Objective functions modification of GA optimized PID controller for brushed DC motor

Zahir, A. A. M.; Alhady, S. S. N.; Wahab, Aeizaal Azman A.; Ahmad, Musyaffa'

doi:10.11591/ijece.v10i3.pp2426-2433

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…The process continues in a loop until when there are no significant changes in the generations which is the condition for termination of the optimisation process. Details on the design of PID controllers for delay-free and time-delay systems using GA can be found in [16,17] and [29] respectively. respectively, that yielded the Min-ITAE that gives Min(Min-ITAE).…”

Section: Fig 2 Flowchart Of Ga Process For Optimising Pidmentioning

confidence: 99%

Selection of PID controller design plane for time-delay systems using genetic algorithm

Ajiboye

Opadiji

Popoola

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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The design of a Proportional-Integral-Derivative (PID) controller with proportional, integral, and derivative, gain, k p , k i , and k d , respectively, for a time-delay system, is quite common, particularly in the k i - k d plane, for a fixed k p or in the k p - k i plane, for a fixed k d . These design methods have been widely reported in the literature, however, the process of investigating the effects of using any of these design planes on system performance has not been given serious attention hence the need for this study. The stability region in the k i - k d and k p - k i design plane for a fixed value of k p and k d respectively were determined. For every determined stability region, the optimum value of controller gains in the plane was determined using a genetic algorithm (GA) with the integral of time multiplied by absolute error (ITAE) used as the objective function. The optimum value of the fixed gains was graphically determined by plotting the minimum of ITAE (Min-ITAE) for each stability region against the fixed gains. The overall optimum controller gains are the fixed gain that gives minimum of Min-ITAE (Min (Min-ITAE)) and the gains that resulted in Min-ITAE that yielded the Min (Min-ITAE). Using the determined overall optimum controller gains, the system closed-loop step response was plotted for the two design planes and the time domain performance measures (TDPMs) were determined. Based on TDPMs obtained for examples 1, 2, and 3, the k i -k d design plane yielded a faster response while the k p - k i design plane yielded a response that closely tracks the input irrespective of the system type and order. The study will enable control system designers to select the design plane that will give the best system performance right from the start of controller design without involving trial and error once the system transfer function and design specifications are known.

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Section: Fig 2 Flowchart Of Ga Process For Optimising Pidmentioning

confidence: 99%

Selection of PID controller design plane for time-delay systems using genetic algorithm

Ajiboye

Opadiji

Popoola

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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“…Evolutionary algorithms are a class of optimization algorithms based on the principles of biological evolution in nature, which are used to find optimal or near-optimal solutions in the search space. Evolutionary algorithms mainly include evolutionary programming (EP) [19], the genetic algorithm (GA) [20,21], genetic programming (GP) [22], etc. Swarm intelligence algorithms are a class of optimization algorithms based on the behavior of groups in nature, which achieve global search or optimization problem solving by simulating the collaboration and cooperation among individuals in a group.…”

Section: Introductionmentioning

confidence: 99%

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network

Zheng

Pan

et al. 2023

Sensors

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The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

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“…Darwin's theory of evolution is fundamental to the evolutionary algorithmic group, the most popular paradigms of which are the genetic algorithm (GA) [11], evolutionary programming [12], differential evolution [13], evolutionary strategy [14] and genetic programming [15]. However, falling into local minima and difficulty finding the optimum solution to the multimodal problem are the main drawbacks of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimum PID controller for airplane wing tires based on gravitational search algorithm

2021

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In this paper, the gravitational search algorithm (GSA) is proposed as a method for controlling the opening and closing of airplane wing tires. The GSA is used to find the optimum proportional-integral-derivative (PID) controller, which controls the wing tires during take-off and landing. In addition, the GSA is suggested as an approach for overcoming the absence of the transfer function, which is usually required to design the optimum PID. The use of the GSA is expected to improve the system. Two of the most popular optimisation algorithms-the harmony search algorithm (HSA) and the particle swarm optimisation (PSO)-were used for the sake of comparison. Moreover, the GSA-, HSA- and PSO-based optimum PID controllers were compared with one of the most important PID tuning methods, the Ziegler-Nichols (ZN) method. In this study, the integral time absolute error (ITAE) was used as a fitness function. First, four transfer functions for different applications were used to compare the performance of the GSA-based PID (PID-GSA), HSA-based PID (PID-HSA), PSO-based PID (PID-PSO) and Ziegler-Nichols-based PID (PID-ZN). Next, the GSA was used to design the optimum PID controller for the opening and closing systems of the airplane wing tires. The results reveal that the GSA provides better outcomes in terms of ITAE when compared with the other adopted algorithms. Furthermore, the GSA demonstrates a fast and robust response to reference variation.

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Objective functions modification of GA optimized PID controller for brushed DC motor

Cited by 12 publications

References 22 publications

Selection of PID controller design plane for time-delay systems using genetic algorithm

Selection of PID controller design plane for time-delay systems using genetic algorithm

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network

Optimum PID controller for airplane wing tires based on gravitational search algorithm

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