A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

Castillo, Oscar; Amador-Angulo, Leticia; Castro, Juan R.; García-Valdéz, Mario

doi:10.1016/j.ins.2016.03.026

Cited by 384 publications

(146 citation statements)

References 29 publications

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“…Tables [12][13][14][15][16][17] show the values of z, "S" means that evidence of significance is found and "N.S" refers to a case in which no evidence of significance is found. In all Tables 12-17 the result of the Evidence of the first row represents the comparison between FHS and HS, the result of the Evidence of the second row represents the comparison between FHS2 and HS, and finally the result of the Evidence of the third row is the comparison between FHS and FHS2 methods.…”

Section: Statistical Comparisonmentioning

confidence: 99%

“…The proposed method is called fuzzy harmony search algorithm (FHS) that performs an adaptation of parameters with Type-1 and interval Type-2 fuzzy logic and it is used to optimize fuzzy tracking controllers so that they follow desired trajectories for benchmark control problems. Other metaheuristics that have been used to solve optimization problems based on fuzzy logic are [10][11][12][13][14][15]. In previous works this HS algorithm has been used for different optimization problems, as can be found in [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

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This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

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Section: Statistical Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

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“…The performance results obtained from Formulas (20) and (21) are shown in Table 3. It can be clearly seen that the IT2-FM has a smaller RMSE value and a higher VAF value compared to the corresponding T1-FM.…”

Section: Performance Comparison Of Type-1 and Interval Type-2 Fuzzy Amentioning

confidence: 99%

“…The IT2-FLS extends the reasoning and design freedom of the fuzzy system because of the expanding dimension performance provided by the footprint of uncertainty (FOU) [17]. In [18][19][20], it has been shown that IT2-FLS is superior for handling uncertainties and nonlinearities compared with the conventional fuzzy logic system.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Zhao

Cao

et al. 2017

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Multivariable coupling, nonlinear and large time delays exist in the coil outlet temperature (COT) control system of the ethylene cracking furnace, which make it hard to achieve accurate control over the COT of the furnace in actual production. To solve these problems, an inverse controller based on an interval type-2 fuzzy model control strategy is introduced. In this paper, the proposed control scheme is divided into two parts: one is the approach structure part of the interval type-2 fuzzy model (IT2-FM), which is utilized to approach the process output. The other is the interval type-2 fuzzy model inverse controller (IT2-FMIC) part, which is utilized to control the output process to achieve the target value. In addition, on the cyber-physical system platform, the actual industrial data are used to test and obtain the mathematical model of the COT control system of the ethylene cracking furnace. Finally, the proposed inverse controller based on the IT2-FM control scheme has been implemented on the COT control system of the ethylene cracking furnace, and the simulation results show that the proposed method is feasible.

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“…Therefore, these last years, an advanced form of FLS, called type-2 FLS (T2-FLS), has attracted considerable attention and becomes more and more imposed in designing robust controllers for uncertain complex processes, including robot systems [15][16][17][18]. One reason is that a T2-FS is characterized by a membership function (MF) that includes a footprint of uncertainty (FOU), which makes it possible to handle linguistic uncertainties more effectively than T1-FS [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators

et al. 2018

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This paper develops a new robust tracking control design for n-link robot manipulators with dynamic uncertainties, and unknown disturbances. The procedure is conducted by designing two adaptive interval type-2 fuzzy logic systems (AIT2-FLSs) to better approximate the parametric uncertainties on the system nominal. Then, in order to achieve the best tracking control performance and to enhance the system robustness against approximation errors and unknown disturbances, a new control algorithm, which uses a new synthesized AIT2 fuzzy sliding mode control (AIT2-FSMC) law, has been proposed. To deal with the chattering phenomenon without deteriorating the system robustness, the AIT2-FSMC has been designed so as to generate three adaptive control laws that provide the optimal gains value of the global control law. The adaptation laws have been designed in the sense of the Lyapunov stability theorem. Mathematical proof shows that the closed loop control system is globally asymptotically stable. Finally, a 2-link robot manipulator is used as case study to illustrate the effectiveness of the proposed control approach.

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A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

Cited by 384 publications

References 29 publications

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators

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