Modeling and simulation of four-wheel steering unmanned ground vehicles using a PID controller

Haytham, A.; Elhalwagy, Yehia Z.; Wassal, Amr G.; Darwish, Nevin M.

doi:10.1109/icengtechnol.2014.7016751

Cited by 20 publications

(13 citation statements)

References 4 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where ( x r , y r ) is the center position of the rear axle, and l is the distance between the front and rear axles. Solve equations (17) and (18) to yield 40,41…”

Section: Obstacle Avoidance Steering Control Strategy Designmentioning

confidence: 99%

Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

Zhao

Cao

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

show abstract

“…where ( x r , y r ) is the center position of the rear axle, and l is the distance between the front and rear axles. Solve equations (17) and (18) to yield 40,41…”

Section: Obstacle Avoidance Steering Control Strategy Designmentioning

confidence: 99%

Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

Zhao

Cao

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Penerapan sistem kendali PID juga telah diterapkan untuk pengendalian UGV yang beroperasi di lahan pertanian dengan penerapan secara pemodelan sistem dan simulasi [12] dan [13]. Dalam penelitianpenelitian tersebut penerapan sistem kendali hanya dilakukan sebatas pada tahap simulasi, dan belum melakukan implementasi penerapan sistem kendali UGV secara hardware.…”

Section: Pendahuluanunclassified

Untitled

Purbowaskito¹,

Hsu²

2017

INFOTEL

View full text Add to dashboard Cite

Abstrak -Saat ini, penelitian terhadap penggunaan UGV dalam bidang industri pertanian dilakukan secara intensif. UGV dapat diaplikasikan dalam bidang industri pertanian dikarenakan operasinya yang fleksibel dan bentuknya yang sesuai dengan kondisi pada lahan pertanian. Salah satu faktor penting dalam pengoperasian UGV di lahan pertanian adalah pengendalian kecepatan geraknya. Kecepatan gerak UGV secara langsung ditentukan oleh kecepatan sudut dari motor DC yang digunakan sebagai tenaga penggerak pada roda-roda UGV. Artikel ini akan mendeskripsikan mengenai perancangan sistem kendali untuk mengendalikan kecepatan motor DC penggerak pada roda UGV. Pengendalian UGV ini dirancang menggunakan sistem kendali PID dimana berdasarkan metode trial dan eror dihasilkan nilai gain persamaan PID sebagai berikut KP = 0.03, KI = 0.0000001, dan KD = 0.005. Kecepatan UGV pada penelitian ini diuji dengan menggunakan dua input perintah kecepatan sudut dalam pemrogramannya. Berdasarkan pengujian sistem kendali menggunakan input 5500 RPM dan 4500 RPM diperoleh hasil bahwa UGV dapat bergerak pada jalur lurus dengan kecepatan yang konstan dengan nilai 0.528 m/s dan 0.431 m/s. Input perintah pada program berupa nilai RPM pada persamaan sistem kendali menghasilkan pengendalian motor DC yang tepat dibandingkan input program berupa nilai PWM secara langsung untuk menggoperasikan motor DC.Kata kunci -UGV, kecepatan, sistem kendali, motor DC Abstract -Nowadays, researches on the UGV usage for agricultural industry are done intensively. UGVs can be applied in agricultural industry sector due to its flexible operation and its suitable shape for agricultural field conditions. An important factor in the UGV operation on agricultural field is the controlling of UGV motion velocity. Directly, the UGV motion velocity is determined by the angular velocity of the DC motors used as the UGV propulsion motor on its wheels. This paper aims to describe the control system design for controlling the UGV propulsion DC motor speed. The UGV control is designed by using the PID control system where based on the trial and error method the PID gains are obtained as follows, KP = 0.03, KI = 0.0000001, and KD = 0.005. The UGV velocity is tested by using two input commands on its programming as the angular velocity. Based on the control system testing with two input commands of 5500 RPM and 4500 RPM, it is obtained that UGV can move on a straight path with constant velocity at 0.528 m/s and 0.431 m/s. The input commands on the UGV programing in RPM values on the designed control system resulting in the proper DC motor speed controller compare to the input commands in PWM values directly to operate the DC motor.

show abstract

“…The obtained results show that these models can be used for control purposes. Haytham et al [6] presented modelling of a four-wheel steering Unmanned Ground Vehicles. Moreover, PID controllers are designed and optimal genetic algorithm is developed to tune the controllers of vehicle.…”

Section: Introductionmentioning

confidence: 99%