Design, Implementation and Nonlinear Control Analysis of a Furuta Pendulum System

Méndez, Efraín; Lopez, Juan R.; Macias-Hidalgo, Israel; Ramírez-Cadena, Miguel; Martínez, Ascensión Hernández; Lozoya-Santos, Jorge de J.; Ramírez-Mendoza, Ricardo A.; Molina, Arturo

doi:10.1109/iccre.2019.8724376

Cited by 5 publications

(16 citation statements)

References 12 publications

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“…Additionally, other studies such as [10], validates that the adaptive features of the MRAC can create a more robust system, features that are also translated to all the MRAC derivations. The previous premise can be clearly validated by the work presented in [11], where an MRAC combined with a Proportional Integrative Derivative (MRAC-PID) is implemented in a real Furuta pendulum testbed, achieving through the simulated and the experimental responses the validation of the method, for the upright stabilization issue of the pendulum.…”

Section: Introductionmentioning

confidence: 81%

“…Henceforth, it is important to highlight that this paper is an extension of the [11] work, originally presented in the ICMEAE 2019 (6th International Conference on Mechatronics, Electronics and Automatation Engineering) and ICCRE 2019 (4th International Conference on Control and Robotics Engineering); where the design, modelling and a first control approach for the Furuta Pendulum was presented.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the [11] implementation had its main drawback, in the great amount of processing resources consumed from the Microcontroller Unit (MCU), which in some instances compromised the correct and precise sampling rate of the data, which leaded to unexpected noise in the error signal and therefore to practical issues in the implementation. Therefore, this proposal takes the obtained results from the MRAC-PID approach, improving the behavior of the system through the implementation of an ANN based controller.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the main objective of designed ANN in this work, is to emulate the behavior of the MRAC-PID controller implemented in [11], extending the operating margins and maintaining the stability gained by the original MRAC-PID, but with lower computational effort; which is indeed, a common characteristic of the implementation of ANN's (as discussed in [13] and [10]).…”

Section: Introductionmentioning

confidence: 99%

“…Then, the results in this work where acquired through the embedded implementation in the real experimental testbed. It is critical for this work to highlight that, the ANN is trained through the input/output data acquired from the stabilization MRAC-PID controller implemented in [11], in order to achieve the improved behavior of the controller with less computational effort.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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