Genetic Algorithm Optimization of PID Controller for Brushed DC Motor

Zahir, A. A. M.; Alhady, S. S. N.; Othman, W. A. F. W.; Ahmad, Muhammad Shakil

doi:10.1007/978-981-10-8788-2_38

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…The modification improve the ISE function and produce better value than ITAE function compared with before modification. Compare with previous work in [24] the original ITAE produce slower settling time with the presence of overshoot. The additional of overshoot and steady state error component into ITAE objective function eliminates overshoot percentage and reduce settling time, however modified ITAE function still produced slower rise time compared with original ITAE.…”

Section: Results and Analysismentioning

confidence: 56%

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

Zahir

Alhady

Wahab

et al. 2020

IJECE

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PID Optimization by Genetic Algorithm or any intelligent optimization method is widely being used recently. The main issue is to select a suitable objective function based on error criteria. Original error criteria that is widely being used such as ITAE, ISE, ITSE and IAE is insufficient in enhancing some of the performance parameter. Parameter such as settling time, rise time, percentage of overshoot, and steady state error is included in the objective function. Weightage is added into these parameters based on users’ performance requirement. Based on the results, modified error criteria show improvement in all performance parameter after being modified. All of the error criteria produce 0% overshoot, 29.51%-39.44% shorter rise time, 21.11%-42.98% better settling time, 10% to 53.76% reduction in steady state error. The performance of modified objective function in minimizing the error signal is reduced. It can be concluded that modification of objective function by adding performance parameter into consideration could improve the performance of rise time, settling time, overshoot percentage, and steady state error

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Section: Results and Analysismentioning

confidence: 56%

“…Previously in [24], authors compared the Genetic Algorithm tuning of PID with Ziegler Nichols and Skogestad IMC. Author had previously compared the modified ITAE function with classical design formulas.…”

Section: Pid Tuned By Genetic Algorithmmentioning

confidence: 99%

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

Zahir

Alhady

Wahab

et al. 2020

IJECE

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show abstract

“…Every individual is assumed as a vector position and represents a potential solution to optimization problems. The formula for calculating speed (V) and position (X) is shown by (6) and 7, respectively [18].…”

Section: A Optimal Pid Controlmentioning

confidence: 99%

“…This tuning method can be categorized into i) empirical methods such as Ziegler-Nichols (ZN) and Cohen-Coon (CC), ii) analytical methods such as root locus (RL) and frequency response (FR), and iii) optimization methods such as using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) [2], [4], [5]. The optimization method is now widely used because of its ability to optimize the performance of PID controls as in [6], [7].…”

Section: Introductionmentioning

confidence: 99%

BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

Praptodiyono

Maghfiroh

Hermanu

2020

indones.j.electron.telecommun.

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The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

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“…The Ziegler Nichols method is a well-known for tuning PID parameters (Azman et al, 2017 ). However, it produces oscillatory responses and yields overshoot problems (Zahir et al, 2018 ). Moreover, it cannot be applied in the online-tuning of parameters, and the control performance can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Park

Quynh

et al. 2021

Front. Neurorobot.

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This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

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Genetic Algorithm Optimization of PID Controller for Brushed DC Motor

Cited by 25 publications

References 11 publications

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

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

BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

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