Balancing control of bicycle robot

Suebsomran, Anan

doi:10.1109/cyber.2012.6392529

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Seperti pada penelitian (Huang, dkk, 2017), dibangun robot humanoid kecil dan sepeda dengan ukuran yang sebanding. Seperti pengendara manusia, robot ini dirancang untuk mengayuh, menyeimbangkan, dan mengarahkan sepeda (Suebsomran, 2012). Penelitian ini secara khusus fokus pada desain sistem kontrol untuk robot untuk menyeimbangkan dan mengarahkan sepeda menggunakan stang.…”

Section: Pendahuluanunclassified

Kontrol Keseimbangan Robot Hexapod EILERO menggunakan Fuzzy Logic

2021

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ABSTRAKPenelitian dengan topik robot hexapod telah banyak dikembangkan, namun sampai saat ini masih sedikit yang mengulas tentang kontrol keseimbangannya. Permasalahan yang kerap muncul adalah ketika robot berada dalam bidang miring, robot dapat terjatuh jika robot tidak dapat menyeimbangkan badan. Begitu pula dengan robot hexapod EILERO yang telah kami bangun. Untuk mengatasi permasalahan itu, selain pemodelan kinematik dan kinematika terbalik yang tepat, juga diperlukan suatu sistem keseimbangan yang baik. Dalam penelitian ini, kami menggunakan fuzzy logic untuk mengontrol keseimbangan robot EILERO dengan umpan balik data kemiringan dari sebuah sensor IMU. Setelah melalui beberapa pengujian yang komprehensif, didapatkan hasil bahwa robot dapat menyeimbangkan diri pada kondisi kemiringan papan pijakan antara -15° dan 15° pada orientasi kemiringan roll dan pitch. Robot mampu merespon dengan capaian steady state di bawah 3000 ms. Dengan demikian, robot EILERO semakin stabil dalam melintasi bidang yang tidak datar.Kata kunci: hexapod, EILERO, kinematika terbalik, fuzzy logic ABSTRACTResearch on the topic of the hexapod robot has been developed a lot, but until now there is little that has been discussed about balance control. The problem that often arises is that when the robot is on an inclined plane, the robot can fall if the robot cannot balance its body. Likewise with the EILERO hexapod robot that we have built. To solve this problem, besides proper kinematic modeling and inverse kinematic modeling, a good balance system is also needed. In this study, we used fuzzy logic to control the balance of the EILERO robot, with tilt data feedback from an IMU sensor. After going through several comprehensive tests, the results show that the robot can balance itself on the slope of the stepboards between -15 ° and 15 ° in the orientation of roll and pitch tilt. The robot is able to respond with steady state achievements below 3000 ms. Thus, the EILERO robot is increasingly stable in traversing uneven planes.Keywords: hexapod, EILERO, inverse kinematic, fuzzy logic

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Section: Pendahuluanunclassified

Kontrol Keseimbangan Robot Hexapod EILERO menggunakan Fuzzy Logic

2021

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“…So we lock the front-bar at 45 • and employ the driving torque as in Eq. (9). Besides, we only consider the uniform circular motion, so the resultant moment (about P A P B ) due to the gravity and the centripetal force should be zero, which leads to…”

Section: Controller For Circular Motionmentioning

confidence: 99%

“…Bui et al 8 were also concerned with the flywheel regulator in their design, and what is amazing is that their robot achieved a stable balance with zero moving velocity in a real experiment. Suebsomran 9 proposed upper and lower flywheels in their bicycle robot design. In a simulation, they showed that the flywheels' rotation produced a suitable torque to stabilize the bicycle in a given roll angle.…”

Section: Introductionmentioning

confidence: 99%

Simple realization of balanced motions under different speeds for a mechanical regulator-free bicycle robot

2014

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SUMMARYMechanical regulator-free bicycle robots have lighter weight and fewer actuators than the traditional regulator-based bicycle robots. In order to deal with the difficulty of maintaining balance for this kind of bicycle robot, we consider a front-wheel drive and mechanical regulator-free bicycle robot. We present the methodologies for realizing the robot's ultra-low-speed track-stand motion, moderate-speed circular motion and high-speed rectilinear motion. A simplified dynamics of the robot is developed using three independent velocities. From the dynamics, we suggest there may be an underactuated rolling angle in the system. Our balancing strategies are inspired by human riders' experience, and our control rules are based on the bicycle system's underactuated dynamics. In the case of track-stand and circular motion, we linearize the frame's rolling angle and configure the robot to maintain balance by the front-wheel's motion with a fixed front-bar turning angle. In the case of the rectilinear motion, we linearize both front-bar steering angle and front-wheel rotating angle, and configure the system to maintain balance by the front-bar's turning with a constant front-wheel rotating rate. Numerical simulations and physical experiments are given together to validate the effectiveness of our control strategies in realizing the robot's proposed three motions.

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“…There exists a considerable number of works on autonomous bicycles carried out since some years. Paper [1] developed a bicycle robot with balancing control. Paper [2] controlled the balance of a two-wheeled vehicle.…”

Section: Introductionmentioning

confidence: 99%

Open-loop Control Strategies and Feasible System Response for a Rear-Axle Bicycle Robot

Masiala¹

2023

Preprint

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This paper aims at computing feasible control strategies and the corresponding feasible state trajectories to drive an autonomous rear-axle bicycle robot from a given initial state to a final state such that the total running cost is minimized. Pontryagin’s Minimum Principle is applied and derives the optimality conditions from which the feasible control functions, expressed as functions of state and costate variables, are substituted into the combined state-costate system to obtain a new free-control state-costate nonlinear system of ordinary differential equations. A computer program was written in Scilab to solve the combined state-costate system and obtain the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations were provided to show the effectiveness and the reliability of the approach.

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Balancing control of bicycle robot

Cited by 7 publications

References 8 publications

Kontrol Keseimbangan Robot Hexapod EILERO menggunakan Fuzzy Logic

Kontrol Keseimbangan Robot Hexapod EILERO menggunakan Fuzzy Logic

Simple realization of balanced motions under different speeds for a mechanical regulator-free bicycle robot

Open-loop Control Strategies and Feasible System Response for a Rear-Axle Bicycle Robot

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