Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

Peraza, Cinthia; Valdez, Fevrier; Castro, Juan R.; Castillo, Oscar

doi:10.1155/2018/3092872

Cited by 34 publications

(16 citation statements)

References 40 publications

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“…The variables as mentioned above are the iterations representing the input variables, and the c3 and c4 parameters are the iterations representing the output variables. The knowledge representation of the variables is shown in the following equations [28]: In the design of the T1 fuzzy system, input and output variables are used: in this case, one input and two outputs. The variables as mentioned above are the iterations representing the input variables, and the c 3 and c 4 parameters are the iterations representing the output variables.…”

Section: Proposed Approach (Fgso)mentioning

confidence: 99%

Section: Proposed Approach (Fgso)mentioning

confidence: 99%

“…The statistical test used to perform the statistical comparison between the methods mentioned above is the z test. In Table 4, we can find the parameters used [17,28]. Table 4.…”

Section: Statistical Comparisonmentioning

confidence: 99%

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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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Section: Proposed Approach (Fgso)mentioning

confidence: 99%

Section: Proposed Approach (Fgso)mentioning

confidence: 99%

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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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“…Peraza et al put forward a method referred to as the parameter adaptation in an FLC based harmony search algorithm (HS) for the optimization of ball and beam controller. Type 1 and type 2 fuzzy logic methods were analyzed utilizing some error performance criteria [12]. Mehedi et al designed and employed a fractional degree controller on a two degree of freedom the ball and beam system.…”

Section: Introductionmentioning

confidence: 99%

Position controller design and implementation of ball and beam system with SMC and PD control methods

Abut¹

2020

Energy eng. control syst.

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Today, several methods are proposed and tested for controlling many nonlinear and unstable systems. This study employed the sliding mode control (SMC) and proportional-derivative (PD), which are used to control the position and modeling of ball and beam system that is a fundamental system used to test the control methods. Such systems are nonlinear and unstable due to their nature. Therefore, these systems are affected by external disturbances and this leads to a decrease in the control quality. The study tested the system by utilizing the classical PD and SMC methods, and the results were assessed by employing the Integral-Square-Error (ISE) performance criterion. The system results were provided as graphics and tables. Besides, the results were compared and analyzed.

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“…Harmony search (HS) algorithm is a well-known metaheuristic algorithm, introduced by Geem et al [3] by mimicking the musician's process in creating a new musical harmony [8], [9]. The HS algorithm is used in different fields of optimization problems, such as engineering [10], [11], water distribution [12], structural optimization [6], music ensemble [13], and university timetable [14], Software testing [15]- [18].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Harmony Search Algorithm With Grey Wolf Optimizer and Modified Opposition-Based Learning

et al. 2019

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Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. Then, a modified version of opposition-based learning technique has been applied to the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth. The proposed hybrid algorithm for global optimization problems is called GWO-HS. The GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. Then, the GWO-HS has been compared with recent HS variants and other wellknown metaheuristic algorithms. The results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.

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Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

Cited by 34 publications

References 40 publications

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

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

Position controller design and implementation of ball and beam system with SMC and PD control methods

Hybrid Harmony Search Algorithm With Grey Wolf Optimizer and Modified Opposition-Based Learning

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