Automatic Steering Control Research Articles

Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot’s path is marked by a distinct line or track, which the robot follows to move. Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.

Characterizing the use of Tesla’s Auto Lane Change Feature in Driver-Initiated Maneuvers

Tesla auto lane change is a feature within Autopilot (AP) – a collection of automated driving features designed to assist the driver with automatic steering and adaptive cruise control. In order to determine how drivers engage in maneuvers performed automatically by AP, 5,154 manual and automated lane change events along with video, system data, and vehicle kinematics were extracted from over 691 hours of naturalistic driving from 19 different drivers. Findings indicate that drivers performed manual lane changes more frequently than auto lane changes despite the use of Autopilot being more prevalent than manual driving on motorways. Auto lane changes occurred at higher speed and were associated with fewer acceleration and braking events and narrower distributions of kinematic characteristics. The lower frequency and kinematic variability in auto lane changes may suggest that drivers use this feature in a limited set of circumstances.

Design and optimization of an automatic hydraulic steer-by-wire system for an agricultural chassis

To meet the requirements of fast steering at low vehicle speed and slow steering at high vehicle speed, the automatic steering of agricultural chassis must control both the wheel steering angle and the steering angle’s angular speed. This study applied hydraulic steer-by-wire technology to the automatic steering control of agricultural chassis. First, the transmission mechanism of the designed steering system was optimized. According to the rule of least squares, aiming at the minimum sum of squares of errors between 10 ideal outer wheel angles and real outer wheel angles, the optimal solution of hole spacing on both sides of the steering hydraulic cylinder piston rod was 925 mm. The outer wheel angle error of the optimized steering mechanism throughout the steering stroke was less than 0.15°. Additionally, a hydraulic steer-by-wire system was developed, and the parameters of its critical components were calculated. Then, the compound control strategy of the steering cylinder piston rod displacement and moving speed was formulated for this automatic steering system. The entire control system included a valve control signal calculation model, an initial velocity calculation model, a correction velocity calculation model, and an attenuation velocity calculation model, and the formulae of each model were deduced. Based on the optimized parameters and the developed control strategy, a simulation model was built in AMESim, and simulation results showed that the proposed control strategy could achieve simultaneous controls of piston rod displacement and speed at different vehicle speeds and loads. The horizontal and vertical displacements of the right wheel center were plotted for typical vehicle speeds and steering commands. The results of this study provided a new idea for the application of hydraulic steer-by-wire technology in the automatic steering of agricultural chassis. Keywords: optimal design, steering mechanism, automatic steering, simulation analysis DOI: 10.25165/j.ijabe.20221501.6755 Citation: Jin Y H, Yuan Y W, Zhu L C, Wang D C, Zhao B, Fang X F. Design and optimization of an automatic hydraulic steer-by-wire system for an agricultural chassis. Int J Agric & Biol Eng, 2022; 15(1): 132–138.

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

A novel robust event‐triggered fault tolerant automatic steering control approach of autonomous land vehicles under in‐vehicle network delay

AbstractThis paper proposes a novel robust event‐triggered fault tolerant automatic steering control strategy for autonomous land vehicles to achieve path tracking and vehicle lateral motion control under in‐vehicle network delay. From the practical point of view, the parameter uncertainties, time delay, and actuator fault are simultaneously introduced to make the designed controller robust to more extensive and challenging driving conditions. A novel polytope reduction and norm‐bounded uncertainty reduction method is used to effectively handle the time‐varying velocity and tire cornering stiffness uncertainties. Then, the uncertain vehicle model can be reconstructed as an uncertain network control system model with time delay and actuator fault. Due to the inevitable time delay and actuator fault, a new cubic absolute‐value Lyapunov function is developed to guarantee the asymptotical stability of the closed control system with the H∞ performance, and the modified project‐based adaptive law is applied to strengthen the fault tolerant ability. Furthermore, a progressive event‐triggered mechanism is proposed for the collaborative design of robust fault tolerant automatic steering controller, which can signally improve the communication resource utilization of the limited bandwidth in‐vehicle network. Finally, the performance comparisons of different controllers are presented. These results prove that the proposed controller can simultaneously guarantee the path tacking performance and lateral dynamics stability, and save the communication resource of the in‐vehicle network.

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic. This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods (including medicines) that is needed to prevent infection and treatment for infected patients. The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic. The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways, particularly in the control of autonomous medic robots. Health officials aim to curb the spread of COVID-19 among medical, nursing staff and patients by using intelligent robots. We propose an advanced controller for a service robot to be used in hospitals. This type of robot is deployed to deliver food and dispense medications to individual patients. An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction. These criteria were met by using two controllers simultaneously: A deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative (PID) controller for automatic steering and speed control. © 2021 Tech Science Press. All rights reserved.

Computer Vision-Control-Based CNN-PID for Mobile Robot

With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed control. Two controllers were used simultaneously to achieve this. Convolutional neural networks (CNNs) are used for target tracking and trajectory prediction, and a proportional-integral-derivative controller is designed for automatic steering and speed control. This study makes use of a Raspberry PI, which is responsible for controlling the robot car and performing inference using CNN, based on its current image input.

Automatic Steering Control of an Unmanned Transport Truck in Off-road Environments

Automatic Steering Control of an Unmanned Transport Truck in Off-road Environments

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

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

Development of autonomous navigation controller for agricultural vehicles

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on the driving skills of the operator. Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades, which contributes considerably to straight-line navigation. The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three wheel-type vehicles were used as the test platform featuring automatic steering, hydrostatic transmission and speed control, which included a rice transplanter, a high-clearance sprayer and a tractor. A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble. A path planning method was proposed to create a straight-line reference path by giving two points, and the target path was determined according to the vehicle initial status and working assignment. Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland. The navigation controller hardware was fabricated for program execution, data processing and information communication with peripherals. A human-machine interface was designed for the operator to complete basic setting, path planning and navigation control by providing controls. Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland. Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation. The lateral error in straight-line tracking was no more than 6 cm, 6 cm and 5 cm for the rice transplanter, the high-clearance sprayer and the tractor, respectively. And the maximum lateral RMS error was 3.10 cm, 4.75 cm, 2.21 cm in terms of straight-line tracking, which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles. Keywords: autonomous navigation, navigation controller, agricultural vehicles, straight-line tracking, straight path, headland turning DOI: 10.25165/j.ijabe.20201304.5470 Citation: Yin X, Wang Y X, Chen Y L, Jin C Q, Du J. Development of autonomous navigation controller for agricultural vehicles. Int J Agric & Biol Eng, 2020; 13(4): 70–76.

Design of Electric Steering Wheel for Agricultural Machinery

Aiming at the automatic steering control system of agricultural machinery, this paper puts forward the method of using S7-200 intelligent PLC of Siemens company as the core controller, which uses the high-speed pulse signal output by the controller to control the stepper motor to drive the rotation of the steering shaft, realizes the automatic steering through the control of the motor speed and steering, uses the encoder to detect the rotation angle of the steering shaft to realize the feedback control, and selects the touch screen to realize the user interaction scheme.

Automated Steering Control System for Reverse Parking Maneuver of Semi-Trailer Vehicles Considering Motion Planning by Simulation of Feedback Control System

With the increase in the demand for road freight transportation, semi-trailers are being increasingly preferedowing to their large maximum load capacity. However, for such vehicles, excellent driving skills are required because unique steering is often necessary during reverse parking. In this paper, the concept of a parking assist system and path tracking controller is proposed. The control system consists of a pure pursuit motion planner for handling the reference path tracking and a feedback controller for stabilizing the hitch angles. We propose a control method to realize the ideal control performance of an actual vehicle subjected to unmeasured disturbance. An actual full-scale vehicle experiment is conducted and the effectiveness of the proposed approach is verified by evaluating the error from the target parking position.

Velocity-based robust fault tolerant automatic steering control of autonomous ground vehicles via adaptive event triggered network communication

This paper proposes a velocity-based robust fault tolerant automatic steering control approach for autonomous ground vehicles via an adaptive event triggered network communication mechanism. Firstly, as the vehicle longitudinal velocity is not fixed but time-varying, a polytope with finite vertices is adopted to construct a linear parameter varying vehicle system model. Secondly, since network-induced delay and actuator fault inevitably occur in the control system, a novel Lyapunov function is formulated to guarantee the asymptotical stability of the closed-loop system. Moreover, an advanced event triggered communication scheme is proposed for the co-design of robust automatic steering controller, which is quite effective to reduce network communication burden and enhance resource utilization of the shared band-limited in-vehicle communication network. Finally, numerical simulations on two cases are carried out to evaluate and verify the effectiveness and robustness of the proposed controller. Compared with existing studies, the proposed controller contributes greatly to the economization of communication resource and improvement of path following performance and lateral stability.

Development of autonomous navigation controller for agricultural vehicles

Agricultural vehicles are adopted to undertake farming tasks by traversing along crop rows in the field. Working quality depends significantly on the driving skills of the operator. Automatic guidance has been introduced into agriculture to achieve high-accuracy path tracking during the last decades, which contributes considerably to straight-line navigation. The objective of this research was to develop an autonomous navigation controller that allowed movement autonomy for various agricultural vehicles. Three wheel-type vehicles were used as the test platform featuring automatic steering, hydrostatic transmission and speed control, which included a rice transplanter, a high-clearance sprayer and a tractor. A dual-antenna RTK-GNSS receiver was attached to the vehicles to provide spatial information on both positioning and heading by using the RTX service from Trimble. A path planning method was proposed to create a straight-line reference path by giving two points, and the target path was determined according to the vehicle initial status and working assignment. Headland turning was comprehensively taken into account by listing different turn patterns in order to realize autonomous navigation at the headland. The navigation controller hardware was fabricated for program execution, data processing and information communication with peripherals. A human-machine interface was designed for the operator to complete basic setting, path planning and navigation control by providing controls. Field experiments were conducted to evaluate the performance and versatility of the newly developed autonomous navigation controller in guiding agricultural vehicles to follow straight paths and turn at the headland. Results showed that an appropriate turn pattern was automatically executed when finishing straight-line navigation. The lateral error in straight-line tracking was no more than 6 cm, 6 cm and 5 cm for the rice transplanter, the high-clearance sprayer and the tractor, respectively. And the maximum lateral RMS error was 3.10 cm, 4.75 cm, 2.21 cm in terms of straight-line tracking, which indicated that the newly developed autonomous navigation controller was versatile and of high robustness in guiding various agricultural vehicles. Keywords: autonomous navigation, navigation controller, agricultural vehicles, straight-line tracking, straight path, headland turning DOI: 10.25165/j.ijabe.20201304.5470 Citation: Yin X, Wang Y X, Chen Y L, Jin C Q, Du J. Development of autonomous navigation controller for agricultural vehicles. Int J Agric & Biol Eng, 2020; 13(4): 70–76.

Research on Stability Control Method of Vehicle Based on Steering Torque Superposition

At present, most stability control systems based on active steering are superposition control of steering Angle. For the steering system configuration commonly used in automatic driving vehicles, the active steering control system of steering angle superposition cannot be realized. In order to achieve man-machine collaborative driving, the co-driving intelligent vehicles usually realize automatic steering control based on the torque superposition, which solves the drawback that the driver of the Angle superposition cannot participate in the steering. In this paper, an active steering control method of superposition of steering torque based on Receding horizon control is proposed for man-machine collaborative driving of co-drive intelligent vehicles. This control method combines steering system with vehicle dynamics system, and takes steering motor torque as the sole control input, which ensures that the driver has the ultimate control right and makes the control of the system more stable. In order to solve the system uncertainties caused by model mismatch and external disturbances, the optimal control rate in predictive time domain can be calculated according to real-time vehicle state parameters by receding horizon control. In the design of the controller, the yaw rate deviation is the input of the controller, and then the calculated superposition torque is input into the vehicle model in real time, and the steering wheel angle is observed as a state variable. The control strategy can effectively compensate for the uncertainty of the system such as model mismatch and external disturbance, and enhance the robustness of the system.

Research on modeling and compensation control strategy of automatic steering system.

A novel compensation control strategy is proposed to compensate for automatic steering system controller limitations in instability under nonlinear interference condition. In this article, the structure and mechanism of automatic steering system are briefly introduced at first. The nonlinear dynamic models of automatic steering system and vehicle are established, and the interference torque models of automatic steering system caused by different speed are then derived through the mechanism of tire structure. Simulations under different road conditions and different vehicle velocities are then used to validate the influence of nonlinear factors on the automatic steering system. Finally, the compensation controller is designed, and the effectiveness of designed controller has been verified by simulation results, in which the dynamic control effect and tracking accuracy of designed controller have been improved significantly. Finally, the novel controller is designed based on Simulink results, and test results reconfirm the partial performance of controller effectively.

Automatic Steering Control Algorithm Based on Compound Fuzzy PID for Rice Transplanter

In order to realize automatic steering controls of rice transplanters in paddy fields, an automatic steering control algorithm is essential. In this study, combining the fuzzy control with the proportional-integral-derivative (PID) control and the kinematics model, a compound fuzzy PID controller was proposed to adjust the real time data of the PID parameters for the automatic steering control. The Kubota SPU-68C rice transplanter was then modified with the new controller. Next, an automatic steering control experimental with the modified transplanter was carried out under two conditions of linear tracking and headland turning in verifying the automatic steering effect of the transplanter in different steering angle situations. The results showed that the deviation with the new controller and the modified transplanter was acceptable, with maximum deviation in linear tracking of 7.5 cm, the maximum headland turning a deviation of 11.5 cm, and the average a deviation of less than 5 cm. In conclusion, within the allowable deviation range of the field operation of the rice transplanter, the proposed algorithm successfully realized automatic steering controls of the transplanter under different steering angles.

Effects of Human Understanding of Automation Abilities on Driver Performance and Acceptance of Lane Change Collision Avoidance Systems

Highly sophisticated and reliable driver assistance systems can fail if a driver does not fully understand the functionalities and capabilities of the system. A driving experiment was designed to evaluate how drivers’ understanding of a driving assistance system and its authority impact on safety and performance in critical conditions. Two groups of 24 drivers each had to avoid several impending hazards, while each group received a different type of assistance for avoiding collisions during a lane change: a haptic feedback force through the steering wheel or an automatic steering control. The different assistance systems were applied to different hazards, resulting in significant differences in driver’s reactions and behavior. Collision data showed that both systems were equally efficient when driver’s expectations, system capabilities, and the hazard encountered were in line. More collisions were observed when both systems reached functional limits that were misunderstood by the drivers. However, the impact of these limitations varied, depending on the driver’s ability to retake control and recover the critical situation. The overall driver’s subjective post-hazard assessments were significantly affected by the smoothness of control authority transfer and the types of critical conditions. This indicates the necessity of an adaptive automation that can strike a balance between the processing abilities of the human and the system, and hazardous contexts encountered.

Recent Advancements in Automation to Enhance Vehicle Technology for Human Centred Interactions

Human vehicle interactions found massive development in the automobile industry for designing new generation vehicles to ensure safe and secure travel. This article focused on the review of evolution of Human Machine Interactions (HMI) in different stages of evolution from late 2000 to present day. This paper aimed to investigate development of interactive braking systems, role of multimodal actions in system recognition, necessity of cognitive vehicle interactions and automatic steering control. The studies have been taken with satisfying parameters which could be considered as performance metric for accomplishing the expected goals and responsibilities of human-centered automation. Automatic brake systems and voice based control are some of the mile stones in the development path but still real time factors like societal, legal and human behaviors are required to be addressed in order to overcome the hurdles before such systems are truly to be introduced on roads. In general, usage of guidance systems will improve the driver performance and some longitudinal research needed to investigate the effects of support systems for the attainment of technical feasibility, in near future.

Design of robust output-feedback-based automatic steering controller for unmanned electric vehicles

This paper presents a robust output-feedback-based automatic steering control scheme for unmanned electric vehicles (UEV). To address the challenging problem, an uncertain dynamic model of the UEV is firstly developed, in which the uncertainties in the tyre cornering stiffness and the actuator faults are included. Then, to deal with the uncertain features and actuator faults of UEV, a robust output-feedback-based control algorithm for automatic steering system of UEV is proposed to achieve the prescribed transient for path tracking errors while keeping all other closed-loop signals bounded. Finally, simulation and experimental results demonstrate that the proposed automatic steering control scheme can effectively improve the tracking performance of UEV.