Balance control of ball-beam system using redundant manipulator

Ryu, Kwanghyun; Oh, Yonghwan

doi:10.1109/icmech.2011.5971319

Cited by 13 publications

(5 citation statements)

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“…System design consideration to be robust and reliable. Automatic PID tuning is performed using embedded C so that the system parameters can adjust to deal with unknown disturbances to the hardware model [7]. Cascade control methodologies are used alongside primary and secondary controller, output and system response are accordingly analysed.…”

Section: Methodsmentioning

confidence: 99%

Tuning of Ball and Beam System using Cascade Control

Garai

Balasubramanyam

2020

IJEMR

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This project presents a new strategy to approach cascade control tuning by using image processing and machine learning to generate required control parameters to be set into the controller. For test bed purpose a ball balance upon a horizontal beam is used which is actuated with a stepper motor, angle of the balance to be scanned by a camera and analyzed using python alongside an ultrasonic sensor to measure real time distance to the ball, set point to be set by user and control action generated upon generating required information from hardware frame.

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Section: Methodsmentioning

confidence: 99%

Tuning of Ball and Beam System using Cascade Control

Garai

Balasubramanyam

2020

IJEMR

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“…Gaussian white noise merupakan sinyal acak yang memiliki intensitas yang sama dengan frekuensi yang berbeda yang berpengaruh terhadap respons sistem [10]. Gambar Dengan penerapan optimal feedback yang dinyatakan dalam bentuk blok diagram dapat dinyatakan bahwa untuk mencari sinyal kontrol optimal 𝑢 diperlukan gain 𝐾 dan gain 𝐾𝑓 yang optimal [11]. Agar sistem tetap stabil, diperlukan penguatan pengendali dan estimator yang stabil pula.…”

Section: Desain Kendali Lqg Sistem Ball and Beamunclassified

Linierisasi Model Sistem Ball and Beam dan Metode Kendali LQG Berbasis MATLAB-Simulink

Suwardi,

Choir,

Nurrahma

et al. 2023

JuLIET

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– There are some unstable control systems, it can be an important problem in control, like systems in aerospace and flight. This can be studied simply in the ball and beam system. The ball and beam system uses an LQG (Linear Quadratic Gaussian) controller to determine the effect of stability and optimal system to reach the set point and the results of the slope angle on the beam at the set point. There is a combination of LQR with the element matrix parameters Q and R which can be searched using a trial and error technique with the condition that the Q matrix is a positive semidefinite while the R matrix is a positive definite matrix. As well as the use of gain Kalman filter as an estimator. Two experiments are given on the Q and R matrix elements which will be used in three distance/position displacement tests. The design of LQG on ball and beam systems is carried out in the MATLAB-Simulink software. Then the results of the angle of inclination of the beam (beam) obtained 14° to make the ball reach a set point of 10 cm, 17° for a set point of 20 cm, and 28° for a set point of 30 cm. Keywords – ball and beam, angle, LQG, LQR, linearizationIntisari –Sistem kontrol yang tidak stabil menjadi sebuah masalah yang penting dalam pengontrolan, seperti sistem dalam aerospace dan penerbangan. Hal ini dapat dipelajari secara sederhana pada sistem ball and beam. Pada sistem ball and beam digunakan pengontrol LQG (linear quadratic gaussian) untuk mengetahui pengaruh stabilitas maupun optimalnya sistem mencapai set point serta hasil kemiringan sudut pada balok (beam) pada titik set point. Terdapat kombinasi LQR dengan parameter matriks elemen Q dan R yang dapat dicari menggunakan teknik trial and error dengan syarat matriks Q ialah semidefinite positive sedangkan matriks R ialah matrik definit positif Serta penggunaan gain filter Kalman sebagai estimator. Dilakukan dua eksperimen pada elemen matriks Q dan R yang akan digunakan pada tiga pengujian perpindahan jarak/posisi. Rancangan LQG pada sistem ball dan beam dilakukan di software MATLAB-Simulink. Diperoleh hasil sudut kemiringan beam sebesar 14° untuk membuat ball mencapai di posisi set point 10 cm, 17° untuk set point 20 cm, dan 28° untuk set point 30 cm.Kata kunci – ball and beam, sudut, LQG, LQR, linierisasi

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“…Other attempts have also been made using conventional control theory. In Ryu and Oh (2011) , they discuss how estimation of the ball velocity is often a large source of error in ball-beam control systems as it is often estimated as the derivative of the measured ball position. He proposes a state space Linear Quadratic Regulator (LQR) controller as a potential solution to these problems and as a general improvement over standard PID control.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

Grimshaw

Oyekan

2021

Front. Robot. AI

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The current pandemic has highlighted the need for rapid construction of structures to treat patients and ensure manufacturing of health care products such as vaccines. In order to achieve this, rapid transportation of construction materials from staging area to deposition is needed. In the future, this could be achieved through automated construction sites that make use of robots. Toward this, in this paper a cable driven parallel manipulator (CDPM) is designed and built to balance a highly unstable load, a ball plate system. The system consists of eight cables attached to the end effector plate that can be extended or retracted to actuate movement of the plate. The hardware for the system was designed and built utilizing modern manufacturing processes. A camera system was designed using image recognition to identify the ball pose on the plate. The hardware was used to inform the development of a control system consisting of a reinforcement-learning trained neural network controller that outputs the desired platform response. A nested PID controller for each motor attached to each cable was used to realize the desired response. For the neural network controller, three different model structures were compared to assess the impact of varying model complexity. It was seen that less complex structures resulted in a slower response that was less flexible and more complex structures output a high frequency oscillation of the actuation signal resulting in an unresponsive system. It was concluded that the system showed promise for future development with the potential to improve on the state of the art.

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Balance control of ball-beam system using redundant manipulator

Cited by 13 publications

References 13 publications

Tuning of Ball and Beam System using Cascade Control

Tuning of Ball and Beam System using Cascade Control

Linierisasi Model Sistem Ball and Beam dan Metode Kendali LQG Berbasis MATLAB-Simulink

Applying Deep Reinforcement Learning to Cable Driven Parallel Robots for Balancing Unstable Loads: A Ball Case Study

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