Swinging up the Furuta Pendulum and its Stabilization Via Model Predictive Control

Seman, Pavol; Rohal’-Ilkiv, Boris; ́as, Martin Juh; Salaj, Michal

doi:10.2478/jee-2013-0022

Cited by 24 publications

(14 citation statements)

References 11 publications

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“…Attached to the arm is a pendulum, which is free to swing in the vertical (V) plane [26]. The reason is that the RIP carries many interesting features, such as non-linearity, fast dynamics, multiple variables, and multidimensional spatial motion, allowing evaluation of several elements of modern control theory [26]- [28].…”

Section: B Ann In Identification For Controlmentioning

confidence: 99%

“…10 shows the RIP prototype used for this study, whose parameters are presented in Table 1. This work does not intend to perform the mathematical description of the RIP, as well as the design methods for controlling it, which is well described elsewhere [26], [28], [36].…”

Section: B System Descriptionmentioning

confidence: 99%

“…with A first-order low pass filter (LPF) is applied to all four outputs of the RIP, as presented in (28) for the arm angle. These filters reduce the noise of the RIP output signals.…”

Section: B System Descriptionmentioning

confidence: 99%

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Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

et al. 2021

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Artificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could be challenging for them. The family of paraconsistent logics (PL) is a powerful tool that can deal with uncertainty and contradictory information, so getting attention from researchers for its implications and applications in artificial intelligence. This investigation describes a novel activation function reasoned on the paraconsistent annotated logic by two-value annotations (PAL2v) rules, a variation of PL, allowing the design of a new paraconsistent neural net (PNN), applied in model identification for control (I4C) of a closed-loop rotary inverted pendulum (RIP) system.

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Section: B Ann In Identification For Controlmentioning

confidence: 99%

Section: B System Descriptionmentioning

confidence: 99%

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Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

et al. 2021

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“…The swinging-up routine can be made through energy control, as explored by Furuta itself in [4]. Other proposed solutions for the task is the predictive controller ( [16]), and even solved through feedforward approaches such as presented in [17]. On the other hand, the second control objective lies in the stabilization of the pendulum, in order to maintain it in an upright position by correcting any disturbance, only through the applied torque of the arm.…”

Section: Control Objectivesmentioning

confidence: 99%

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

Méndez¹,

Baltazar-Reyes²,

Macias³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Nowadays, process automation and smart systems have gained increasing importance in a wide variety of sectors, and robotics have a fundamental role in it. Therefore, it has attracted greater research interests; among them, Underactuated Mechanical Systems (UMS) have been the subject of many studies, due to their application capabilities in different disciplines. Nevertheless, control of UMS is remarkably more difficult compared to other mechanical systems, owing to their non-linearities caused by the presence of fewer independent control actuators with respect to the degrees of freedom of the mechanism (which characterizes the UMS). Among them, the Furuta Pendulum has been frequently listed as an ideal showcase for different controller models, controlled often through non-lineal controllers like Sliding-Mode and Model Reference Adaptive controllers (SMC and MRAC respectively). In the case of SMC the chattering is the price to be paid, meanwhile issues regarding the coupling between control and the adaptation loops are the main drawbacks for MRAC approaches; coupled with the obvious complexity of implementation of both controllers. Hence, recovering the best features of the MRAC, an Artificial Neural Network (ANN) is implemented in this work, in order to take advantage of their classification capabilities for non-linear systems, their low computational cost and therefore, their suitability for simple implementations. The proposal in this work, shows an improved behavior for the stabilization of the system in the upright position, compared to a typical MRAC-PID structure, managing to keep the pendulum in the desired position with reduced oscillations. This work, is oriented to the real implementation of the embedded controller system for the Furuta pendulum, through a Microcontroller Unit (MCU). Results in this work, shows an average 58.39% improvement regarding the error through time and the effort from the controller.

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“…Although nonlinear control does not deal with model approximation problems such as approximation errors or underfitting parameters, linear control is a powerful approach which can enable a simpler stability analysis and control design due to the extensively studied linear theory. For instance, a linear model predictive control for the pendulum balance is combined with swing-up strategies in (Seman et al, 2013), and the work in (Ramírez-Neria et al, 2014) present a linear controller whose input is based on a linear observer, with a control strategy obtained under an active disturbance rejection control (ADRC) scheme.…”

Section: Introductionmentioning

confidence: 99%

A Switched Control Strategy for Swing-Up and State Regulation for the Rotary Inverted Pendulum

Solís¹,

Olivares²,

Allende³

2019

STUD INFORM CONTROL

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The rotary inverted pendulum (RIP) is an underactuated mechanical system that can be balanced in the upward position, by applying the linear control theory based on a nonlinear dynamical model linearized around that position. Its structure makes it possible to implement controllers designed for energy minimization in the non-linearizable region. Due to the limited validity of the resulting linear model, a nonlinear control is needed to automatically move the pendulum close to the linearized region, starting from its stable downward position. A RIP platform has been built to experimentally test a switched control strategy design, which combines an energy-based nonlinear controller for swinging up the pendulum and a linear quadratic regulator for the balancing task. Experimental results validate the performance of this approach on the physical prototype.

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Swinging up the Furuta Pendulum and its Stabilization Via Model Predictive Control

Cited by 24 publications

References 11 publications

Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

A Switched Control Strategy for Swing-Up and State Regulation for the Rotary Inverted Pendulum

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