Analysis of aircraft lateral path tracking accuracy and its implications for separation standards

2013 ◽  
Author(s):  
M. Cramer ◽  
L. Rodriguez
Keyword(s):  
Path Tracking ◽  
Tracking Accuracy ◽  
Separation Standards

Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots

2013 ◽  
Vol 418 ◽  
pp. 10-14 ◽  
Author(s):  
Hong Ji Zhang ◽  
Yuan Yuan Ge
Keyword(s):  
Tracking Control ◽  
Control Algorithm ◽  
Control Object ◽  
Learning Objects ◽  
Path Tracking ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Path Tracking Algorithm ◽  
Robot Path

For conventional fuzzy path tracking controller need to manually updated the control parameters in order to get better tracking control deficiencies and the lack of robustness of the problem when the control object is disturbed. Parameters self-adjusting tracking algorithm is proposed based on Cerebellum Model Articulation Controller(CMAC) and fuzzy logic composite of the control. The CAMC control logarithm first charged with tracking through learning objects charged with approximation of the object model, to learning cycle worth to control corresponding to the amount of correction corresponding weight value according to the error between input and output of the system and set the learning rate. When the object or environment changes can make the control performance of the system is automatically adjusting within a certain range, since the role of the CAMC. Tracking experiments show that. The tracking control algorithm has high tracking accuracy and good robustness, is conducive to the overall optimization of robot path tracking.

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 A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

2019 ◽  
Vol 9 (22) ◽  
pp. 4739 ◽  
Author(s):  
Yao ◽  
Tian
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Tracking Error ◽  
Path Tracking ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Prediction Horizon ◽  
Tracking Model ◽  
Vehicle Path

Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

scholarly journals A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3689
Author(s):  
Zhiwei He ◽  
Linzhen Nie ◽  
Zhishuai Yin ◽  
Song Huang
Keyword(s):  
Autonomous Vehicles ◽  
Tracking Control ◽  
Linear Time ◽  
Lower Layer ◽  
Path Tracking ◽  
Current Control ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Tracking Accuracy ◽  
Steering Angle

This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

Path tracking based on model predictive control with variable predictive horizon

2021 ◽  
pp. 014233122110038
Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithm ◽  
Path Tracking ◽  
Response Analysis ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Comprehensive Performance ◽  
Path Tracking Method

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.

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 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.

scholarly journals Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm

Actuators ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 22
Author(s):  
Liang Wang ◽  
Zhiqiang Zhai ◽  
Zhongxiang Zhu ◽  
Enrong Mao
Keyword(s):  
Genetic Algorithm ◽  
Tracking Control ◽  
Population Genetic ◽  
Acute Angle ◽  
Path Tracking ◽  
Tracking Performance ◽  
Obtuse Angle ◽  
Tracking Accuracy ◽  
Multiple Population ◽  
Population Genetic Algorithm

To improve the path tracking accuracy of autonomous tractors in operation, an improved Stanley controller (IMP-ST) is proposed in this paper. The controller was applied to a two-wheel tractor dynamics model. The parameters of the IMP-ST were optimized by multiple-population genetic algorithm (MPGA) to obtain better tracking performance. The main purpose of this paper is to implement path tracking control on an autonomous tractor. Thus, it is significant to study this field because of smart agricultural development. According to the turning strategy of tractors in field operations, five working routes for tractors were designed, including straight, U, Ω, acute-angle and obtuse-angle routes. Simulation tests were conducted to verify the effectiveness of the proposed IMP-ST in tractor path tracking for all routes. The lateral root-mean-square (RMS) error of the IMP-ST was reduced by up to 36.84% and 48.61% compared to the extended Stanley controller and the original Stanley controller, respectively. The simulation results indicate that the IMP-ST performed well in guiding the tractor to follow all planned working routes. In particular, for the U and Ω routes, the two most common turning methods in tractor field operations, the path tracking performance of the IMP-ST was improved by 41.72% and 48.61% compared to the ST, respectively. Comparing and analyzing the e-Ψ and β-γ phase plane of the three controllers, the results indicate that the IMP-ST has the best control stability.

Loose Guide for Passive Omnidirectional Mobility Aid

2006 ◽  
Vol 18 (4) ◽  
pp. 511-518 ◽  
Author(s):  
Naemeh Nejatbakhsh ◽  
◽  
Kazuhiro Kosuge ◽  
Keyword(s):  
Passive Control ◽  
The Elderly ◽  
Path Tracking ◽  
Indoor Navigation ◽  
Human Interaction ◽  
Tracking Accuracy ◽  
Objective System ◽  
Mobility Aid ◽  
Magneto Rheological ◽  
And Control

This paper details the design and control of an intelligent mobility aid for the elderly and gait-disabled, called Omni RT Walker (ORTW). Omni RT Walker-II, version 2 of ORTW, consists of an omnidirectional platform and uses magneto-rheological brakes for passive control. ORTW-II enables the elderly to use the driving skills they possess while supplementing movement that may have declined due to their age or fatigue. We choose indoor navigation as the task to be realized by shared control of ORTW-II. Unlike most path tracking methods, which attempt to lead an objective system on a desired trajectory, our new algorithm restricts mobility to a pathway called thePotential Canal, while mobility is conducted by the user. In systems with direct human interaction similar to mobility aids, our proposal is expected to increase user-dependability in system operation while increasing user freedom and safety. A collision-free Potential Canal is maintained using realtime modification based on environmental information. Experimental results are included to demonstrate path tracking accuracy and quality.

scholarly journals A Moving Path Tracking Method of the Thunderstorm Cloud Based on the Three-Dimensional Atmospheric Electric Field Apparatus

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xu Yang ◽  
Hongyan Xing ◽  
Wei Xu ◽  
Xinyuan Ji
Keyword(s):  
Electric Field ◽  
Elevation Angle ◽  
Three Dimensional ◽  
Path Tracking ◽  
Atmospheric Electric Field ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Thunderstorm Cloud ◽  
Radar Map ◽  
Moving Path

In order to obtain the position of thunderstorm cloud in real time and make it possible to track the thunderstorm cloud motion, a method is proposed for tracking the moving path of thunderstorm cloud, with the aid of the three-dimensional atmospheric electric field apparatus (AEFA). According to the method of images, we establish a spatial model for tracking the moving path. Based on the model, we define the dynamic parameters of thunderstorm cloud position. Subsequently, to realize the moving path tracking of thunderstorm cloud, its coordinates are associated with the time points. Besides, we use the relationship between electric field component measurement error, horizontal angle, elevation angle, and the tracking accuracy to analyze the tracking performance. Finally, a fusion system combining an electric field measurement unit, electric field calibration unit, and permittivity measurement unit is designed to meet the actual needs. The results show that the method can accurately track the thunderstorm cloud moving path and has a better effect. In addition, the method can also be combined with a radar map, thus better predicting the development of the thunderstorm cloud.

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