Design of Fuzzy Supervisor-Based Adaptive Process Control Systems

Chapter
First Online:
Part of the Intelligent Systems Reference Library book series (ISRL, volume 107)

Abstract

The modern industrial processes are difficult to model and control by classical means for their nonlinearity, inertia, model uncertainty and varying parameters. The adaptive fuzzy logic controllers (AFLCs) improve the system performance but are computationally hard to design and embed in programmable logic controllers (PLCs) for wider industrial applications. In this chapter a design approach for simple AFLCs is suggested, based on main controllers—linear, FLC or parallel distributed compensation (PDC), and fuzzy logic supervisors (FLSs) for on-line auto-tuning of their gains or scaling factors. The effect is a continuous adaptation of the control surface in response to plant changes. Approximation of the designed AFLC to a PDC equivalent on the basis of neuro-fuzzy and optimization techniques enables the stability analysis of the AFLC system using the indirect Lyapunov method and also its PLC implementation. The AFLC is applied for the real time control of the processes in a chemical reactor, a dryer, a two-tank and an air-conditioning systems, decreasing overshoot, settling time, control effort and coupling compared to classical FLC and linear control systems.

Keywords

Adaptive fuzzy logic control Design Genetic algorithms Inertial processes Nonlinear dynamic systems Stability Supervisor 

List of Abbreviations

ADC

Analog-to-digital converter

AFLC

Adaptive fuzzy logic control/controllers

DAC

Digital-analog converter

DAQ

Data acquisition

FIM

Fuzzy inverse model

FL

Fuzzy logic

FU

Fuzzy unit

FLC

Fuzzy logic control/controllers

FLS

Fuzzy logic supervisor

FSOC

Fuzzy self-organizing control

GA

Genetic algorithms

HVAC

Heating ventilation and air-conditioning

IMC

Internal model controller

ISE/IAE

Integral squared error/integral of absolute error

KBM

Knowledge-based modifier

LMI

Linear matrix inequality

MF

Membership function

MRFLC

Model reference FLC

NF

Neuro-fuzzy (model, structure, control etc.)

PDC

Parallel distributed compensation

PD/PI/PID

Proportional plus derivative/proportional plus integral/proportional plus integral plus derivative (for a linear control algorithm)

PLC

Programmable logic controllers

PWM

Pulse-width modulation

RH

Relative humidity

SFU

Supervisor fuzzy unit

SISO/TISO/TITO/MIMO

Single-input-single-output/two-input-single-output/two-input-two-output/multi-input-multi output

2I

Two-input

ScF

Scaling factor (normalization/denormalization gains of a FLC)

TSK

Takagi-Sugeno-Kang fuzzy model

ZN

Ziegler-Nichols linear plant model (also a three parameter model)

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Notes

Acknowledgments

The author would like to express her gratitude to Prof. Lakhmi C. Jain from the University of Canberra, Australia for his invaluable assistance and the many discussions that helped improving the presentation of the research, to the reviewers for their competent comments and recommendations and to the Bulgarian diploma and Ph.D. students who were involved in many of the experiments and the investigations, contained in this chapter.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of AutomationTechnical University of SofiaSofiaBulgaria

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