Adaptive steering control for uncertain vehicle dynamics with crosswind effects and steering angle constraints

2008 ◽  
Author(s):  
Nazli E. Kahveci
Keyword(s):  
Vehicle Dynamics ◽  
Steering Control ◽  
Steering Angle

Comparison of Analytical and Empirical Models to Capture Variations in Off-Road Vehicle Dynamics

2005 ◽  
Author(s):  
Paul J. Pearson ◽  
David M. Bevly
Keyword(s):  
Empirical Data ◽  
Vehicle Dynamics ◽  
Experimental Validation ◽  
Inertial Sensors ◽  
Analytical Models ◽  
Control Algorithms ◽  
Model Parameters ◽  
Steering Control ◽  
Steering Angle ◽  
Yaw Rate

This paper develops two analytical models that describe the yaw dynamics of a farm tractor and can be used to design or improve steering control algorithms for the tractor. These models are verified against empirical data. The particular dynamics described are the motions from steering angle to yaw rate. A John Deere 8420 tractor, outfitted with inertial sensors and controlled through a PC-104 form factor computer, was used for experimental validation. Conditions including different implements at varying depths, as would normally be found on a farm, were tested. This paper presents the development of the analytical models, validates them against empirical data, and gives trends on how the model parameters change for different configurations.

Vehicle coupled bifurcation analysis of steering angle and driving torque

2021 ◽  
pp. 095440702098540
Author(s):  
Xianbin Wang ◽  
Shuming Shi
Keyword(s):  
Bifurcation Analysis ◽  
Hybrid Method ◽  
Vehicle Dynamics ◽  
Front Wheel ◽  
Steering Angle ◽  
Driving Mode ◽  
Vehicle System ◽  
Autonomous Equation ◽  
Real Coded Genetic Algorithm ◽  
Driving Torque

The mechanism of vehicle dynamics steering bifurcation has almost been confirmed. But the present steering bifurcation mechanism cannot explain the bifurcation phenomena caused by the driving torque. As a result, the vehicle coupled bifurcation analysis of the steering angle and driving torque has not been studied. Based on the five degrees of freedom (5DOF) vehicle system dynamics model with driving torque involved, the vehicle dynamics equilibriums under different driving torque and driving mode were searched by a hybrid method in this paper. The hybrid method combined the real-coded Genetic Algorithm with Quasi-Newton gradient method. According to the definition of static bifurcation of nonlinear systems, the equilibrium bifurcation of 5DOF vehicle system was confirmed. Then, the 5DOF vehicle system model was transformed into autonomous equation with the front wheel steering angle as intermediate variable. From the two aspects of constant steering angle amplitude and constant driving torque, the bifurcation diagrams of different driving mode were calculated. The vehicle coupled bifurcation characteristics of steering angle and driving torque were analyzed. The results show that the values of the driving torque will directly affect the bifurcation characteristics of vehicle dynamics system. The coupled feature of the front wheel steering angle and driving torque effect on vehicle bifurcation is obvious.

Vehicle Dynamics Control Based on Linear Quadratic Regulator

2009 ◽  
Vol 16-19 ◽  
pp. 876-880
Author(s):  
Si Qi Zhang ◽  
Tian Xia Zhang ◽  
Shu Wen Zhou
Keyword(s):  
Control Strategy ◽  
Vehicle Dynamics ◽  
Linear Quadratic Regulator ◽  
Active Safety ◽  
Vehicle Stability ◽  
Steering Control ◽  
Linear Quadratic ◽  
Yaw Rate ◽  
Vehicle Dynamics Control ◽  
Yaw Moment

The paper presents a vehicle dynamics control strategy devoted to prevent vehicles from spinning and drifting out. With vehicle dynamics control system, counter braking are applied at individual wheels as needed to generate an additional yaw moment until steering control and vehicle stability were regained. The Linear Quadratic Regulator (LQR) theory was designed to produce demanded yaw moment according to the error between the measured yaw rate and desired yaw rate. The results indicate the proposed system can significantly improve vehicle stability for active safety.

scholarly journals Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

2021 ◽  
Author(s):  
Md Khairul Islam ◽  
Mst. Nilufa Yeasmin ◽  
Chetna Kaushal ◽  
Md Al Amin ◽  
Md Rakibul Islam ◽  
...  
Keyword(s):  
Deep Neural Network ◽  
Mean Squared Error ◽  
Comparative Investigation ◽  
Training Data ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Steering Control ◽  
Steering Angle ◽  
Squared Error ◽  
Entire Planet

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

scholarly journals Optimal Geno-Fuzzy Lateral Control of Powered Parachute Flying Vehicles

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 400
Author(s):  
Hanafy M. Omar
Keyword(s):  
Vehicle Dynamics ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Maximum Error ◽  
Steering Angle ◽  
Physical Constraints ◽  
Six Degrees Of Freedom ◽  
Fuzzy Membership Functions ◽  
Yaw Angle

In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of the system, such as the maximum error of the yaw angle and the maximum allowed steering angle, are naturally included in the designed controller. The effectiveness of the proposed controller was assessed using the nonlinear six degrees of freedom (6DOF) mathematical model of the PPC. The genetic algorithm (GA) optimization technique was used to optimize the distribution of the fuzzy membership functions in order to improve the performance of the suggested controller. The robustness of the proposed controller was evaluated by changing the values of the parafoil aerodynamic coefficients and the initial flight conditions.

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

2019 ◽  
Vol 9 (13) ◽  
pp. 2666 ◽  
Author(s):  
Junnan Yin ◽  
Dequan Zhu ◽  
Juan Liao ◽  
Guangyue Zhu ◽  
Yao Wang ◽  
...  
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Maximum Deviation ◽  
Fuzzy Pid ◽  
Steering Control ◽  
Time Data ◽  
Steering Angle ◽  
Fuzzy Pid Controller ◽  
Allowable Deviation ◽  
Automatic Steering

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.

Analysis of the vehicle driving stability region based on the bifurcation of the driving torque and the steering angle

2017 ◽  
Vol 231 (7) ◽  
pp. 984-998 ◽  
Author(s):  
Shuming Shi ◽  
Ling Li ◽  
Xianbin Wang ◽  
Hongfei Liu ◽  
Yuqiong Wang
Keyword(s):  
Vehicle Dynamics ◽  
Stability Region ◽  
Integrated Control ◽  
Steering Angle ◽  
Vehicle Handling ◽  
Vehicle System ◽  
Non Linear ◽  
Driving Stability ◽  
Driving Torque ◽  
Dynamics Stability

Integrated control systems for vehicle-handling stability are usually based on the steering bifurcation mechanism. The best integrated control performance is obtained by coordinating different control methods. However, in vehicle steering and driving conditions, the coupling characteristics of the longitudinal forces and the lateral forces of the tyres must lead to changes in the bifurcation characteristics. The corresponding vehicle dynamics stability region has to be redetermined. The corresponding integrated control method also needs to be adjusted. Therefore, in combination with the physical significance of the dynamics equilibrium point of the vehicle system, the definition of the driving stability region of the vehicle based on the characteristics of the driving torque and the steering angle bifurcation is proposed. With the concept presented above, the five-degree-of-freedom non-linear vehicle system model for the driving stability region of the vehicle was solved. The simulation results show that, according to the driving stability region of the vehicle, the vehicle dynamics stability with different driving torque inputs and different front-wheel steering-angle inputs can be accurately estimated. The study of the driving stability region of the vehicle is beneficial for engineering applications in non-linear automotive dynamics research. In addition, it provides the theoretical basis for integrated control of the vehicle-handling stability.

Design of an Optimal Control Strategy for an Active Front Steering System

2006 ◽  
Author(s):  
Avesta Goodarzi ◽  
Ebrahim Esmailzadeh ◽  
Babak Nadarkhani
Keyword(s):  
Optimal Control ◽  
Vehicle Dynamics ◽  
Steering System ◽  
Lateral Acceleration ◽  
Manufacturing Companies ◽  
Vehicle Dynamic ◽  
Steering Control ◽  
Optimal Control Strategy ◽  
Active Front Steering ◽  
Innovative System

The concept of active steering control (ASC) has been considered by several researchers as well as auto manufacturing companies during recent years. This innovative system permits any correction of the driver’s steering angle in order to achieve the desired vehicle dynamic behavior. An optimal control law to evaluate the steering angle’s correction of the front wheels, being part of an active front steering system (AFS), has been developed. For this purpose a specific lateral vehicle dynamics index is defined in which way that the minimization of the performance index lead to improved vehicle dynamics. The optimal values of the control law’s gains are determined analytically. The performance of the proposed control system has been verified using 8-DOF nonlinear vehicle dynamic model. The simulation results illustrate that considerable improvement in vehicle handling is achieved particularly for the cases of the low and mid-range lateral acceleration maneuvers.

scholarly journals Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

2020 ◽  
Vol 53 (3-4) ◽  
pp. 501-518
Author(s):  
Chaofang Hu ◽  
Lingxue Zhao ◽  
Lei Cao ◽  
Patrick Tjan ◽  
Na Wang
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Obstacle Avoidance ◽  
Predictive Control ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Steering Control ◽  
Steering Angle ◽  
Ground Vehicle ◽  
Tracking Controller

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

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