Performance Evaluation of an On-Line Self-Learning Fuzzy Logic Controller Applied to Non-Linear Processes

Ghwanmeh, Sameh; Jones, Karl O.; Williams, David J.

doi:10.1016/s1474-6670(17)43167-3

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The input signals e(kT), ce(kT) are mapped from real values to normalized values on the universe of discourse using scaling factors, GE and GC respectively. The fuzzy sets of the controller are formed on a discrete universe of discourse of a similar structure as presented by Ghwanmeh et al (1995), i.e., 13 elements for the error defined in the range {−6,+6}, 13 elements for the change in error defined in the range {-6,+6} and 15 elements for the controller output defined in the range {−7,+7}. The membership functions are triangular, symmetrical about the centre and any two adjacent membership functions have a cross-point level of 0.7.…”

Section: The Basic Levelmentioning

confidence: 99%

Design considerations for a Model Reference Fuzzy Adaptive Controller

Banerjee

Jones

Williams

2001

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

A major difficulty with fuzzy logic control is the development of an adequate rule-base and its modification such that the desired performance is maintained with changing operating conditions. Rule elicitation is a lengthy process and specific to each application. To overcome this problem, the concepts of self-organizing fuzzy logic control and other self-learning approaches have been widely investigated. In this paper, a different type of learning fuzzy control algorithm has been discussed. The control method has been called Model Reference Fuzzy Adaptive Control (MRFAC) indicating the method of adaptation. A reference model is used to provide performance feedback for automatically synthesizing and modifying a fuzzy controller’s rule-base on-line, as new information on how to control the system is gathered. The method employs a simple iterative adaptation algorithm compared to other methods in the literature. The design issues involving the method have been thoroughly investigated. Extensive simulation tests have been executed on the MRFAC to examine the effectiveness of choice of its different parameters. The results provide an useful insight into the design considerations involving the MRFAC.

show abstract

Section: The Basic Levelmentioning

confidence: 99%

Design considerations for a Model Reference Fuzzy Adaptive Controller

Banerjee

Jones

Williams

2001

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…To address these failures, much approaches have explored nonlinear control strategies for multi-tank systems. Such approaches include controls related to nonlinear sliding modes [8], nonlinear backstepping [10], constrained prediction [11], convolution network [12], and fuzzy methods [13][14][15], which are some of the techniques employed to tackle the challenges posed by these nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Fractional Transformation-Based Decentralized Robust Control of a Coupled-Tank System for Industrial Applications

Rahman,

Leiva,

Ghaffar

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

Petrochemical and dairy industries, waste management, and paper manufacturing fall under the category of process industries where flow and liquid control are essential. Even when liquids are mixed or chemically treated in interconnected tanks, the fluid and flow should constantly be observed and controlled, especially when dealing with nonlinearity and imperfect plant models. In this study, we propose a nonlinear dynamic multiple-input multiple-output (MIMO) plant model. This model is then transformed through linearization, a technique frequently utilized in the analysis and modeling of fractional processes, and decoupling for decentralized fixed-structure H-infinity robust control design. Simulation tests based on MATLAB and SIMULINK are subsequently executed. Numerous assessments are conducted to evaluate tracking performance, external disturbance rejection, and plant parameter fluctuations to gauge the effectiveness of the proposed model. The objective of this work is to provide a framework that anticipates potential outcomes, paving the way for implementing a reliable controller synthesis for MIMO-connected tanks in real-world scenarios.

show abstract

“…Fuzzy logic is as a powerful problem-solving methodology with a great number of applications in level control. Intelligent control including fuzzy control [31][32][33][34][35][36], neural network control [37], and genetic algorithms [38] has also been applied to liquid level system.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization via Cuckoo Optimization Algorithm of Fuzzy Controller for Liquid Level Control

Balochian

Ebrahimi

2013

Journal of Engineering

View full text Add to dashboard Cite

Cuckoo optimization algorithm (COA) is one of the latest evolutionary algorithms. Finding the best optimal point, rapid convergence, and simplicity in determining algorithm parameters are some merits of COA. In this paper, COA is applied to tuning optimal fuzzy parameters for Sugeno-type fuzzy logic controllers (S-FLCs) which are used for liquid level control. A programmable logic controller (PLC) is used with fuzzy controller. For this purpose, a liquid level control set and PLC have been assembled together. MATLAB/Simulink program has been used to achieve the optimal parameters of the membership functions. The results show clearly that the optimized FLC using COA has better performance compared to manually adjustments of the system parameters for different datasets.

show abstract

Performance Evaluation of an On-Line Self-Learning Fuzzy Logic Controller Applied to Non-Linear Processes

Cited by 4 publications

References 4 publications

Design considerations for a Model Reference Fuzzy Adaptive Controller

Design considerations for a Model Reference Fuzzy Adaptive Controller

Fractional Transformation-Based Decentralized Robust Control of a Coupled-Tank System for Industrial Applications

Parameter Optimization via Cuckoo Optimization Algorithm of Fuzzy Controller for Liquid Level Control

Contact Info

Product

Resources

About