Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers

Flores-Moran, Eduardo; Yánez-Pazmiño, Wendy; Barzola-Monteses, Julio

doi:10.1109/carpathiancc.2018.8399621

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…Unlike works [31][32][33], our algorithm does not run in simulation. However, it is a process that runs in line from the developed Graphical User Interface (GUI).…”

Section: Controller and Tuningmentioning

confidence: 99%

A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

Cruz-Miguel

García-Martínez

Rodríguez‐Reséndiz

et al. 2020

Sensors

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Servo systems are feedback control systems characterized by position, speed, and/or acceleration outputs. Nowadays, industrial advances make the electronic stages in these systems obsolete compared to the mechanical elements, which generates a recurring problem in technological, commercial and industrial applications. This article presents a methodology for the development of an open-architecture controller that is based on reconfigurable hardware under the open source concept for servo applications. The most outstanding contribution of this paper is the implementation of a Genetic Algorithm for online self tuning with a focus on both high-quality servo control and reduction of vibrations during the positioning of a linear motion system. The proposed techniques have been validated on a real platform and form a novel, effective approach as compared to the conventional tuning methods that employ empirical or analytical solutions and cannot improve their parameter set. The controller was elaborated from the Graphical User Interface to the logical implementation while using free tools. This approach also allows for modification and updates to be made easily, thereby reducing the susceptibility to obsolescence. A comparison of the logical implementation with the manufacturer software was also conducted in order to test the performance of free tools in FPGAs. The Graphical User Interface developed in Python presents features, such as speed profiling, controller auto-tuning, measurement of main parameters, and monitoring of servo system vibrations.

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“…Unlike works [31][32][33], our algorithm does not run in simulation. However, it is a process that runs in line from the developed Graphical User Interface (GUI).…”

Section: Controller and Tuningmentioning

confidence: 99%

A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

Cruz-Miguel

García-Martínez

Rodríguez‐Reséndiz

et al. 2020

Sensors

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“…The PID controller has 3 parameters, namely KP (Proportional Constants), KI (Integral Constants), KD (Derivative Constants) in designing the controllers, the goal is to get the optimal system response according to the desired design specifications [2]. There are several methods of PID tuning, including the Ziegler-Nichols method [3] and the Fuzzy method [4] [5]. Most of these tuning methods require a broad knowledge of the control system.…”

Section: Introductionmentioning

confidence: 99%

Tuning of PID Controller Parameters with Genetic Algorithm Method on DC Motor

Suseno

Ma’arif

2021

IJRCS

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Proportional Integral Derivative (PID) controllers are used in general to control a system, for example a DC motor system. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (KP), Integral Gain (KI) and Derivative Gain (KD). In this case, the genetic algorithm method is used which can give better results in each iteration. Genetic algorithms are one of the smart methods inspired by the process of natural selection, the process that causes biological evolution, this concept is applied to tuning PID parameters. This research uses the Matlab simulation method and applies the simulation results to the DC motor hardware using the Arduino Uno. The genetic algorithm method gives a system that has a better steady time and a smaller maximum spike than the Trial and Error method. The test process produced the two best data with an overshoot value = 2, settling time = 13.5 and rise time of 2.7872 and the PID parameter value for mutation of 1 was KP = 3.7500; KI = 1.3184 and KD = 0.2051. Then the value of the best PID parameter on Crossover is 0.4, which is KP = 4.2090; KI = 1.2012 and KD = 0.2539 with an overshoot value = 2, settling time = 18 and rise time = 2.6462.

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“…DC motor is controlled with PI linear control theory into the fuzzy control structure [4]- [6]. The combination of FLC and PID controllers has also been described, the PID control method combined with the fuzzy method [7]- [9]. In addition, neural networkbased control has also been developed.…”

Section: Introductionmentioning

confidence: 99%

Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

Aribowo

Muslim

Achmad

et al. 2021

J. Elektron. dan Telekomun.

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This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.

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Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers

Cited by 21 publications

References 13 publications

A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

Tuning of PID Controller Parameters with Genetic Algorithm Method on DC Motor

Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor

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