Motion control of nonholonomic robots at low speed

Many applications in robotics require precise tracking of the prescribed path. The aim of this article is to develop and verify by computer simulation a control design method which ensures that the “output” of the robot will move along a prescribed path. A virtual vehicle approach algorithm was used to track a predefined vehicle path. The idea behind this algorithm is that the movement of a virtual vehicle on a predefined path is controlled by a differential equation whose input is a control deviation representing the distance between a real and a virtual vehicle. The main advantage of the path-following approach is that, based on this approach, the feedback realized is invariant to the path.

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Vision-based curved lane keeping control for intelligent vehicle highway system

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651818810621 ◽

2018 ◽

Vol 233 (8) ◽

pp. 961-979 ◽

Cited By ~ 2

Author(s):

Kawther Osman ◽

Jawhar Ghommam ◽

Hasan Mehrjerdi ◽

Maarouf Saad

Keyword(s):

Motion Control ◽

Control Design ◽

Path Following ◽

Intelligent Vehicle ◽

Lateral Motion ◽

Vision Sensor ◽

Motion Error ◽

Guidance Algorithm ◽

Lane Keeping ◽

Highway System

This article addresses the coordinated longitudinal and lateral motion control for an intelligent vehicle highway system. The strategy of this work consists of defining the edges of the traveled lane using a vision sensor. According to the detected boundaries, a constrained path-following method is proposed to drive the longitudinal and the lateral vehicle’s motion. Error constraints of the intelligent vehicle highway system position are manipulated by including the function of barrier Lyapunov in designing the guidance algorithm for the intelligent vehicle highway system. To calculate the necessary forces that would steer the vehicle to the desired path, a control design is proposed that integrates the sign of the error for the compensation of the uncertain vehicle’s parameters. The Lyapunov function is later used to minimize the path-following errors and to guarantee a stable system. The efficiency of the developed approach is proved by numerical simulations.

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A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

Energies ◽

10.3390/en14020517 ◽

2021 ◽

Vol 14 (2) ◽

pp. 517

Author(s):

Dániel Fényes ◽

Balázs Németh ◽

Péter Gáspár

Keyword(s):

Autonomous Vehicles ◽

Design Method ◽

Nonlinear Modeling ◽

Autonomous Vehicle ◽

Control Design ◽

Path Following ◽

Simulation Software ◽

Model Parameters ◽

Modeling And Control ◽

And Control

This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated.

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Motion Control of a 4WS4WD Path-Following Vehicle: Dynamics-Based Steering and Driving Models

Shock and Vibration ◽

10.1155/2021/8861159 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Zhonghua Zhang ◽

Caijin Yang ◽

Weihua Zhang ◽

Yanhai Xu ◽

Yiqiang Peng ◽

...

Keyword(s):

Dynamic Model ◽

Motion Control ◽

Degrees Of Freedom ◽

Path Following ◽

Coordinate Space ◽

Vehicle Model ◽

Control Models ◽

Path Following Control ◽

Vehicle Path ◽

And Control

This paper deals with a four-wheel-steering four-wheel-driving (4WS4WD) vehicle under the path-following control. Focuses are placed on the motion control of the vehicle, and the drive forces and steering angles for achieving accurate path-following by the vehicle are determined. In this research, a nonlinear vehicle model of three degrees of freedom (DOFs) is used. The vehicle path-following dynamics are modeled using the classical mass-damper-spring vibration theory, which is described by three ordinary differential equations of second order with lateral, heading and velocity deviations, and control parameters. Combined with the vehicle path-following dynamic model, the nonlinear vehicle dynamic model is decoupled in generalized coordinate space. The required drive forces and steering angles for the vehicle path-following controllers are thus calculated and control models are obtained. Theoretical analysis for steering and driving control models is also carried out. It discloses that control models can maintain good performance against uncertainties. The vehicle path-following control is exhibited by dynamic simulation in CarSim with consideration of a complex vehicle model and a variable-curvature planned path. Numerical results obtained are analyzed and show control models have capable of dealing with a complex path-following problem. This paper provides a new insight into understanding path-following control of a 4WS4WD vehicle at the generalized vibration level.

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Motion Control of a 4WS4WD Path-Following Vehicle: Further Studies on Steering and Driving Models

Shock and Vibration ◽

10.1155/2021/8830841 ◽

2021 ◽

Vol 2021 ◽

pp. 1-25

Author(s):

Zhonghua Zhang ◽

Caijin Yang ◽

Weihua Zhang ◽

Yanhai Xu ◽

Yiqiang Peng ◽

...

Keyword(s):

Motion Control ◽

Deep Level ◽

Dynamic Environment ◽

Path Following ◽

Complex Problem ◽

Level Control ◽

Control Models ◽

Path Following Control ◽

Vehicle Path ◽

Vehicle Dynamic Model

Further research on motion control of a 4WS4WD path-following vehicle is carried out in this paper. Focuses are placed on understanding and testing the vehicle path-following control models developed previously at a deep level. Control models are in relation to parameters introduced, and the effects of these parameters are discovered. Control models are interpreted by dynamic simulation using a 3DOF vehicle model with three cases. Three kinds of planned paths are considered in these cases to test control performances, which include the straight, circular, and sinusoidal paths. Interesting dynamic results are obtained and analyzed qualitatively, e.g., various steering modes. Simulation studies are extended with consideration of a fine vehicle dynamic model established in CarSim and a complex path composed of straight and curved segments. Control models are examined in a complex problem, and results obtained show that they are validated with robustness in dynamic environment.

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A non-overshooting controller for vehicle path following

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331221994384 ◽

2021 ◽

pp. 014233122199438

Author(s):

Tong Xu ◽

Dong Wang ◽

Weigong Zhang

Keyword(s):

Field Experiments ◽

Fitness Function ◽

Path Following ◽

Settling Time ◽

Distribution Model ◽

Positioning Errors ◽

Guidance Law ◽

Initial Control ◽

Vehicle Path ◽

Pavement Construction

Unmanned pavement construction is of great significance in China, and one of the most important issues is how to follow the designed path near the boundary of the pavement construction area to avoid curbs or railings. In this paper, we raise a simple yet effective controller, named the proportional-integral-radius and improved particle swarm optimization (PIR-IPSO) controller, for fast non-overshooting path-following control of an unmanned articulated vehicle (UAV). Firstly, UAV kinematics model is introduced and segmented UAV steering dynamics model is built through field experiments; then, the raw data collected by differential global positioning system (DGPS) is used to build the measurement error distribution model that simulates positioning errors. Next, line of sight (LOS) guidance law is introduced and the LOS initial parameter is assigned based on human driving behavior. Besides, the initial control parameters tuned by the Ziegler-Nichols (ZN) method are used as the initial iterative parameters of the PSO controller. An improved PSO fitness function is also designed to achieve fast non-overshoot control performance. Experiments show that compared with the PSO, ZN and ZN-PSO controller, the PIR-PSO-based controller has significantly less settling time and almost no overshoot in various UAV initial states. Furthermore, compared with other controllers, the proposed PIR-IPSO-based controller achieves precise non-overshoot control, relatively less settling time and centimeter-level positioning error in various initial deviations.

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