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Technical Brief

Design and Analysis of Adaptive Control Systems Over Wireless Networks

[+] Author and Article Information
Ali Albattat

Department of Mechanical and Aerospace Engineering,
Missouri University of Science and Technology,
Rolla, MO 65409

Benjamin Gruenwald

Department of Mechanical Engineering,
University of South Florida,
Tampa, FL 33620

Tansel Yucelen

Department of Mechanical Engineering,
University of South Florida,
Engineering Building C 2209,
4202 East Fowler Avenue,
Tampa, FL 33620
e-mail: tyucelen@gmail.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 18, 2016; final manuscript received October 18, 2016; published online May 9, 2017. Assoc. Editor: Yongchun Fang.

J. Dyn. Sys., Meas., Control 139(7), 074501 (May 09, 2017) (8 pages) Paper No: DS-16-1037; doi: 10.1115/1.4035094 History: Received January 18, 2016; Revised October 18, 2016

In this paper, we study the design and analysis of adaptive control systems over wireless networks using event-triggering control theory. The proposed event-triggered adaptive control methodology schedules the data exchange dependent upon errors exceeding user-defined thresholds to reduce wireless network utilization and guarantees system stability and command following performance in the presence of system uncertainties. Specifically, we analyze stability and boundedness of the overall closed-loop dynamical system, characterize the effect of user-defined thresholds and adaptive controller design parameters to the system performance, and discuss conditions to make the resulting command following performance error sufficiently small. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

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Figures

Grahic Jump Location
Fig. 1

Event-triggered adaptive control system

Grahic Jump Location
Fig. 2

An example showing the effect of user-defined thresholds and the adaptive controller design parameters to the ultimate bound given by Eq. (28): (a) effect of γ∈[1,100] and L∈[0,10] to the ultimate bound given by Eq. (28) for ϵx=1 and ϵu=1, where the arrow indicates the direction γ is increased (dashed line denotes the case with γ = 100), (b) effect of ϵx∈[0,1.5] to the ultimate bound given by Eq. (28) for ϵu=1, L∈[0,10], and γ = 100, where the arrow indicates the direction ϵx is increased (dashed line denotes that case with ϵx=1), and (c) effect of ϵu∈[0,3] to the ultimate bound given by Eq. (28) for ϵx=1, L∈[0,10], and γ = 100, where the arrow indicates the direction ϵu is increased (dashed line denotes that case with ϵu=1)

Grahic Jump Location
Fig. 3

Command following performance for the proposed event-triggered adaptive control approach: (a) ϵx=0.1, ϵu=0.1, γ=2.5, and L = 0, (b) ϵx=0.1, ϵu=0.1, γ=2.5, and L=5I, (c) ϵx=0.1, ϵu=0.1, γ = 20, and L=5I, and (d) ϵx=0.1, ϵu=0.1, γ = 40, and L=5I

Grahic Jump Location
Fig. 4

Event triggers in Figs. 3(a)3(d) and comparison with a conventional periodic strategy in terms of state and control transmission: (a) State and control event triggers for the cases presented in Figs. 3(a)3(d) and (b) comparison of the proposed event-triggered adaptive control approach in Figs. 3(a)3(d) with a conventional periodic strategy

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