Shooting swarm algorithm for solving two-point boundary value problems

Altamir, Suhaib Abduljabbar; Kahya, Mohammed Abdulrazaq; Aladool, Azzam

doi:10.11591/ijeecs.v29.i1.pp553-561

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After finishing the compression, the fuel mass will start the combustion stage. In the combustion stage, the developed torque is related to the air (𝐴) to fuel (𝐹) ratio (𝛿), speed, spark advance 𝜎, and air mass (𝑚 𝑎 ) according to (6).…”

Section: 𝑚 𝑖 ̇= 𝑦(𝜃)𝑞(𝑃)mentioning

confidence: 99%

Optimal proportional-integral speed control for closed-loop engine timing system

Albatran,

Harasis

2024

IJEECS

View full text Add to dashboard Cite

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

show abstract

Section: 𝑚 𝑖 ̇= 𝑦(𝜃)𝑞(𝑃)mentioning

confidence: 99%

Optimal proportional-integral speed control for closed-loop engine timing system

Albatran,

Harasis

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…The mayfly optimization algorithm proves superior in image multilevel thresholding compared to other algorithms [35]. In the field of mathematics, the shooting swarm algorithm based on PSO and FA is introduced to solve boundary value problems, yielding accurate results and significantly reducing required iterations for problem solutions [36]. For PV system optimization, different algorithms are used to demonstrate quick and efficient maximum power point operation of PV system such as the hybrid FA-perturb and observe (P&O) algorithm [37], improved Seagull optimizer (SO) with modified variable step sizes perturb [38] and observe and evolutionary algorithms (genetic algorithm, FA, and fruit fly optimization) [39].…”

Section: Introductionmentioning

confidence: 99%

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Jallad,

Badran

2024

Bulletin EEI

View full text Add to dashboard Cite

This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.

show abstract

Shooting swarm algorithm for solving two-point boundary value problems

Cited by 2 publications

References 17 publications

Optimal proportional-integral speed control for closed-loop engine timing system

Optimal proportional-integral speed control for closed-loop engine timing system

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Contact Info

Product

Resources

About