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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Turki Y. Abdalla
University of Basrah
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
A modified artificial bee colony based fuzzy motion tracking scheme for mobile robot Abdulkareem Younis Abdalla; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3739

Abstract

In this study a new modified artificial bee colony algorithm for the optimization of the fuzzy control scheme for motion tracking of mobile robot is developed. The modification is based on using some features from the particle swarm optimization algorithm to improve solution quality. The modified artificialbee colony (MABC) balance the exploration and exploitation of the original one. This balancing results in going through the global search space and increases the convergence speed and solution accuracy. MABC is then used for the design of an efficient fuzzy system that perform motion tracking for mobile robot more accurate through minimizing a suitable selected objective function. Results illustrate the high quality of the proposed method.
A new modified grasshopper optimization algorithm Abdulkareem Y. Abdalla; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4083

Abstract

The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.
A PSO optimized RBFNN and STSMC scheme for path tracking of robot manipulator Atheel K. Abdul Zahra; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5018

Abstract

This article presents the design of super twisting sliding mode control (STSMC) based on radial basis function neural network (RBFNN) for path tracking of two link robot manipulator. The proposed controller is utilized to guarantee and achieve that the surface of sliding can be in equilibrium point within a short time and avoid the problem of chattering at the output. The Lyapunov theory is used in presenting a new convergence proof. Also, the particle swarm optimization (PSO) algorithm is employed to give the optimal parameter values of the proposed controller. Simulation results explain the goodness of the proposed control method for trajectory tracking of 2-link robot manipulator when compared with SMC strategy. Results demonstrate that the the percentage improvement in mean square error (MSE) of using STSMC when compared with the standard SMC are 15.36%, 16.94% and 12.92%, for three different cases respectively.
Co-Authors Abdulkareem Y. Abdalla Abdulkareem Younis Abdalla Ali A. Abed Atheel K. Abdul Zahra Noor Faleh Abdul Hassan