Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional Order PIλDμ Controllers

Patel, Himanshukumar R.; Shah, Viral B.

doi:10.5772/intechopen.90359

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…2. Optimizing IT2FL with the FPA using the basic linguistic information, would offer an effective decisions [36,37]. The FPA parameters and values used include; number of generation = 1000, population size = 100, ∈ = 0.8, = 0.1, and = 1.5.…”

Section: Fpa-optimization Of It2fl For Telemedical Monitoring and Prediction Of Cardiac Patientsmentioning

confidence: 99%

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Umoh

Abayomi

Udoh

et al. 2021

Advances in Intelligent Systems and Computing

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Determining the best parameters of the membership functions (MFs) in developing interval type-2 fuzzy logic controller (IT2FLC) is always difficult and complex because of the presence of high level of uncertainty and imprecision. Flower Pollination Algorithm (FPA) is a nature-inspired global optimization technique that imitates the pollination behavior of flowering plants, which avoids trapping in local optimum solution. This paper describes the optimization of IT2FLC using FPA for telemedical problem and applies to predict shock level in cardiac patients. The optimized IT2FLC is used to handle the uncertainties inherent in the membership functions (MFs) of the type-2 using cardiac patients' data for remote vital signs monitoring. The parameters of the MFs of the IT2FLC had not been previously optimized as this study considers applying Flower Pollination Algorithm (FPA) to optimize interval Footprint of Uncertainty (FOU) MFs of IT2FLS. We use the FPA to find the best MFs parameters of the IT2FL and the choice of FPA is because of the linear nature of the algorithm which gives attractive performances when compared to state-of-the-art algorithms. The empirical comparison is made on the designed system using cardiac patients' health datasets obtained both from real-time and clinical scenarios. Simulation results indicate that FPAbased interval type-2 fuzzy algorithm outperforms those results of a single IT2FL algorithm.

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Section: Fpa-optimization Of It2fl For Telemedical Monitoring and Prediction Of Cardiac Patientsmentioning

confidence: 99%

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Umoh

Abayomi

Udoh

et al. 2021

Advances in Intelligent Systems and Computing

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“…The nature-based metaheuristic algorithms were used to optimize the type-1 fuzzy logic controllers for fault-tolerant control application in chemical process application (i.e. nonlinear level control process) depicted in Patel and Shah (2020) with statistical results and fault recovery time analysis.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Patel

2022

IJICC

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PurposeFuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness (LOE). To optimize the fuzzy controller, type-1 harmonic search (HS) and interval type-2 (HS) will be used.Design/methodology/approachThe type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control (FTC) applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness (LOE) and also the same controller will be tested on DC motor angular position control with and without noise.FindingsThe key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller's membership function optimization. This is done in order to improve the controller's performance, bringing the process value of the two-tank level control process closer to the target process value (set point). It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system, which can handle more uncertainty than a type-1 fuzzy system.Originality/valueThe type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise. Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.

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“…In reference to Priyadarshi et al (2018) and Templos-Santos and Aguilar-Mejia (2019), FPA is used in engineering problems, like maximum power point tracking (MPPT) of pumping system govern by photovoltaic energy, permanent magnet synchronous motor (PMSM) speed control using conventional PI controller. Also in Patel et al (2020a), fuzzy FPA is used to control dihybrid level control system under the external disturbances. In recent, FPA algorithm proves capability in renewable energy sector application, the FPA is used for MPPT using innovative wave energy converter system (Zhao et al , 2019; Sun et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The FPA algorithm is used for optimization engineering and non-engineering problem (Priyadarshi et al , 2018; Templos-Santos and Aguilar-Mejia, 2019; Patel et al , 2020a; Zhao et al , 2019; Sun et al , 2018; Jain et al , 2018), however, the use of the metaheuristic algorithm in fault-tolerant control (FTC) applications is novel and proposed in the article, in addition, the metaheuristic FPA is used to design adaptive parameterization of type-1 fuzzy controller membership functions (MFs) for process control benchmark control problem with uncertainties. In recent (Priyadarshi et al , 2018; Templos-Santos and Aguilar-Mejia, 2019; Patel et al , 2020a; Lagunes et al ., 2018a, b; Bernal et al , 2019; Patel and Shah, 2022a, b; Patel, 2021), various metaheuristic algorithms are used to optimize fuzzy controllers for different engineering applications but due to advantages of the FPA (i.e. fewer parameters for tuning, random flight based algorithm, etc.)…”

Section: Introductionmentioning

confidence: 99%

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Patel

Shah

2022

IJICC

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PurposeIn recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approachThe fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.FindingsOne case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/valueThe main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

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Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional Order PI^λD^μ Controllers

Cited by 5 publications

References 29 publications

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem

Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

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