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Research Papers

Comparison of Different Model-Free Control Methods Concerning Real-Time Benchmark

[+] Author and Article Information
Elmira Madadi

Chair of Dynamics and Control,
University of Duisburg-Essen,
Duisburg 47048, Germany
e-mail: elmira.madadi@uni-due.de

Yao Dong

Chair of Dynamics and Control,
University of Duisburg-Essen,
Duisburg 47048, Germany
e-mail: yao.dong@stud.uni-due.de

Dirk Söffker

Chair of Dynamics and Control,
University of Duisburg-Essen,
Duisburg 47048, Germany
e-mail: soeffker@uni-due.de

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received April 19, 2017; final manuscript received July 17, 2018; published online August 16, 2018. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 140(12), 121014 (Aug 16, 2018) (9 pages) Paper No: DS-17-1204; doi: 10.1115/1.4040967 History: Received April 19, 2017; Revised July 17, 2018

The design of accurate model often appears as the most challenging tasks for control engineers especially focusing to the control of nonlinear system with unknown parameters or effects to be identified in parallel. For this reason, development of model-free control methods is of increasing importance. The class of model-free control approaches is defined by the nonuse of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore, the major criteria to evaluate model-free control performance are aspects regarding robustness against unknown inputs and disturbances and related achievable tracking performance. In this contribution, a detailed comparison of three different model-free control methods (intelligent proportional-integral-derivative (iPID) using second-order sliding differentiator and two variations of model-free adaptive control (using modified compact form dynamic linearization (CFDL) as well as modified partial form) is given. Using a three-tank system benchmark, the experimental results are validated concerning the performance behavior. The results obtained demonstrate the effectiveness of the methods introduced.

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Figures

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Fig. 1

Block diagram of the proposed iPID via second-order sliding differentiator [15]

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Fig. 2

Test rig of three-tank system at the Chair of Dynamics and Control (UDuE): (1) pressure sensor, (2) two way switch valve, (3) ball valve, (4) programmable logic controller, (5) digital/analog module, (6) analog/digital module, and (7) tanks

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Fig. 3

Sketch of the three-tank system

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Fig. 4

Comparison of derivative signals with and without second-order sliding differentiator

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Fig. 5

Derivative signal of second-order sliding differentiator with and without filters

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Fig. 6

Block diagram of the proposed modified model-free adaptive control method

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Fig. 7

Water level control for different model-free control methods

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Fig. 8

Water level control of different model-free control methods with disturbance

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Fig. 9

Comparison of different control methods by means of criterion (37), with and without disturbance

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Fig. 10

Criterion (37) for different parameter values

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Fig. 11

Performance statistics for different parameter values for both modified model-free controllers

Tables

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