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Automatic guidance of agricultural vehicles requires automatic execution of operation commands received from the navigation controller by using electronically controlled mechanisms for wheel steering, speed changing and work implementing. Automatic steering contributes as a prerequisite technique in automatic and semi-automatic agricultural navigation. This research aimed to develop an electric automatic steering system that was compact in its structure and integrated into original steering mechanism in a simply and convenient way for aftermarket modification. A brushless motor and reducer assembly was utilized to provide an adequate steering torque instead of manual maneuver. A rapid assembling approach was proposed by passing the steering shaft through the hollow output shaft. A digital proportional-integral-differential (PID) algorithm was implemented to calculate the rotation speeds and directions by comparing the desired angle and the actual angle, which was implemented in a printed circuit board with a microcontroller unit (MCU) and interface chips. An unmanned wheeled tractor was applied as test platform to integrate the newly developed electric automatic steering system. Tests were conducted to evaluate its performance in terms of stability and responsiveness. An autonomous navigation system guided the tractor along target paths in the field by sending steering commands to the electric automatic steering system. The results show that the steering angle error was less than 0.81° when desired steering angle was less than 10°. The lateral error difference was no more than 4.76 cm when repeating following the same target path, which indicated that the electric automatic steering system responded accurately and robustly to steering commands. [ABSTRACT FROM AUTHOR]

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Abstract 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.

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions. [ABSTRACT FROM AUTHOR]

This article presents an application of a Sigmoid-plane (S-plane) adaptive control algorithm to an automatic steering system in the presence of uncertain parametric of the unmanned surface vessel (USV) and the unknown disturbance of the marine environment. Due to technical difficulties such as sensor noise, the marine environment disturbance is assumed to be unmeasured. To overcome this problem, an S-plane control is designed to resist marine environment disturbances by the improved adaptive term. Based on the gradient method, the USV heading model reference adaptive controller of the USV is designed, so that the USV has a certain ability to resist model parameter changes. Considering the uncertainty of marine environment interference, a model-reference-based S-plane adaptive controller for the USV is designed. After the reference of the USV course model, the S-plane adaptive control approach is employed to reduce the effect of the marine environment. It is proved that the USV course control system is stable, despite the adverse bad sea conditions. Finally, the course control simulation experiment for the USV with the unknown marine environmental interference is carried out. To demonstrate the benefits of the S-plane adaptive controller, the results are presented in comparison with a Lyapunov method controller. The results show that the S-plane adaptive controller is robust to the changed model parameter and external disturbances. [ABSTRACT FROM AUTHOR]

ABSTRACT The need for accuracy in sugarcane machine traffic has boosted the adoption of automatic steering systems. These have been used in tractors pulling transhipment trailers. Such sets are long and articulated, which hinders their performance and benefits due to alignments in curved and laterally sloped paths. In this sense, this study aimed to quantify shifts in direction to which tractors and transshipment trailers are subjected while traveling straight and curved paths on different terrain slopes. We evaluated an internal transport system composed of one tractor and two three-axle transshipment trailers, equipped with GNSS receivers and RTK correction system, for their respective positioning. Pass-to-pass errors were evaluated by differences in orthogonal distance between antenna positioning and a reference line. Results showed that misalignment errors of tractor and trailer sets are strongly associated with path type and terrain lateral slope, besides being increasingly affected between set parts. Trailer path errors were above acceptable limits, thereby complementary solutions are needed regarding tractor automatic steering.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, the technology of automatic tractor navigation based on satellite positioning has greatly improved the efficiency and accuracy of tractor field work. Automatic steering contributes is one of the key technologies to realize the automation and intelligentization of agricultural mechanization. It costs much time to install and test automatic steering systems for tractors in the field due to complicated conditions. An automatic steering test bench was developed to reduce time consumed in the field by conducting simulation tests on accuracy and reliability. The developed automatic steering system can be applied to the tractor after obtaining satisfactory results on the test bench, which will greatly shorten the development cycle and improve the precision of the system. In this study, a 120-horsepower tractor front axle was selected. Through the design and calculation of mechanical structure, hydraulic system and electrical control system, the tractor automatic steering test bench was built. The mechanical body consist of a tractor front axle assembly, a loading device and a mechanical frame. The hydraulic interface was reserved for post-installation of automatic steering devices. Two inertial measurement units were used to test the steering system performance by recording the rotation angle of front wheels and the steering wheel. The steering wheel had a steering clearance of 16.48° and an average wheel delay time of 0.14s. In the general range and small angle range, there is a small deviation between the actual corner and the theoretical corner. Responsibility and stability met requirements for agricultural machinery steering. Experimental results show that the test bench has stable performance in terms of status detection, steering control and measurement analysis, which could meet requirements for verifying working parameters of automatic steering devices. The research provides an efficient and reliable test bench for commissioning and performance testing of agricultural machinery automatic steering.

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions.

This report defines the transit service needs that can be met by use of lane assistance systems and the requirements that these systems must meet in order to be useful and safe. The work is based on review of the existing literature and research on the subject of lane assistance, combined with case studies of several transit properties that could potentially benefit from use of lane assist systems. The project team has conducted workshops involving participation by a broad mix of people representing the transit properties in order to learn about the needs that they perceive, as well as both benefits and risks that they perceive associated with use of lane assist technology. Because these transit properties are significantly different from each other, their needs are also diverse.

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. [ABSTRACT FROM AUTHOR]

Highlights • Controlled traffic results in more favorable physical properties to plants. • Controlled traffic condenses the compaction to the inter-row center. • Greater root dry biomass was obtained when used in controlled traffic. • Controlled traffic generates increased sugarcane yield. Abstract The mechanized sugarcane harvesting using larger and heavier machines has accentuated soil compaction. The aim of this study was to quantify the impact of harvesting controlled traffic in sugarcane cultivated under different row spacing on physical-mechanical soil attributes, root system, and yield. The experiment was carried out in an Oxisol at Nova Europa county, São Paulo state, Southeastern Brazil, with three treatments: sugarcane cultivated with single-row spacing of 1.50 m and mechanized harvesting with no and with controlled traffic management (SR and SRCT, respectively) and sugarcane cultivated with double-combined row spacing of 1.50 × 0.90 m, and mechanized harvesting with controlled traffic management (DRCT). Soil samples were taken at the planting row (R), under the wheels track of the machinery (IRC) as well as in at mid-distance between the plant row and the traffic region, identified as seedbed (S), at the depths of: 0.00–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40, 0.40–0.50, 0.50–0.70, and 0.70–1.00 m. The following soil physical attributes were assessed: bulk density, soil porosity, and soil penetration resistance, in addition to the measure biometric variables of stalk diameter, plant height and yield. Root parameters were assessed only after the 2015/2016 harvest. Controlled traffic provided more favorable soil physical properties to plant growth as expressed by lower bulk density and higher soil macroporosity both in the planting row and seedbed. Regardless of the treatment, differences were found in the soil physical properties among the sampling sites R, IRC, and S. For SRCT, an increase of 17.9% root dry biomass has been found while an increase of 18.5% occurred for the DRCT treatment as well as higher density, length, volume, and root area compared to controlled traffic absence. Controlled traffic enabled sugarcane yield gains of 8.2 and 10.3 Mg ha−1 for SRCT in the third and fourth harvests, and 18.8 and 15.7 Mg ha−1 for DRCT in the same harvest sequence. These findings suggest that controlled traffic through automatic steering associated to double row spacing minimizes soil compaction and preserves soil physical conditions for roots as well as increases sugarcane yield. [ABSTRACT FROM AUTHOR]

This paper describes a robust control design for automatic steering of passenger cars. Previous studies [l-3] showed that' reliable automatic driving at highway speed may not be achieved under practical conditions with look-down reference systems which use only one sensor at the front bumper to measure the lateral displacement of the vehicle from the lane reference. An additional lateral displacement. sensor is added here at the tail bumper to solve the automatic steering control problem. The control design is performed stepwise: First, an initial controller is determined using the parameter space approach in an invariance plane. This controller is then refined to accommodate practical constraints and finally optimized using the multi-objective optimization program MOPS. The performance and robustness of the final controller was verified experimentally at California PATH in a series of test runs.

[Display omitted] • The steering motor provided adequate steering torque. • Fuzzy control realized estimation of the resistive torque. • EASS adapted effectively to various agricultural vehicles. • Self-adjusting of PID parameters improved the steering accuracy. • EASS promoted the utilization of automatic navigation economically. Automatic steering is a crucial technology for achieving automatic navigation and unmanned driving of agricultural machinery. In this research, agricultural wheeled vehicles with the HPAS (Hydraulic Power Assistant Steering) system were selected as the research object and the EASS (Electric Automatic Steering System) was proposed by using fuzzy control to realize effective steering instead of human drivers. The PID (Proportional Integral Differential) algorithm was used to process the difference between the desired and actual steering angles and to realize automatic control of rotation of directional wheels. Fuzzy control was adopted to achieve self-adjusting of PID parameters and to optimize working state of the steering motor in real-time for different agricultural wheeled vehicles under various operating conditions. Fuzzy control rules were explored and formulated by analyzing variation of the steering angle under different resistive torque values. For evaluation of its feasibility, the newly developed EASS was applied in automatic navigation of a rice transplanter, a tractor, and a high-clearance sprayer. Field testing results showed that the maximum path tracking lateral error and heading error were 4.39 cm and 2.13°, respectively. And the maximum RMS (Root Mean Square) errors were 3.75 cm and 1.02°, respectively. Therefore, the fuzzy control-based the EASS in this research was applicable to various agricultural wheeled vehicles. [ABSTRACT FROM AUTHOR]

In order to distinguish the perception and controller of end-to-end automatic steering models, this paper improved the structure of the deep learning network. By pre-training an autoencoder and gelling a good road feature alter encoding, it used the encoder as a perception and trained with the steering wheel angle prediction controller model by end-to-end approach. The training results show that the improved automatic steering network model converges faster, and the predicted angle can change well with the change of the actual angle. By using the decoder and the feature map reverse propagation method respec­tively , this paper restored the road pictures and visualized the road characteristics that the automatic steering model focused on. [ABSTRACT FROM AUTHOR]

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.

Within the context of more and more autonomous vehicles, an automatic lateral control device (AS: Automatic Steering) was used to steer the vehicle along the road without drivers’ intervention. The device was not able to detect and avoid obstacles. The experiment aimed to analyse unexpected obstacle avoidance manoeuvres when lateral control was delegated to automation. It was hypothesized that drivers skirting behaviours and eye movement patterns would be modified with automated steering compared with a control situation without automation. Eighteen participants took part in a driving simulator study. Steering behaviours and eye movements were analysed during obstacle avoidance episodes. Compared with driving without automation, skirting around obstacles was found to be less effective when drivers had to return from automatic steering to manual control. Eye movements were modified in the presence of automatic steering, revealing further ahead visual scanning of the driving environment. Resuming manual control is not only a problem of action performance but is also related to the reorganisation of drivers’ visual strategies linked to drivers’ disengagement from the steering task. Assistance designers should pay particular attention to potential changes in drivers’ activity when carrying out development work on highly automated vehicles. [ABSTRACT FROM AUTHOR]

It is well known that backward motion control of an articulated vehicle is difficult because it is an open loop unstable system and such motion is also dangerous due to ‘jackknifing’. In this paper, an anti-jackknife reverse tracking control strategy for autonomous articulated vehicles is proposed based on the combined longitudinal and lateral control scheme. In the proposed lateral-longitudinal control scheme, the major task is to control the reverse heading of the trailer by automatic steering strategies that observe both the anti-jackknife condition and input limitations. The main contribution of this paper is the development of globally asymptotic anti-jackknife stabilising and tracking controls of heading angles with both state and input constraints considered a priori. The proposed control inherently has an anti-windup mechanism that prevents the hitch angle from going beyond any specified critical value to avoid jackknifing, during which time, the steering angle remains at its limit. Stability of the controller is theoretically proven via the Lyapunov argument. Effectiveness of the proposed approach is demonstrated by CarSim and Simulink joint simulations. [ABSTRACT FROM PUBLISHER]

This paper is concerned with optimal trajectory control for two unmanned aerial vehicles (UAVs) in a multisource localization environment. The received signal strength (RSS) at the UAVs in specified time intervals permits passive differential RSS (DRSS)-based localization of multiple radio frequency (RF) sources with unknown transmit powers. A steering algorithm is proposed to update the UAV waypoints in order to minimize the summation of the uncertainty of the source locations. The UAV paths are optimized by maximizing the determinant of the Fisher Information Matrix (FIM). The FIM is approximated at successive waypoints using the estimated locations of the sources. In addition to maximizing the localization accuracy, the objectives of the proposed trajectory control are to minimize the number of UAVs, the mission time and the path length. As the DRSS is a non-linear measurement, an extended Kalman filter (EKF), which is a non-linear filtering technique, is considered in this paper. The efficiency of the approach is depicted through simulations. [ABSTRACT FROM AUTHOR]

The influence of satellite guidance on the operating precision of a John Deere 7230R tractor with a Väderstad Carrier 350 soil cultivation multi-implement was evaluated. The tractor-implement unit was operated in three steering modes: conventional when the tractor was operated manually and two steering modes, one which relied on satellite navigation with free access to the SF1 correction signal and one using paid subscription for the SF2 correction signal. The automatic steering system consisted of John Deere AutoTrac parallel tracking, StarFire 3000 antenna and GreenStar 3 CommandCenter display. In each mode, operating precision was tested at three travel speeds: 3, 6 and 12 km h −1 . The experiment was carried out in 2013, on a farm in the region of Mazowsze, Poland, on a experimental plot with a surface area of 1.5 ha. Operating precision was analysed based on the combined operating width of two adjacent passes. Deviations from the optimal in-field operating routes, overlapped area (overlaps) and missed area (omissions) were determined. [ABSTRACT FROM AUTHOR]

Automatic steering devices for farm machinery and tractors have the task to relieve the driver from the physical and mental stress of monotonous steering work. Simultaneously, they are intended to help exploit machines and tractors closer to their full performance and improve the quality of work. However, it is not possible to rule out malfunctions or breakdowns of sensors, gaps in guidance lines, obstacles in the field such as ditches or pylons, temporary hazards like the sudden appearance of animals in front of a moving machine or a temporary lack of concentration of the operator. Most of these problems may be prevented by a safety system for automatically steered agricultural machinery developed at the Department of Agricultural Engineering of the Martin-Luther-University in Halle. The function of the components of the safety system and preliminary results obtained in field experiments are introduced and discussed.

Satellite positioning systems are widely used for determining the position of moving machines and vehicles. Different methods for improving the accuracy and reliability are often used by fusing dead reckoning systems to the satellite positioning. To develop these fusions and to determine sufficient positioning accuracies, a realistic simulator for positioning data is required. In this study, we developed a GNSS (Global Navigation Satellite System) error simulator to support the development of navigation capabilities. The simulator was based on real dynamic tests driven with an agricultural tractor. The driving speed of the tests was approximately 9 km/h and the GNSS measurement rate was 10 Hz. We used Allan variance calculations to determine the characteristics of the errors and Geary’s C to determine the spatial autocorrelation of the error in agricultural fields. With these methods, we constructed a feasible error simulator capable of simulating positioning errors and positioning reliability for all typical GNSS positioning accuracy levels. The simulator uses White noise, Random walk, Flicker noise, Rate ramp, a First order Gauss–Markov process and the spatial autocorrelation map to generate errors for horizontal coordinates. [ABSTRACT FROM AUTHOR]

The article discusses a study which examines the impact of vehicle steering task automation (VSTA) and implement control and monitoring task automation (ICMTA) on the performance of agricultural vehicle drivers. The tests were performed using the tractor driving simulation (TDS) from the University of Manitoba. Findings show the significant contribution of automation to the decline of drivers' reaction time and number of errors.

The aim of this study was to develop a general-purpose electric off-road robot vehicle by using automatic control technologies. The vehicle prototype used in this study was a commercially-purchased electricity utility vehicle that was designed originally for manual operations. A manipulating unit, an automatic steering system and a speed control system were developed and integrated into a CAN-bus network for operating on functions (forward, reverse, park or stop), realizing desired steering angles and maintaining a constant speed, respectively, in the mode of automation. An autonomous navigation system based on RTK-GPS and IMU was used to evaluate the performance of the newly developed off-road robot. Field tests showed that the maximum error in speed control was 0.29 m/s and 0.22 m/s for speed tests and autonomous runs, respectively. The lateral offset was less than 10 cm in terms of straight paths, indicating that the automatic steering control system was of good performance [ABSTRACT FROM AUTHOR]

An intelligent vehicle-road model was established based on vehicle dynamics and it was transformed to an error-based equation in state space by linear transformation. According to the sliding mode variable structure control theory, the automatic steering control algorithm with exponential reaching law was proposed. Then the effect of reaching law parameters on system dynamic quality was analyzed in detail and a novel method to select the reaching law parameters was proposed. Simulation was conducted to validate the proposed algorithm. The results show that the developed automatic steering sliding mode algorithm not only follows the road with small error, but also improves the vehicle dynamic performance. [ABSTRACT FROM AUTHOR]

The present paper proposes an automatic path-tracking controller of a four-wheel steering (4WS) vehicle based on the sliding mode control theory. The controller has an advantage in that the front- and rear-wheel steering can be decoupled at the front and rear control points, which are defined as centres of percussion with respect to the rear and front wheels, respectively. Numerical simulations using a 27-degree-of-freedom vehicle model demonstrated the following characteristics: (1) the automatic 4WS controller has a more stable and more precise path-tracking capability than the 2WS controller, and (2) the automatic 4WS controller has robust stability against system uncertainties such as cornering power perturbation, path radius fluctuation, and cross-wind disturbance. [ABSTRACT FROM AUTHOR]

Methods and experimental results of a collision-avoidance, driver-assistance system are described with automatic object detection, trajectory prediction, and track following, with controlled braking and steering. The objects are detected by a fusion of Light Detection and Ranging (LIDAR) scanning and video camera pictures that show the location, size and speed of objects in front of the car. A desired trajectory is calculated depending on the distance, the width of a swerving action and differential speed. For the trajectory control, different control methods were designed and tested experimentally, such as velocity-dependent linear feedback and feedforward control, nonlinear asymptotic output tracking and nonlinear flatness-based control, using extended one-track models with vehicle state estimation for the sideslip angle and cornering stiffness. Automatic braking is realised with an electrohydraulic brake and automatic steering with an active front steering. Various control systems are compared by simulations and real test drives showing the behaviour of a Volkswagen Golf with automatic braking and/or automatic swerving to a free lane, thus avoiding hitting a suddenly appearing obstacle. The research project PRORETA was a four-year cooperation between Continental Automotive Systems and Darmstadt University of Technology. [ABSTRACT FROM AUTHOR]

This article discusses the design, implementation, and testing of a dual-path front hydrostatic drive wheel, rear caster wheel off-road vehicle. A mathematical model of the engine and major machine components was used to simulate vehicle behavior and design a drive-by-wire control system for stability and turning. The drive-by-wire system was implemented on the machine, and the model and controller were experimentally verified through testing. The physical system model contains a simplified diesel engine, hydraulic pumps and motors, gearboxes, wheels, and associated interconnecting components. The diesel engine was coupled to two hydraulic pumps via a gear box, with each hydraulic pump independently supplying flow to each hydraulic wheel motor. External disturbances were applied to the contact patch of each drive wheel. The force on each wheel was determined independently and combined to simulate vehicle acceleration, rotation, or both. A model-based controller was designed for and implemented on the machine. In the simulation, the control algorithm used steering input, speed input, and yaw rate to control pump displacement for each wheel. The controller adjusted ground drive pump displacements to maintain directional (yaw) stability when a disturbance was applied to either wheel. The controller algorithm was also implemented and verified on a test vehicle by removing the existing mechanical linkages previously used to control the hydraulic pump displacements and retrofitting electrohydraulic pump control. A joystick replaced the steering wheel and propulsion lever and was used to provide the driver inputs to the electronic controller. Testing the vehicle demonstrated positive results with stable operation up to 20 km/h (12 mph). [ABSTRACT FROM AUTHOR]

This paper presents the design and experimental implementation of a genetic fuzzy controller for automatic steering of a small-scaled vehicle. We first derive a dynamic model of the vehicle via system identification and show that the model exhibits similar characteristics to full-sized vehicles. Subsequently, a stable fuzzy proportional-derivative controller is designed and optimized by genetic algorithms. The control system is transformed into a Luré system, and Lyapunov's direct method is used to guarantee the stability of the control system. Experimental studies suggest that the control system is insensitive to parametric uncertainty, load, and disturbances. The performance of the proposed controller is also compared against a conventional proportional derivative (PD) controller. Experimental results confirm that it outperforms the conventional PD controller, particularly in terms of robustness. [ABSTRACT FROM AUTHOR]