Model identification of rotary inverted pendulum using artificial neural networks

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

doi:10.1109/RDCAPE.2015.7281385

Model identification of rotary inverted pendulum using artificial neural networks

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 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 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 language	English
Title of host publication	2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	146-150
Number of pages	5
ISBN (Electronic)	9781479972470
DOIs	https://doi.org/10.1109/RDCAPE.2015.7281385
Publication status	Published - 28-09-2015
Event	International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015 - Noida, India Duration: 12-03-2015 → 13-03-2015

Conference

Conference	International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015
Country/Territory	India
City	Noida
Period	12-03-15 → 13-03-15

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Energy Engineering and Power Technology

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10.1109/RDCAPE.2015.7281385

Cite this

Chandran, D., Krishna, B., George, V. I., & Thirunavukkarasu, I. (2015). Model identification of rotary inverted pendulum using artificial neural networks. In 2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015 (pp. 146-150). Article 7281385 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RDCAPE.2015.7281385

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title = "Model identification of rotary inverted pendulum using artificial neural networks",

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 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.",

author = "Deepak Chandran and Bipin Krishna and George, {V. I.} and I. Thirunavukkarasu",

year = "2015",

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doi = "10.1109/RDCAPE.2015.7281385",

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booktitle = "2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015",

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Chandran, D, Krishna, B, George, VI & Thirunavukkarasu, I 2015, Model identification of rotary inverted pendulum using artificial neural networks. in 2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015., 7281385, Institute of Electrical and Electronics Engineers Inc., pp. 146-150, International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015, Noida, India, 12-03-15. https://doi.org/10.1109/RDCAPE.2015.7281385

Model identification of rotary inverted pendulum using artificial neural networks. / Chandran, Deepak; Krishna, Bipin; George, V. I. et al.
2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 146-150 7281385.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Model identification of rotary inverted pendulum using artificial neural networks

AU - Chandran, Deepak

AU - Krishna, Bipin

AU - George, V. I.

AU - Thirunavukkarasu, I.

PY - 2015/9/28

Y1 - 2015/9/28

N2 - 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 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.

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 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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BT - 2015 International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2015

Y2 - 12 March 2015 through 13 March 2015

ER -

Model identification of rotary inverted pendulum using artificial neural networks

Abstract

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