scholarly journals Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qing Ye ◽  
Ruochen Wang ◽  
Chi Zhang ◽  
Yingfeng Cai
Keyword(s):  
Dynamic Models ◽  
Hierarchical Control ◽  
Great Influence ◽  
Control Process ◽  
Steering System ◽  
Path Tracking ◽  
Intelligent Vehicle ◽  
Tracking Accuracy ◽  
Motion System ◽  
Antilock Braking System

In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.

Analyzing the influence of automatic steering system on the trajectory tracking accuracy of intelligent vehicle

2018 ◽  
Vol 121 ◽  
pp. 188-196 ◽  
Author(s):  
Ruochen Wang ◽  
Qing Ye ◽  
Yingfeng Cai ◽  
Yong Wang ◽  
Xing Xu ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Steering System ◽  
Intelligent Vehicle ◽  
Tracking Accuracy ◽  
Automatic Steering

scholarly journals Research on modeling and compensation control strategy of automatic steering system

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987502
Author(s):  
Qing Ye ◽  
Ruochen Wang ◽  
Yinfeng Cai ◽  
Long Chen
Keyword(s):  
Control Strategy ◽  
Dynamic Models ◽  
Dynamic Control ◽  
Steering System ◽  
Tracking Accuracy ◽  
Control Effect ◽  
Compensation Control ◽  
Automatic Steering ◽  
Nonlinear Dynamic Models ◽  
System Controller

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.

A novel path tracking approach considering safety of the intended functionality for autonomous vehicles

2021 ◽  
pp. 095440702110205
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Coordinated Control ◽  
Steering System ◽  
Front Wheel ◽  
Path Tracking ◽  
Hierarchical Architecture ◽  
Automatic Steering ◽  
The Stability ◽  
Control Layer

To reduce the adverse effect of the functional insufficiency of the steering system on the accuracy of path tracking, a path tracking approach considering safety of the intended functionality is proposed by coordinating automatic steering and differential braking in this paper. The proposed method adopts a hierarchical architecture consisting of a coordinated control layer and an execution control layer. In coordinated control layer, an extension controller considering functional insufficiency of the steering system, tire force characteristics and vehicle driving stability is proposed to determine the weight coefficients of automatic steering and the differential braking, and a model predictive controller is designed to calculate the desired front wheel angle and additional yaw moment. In execution control layer, a H∞ steering angle controller considering external disturbances and parameter uncertainty is designed to track desired front wheel angle, and a braking force distribution module is used to determine the wheel cylinder pressure of the controlled wheels. Both simulation and experiment results show that the proposed method can overcome the functional insufficiency of the steering system and improve the accuracy of path tracking while maintaining the stability of the autonomous vehicle.

Modeling and trajectory tracking control of a two-wheeled mobile robot: Gibbs–Appell and prediction-based approaches

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1551-1570 ◽  
Author(s):  
Hossein Mirzaeinejad ◽  
Ali Mohammad Shafei
Keyword(s):  
Trajectory Tracking ◽  
Dynamic Models ◽  
Sliding Mode ◽  
Tracking Accuracy ◽  
Wheeled Mobile Robots ◽  
Tracking Errors ◽  
Trajectory Tracking Control ◽  
Control Laws ◽  
Control Approach ◽  
Prediction Time

SUMMARYThis study deals with the problem of trajectory tracking of wheeled mobile robots (WMR's) under non-holonomic constraints and in the presence of model uncertainties. To solve this problem, the kinematic and dynamic models of a WMR are first derived by applying the recursive Gibbs–Appell method. Then, new kinematics- and dynamics-based multivariable controllers are analytically developed by using the predictive control approach. The control laws are optimally derived by minimizing a pointwise quadratic cost function for the predicted tracking errors of the WMR. The main feature of the obtained closed-form control laws is that online optimization is not needed for their implementation. The prediction time, as a free parameter in the control laws, makes it possible to achieve a compromise between tracking accuracy and implementable control inputs. Finally, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of some trajectory tracking maneuvers.

Development of Path Tracking Control Algorithm for a 4 DOF Spatial Manipulator Using PID Controller

2017 ◽  
pp. 314-327
Author(s):  
Pradeep Reddy Bonikila ◽  
Ravi Kumar Mandava ◽  
Pandu Ranga Vundavilli
Keyword(s):  
Pid Controller ◽  
Dynamic Models ◽  
Control Method ◽  
Robotic Manipulator ◽  
Industrial Applications ◽  
Path Tracking ◽  
End Effector ◽  
Straight Line ◽  
Feedback Control Method ◽  
Manipulator Arm

The path tracking phenomenon of a robotic manipulator arm plays an important role, when the manipulators are used in continuous path industrial applications, such as welding, machining and painting etc. Nowadays, robotic manipulators are extensively used in performing the said tasks in industry. Therefore, it is essential for the manipulator end effector to track the path designed to perform the task in an effective way. In this chapter, an attempt is made to develop a feedback control method for a 4-DOF spatial manipulator to track a path with the help of a PID controller. In order to design the said controller, the kinematic and dynamic models of the robotic manipulator are derived. Further, the concept of inverse kinematics has been used to track different paths, namely a straight line and parabolic paths continuously. The effectiveness of the developed algorithm is tested on a four degree of freedom manipulator arm in simulations.

An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 3246-3262 ◽  
Author(s):  
Bing Zhang ◽  
Changfu Zong ◽  
Guoying Chen ◽  
Guiyuan Li
Keyword(s):  
Electric Vehicle ◽  
Model Prediction ◽  
Hierarchical Control ◽  
Path Tracking ◽  
Vehicle Dynamic ◽  
Independent Control ◽  
Prediction Horizon ◽  
Adaptive Prediction ◽  
Model Prediction Control ◽  
Prediction Control

An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

Identification of power steering system dynamic models

Mechatronics ◽  
1998 ◽  
Vol 8 (3) ◽  
pp. 255-270 ◽  
Author(s):  
Amuliu Bogdan Proca ◽  
Ali Keyhani
Keyword(s):  
Dynamic Models ◽  
Steering System ◽  
System Dynamic ◽  
Power Steering System ◽  
Power Steering

Lateral robust iterative learning control for unmanned driving robot vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 792-808
Author(s):  
Shuhua Su ◽  
Gang Chen
Keyword(s):  
Dynamic Model ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Steering System ◽  
Path Tracking ◽  
Iterative Learning ◽  
Control Law ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

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