turbine in the positive wind direction. Partial wake shading is considered for wake steering control and full wake shading is considered for induction control. An ambient wind speed of 8m/s is used and a representative sea state is selected. The test cases are defined based on different blade pitch and yaw angles of the upstream turbine. Power generation of the offshore wind farm is studied under different test cases. A decouped analysis approach is used to investigate drivetrain response. Global responses are obtained from FAST.Farm. These loads are used as input of the 10 MW wind turbine drivetrain model for the gears and bearings load response analysis. Results show that both induction and wake steering control lead to a limited increase in power generation of the wind farm. Additionally, both control methods affect the drivetrain response statistics, while the features are different. This study facilitates a better understanding on drivetrain dynamic behaviour in a wind farm perspective, which serves as a reference for the wind farm optimizaton in the future. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

The hydra-power articulated steering vehicles possess brilliant maneuverability and efficiency, and they were widely applied in mining, construction, agriculture, and forestry. However, the steering characteristic also deduced a serious handling stability problem of this type of vehicle, i.e., oscillation in yaw motion. Previous research only analyzed the stability of the vehicle dynamical system or provided a passive structural method to suppress the oscillation of articulated vehicles. This work presents a novel model-based steering control of articulated steering vehicles. A coupled nonlinear dynamic model was established firstly, in which nonlinear models of the hydraulic system and dynamic model of articulated frames were included. Then the coupled model was validated in time and frequency domain by a field test. The susceptibility of different factors of the system oscillation was investigated by simulation based on the validated model. On this foundation, an optimized scheme of the hydraulic steering system was provided. Further a novel control strategy, in which the articulation angle and corresponding angular velocity were considered together as the control variables of the system, was embedded into the optimized system. Comparing results in dynamic responses of articulated frames, ripples in the hydraulic steering system shown the effectiveness and superiority of the presented method.

Previous studies argued that body turns are executed in an ordered sequence: the eyes turn first, followed by the head and then by the trunk. The purpose of this study was to find out whether this sequence holds even if body turns are not explicitly instructed, but nevertheless are necessary to reach an instructed distal goal. We asked participants to shop for grocery products in a simulated supermarket. To retrieve each product, they had to walk down and aisle, and then turn left or right into a corridor that led towards the target shelf. The need to make a turn was never mentioned by the experimenter, but it nevertheless was required in order to approach the target shelf. Main variables of interest were the delay between eye and head turns towards the target shelf, as well as the delay between head and trunk turns towards the target shelf. We found that both delays were consistently positive, and that their magnitude was near the top of the range reported in literature. We conclude that the ordered sequence of eye - then head - then trunk turns can be observed not only with a proximal, but also with a distal goal.

The objective of this study is to analyse and control the cornering behaviour of an Articulated All-Terrain Tracked Vehicle (ATV). The ATV is characterized by two units connected through a mechanical multiaxial joint designed to overcome extreme longitudinal and side slopes. The hydraulic actuation of the joint enables an articulated steering feature thus avoiding any thrusts adjustment as it happens for skid-steering vehicles. A direct curvature controller is presented for analysing the steady-state ATV behaviours through a nonlinear model. Furthermore, a hitch angle controller is introduced to overcome the necessity of a curvature feedback measurement. The methodology is verified by simulating typical manoeuvres adopted for evaluating vehicle handling performance.

The steering control of the autonomous vehicles represents an avital issue in the vehicular system. The model predictive control was proved as an effective controller. However, the representation of the model predictive control (MPC) by a large prediction horizon and control horizon requires a large number of parameters and it is complicated. Discrete-time Laguerre functions can cope with this issue to represent the MPC with few parameters. Whilst, the Laguerre functions require a proper tuning for its parameters in order to provide a good response with MPC. This paper introduces a new design method to tune the parameters of the MPC with the Laguerre function by a new artificial intelligence (AI) technique named social ski driver algorithm (SSDA). The proposed MPC based on the SSDA is applied to adjust the steering angle of an autonomous vehicle including vision dynamics. Further test scenarios are created to ensure the effectiveness of the proposed control to cope with the variations of road curvatures.