Path‐Tracking Model Regulation

2021 ◽  
pp. 159-176
Keyword(s):  
Path Tracking ◽  
Tracking Model

Constraint-oriented integrated longitudinal and lateral robust control for connected and automated vehicle platoons

2021 ◽  
pp. 107754632110026
Author(s):  
Zeyu Yang ◽  
Jin Huang ◽  
Zhanyi Hu ◽  
Diange Yang ◽  
Zhihua Zhong
Keyword(s):  
Robust Control ◽  
Estimation Error ◽  
Uniform Boundedness ◽  
Path Tracking ◽  
Adaptive Robust Control ◽  
Connected Vehicle ◽  
Vehicle Platoon ◽  
Tracking Model ◽  
Vehicle Platoons ◽  
Following Error

The coupling, nonlinearity, and uncertainty characteristics of vehicle dynamics make the accurate longitudinal and lateral control of an automated and connected vehicle platoon a tough task. Little research has been conducted to fully address the characteristics. By using the ideology of constraint-following control this article proposes an integrated longitudinal and lateral adaptive robust control methodology for a vehicle platoon with a bidirectional communication topology. The platoon control objectives contain the path tracking stability, the platoon internal stability, and the string stability. First, we establish the nonlinear kinematics path tracking model and the coupled vehicle longitudinal and lateral dynamical model that contains time-varying uncertainties. Second, we design a series of nonlinear equality constraints that directly guarantee the control objectives based on the kinematic relations. On this basis, an adaptive robust constraint-following control is proposed. It is shown that the control guarantees the uniform boundedness and the uniform ultimate boundedness of the constraint-following error and the uncertainty estimation error. Finally, simulation results are provided to validate the effectiveness of the proposed methodology.

scholarly journals Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

2021 ◽  
Author(s):  
Praveenkumar Babu ◽  
Eswaran Parthasarathy
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Performance Analysis ◽  
Hidden Variables ◽  
Path Tracking ◽  
Multiple Objects ◽  
Linear Measurements ◽  
Filtering Algorithm ◽  
Tracking Model ◽  
State Estimations

<div>In the presence of uncertainty, one of the most difficult issues for tracking in control systems is to estimate the accuracy and precision of hidden variables. Kalman filter is considered as the widely adapted estimation algorithm for tracking applications. However, tracking of multiple objects is still a challenging task to achieve better results for prediction and correction. To solve this problem, a multi-dimensional Kalman filter is proposed using state estimations for tracking multiple objects. This paper also presents the performance analysis of proposed tracking model for linear measurements. The steady?state and covariance equations are derived and their co-efficients are updated. The multi-dimensional Kalman filter is evaluated mathematically for linear dynamic systems. The path tracking based on Kalman filter and multi-dimensional Kalman filter is also analyzed. The true and filtered responses of our proposed filtering algorithm for multiple object tracking are observed. The output covariance produces steady state values after four number of samples. The simulation results shows that the performance of our proposed filtering algorithm is 2x times effective than conventional Kalman filter for objects moving in linear motion and proves that proposed filter is suitable for real?time implementation.</div><div><br> </div>

scholarly journals Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

2021 ◽  
Author(s):  
Praveenkumar Babu ◽  
Eswaran Parthasarathy
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Performance Analysis ◽  
Hidden Variables ◽  
Path Tracking ◽  
Multiple Objects ◽  
Linear Measurements ◽  
Filtering Algorithm ◽  
Tracking Model ◽  
State Estimations

<div>In the presence of uncertainty, one of the most difficult issues for tracking in control systems is to estimate the accuracy and precision of hidden variables. Kalman filter is considered as the widely adapted estimation algorithm for tracking applications. However, tracking of multiple objects is still a challenging task to achieve better results for prediction and correction. To solve this problem, a multi-dimensional Kalman filter is proposed using state estimations for tracking multiple objects. This paper also presents the performance analysis of proposed tracking model for linear measurements. The steady?state and covariance equations are derived and their co-efficients are updated. The multi-dimensional Kalman filter is evaluated mathematically for linear dynamic systems. The path tracking based on Kalman filter and multi-dimensional Kalman filter is also analyzed. The true and filtered responses of our proposed filtering algorithm for multiple object tracking are observed. The output covariance produces steady state values after four number of samples. The simulation results shows that the performance of our proposed filtering algorithm is 2x times effective than conventional Kalman filter for objects moving in linear motion and proves that proposed filter is suitable for real?time implementation.</div><div><br> </div>

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 Towards Autonomous Driving: Review and Perspectives on Configuration and Control of Four-Wheel Independent Drive/Steering Electric Vehicles

Actuators ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 184
Author(s):  
Peng Hang ◽  
Xinbo Chen
Keyword(s):  
Electric Vehicles ◽  
Active Fault ◽  
Kinematic Model ◽  
Autonomous Driving ◽  
Stability Control ◽  
Path Tracking ◽  
Rollover Prevention ◽  
Common Control ◽  
Tracking Model ◽  
And Control

In this paper, the related studies of chassis configurations and control systems for four-wheel independent drive/steering electric vehicles (4WID-4WIS EV) are reviewed and discussed. Firstly, some prototypes and integrated X-by-wire modules of 4WID-4WIS EV are introduced, and the chassis configuration of 4WID-4WIS EV is analyzed. Then, common control models of 4WID-4WIS EV, i.e., the dynamic model, kinematic model, and path tracking model, are summarized. Furthermore, the control frameworks, strategies, and algorithms of 4WID-4WIS EV are introduced and discussed, including the handling of stability control, rollover prevention control, path tracking control and active fault-tolerate control. Finally, with a view towards autonomous driving, some challenges, and perspectives for 4WID-4WIS EV are discussed.

Path Tracking Controller Design of Automatic Guided Vehicle Based on Four-Wheeled Omnidirectional Motion Model

2020 ◽  
Vol 17 (2) ◽  
pp. 7996-8010
Author(s):  
X. Wu ◽  
Y. Yang
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Threshold Value ◽  
Position Angle ◽  
Path Tracking ◽  
Global Coordinate System ◽  
Automatic Guided Vehicle ◽  
Input Variables ◽  
Industrial Pc

This paper presents a new design of omnidirectional automatic guided vehicle based on a hub motor, and proposes a joint controller for path tracking. The proposed controller includes two parts: a fuzzy controller and a multi-step predictive optimal controller. Firstly, based on various steering conditions, the kinematics model of the whole vehicle and the pose (position, angle) model in the global coordinate system are introduced. Secondly, based on the modeling, the joint controller is designed. Lateral deviation and course deviation are used as the input variables of the control system, and the threshold value is switched according to the value of the input variable to realise the correction of the large range of posture deviation. Finally, the joint controller is implemented by using the industrial PC and the self-developed control system based on the Freescale minimum system. Path tracking experiments were made under the straight and circular paths to test the ability of the joint controller for reducing the pose deviation. The experimental results show that the designed guided vehicle has excellent ability to path tracking, which meets the design goals.

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