An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors

Lotfy, Ahmed; Kaveh, Masoud; Mosavi, Mohammad Reza; Rahmati, A.

doi:10.1007/s10470-020-01599-9

Cited by 39 publications

(28 citation statements)

References 25 publications

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“…It is the reason why DC motor is very popular in industrial applications and household appliances that requires motor speed and position control. DC motors are most compatible with level speed control and are therefore applied in many adaptable speed drives [1].…”

Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

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Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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“…The PSO procedure is used to find the best fuzzy limits for minimizing the impartial functions. Since of being fuzziness and existence of doubt in input variables to fuzzy schemes, and also their easy application, fuzzy managers are introduced as one of valuable optimization tools in manufacturing particularly in DC engines are projected by [12].In this paper we use Ziegler-Nichols modification process for modification of a Proportional-Integral (P-I) and Integral-Proportional (I-P) manager for a DC motor to regulator its speed [13].…”

Section: Introductionmentioning

confidence: 99%

“…The paper numbers [1][2][3][4], [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], proposed the sun tracking system controller , dual-axis solar tracking development and Experimental Study for a Two-Axis Sun Tracker,whereas the the references [5][6][7][8][9][10][11][12][13][14][15][16][17][18] explanation for different techniques for control speeds, voltage position by fuzzy ,GA,PSO ,LQR and PLC -base fuzzy .In current years, devoted efforts have been made to reach effective solar photovoltaic (PV) schemes with enhanced performance. The paper numbers [28][29] to the end references explanation the hybrid of different techniques for example neural-fuzzy , Discrete-time ripple correlation , reference adaptive control, wavelet radial basis function neural networks, Radial basis function network based on time variant multi-objective particle swarm optimization and Differential Evolution algorithm based Radial Basis Function Networks for classification problems in particular, as a strategy for performance improvement, solar tracking system is researched in-depth to reach the improvement, solar tracking system efficiency and quality.…”

Section: Introductionmentioning

confidence: 99%

PLC Fuzzy PID Controller of MPPT of Solar Energy Converter

Gizi

2021

Wseas Transactions on Systems and Control

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Development of the Maximum-Power-Point-Tracking (MPPT) scheme for solar mounts and rectifiers leftovers interesting. We design of high-sensitive fuzzy (HSF) Proportional-Integral-Derivative (PIDC) controller by means of Matlab and Programmable-Logic-Controllers (PLCs) for an adjusted of the MPPT scheme. This proposal is founded on a synergistic mixture of the Radial-Basis-Function-Neural Network (RBF-NN), Genetic Algorithm (GA), and Sugeno-Fuzzy-Logic (SFL) systems. The finest limits of PIDC and MPPT are strong-minded through optimization, wherever RBF-NN is adjusted by means of GA to reach the best key. Also, RBF-NN is rummage-sale to improve the PID limits (got from GA) for scheming HSFL-PIDC of the MPPT scheme. The HSFL-PIDC controller is extra planned to transfer in PLCs (STEP 75.5) for implementing the photovoltaic (PV) scheme. The all-inclusive scheme is extra tuned by solar limits under numerous operational circumstances to advance the solar recital in terms of accusing and correcting. The recital of the planned analog employed MPPT controller is assessed by interfacing it with a hardware prototype of dual photovoltaic (PV) scheme. The mixture controller configured the regulator signal founded on the interaction and in that way reduces the voltage mistake and the oscillation in the voltage regulator process. The achieved scheme is established to be well-organized and robust in refining solar charging and rectifying capability .

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“…Evolutionary Algorithms (EA) are considered as an alternative that leads to faster results than traditional tuning strategies to improve PI-Fuzzy controller parameters for DC motor speed control [9]- [12]. Bulut in their study, seek impact of the generation number to find rule base of a Fuzzy-PI Controller using genetic algorithms [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

Bulut¹

2021

Preprint

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This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit.

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An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors

Cited by 39 publications

References 25 publications

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Improving neural network using a sine tree-seed algorithm for tuning motor DC

PLC Fuzzy PID Controller of MPPT of Solar Energy Converter

Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

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