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Research Papers

A Decentralized Neuro-Adaptive Control Scheme to Suppress Chaotic/Hyperchaotic Dynamics of Smart Valves Network

[+] Author and Article Information
Peiman Naseradinmousavi

Dynamic Systems and Control Laboratory,
Department of Mechanical Engineering,
San Diego State University,
San Diego, CA 92115
e-mails: pnaseradinmousavi@mail.sdsu.edu;
peiman.n.mousavi@gmail.com

Hashem Ashrafiuon

Professor
Director of Center for Nonlinear
Dynamics and Control,
Department of Mechanical Engineering,
Villanova University,
Villanova, PA 19085
e-mail: hashem.ashrafiuon@villanova.edu

Mostafa Bagheri

Dynamic Systems and Control Laboratory,
Department of Mechanical Engineering,
San Diego State University,
San Diego, CA 92115
e-mail: mbagheri@sdsu.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received December 2, 2017; final manuscript received March 9, 2018; published online April 3, 2018. Assoc. Editor: Zaihua Wang.

J. Comput. Nonlinear Dynam 13(5), 051008 (Apr 03, 2018) (11 pages) Paper No: CND-17-1539; doi: 10.1115/1.4039627 History: Received December 02, 2017; Revised March 09, 2018

In this effort, we utilize a decentralized neuro-adaptive scheme in extinguishing both the chaotic and hyperchaotic dynamics of the so-called “Smart Valves” network. In particular, a network of two dynamically interconnected bidirectional solenoid actuated butterfly valves undergoes the harmful chaotic/hyperchaotic dynamics subject to some initial conditions and critical parameters. Crucial trade-offs, including robustness, computational burden, and practical feasibility of the control scheme, are thoroughly investigated. The advantages and shortcomings of the decentralized neuro-adaptive method are compared with those of the direct decentralized adaptive one to yield a computationally efficient, practically feasible, and robust scheme in the presence of the coupled harmful responses.

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References

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Figures

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Fig. 1

(a) A schematic configuration of two bidirectional solenoid actuated butterfly valves subject to the sudden contraction and (b) a coupled model of two butterfly valves in series without actuation

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Fig. 2

(a) The experimental work setup and (b) the experimentally measured total flow loads

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Fig. 3

(a) The coupled sets' phase portraits for initial1 and (b) the coupled sets' phase portraits for initial2

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Fig. 4

(a) The Lyapunov exponents for initial1, (b) the positive Lyapunov exponents for initial2 versus different approach angles (θ), (c) the Poincaré map for initial1 of the upstream set, (d) the Poincaré map for initial1 of the downstream set, (e) the Poincaré map for initial2 of the upstream set, and (f) the Poincaré map for initial2 of the downstream set

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Fig. 5

The estimation process for the decentralized neuro-adaptive scheme suppressing the coupled chaotic dynamics; the lower and upper lines stand for the upstream and downstream agents

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Fig. 6

The direct decentralized adaptive scheme's parameter estimation for sample ζi,w and ηi of both the upstream and downstream agents; (a) and (c): the lower and upper lines stand for the upstream and downstream agents and (b): the lower and upper lines indicate the downstream and upstream agents

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Fig. 7

The estimation process for the decentralized neuro-adaptive scheme suppressing the coupled hyperchaotic dynamics; the lower and upper lines stand for the upstream and downstream agents

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Fig. 8

(a) The neuro-adaptive control inputs, (b) the direct-based control inputs, (c) the neuro-adaptive magnetic torques, and (d) the direct-based magnetic torques

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Fig. 9

(a) The neuro-adaptive valves' rotation angles, (b) the direct-based valves' rotation angles, (c) the neuro-adaptive error signals, and (d) the direct-based error signals

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Fig. 10

(a) The neuro-adaptive control inputs, (b) the direct-based control inputs, (c) the neuro-adaptive magnetic torques, and (d) the direct-based magnetic torques

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Fig. 11

(a) The neuro-adaptive valves' rotation angles, (b) the direct-based valves' rotation angles, (c) the neuro-adaptive error signals, and (d) the direct-based error signals

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