Low cost two-wheels self-balancing robot for control education powered by stepper motors

Borja, José Antonio Tumialán; Alvarado, I.; Peña, David Muñoz de la

doi:10.1016/j.ifacol.2020.12.2660

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Authors in [41,42] describe an experimental, Arduino-based, low-cost, self-balancing robot designed for control teaching at the University of Seville. The fundamental idea is that by building and controlling this robot, students may learn electronics, computer programming, modelling, control, and signal processing.…”

Section: Arduino-based Low-cost Solutionsmentioning

confidence: 99%

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

et al. 2022

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The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

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Section: Arduino-based Low-cost Solutionsmentioning

confidence: 99%

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

et al. 2022

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“…The specifics of the robot, including its construction and components, are very similar to the description provided in [17]. The chassis is made using a 3D printer, and its main components are: a Raspberry Pi, an inertial measuring unit MPU6050, an Arduino NANO, a microstepping motor driver A3967 and two step motors Nema 17.…”

Section: Case Study a Two-wheeled Inverted Pendulum Robotmentioning

confidence: 99%

“…The chassis is made using a 3D printer, and its main components are: a Raspberry Pi, an inertial measuring unit MPU6050, an Arduino NANO, a microstepping motor driver A3967 and two step motors Nema 17. The main difference between this robot and the one described in [17] is the inclusion of the Raspberry Pi for monitoring and controlling the system, as we explain in further detail in Section V-B.…”

Section: Case Study a Two-wheeled Inverted Pendulum Robotmentioning

confidence: 99%

Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum

Krupa¹,

Cámara²,

Alvarado³

et al. 2021

Preprint

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This article presents the real-time implementation of the model predictive control for tracking formulation to control a two-wheeled inverted pendulum robot. This formulation offers several advantages over standard MPC formulations at the expense of the addition of a small number of decision variables, which complicates the inner structure of the matrices of the optimization problem. We implement a sparse solver, based on an extension of the alternating direction method of multipliers, in the system's embedded hardware. The results indicate that the solver is suitable for controlling a real system with sample times in the range of milliseconds using current, readily-available hardware.

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“…In academia, the TWSBR is often used as a research platform to verify advanced control algorithms as its behaviour is comparable to that of the classical inverted pendulum system. Its wheels are usually driven ❒ ISSN: 2089-4864 by an electrico-mechanical system which can be either direct current (DC) motors or stepper motors [9]. The main control objective is to stabilize the robot by driving all the state variables, which are the robot's position, velocity, tilt angle and angular velocity, to approach their desired stable values in the shortest time possible.…”

Section: Introductionmentioning

confidence: 99%

Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator

Lau

Ahmad

Goh

2023

IJRES

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<p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p>

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Low cost two-wheels self-balancing robot for control education powered by stepper motors

Cited by 8 publications

References 4 publications

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum

Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator

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