System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks

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

doi:10.1109/IIC.2015.7150815

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 proceeding › Conference contribution

5 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 language	English
Title of host publication	2015 International Conference on Industrial Instrumentation and Control, ICIC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	612-617
Number of pages	6
ISBN (Electronic)	9781479971657
DOIs	https://doi.org/10.1109/IIC.2015.7150815
Publication status	Published - 06-07-2015
Event	2015 International Conference on Industrial Instrumentation and Control, ICIC 2015 - Pune, Maharashtra, India Duration: 28-05-2015 → 30-05-2015

Conference

Conference	2015 International Conference on Industrial Instrumentation and Control, ICIC 2015
Country/Territory	India
City	Pune, Maharashtra
Period	28-05-15 → 30-05-15

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering
Control and Systems Engineering

Access to Document

10.1109/IIC.2015.7150815

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). Article 7150815 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIC.2015.7150815

@inproceedings{4b63262b289a4389a0ee2dcb4c80a77c,

title = "System Identification of Rotary Double 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 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.",

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

year = "2015",

month = jul,

day = "6",

doi = "10.1109/IIC.2015.7150815",

language = "English",

pages = "612--617",

booktitle = "2015 International Conference on Industrial Instrumentation and Control, ICIC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2015 International Conference on Industrial Instrumentation and Control, ICIC 2015 ; Conference date: 28-05-2015 Through 30-05-2015",

}

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. et al.
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 proceeding › Conference contribution

TY - GEN

T1 - System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks

AU - Chandran, Deepak

AU - Krishna, Bipin

AU - George, V. I.

AU - Thirunavukkarasu, I.

PY - 2015/7/6

Y1 - 2015/7/6

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 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84941115872&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941115872&partnerID=8YFLogxK

U2 - 10.1109/IIC.2015.7150815

DO - 10.1109/IIC.2015.7150815

M3 - Conference contribution

AN - SCOPUS:84941115872

SP - 612

EP - 617

BT - 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015

Y2 - 28 May 2015 through 30 May 2015

ER -

System Identification of Rotary Double Inverted Pendulum using Artificial Neural Networks

Abstract

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this