Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers

Arman Dabiri, Morad Nazari, Eric A. Butcher

Research output: ResearchConference contribution

Abstract

In this paper, an adaptive neural fuzzy inference system (ANFIS)-based control technique is proposed to stabilize dynamical systems with fractional order dampers. For this purpose, a linear quadratic regulator (LQR) is first designed for the analogous linearized integer order systems where the fractional damper is replaced by the combination of an integer spring and an integer damper. Next, the ANFIS-based controller is trained based on the responses of the closed-loop LQR-controlled system under different scenarios such as several initial conditions and/or inputs. Since the number of fuzzy rules increases exponentially by increasing the number of inputs, a fusion function proposed in the literature is used to reduce the number of inputs in the ANFIS-based controller. Hence the number of fuzzy rules is reduced as well. The result of this training is a trained ANFIS-LQR controller that can be used for stabilizing the fractional-order models with fractional order dampers. As an illustrative example, the proposed technique is employed to stabilize an under-actuated double inverted pendulum on the cart with fractional order dampers.

LanguageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1972-1977
Number of pages6
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Other

Other2017 American Control Conference, ACC 2017
CountryUnited States
CitySeattle
Period5/24/175/26/17

Fingerprint

Fuzzy inference
Dynamical systems
Controllers
Fuzzy rules
Pendulums
Fusion reactions

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Dabiri, A., Nazari, M., & Butcher, E. A. (2017). Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers. In 2017 American Control Conference, ACC 2017 (pp. 1972-1977). [7963241] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.23919/ACC.2017.7963241

Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers. / Dabiri, Arman; Nazari, Morad; Butcher, Eric A.

2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1972-1977 7963241.

Research output: ResearchConference contribution

Dabiri, A, Nazari, M & Butcher, EA 2017, Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers. in 2017 American Control Conference, ACC 2017., 7963241, Institute of Electrical and Electronics Engineers Inc., pp. 1972-1977, 2017 American Control Conference, ACC 2017, Seattle, United States, 5/24/17. DOI: 10.23919/ACC.2017.7963241
Dabiri A, Nazari M, Butcher EA. Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers. In 2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc.2017. p. 1972-1977. 7963241. Available from, DOI: 10.23919/ACC.2017.7963241
Dabiri, Arman ; Nazari, Morad ; Butcher, Eric A./ Adaptive neural-fuzzy inference system to control dynamical systems with fractional order dampers. 2017 American Control Conference, ACC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1972-1977
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