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Research Papers

Self-Tuning Robust Fuzzy Controller Design Based on Multi-Objective Particle Swarm Optimization Adaptation Mechanism

[+] Author and Article Information
Edson B. M. Costa

Federal Institute of Education, Science
and Technology,
Department of Electrical Engineering,
Laboratory of Computational Intelligence
and Control - LaCInCo,
Avenue Newton Bello, s/n, Vila Maria,
Imperatriz, CEP 65919-050, Maranhão, Brazil
e-mail: edson.costa@ifma.edu.br

Ginalber L. O. Serra

Federal Institute of Education, Science
and Technology,
Department of Electroelectronics,
Laboratory of Computational Intelligence Applied
to Technology,
Avenue Getúlio Vargas, 04, Monte Castelo,
São Luís CEP 65030-005, Maranhão, Brazil
e-mail: ginalber@ifma.edu.br

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 25, 2016; final manuscript received January 9, 2017; published online May 12, 2017. Assoc. Editor: Srinivasa M. Salapaka.

J. Dyn. Sys., Meas., Control 139(7), 071009 (May 12, 2017) (12 pages) Paper No: DS-16-1055; doi: 10.1115/1.4035758 History: Received January 25, 2016; Revised January 09, 2017

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

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References

Figures

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Fig. 1

Adaptive fuzzy control scheme based on robust stability criterion via MOPSO

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Fig. 2

Experimental setup for real-time temperature control

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Fig. 3

Experimental input/output data for dynamic TS fuzzy modeling of the thermal plant. The AC voltage (RMS) is the input signal of the plant and the temperature (degree Celsius) is the output response.

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Fig. 4

Estimated membership functions by FCM algorithm (solid line) and optimized membership functions by PSO (dashed line)

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Fig. 5

Static curve: thermal plant (“o” line), identified TS fuzzy model (solid line), and optimized TS fuzzy model (dashed line)

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Fig. 6

The cost of the best individual in each generation for optimization of the identified dynamic TS fuzzy model to represent the static curve of the thermal plant

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Fig. 7

Pareto front: multiobjective functions for TS fuzzy controller design in the first (a) and second (b) rules

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Fig. 8

Recursive parametric estimation of the TS fuzzy model: (a)–(d) consequent parameters for rule 1 and (e)–(h) consequent parameters for rule 2

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Fig. 9

Recursive parametric estimation of the TS fuzzy PID controller: (a)–(c) consequent parameters for rule 1 and (d)–(f) consequent parameters for rule 2

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Fig. 10

Instantaneous normalized activation degrees of the first (a) and second (b) rules of the TS fuzzy model/controllers

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Fig. 11

Instantaneous (a) gain and (b) phase margins from the adaptive fuzzy control system

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Fig. 12

(a) Output response of the temperature adaptive fuzzy control system and (b) control action of the adaptive fuzzy controller

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