PID designs using DE and PSO algorithms for damping oscillations in a DC motor speed

Syafaah, Lailis; Widianto,; Pakaya, Ilham; Suhardi, Diding; Irfan, Muhammad

doi:10.1109/eecsi.2017.8239138

Cited by 11 publications

(6 citation statements)

References 4 publications

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“…In other words, the PID controller architecture using M1-PSO causes the overshoot rate in the step response curve to be reduced compared to M2-PSO and classic PSO. The simulation results have shown that the first scenario based on inertia weight function (W3) gives a better response compared to the second scenario and classic PSO and it reaches the global minimum value in fewer iteration numbers compared to the previous study [11,12]. Indeed, the obtained simulation results encourage to apply the proposed approach in practical control systems.…”

Section: Discussion Of Simulation Resultsmentioning

confidence: 78%

“…For example [7][8][9], the Ziegler and Nichols method may provide a high order system with big overshoots, highly oscillatory and longer settling time. To solve these challenges and difficulties, various approaches have been proposed to find optimum PID parameters such as [10] meta-heuristic algorithms [11], differential evolution (DE) [12], Flower Pollination Algorithm (FPA) [13], genetic algorithms (GN) [14], Levenberg-Marquardt Algorithm (LMA) [15], Grey Wolf Optimization Algorithm (GWO) [16], Jaya optimization algorithm (JOA) [17], PSO [18,19], Improved Sine Cosine Algorithm (ISCA) [20]. These are optimization methods that have been introduced to tune the controller parameters for speed control of the DC motor.…”

Section: Introductionmentioning

confidence: 99%

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Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

Mustafa

2021

EEJET

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A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.

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Section: Discussion Of Simulation Resultsmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

Mustafa

2021

EEJET

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“…The MZN tuning formulas are given in Table 2 [20]- [21]. From response curve of T.F in (7) as shown in Figure 5 can be obtained the parameters a, L and T, a= 0.0343, L= 0.0077, T= 0.2259. We will get: Kp= 27.7, Ki=Kp/Ti =87.586 and Kd=Kp*Td =0.1002.…”

Section: Modified Ziegler-nichols Methodsmentioning

confidence: 99%

Implementing optimization of PID controller for DC motor speed control

Rashid

Hussain

2021

IJEECS

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The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.

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“…It is a typical actuator used in various mechanical systems and commercial products, including rolling mills, traction devices, industrial robots, and educational robots [1][2][3][4][5]. These advantages motivate researchers to create various algorithms and strategies for control of DC motor speed and position [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor

Abdelghany,

Okasha Elnady,

Ibrahim

2023

Journal of Robotics and Control

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DC motors are simple and controllable, making them a popular choice for various applications. However, the speed and load characteristics of DC motors can change, making it difficult to control them effectively. This paper proposes an optimum PID controller with fuzzy self-tuning for DC servo motors. The controller uses two steps to adjust the PID gains: The ACS algorithm is employed to identify the optimal PID gains in the first step. A fuzzy logic (FLC) controller is employed in the second stage to further fine-tune the gains. The FLC considers two cost functions: the first function is the sum of the squares of the error between the controlled output and reference input. The second function is a mathematical expression that specifies the required characteristics of the system response. The fuzzy self-tune then uses a set of rules to adjust the PID gains in response to changes in the system. The rules are based on the two cost functions designed to maintain the optimum PID gains for various operating settings. The outcomes of the two functions are: Kp = 5.2381, Ki = 7.0427, and Kd = 0.49468, with rising time = 0.2503, overshoot = 2.5079, and settling time = 10.4824 in the first cost function. The second cost function outcomes are Kp = 8.1381; Ki = 8.6427; and Kd = 0.49468. The FST-PID controller's performance is evaluated using Matlab-Simulink. The proposed controller was tested on a DC servo motor, and the results showed good performance in both steady-state and transient responses. The controller also maintained the optimum PID gains in the event of changes or disturbances. So, the motor's speed can effectively control under a variety of conditions.

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PID designs using DE and PSO algorithms for damping oscillations in a DC motor speed

Cited by 11 publications

References 4 publications

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

Implementing optimization of PID controller for DC motor speed control

Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor

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