Real-time fuzzy adaptive control

Škrjanc, Igor; Kavšek-Biasizzo, Katarina; Matko, Drago

doi:10.1016/s0952-1976(96)00078-4

Cited by 17 publications

(1 citation statement)

References 11 publications

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“…Fuzzy control is well indicated in all the situations and provides a reasonable and effective alternative to classical controllers when the system model becomes complex and inaccurate, and does not allow us to an exact description without mismatch FLC techniques have found many successful applications and demonstrated significant improved performance. Skrjanc et al [6] have presented a fuzzy adaptive cancellation control and compared it with model -reference adaptive control. The comparison has been made by implementing the above for a heat exchanger and it proves to be superior to classical model -reference adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Temperature Control using Soft Computing

Nithya¹,

Sivakumaran²,

Sivashanmuguham³

2012

IJCA

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This paper experimentally investigates the control of an industry based shell and tube heat exchanger. The hot fluid outlet temperature in the shell side is maintained by manipulating the cold fluid temperature in the tube side. The Fuzzy Logic based Controller (FLC) has been implemented in a MATLAB environment using cost effective ADAM's module and compared with the Proportional-Integral (PI) controller. The performance of the controller has been investigated for multiple changes in set points and load changes. The fuzzy logic based controller has higher speed of response and the steady state error for the fuzzy logic control has a small average value than that of the PI control. There is less oscillatory behavior with the fuzzy logic controller, which allows a system to reach steady-state operating conditions in regions where PI controller is not able to perform well. The time domain specifications like rise time, settling time, overshoot and the performance indices, Integral Squared Error (ISE) and Integral Average Error (IAE) have been compared to PI controller.

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Section: Introductionmentioning

confidence: 99%

Experimental Study of Temperature Control using Soft Computing

Nithya¹,

Sivakumaran²,

Sivashanmuguham³

2012

IJCA

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Fuzzy Control of Automatic Brush-Plating Process

Jing

Xu²,

Liu³

et al. 2009

Advances in Soft Computing

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A Robust Evolving Cloud-Based Controller

Angelov

Škrjanc

Blažič

2015

Springer Handbook of Computational Intelligence

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In this chapter a novel online self-evolving cloudbased controller, called Robust Evolving Cloudbased Controller (RECCo) is introduced. This type of controller has a parameter-free antecedent (IF) part, a locally valid PID consequent part, and a center-of-gravity based defuzzification. A firstorder learning method is applied to consequent parameters and reference model adaptive control is used locally in the ANYA type fuzzy rulebased system. An illustrative example is provided mainly for a proof of concept. The proposed controller can start with no pre-defined fuzzy rules and does not need to pre-define the range of the output, number of rules, membership functions, or connectives such as AND, OR. This RECCo controller learns autonomously from its own actions while controlling the plant. It does not use any off-line pre-training or explicit models (e.g. in the form of differential equations) of the plant. It has been demonstrated that it is possible to fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hypersurface acting as a data space) generate and self-tune/learn a non-linear controller structure and evolve it in online mode. Moreover, 75

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Real-time fuzzy adaptive control

Cited by 17 publications

References 11 publications

Experimental Study of Temperature Control using Soft Computing

Experimental Study of Temperature Control using Soft Computing

Fuzzy Control of Automatic Brush-Plating Process

A Robust Evolving Cloud-Based Controller

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