General derivation and analysis for input–output relations in interval type-2 fuzzy logic systems

Aliasghary, Mortaza; Eksin, İbrahim; Güzelkaya, Müjde; Kumbasar, Tufan

doi:10.1007/s00500-014-1340-7

Cited by 8 publications

(8 citation statements)

References 27 publications

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“…U (t) is the general type-2 fuzzy controller output and can be calculated by (24)(25)(26) in the section B.…”

Section: Fractional Order General Type-2 Fuzzy Pid Controller a mentioning

confidence: 99%

“…He also analyzed stability of a single input interval type-2 fuzzy logic controllers [24]. A structure of interval type-2 fuzzy controller was derived in [25] and [26] revisited Karnik-Mendel algorithms in the framework of linear fractional programming. Hagras done some jobs on internal type-2 fuzzy logic, like controlling for autonomous mobile robots [27], realise ambient intelligence in ubiquitous computing environments [28], mobile field workforce area optimization [29] and mobile field workforce area optimization [30].…”

Section: Introductionmentioning

confidence: 99%

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A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Zhong

2020

IEEE Access

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Fractional order PID controller was received attentions in control problems for it had 2 freedom adjustable parameters. Practices had proved that better results could be obtained by introduction of fractional order PID in control problems. And in recently years, fractional order PID combined with fuzzy logic system was gradually got attention, the typical of which was fractional order type-1 fuzzy PID controller. For fractional order PID and fractional order type-1 fuzzy PID controller couldn't deal with the uncertainty of system, so fractional order interval type-2 fuzzy PID controller was applied to solve this problem. But interval type-2 fuzzy sets were a simplification of type-2 fuzzy sets, and there may be have the defect of information loss. A fractional order general type-2 fuzzy PID controller was proposed in this article. The proposed controller based on general type-2 fuzzy logic system, which can make full use of the advantages of general type-2 fuzzy logic system in describing the uncertainty of the system. The fractional order general type-2 fuzzy PID controller utilized a simplified type reduction called NT type reduction algorithm. The NT type reduction algorithm can get the defuzzification result directly and avoided iterative process as KM type reduction commonly used in interval type-2 fuzzy controller. The simulations of 3 processes and a practical inverted pendulum system show that fractional order general type-2 fuzzy PID controller can reduce overshoot, improve system response speed and accelerate system stability time in comparing with other controllers. Especially, when the system has disturbance, parameters uncertainty or structure uncertainty, the fractional order general type-2 fuzzy PID controller has better control effects than other compared controllers. INDEX TERMS Fractional order control, fractional order fuzzy PID, general type-2 fuzzy sets, general type-2 fuzzy logic system, type reduction.

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“…U (t) is the general type-2 fuzzy controller output and can be calculated by (24)(25)(26) in the section B.…”

Section: Fractional Order General Type-2 Fuzzy Pid Controller a mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Zhong

2020

IEEE Access

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“…Among the T2 FSs, interval type-2 fuzzy logic systems (IT2 FLSs) in which the secondary MFs are one, have been introduced to cope with computational burden. IT2 FLS has been successfully used in various control, modeling and stability issues [1][2][3][4][5][6][7][8][9][10]. For example, the stability conditions of IT2 polynomial fuzzy-model-based control system through the sum of the squares approach have been studied in [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Based on Lyapunov stability theory, Sheng et al [10] solved the stability problem of discrete IT2 FCSs. In comparison with type-1 FSs, type-2 FSs provide better modeling and handling uncertainty on the basis of additional degrees of freedom provided by the FOU in their membership functions (MFs) [8]. A comparative study of generalized type-2 fuzzy control systems (GT2FCSs) with respect to IT2 FCSs and T1 FCSs has been considered in [11,12] to prove the efficiency and performance of GT2FCSs in the presence of external noise.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of dynamic interval type‐2 fuzzy control systems in frequency domain

Namadchian

Zare

2020

Asian Journal of Control

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The present paper aims to analyze the stability of dynamic Interval Type-2 fuzzy control systems (IT2 FCSs) with adjustable parameters by investigating the existence of limit cycles based on the describing function (DF) method. To simplify the stability analysis, the following structured procedure is described. First, to avoid complex computation, a simple architecture of the IT2 FCS using two embedded Type-1 fuzzy control systems (T1 FCSs) is proposed. Then, the DF of IT2 FCS is obtained based on the DFs of embedded T1FCSs. Subsequently, integrating the stability equation and parameter plane approaches provides a solution to identify the limit cycle and the asymptotically stability regions. Moreover, particle swarm optimization (PSO) technique is utilized to minimize the limit cycle region. Other focus of this paper is on the stability margins analysis for robust design. For this purpose, a gain-phase margin tester is applied to determine the minimum Gain Margin (GM min ) and Phase Margin (PM min ) when limit cycles can arise. Finally, two simulation examples are given to verify the effectiveness of the proposed approach.

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“…A great deal of attention has been paid to interval type‐2 fuzzy logic systems (IT2FLSs) due to their high performance in practical applications such as load frequency controller , bioreactor control , system identification , induction motor , and wind generators . The results of these experiment and simulation studies demonstrate that the IT2FLS have a superior performance over the type‐1 counterpart . However, we believe that there is no research work related to applying IT2FLS in control of PEMFCs in literature.…”

Section: Introductionmentioning

confidence: 99%

Control of PEM Fuel Cell Systems Using Interval Type‐2 Fuzzy PID Approach

Aliasghary

2018

Fuel Cells

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In this study, an interval type‐2 fuzzy proportional–integral–derivative (IT2FPID) controller is designed to be used in the air flow regulation of polymer electrolyte membrane fuel cell (PEMFC) systems. The proposed controller is able to handle the effect of external disturbances and fix the oxygen excess ratio at an appropriate set‐point. Three crisp singletons for the output and two interval type‐2 membership functions for the inputs are defined for the suggested controller. Two conventional controllers including type‐1 fuzzy PID (T1FPID) and ordinary proportional–integral–derivative (PID) are also investigated to evaluate the performance and efficiency of this technique. The results of various simulations indicate that the IT2FPID controller has a better performance and transient response compared to the other approaches.

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General derivation and analysis for input–output relations in interval type-2 fuzzy logic systems

Cited by 8 publications

References 27 publications

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

A Fractional Order General Type-2 Fuzzy PID Controller Design Algorithm

Stability analysis of dynamic interval type‐2 fuzzy control systems in frequency domain

Control of PEM Fuel Cell Systems Using Interval Type‐2 Fuzzy PID Approach

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