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

Peraza, Cinthia; Valdez, Fevrier; Melín, Patricia

doi:10.3390/a10030082

Cited by 39 publications

(14 citation statements)

References 31 publications

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“…The objective function for FLC is the root mean square error (RMSE) that is represented in Equation (7), and the objective function for PI and PID controller is the Settling Time, although there are other control metrics that are also used, as follows: ISE (Integral of Squared error), IAE (Integral of the Absolute value of the Error), ITSE (Integral of Time-weighted Squared Error), and ITAE (Integral of the Time multiplied by the Absolute value of the Error), respectively, presented in Equations (8)- (11). The metrics used to measure the error in this work were selected based on works published in the literature; most of the authors and experts in the area recommend some of them in works related to control [17,34,36,37].…”

Section: Case Study Used In This Workmentioning

confidence: 99%

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Valdez

Castillo

Caraveo

et al. 2019

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Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics to validate the performance of the speed control obtained with the controllers proposed in this paper.

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Section: Case Study Used In This Workmentioning

confidence: 99%

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Valdez

Castillo

Caraveo

et al. 2019

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“…This paper presents a modification to the GSO metaheuristic with the use of type-1 fuzzy systems for the adjustment of the c 3 and c 4 parameters in the GSO algorithm [15]. In addition to a type-1 fuzzy system extension, we are also using type-2 fuzzy logic to give rise to an interval type-2 fuzzy system (IT2), and this proposal was used for the optimization of the membership functions of the controller of the autonomous mobile robot [16]. Experiments were carried out optimizing the fuzzy controller for the aforementioned study case, and the optimization was initially carried out with the original galactic swarm optimization, after which the T1 and IT2 fuzzy systems were used to perform the fuzzy controller optimization, in the same way that the original GSO algorithm was experimented with by adding noise to the plant and without noise in order to compare the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Bernal

Castillo

Soria

et al. 2019

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Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization of the fuzzy controller. In the proposed approaches, perturbation (noise) was added to the plant in order to find out if our approach behaves well under perturbation to the autonomous mobile robot plant; additionally, we consider our ability to compare the results obtained with the approaches when no perturbation is considered.

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“…Nowadays, metaheuristic optimization methods represent a very interesting alternative for the optimization of complex problems without the mathematical modeling of the problem, and they have been successfully applied in several kinds of application, for example, in control applications [1][2][3], optimizing Artificial Neural Networks [4][5][6], optimizing a controller applied in an complex electromechanical process [7], fuzzy controllers [8,9], etc. On the other hand, dynamic parameter adaptation in metaheuristic methods based on fuzzy logic can improve their optimization performance as can be observed in [10][11][12][13]. However, this dynamic adaptation based on fuzzy logic significantly increases the computational cost of the optimization process.…”

Section: Introductionmentioning

confidence: 99%

“…The main difference is that the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) approach requires only two α-planes to model the GT2 FIS, but the values of α are selected with the optimization criteria for shadowed sets proposed by Pedrycz in [14], and recent examples of the ST2 FIS applied in control problems can be found in [15]. On the other hand, the optimization of fuzzy controllers that was previously presented, for example in [1,3,10,16], is presented. The reason for exploring this application is because the fuzzy controllers have been proven to have good performance in complex applications, for example, in [17].…”

Section: Introductionmentioning

confidence: 99%

Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

et al. 2019

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Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.

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

Cited by 39 publications

References 31 publications

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation

Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

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