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Listing 18 manuscripts matching the search of "Fuzzy Logic Controller"

Modeling of the Inductance Variation and Control of the Switched Reluctance Motor Based on Fuzzy Logic

by Omer Bay, Cetin Elmas

Switched Reluctance Motors (SRNs) aze increasingly popular machines in electrical drives, whose performances are directly related to their operating conditions. Their dynamic characteristics vary as conditions change. Recently, several methods of the modeling of the magnetic saturation of SRNs have been proposed as analytical functions. However, the SRM system is nonlinear and cannot be adequately described by such models. Fuzzy Logic (FL) is known that might overcome this problem. This paper introduces an attempt to use fuzzy logic to model of the inductance variation of the switched reluctance motor and to control the switched reluctance motor using fuzzy model. Fuzzy based modelling does not require an accurate mathematical model which is very difficult to obtain from a switched reluctance motor because of its inherit nonlinearities. The modelling method in this paper differs significantly from previous modelling methods. An application of fuzzy sets to the SRM drive control was also applied to the speed loop, replacing the conventional PI controller. Fuzzy logic controller (FLC) was optimised by using neural network. Simulation results were verified through experimental results and fuzzy logic model was proven to be reasonably accurate. The results of applying the fuzzy logic controller to SRM were compared to those obtained by the application of a conventional PI system. Compared to a PI control the fuzzy logic control provided a better response in terms of accuracy, and insensitivity to changes in operating conditions.

Volume: 10, Issue: 3

Design of a Fuzzy Logic Based Robotic Admittance Controller

by Sameer M. Prabhu, Devendra P. Garg

A fuzzy logic based admittance control approach is proposed for the control of robotic end-effector forces occurring during a typical automated robotic manufacturing task. The proposed admittance control approach provides the necessary nonlinear control actions required in a typical automated robotic manufacturing task, and at the same time enables the incorporation of existing knowledge obtained from the process operator, in the design of the controller. This can significantly reduce the controller development time. The robotic deburring task is used as an example of a typical manufacturing task, although the technique can be easily extended to other manufacturing tasks. Automated robotic deburring offers an attractive alternative to manual deburring in terms of reduced costs and improved quality of the finished parts. The approaches proposed for control of deburring using conventional control techniques require an accurate process model and hence are not ideally suited for control under conditions of uncertainty in the available information about the process. Fuzzy logic control techniques offer an alternative to conventional control methods used for the deburring task. A fuzzy logic rule base is designed, using the knowledge obtained from the deburring operator, to control the deburring robot in the presence of uncertainties in the burr size and location information. The fuzzy logic controller issues corrections to the nominal robot trajectory based on the burrs encountered. The corrected robot trajectory is then input to a robot positional controller. Simulation results are presented to demonstrate the effectiveness of the proposed fuzzy logic based admittance control scheme in controlling the automated robotic deburring operation.

Volume: 4, Issue: 2

Experimental Implementation of An Adaptive Fuzzy Logic Controller for Process Control

by Jun Lu, Gordon Lee, Warren Jasper

Complex industrial processes such as batch chemical reactors, steelmaking, dyeing processes and metal forming offer challenges in control due to the uncertainty and complexity of the processes. Model-based control methods generally require some knowledge of system structure and possibly some bounds on the uncertainty of the system parameters. Such methods as robust control and adaptive control belong to this category. In a case where system identification is not feasible, knowledge-based methods offer an alternative solution to control. One such method, fuzzy logic control (FLC), may be used to simulate the decision making process of an experienced expert. Usually, the control decisions of an expert can be expressed linguistically as a set of heuristic decision rules. The rules are used to build rulebases for FLC; further, algorithms are used to convert the results of the rules to quantitative outputs.This article presents an adaptive multi-input, multi-output (MIMO) fuzzy logic control method which can be applied to unknown or partially unknown systems. This method will leam about the system as it controls the process. While several fuzzy logic control schemes have been developed, including adaptive algorithms, many of these techniques can only handle single input/single output systems. The MIMO method developed here employs optimization techniques with FLC to provide the control inputs. Further, verification of the adaptive FLC method is shown through actual experimental results of a batch direct dyeing process.

Volume: 1, Issue: 3


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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