A fuzzy set theory based control of a phase-controlled converter DC machine drive

Sousa, G.C.D.; Bose, B.K.

doi:10.1109/28.273619

Cited by 137 publications

(17 citation statements)

References 14 publications

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“…Many studies have concluded that the fuzzy logic controller is better in motor control than the PID controller [1][2][3][4][5]. Other studies have insisted that there is not much performance difference between fuzzy logic and PID controllers [6][7][8][9]. Moreover, although many studies insist that the fuzzy logic controller is better than the PID controller, the PID controller is used more than the fuzzy logic controller these days.…”

Section: Introductionmentioning

confidence: 99%

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Kim

2014

International Journal of Fuzzy Logic and Intelligent Systems

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Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

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Section: Introductionmentioning

confidence: 99%

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Kim

2014

International Journal of Fuzzy Logic and Intelligent Systems

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“…Therefore, the use of classical controllers does not meet the requirements for robust performance. The introduction of artificial intelligence (Krohling et al, 1997;Sousa and Bose, 1994) has brought a new era in the industrial drive. Various AI based heuristic controls have shown a good prospect to bring robustness and adaptive nature in constant speed variable torque or constant torque variable speed drive applications (Kukolj et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Speed Control of Switched Reluctance Motor Using New Hybrid Particle Swarm Optimization

A.¹

2012

Journal of Computer Science

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Problem statement:The main objective of this research is to obtain the speed control of switched reluctance motor with minimum settling time and without overshoot. Approach: A new algorithm has been developed with the combination of differential evolution and particle swarm optimization and applied for speed control of switched reluctance motor under sudden change in speed. Also speed control of switched reluctance motor was obtained by other artificial intelligence methods such as fuzzy logic controller, fuzzy PI controller and particle swarm optimization based tuning of fuzzy PI controller. Matlab/Simulink environment was used for the simulation. Results: Results are discussed and tabulated based on the performance of the controllers. Conclusion: From the comparison of all above methods, the algorithm has given better results in speed response than other controllers.

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“…A fuzzy inference tool works with a set of heuristic decision rules expressed in linguistic terms [74][75][76][77] and typically consists of three main components, described in the subsections that follow.…”

Section: Design Of the Fuzzy Inference Toolmentioning

confidence: 99%

A Fuzzy Logic-Based Tool for the Assessment of Corporate Sustainability: A Case Study in the Food Machinery Industry

2017

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This paper develops a quantitative tool for the assessment of sustainability at the company's level, taking into account its three key perspectives, i.e., economic, environmental and social. The proposed framework is grounded on fuzzy logic and in particular on a monotonic hierarchical fuzzy inference tool, as an effective means to gather the judgements and scores against the key performance indicators (KPIs) of each sustainability perspective into an aggregated index. In developing the fuzzy inference tool, we also solve some typical issues related to the assessment of sustainability, e.g., the identification of benchmark values to rate the company's performance against each sustainability KPI. The architecture of the fuzzy inference tool was embodied in an ad hoc software developed in Microsoft Excel TM (Microsoft Corporation, Albuquerque, NM, USA), which makes it easy to use the proposed framework in practical situations. The potential of the software tool developed is illustrated by means of a case study, targeting a food machinery company, for which the overall evaluation of sustainability is carried out.

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A fuzzy set theory based control of a phase-controlled converter DC machine drive

Cited by 137 publications

References 14 publications

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Speed Control of Switched Reluctance Motor Using New Hybrid Particle Swarm Optimization

A Fuzzy Logic-Based Tool for the Assessment of Corporate Sustainability: A Case Study in the Food Machinery Industry

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