scholarly journals Steering Wheel AGV Path Tracking Control Based on Improved Pure Pursuit Model

2021 ◽  
Vol 2093 (1) ◽  
pp. 012005
Author(s):  
Yiyang Wu ◽  
Zhijiang Xie ◽  
Ye Lu
Keyword(s):  
Control Strategy ◽  
Fitness Function ◽  
Path Tracking ◽  
Current Speed ◽  
Steering Wheel ◽  
Tracking Accuracy ◽  
Reference Path ◽  
Optical Module ◽  
Pursuit Control ◽  
Pure Pursuit

Abstract Aiming at the path tracking problem of the AGV transfer platform of an Optical module installing and calibrating system, this paper designs a pure pursuit control strategy in which the preview distance changes adaptively according to the current speed of AGV and the curvature of the reference path. Firstly, AGV kinematics model and pure pursuit model are established according to the geometric relationship. Then fitness function is established with tracking deviation and steering stability, and Particle swarm optimization (PSO) algorithm is used to optimize the preview distance of pure pursuit model of AGV under various working conditions. During the tracking process, AGV selects the optimal preview distance according to the curvature of the reference path and the current speed. The simulation experiment results show that the improved pure pursuit control strategy containing curvature information of reference path can improve the adaptability of AGV when it is tracking complex path, guaranteeing tracking accuracy and steering stability.

scholarly journals Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

2019 ◽  
Vol 10 (1) ◽  
pp. 230 ◽  
Author(s):  
Lingli Yu ◽  
Xiaoxin Yan ◽  
Zongxu Kuang ◽  
Baifan Chen ◽  
Yuqian Zhao
Keyword(s):  
Feedforward Control ◽  
Forward Direction ◽  
Front Axle ◽  
Path Tracking ◽  
Tuning Method ◽  
Tracking Controller ◽  
Pursuit Control ◽  
Pure Pursuit ◽  
The Stability ◽  
Fuzzy Parameter

Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

scholarly journals Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

2022 ◽  
Vol 35 (1) ◽  
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Peng Hang ◽  
Shengyuan Wang
Keyword(s):  
Control Strategy ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
High Speed ◽  
Front Wheel ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Large Curvature ◽  
Yaw Moment

AbstractIt is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions.

scholarly journals Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

2021 ◽  
Author(s):  
Bin Tang ◽  
Zheng-Yi Yang ◽  
Hao-bin Jiang ◽  
Zi-yan Lin ◽  
Zhan-xiang Xu ◽  
...  
Keyword(s):  
Control Strategy ◽  
Potential Field ◽  
Path Tracking ◽  
Lyapunov Theory ◽  
Lateral Acceleration ◽  
Lateral Stability ◽  
Tracking Accuracy ◽  
Commercial Vehicles ◽  
Field Function ◽  
Lane Keeping

Abstract With regard to the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test.

Path tracking coordinated control strategy for autonomous four in-wheel-motor independent-drive vehicles with consideration of lateral stability

2020 ◽  
pp. 095440702094688
Author(s):  
Changle Xiang ◽  
Haonan Peng ◽  
Weida Wang ◽  
Liang Li ◽  
Quan An ◽  
...  
Keyword(s):  
Control Strategy ◽  
Tracking Control ◽  
Control Method ◽  
Coordinated Control ◽  
Path Tracking ◽  
Lateral Stability ◽  
Dynamic Adjustment ◽  
Tracking Accuracy ◽  
Multi Objective ◽  
Yaw Moment

How to enhance and ensure the vehicle yaw/lateral stability and safety performance is a crucial and worthy research problem for the autonomous four in-wheel-motor independent-drive vehicle. In this paper, a hierarchical path tracking control strategy coordinated with the direct yaw moment control for autonomous four in-wheel-motor independent-drive vehicle is proposed with consideration of the lateral stability. In the upper controller, a novel model predictive control method based on the vehicle 7-degree-of-freedom dynamic model is presented to obtain the front wheel steering angle and two virtual generalized forces, the traction and yaw moment. A dynamic adjustment method of target weight based on variance adjustment factor is proposed to realize multi-objective cooperative control of maneuverability, path tracking accuracy, and yaw stability. Moreover, the tire stability margin and its standard deviation are put forward in the objective function of the lower optimal generalized force distributor, making full use of the road adhesion of each wheel. Carsim–Simulink joint simulation results illustrate that the proposed coordinated control strategy could prosper the multi-objective coordinated control. In addition, the path tracking coordinated control strategy could further improve the lateral stability and safety of the vehicle compared with the mere path tracking control strategy, when autonomous four in-wheel-motor independent-drive vehicle is placed under extremely dangerous road conditions.

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

scholarly journals Experimental Study on Antivibration Control of Electrical Power Steering Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhaojian Wang ◽  
Hamid Reza Karimi
Keyword(s):  
Control Strategy ◽  
Controller Design ◽  
Electrical Power ◽  
Steering System ◽  
Steering Wheel ◽  
Bench Test ◽  
Power Steering ◽  
Motor Controller ◽  
Hydraulic Steering ◽  
Wheel Vibration

We focus on the antivibration controller design problem for electrical power steering (EPS) systems. The EPS system has significant advantages over the traditional hydraulic steering system. However, the improper motor controller design would lead to the steering wheel vibration. Therefore, it is necessary to investigate the antivibration control strategy. For the implementation study, we also present the motor driver design and the software design which is used to monitor the sensors and the control signal. Based on the investigation on the regular assistant algorithm, we summarize the difficulties and problems encountered by the regular algorithm. After that, in order to improve the performance of antivibration and the human-like steering feeling, we propose a new assistant strategy for the EPS. The experiment results of the bench test illustrate the effectiveness and flexibility of the proposed control strategy. Compared with the regular controller, the proposed antivibration control reduces the vibration of the steering wheel a lot.

scholarly journals Review and Comparison of Path Tracking Based on Model Predictive Control

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1077 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Path Tracking ◽  
Error Model ◽  
The Real ◽  
Time Performance ◽  
Reference Path ◽  
Reference Velocity ◽  
Positioning Errors

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

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