System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks

Deepak Chandran, Bipin Krishna, V. I. George, I. Thirunavukkarasu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

System Identification has been widely used in obtaining the mathematical model of nonlinear systems. Nonlinear system identification is challenging because of its complexity and unpredictability. The nonlinear system considered in this paper is Rotary Double Inverted Pendulum which is unstable and non-minimum phase system. Inverted pendulum is a well-known benchmark system in control system laboratories which is inherently unstable. In this work full dynamics of the system is derived using classical mechanics and Lagrangian formulation. Artificial neural network is used to identify the model.

Original languageEnglish
Title of host publication2015 International Conference on Industrial Instrumentation and Control, ICIC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages612-617
Number of pages6
ISBN (Electronic)9781479971657
DOIs
Publication statusPublished - 06-07-2015
Event2015 International Conference on Industrial Instrumentation and Control, ICIC 2015 - Pune, Maharashtra, India
Duration: 28-05-201530-05-2015

Conference

Conference2015 International Conference on Industrial Instrumentation and Control, ICIC 2015
CountryIndia
CityPune, Maharashtra
Period28-05-1530-05-15

Fingerprint

Pendulums
Nonlinear systems
Identification (control systems)
Neural networks
Mechanics
Mathematical models
Control systems

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering

Cite this

Chandran, D., Krishna, B., George, V. I., & Thirunavukkarasu, I. (2015). System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks. In 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015 (pp. 612-617). [7150815] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIC.2015.7150815
Chandran, Deepak ; Krishna, Bipin ; George, V. I. ; Thirunavukkarasu, I. / System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks. 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 612-617
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Chandran, D, Krishna, B, George, VI & Thirunavukkarasu, I 2015, System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks. in 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015., 7150815, Institute of Electrical and Electronics Engineers Inc., pp. 612-617, 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015, Pune, Maharashtra, India, 28-05-15. https://doi.org/10.1109/IIC.2015.7150815

System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks. / Chandran, Deepak; Krishna, Bipin; George, V. I.; Thirunavukkarasu, I.

2015 International Conference on Industrial Instrumentation and Control, ICIC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 612-617 7150815.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - System Identification has been widely used in obtaining the mathematical model of nonlinear systems. Nonlinear system identification is challenging because of its complexity and unpredictability. The nonlinear system considered in this paper is Rotary Double Inverted Pendulum which is unstable and non-minimum phase system. Inverted pendulum is a well-known benchmark system in control system laboratories which is inherently unstable. In this work full dynamics of the system is derived using classical mechanics and Lagrangian formulation. Artificial neural network is used to identify the model.

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Chandran D, Krishna B, George VI, Thirunavukkarasu I. System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks. In 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 612-617. 7150815 https://doi.org/10.1109/IIC.2015.7150815