Perancangan Reaction Wheel Inverted Pendulum Sebagai Alat Peraga Sistem Kontrol Berbasis Sistem Kontrol PID

A control system will be easier to understand if demo devices are available that can be used as learning media. Reaction wheel inverted pendulum is an under-actuation device so that the existence of a controller is absolutely necessary. This will be very interesting if used as a teaching aid of a control system. One application of this reaction wheel inverted pendulum is for the humanoid robot balance system. In this research project the physical design of the teaching aids and the design of the controller are carried out. The design starts from designing mechanical part first, starting from the dimensions and shape of the tool to the needs of the motor. Furthermore, a controller is designed that can balance the device automatically. The controller used is based on Arduino. The test results show that the reaction wheel inverted pendulum demo device can work quite well even though there is some drawback.

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Control System Design of Rotary Inverted Pendulum

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.766 ◽

2014 ◽

Vol 608-609 ◽

pp. 766-769

Author(s):

Li Qian Wang ◽

Kai Hu

Keyword(s):

Control System ◽

Control Strategy ◽

Inverted Pendulum ◽

Controller Design ◽

Single Stage ◽

Test Results ◽

Final Test ◽

Loop Control ◽

Classical Control ◽

Rotary Inverted Pendulum

In this paper we study the control system of single stage rotary inverted pendulum, and put forwards the controller design based on the core of STM32. In control strategy we use the classical control theory-PID control algorithm, which realizes the closed-loop control of rotating arm and swing rod for the single stage rotary inverted pendulum. The final test results show that the control strategy is effective.

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Gyroscope and Accelerometer Sensor on the Lanange Jagad Dance Robot Balance System

Buletin Ilmiah Sarjana Teknik Elektro ◽

10.12928/biste.v2i2.922 ◽

2020 ◽

Vol 2 (2) ◽

pp. 51

Author(s):

Ahmad Sopi Samosir ◽

Nuryono Satya Widodo

Keyword(s):

Control System ◽

Success Rate ◽

Humanoid Robot ◽

Balance Control ◽

Humanoid Robots ◽

Filter Method ◽

Accelerometer Sensor ◽

Balance System ◽

Kalman Filter Method ◽

Gyroscope Sensor

In performing dance moves, humanoid robots are expected to move flexibly and not easily fall during dance moves. To reduce the risk of robots falling while performing dance moves, a balance control system using a gyroscope sensor and accelerometer from the MPU6050 is controlled through the Arduino MEGA 2560 PRO. Robots that have balance control, are able to maintain stability in track conditions that have a certain degree of slope. This balance control system uses the Kalman filter method for processing data from the gyroscope sensor and accelerometer in order to reduce the noise that occurs during the robot's balance process. From the results of the test, the percentage of the success rate of robots in rest was 88.8%, the percentage of success when the robot was running was 86.6%, and the percentage of success when the robot was walking with dancing was 75%. From the results of all tests, humanoid robot has a percentage of 83.4% after adding a balance control system and when the humanoid robot does not use balance control will only produce a percentage of success rate of 48.4%.

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1P2-C05 Development of Wheeled Inverted Pendulum Type Humanoid Robot with the Equivalent Upper Body D.O.F to Human : Mechanism design and implementation of control system

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2015._1p2-c05_1 ◽

2015 ◽

Vol 2015 (0) ◽

pp. _1P2-C05_1-_1P2-C05_3

Author(s):

Seonghee JEONG ◽

Masatsugu IRIBE

Keyword(s):

Control System ◽

Mechanism Design ◽

Humanoid Robot ◽

Inverted Pendulum ◽

Upper Body ◽

Design And Implementation ◽

Wheeled Inverted Pendulum

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A Robust Walking Control System of Humanoid Robot

ROBOT ◽

10.3724/sp.j.1218.2010.00484 ◽

2010 ◽

Vol 32 (4) ◽

pp. 484-490

Author(s):

Lun XIE ◽

Zhiliang WANG ◽

Chong WANG ◽

Jiaming XU

Keyword(s):

Control System ◽

Humanoid Robot ◽

Walking Control

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A Case Study on Analytical Analysis of the Inverted Pendulum Real-Time Control System

10.21236/ada373286 ◽

1999 ◽

Cited By ~ 10

Author(s):

Danbing Seto ◽

Lui Sha

Keyword(s):

Control System ◽

Real Time ◽

Inverted Pendulum ◽

Real Time Control ◽

Analytical Analysis ◽

Time Control

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A real-time HIL control system on rotary inverted pendulum hardware platform based on double deep Q-network

Measurement and Control ◽

10.1177/00202940211000380 ◽

2021 ◽

Vol 54 (3-4) ◽

pp. 417-428

Author(s):

Yanyan Dai ◽

KiDong Lee ◽

SukGyu Lee

Keyword(s):

Control System ◽

Reinforcement Learning ◽

Inverted Pendulum ◽

Learning Algorithm ◽

Deep Understanding ◽

Control Engineering ◽

Experience Replay ◽

Real Hardware ◽

Rotary Inverted Pendulum ◽

Reinforcement Learning Algorithm

For real applications, rotary inverted pendulum systems have been known as the basic model in nonlinear control systems. If researchers have no deep understanding of control, it is difficult to control a rotary inverted pendulum platform using classic control engineering models, as shown in section 2.1. Therefore, without classic control theory, this paper controls the platform by training and testing reinforcement learning algorithm. Many recent achievements in reinforcement learning (RL) have become possible, but there is a lack of research to quickly test high-frequency RL algorithms using real hardware environment. In this paper, we propose a real-time Hardware-in-the-loop (HIL) control system to train and test the deep reinforcement learning algorithm from simulation to real hardware implementation. The Double Deep Q-Network (DDQN) with prioritized experience replay reinforcement learning algorithm, without a deep understanding of classical control engineering, is used to implement the agent. For the real experiment, to swing up the rotary inverted pendulum and make the pendulum smoothly move, we define 21 actions to swing up and balance the pendulum. Comparing Deep Q-Network (DQN), the DDQN with prioritized experience replay algorithm removes the overestimate of Q value and decreases the training time. Finally, this paper shows the experiment results with comparisons of classic control theory and different reinforcement learning algorithms.

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Sliding Mode Control for Wheeled Inverted Pendulum

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.971-973.714 ◽

2014 ◽

Vol 971-973 ◽

pp. 714-717 ◽

Cited By ~ 4

Author(s):

Xiang Shi ◽

Zhe Xu ◽

Qing Yi He ◽

Ka Tian

Keyword(s):

Control System ◽

Sliding Mode Control ◽

High Performance ◽

Inverted Pendulum ◽

Sliding Mode ◽

Experimental Results ◽

Control Law ◽

Collaborative Simulation ◽

Mode Control ◽

Wheeled Inverted Pendulum

To control wheeled inverted pendulum is a good way to test all kinds of theories of control. The control law is designed, and it based on the collaborative simulation of MATLAB and ADAMS is used to control wheeled inverted pendulum. Then, with own design of hardware and software of control system, sliding mode control is used to wheeled inverted pendulum, and the experimental results of it indicate short adjusting time, the small overshoot and high performance.

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