zaer abo-hammour - Academia.edu (original) (raw)

Papers by zaer abo-hammour

Applied mathematics & information sciences, 2016

In this paper, the solution of inverse kinematics problem of robot manipulators using genetic alg... more In this paper, the solution of inverse kinematics problem of robot manipulators using genetic algorithms (GA) is presen ted. Two versions of genetic algorithms are used which include th e conventional GA and the continuous GA. The inverse kinemat ics problem is formulated as an optimization problem based on the concep t of minimizing the accumulative path deviation in the absen ce of any obstacles in the workspace. Simulation results show that th e continuous GA outperforms the conventional GA from all asp ects. The superiority of the continuous GA is seen in that it will alway s provide smooth and faster solutions as compared with the co nventional GA.

Journal of urban planning and development, Dec 1, 2021

Abstract Land-use allocation (LUA) is a spatial optimization problem for urban planning in the fu... more Abstract Land-use allocation (LUA) is a spatial optimization problem for urban planning in the future. The solution to this problem could be enhancing the effectiveness of optimization algorithms t...

2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

This paper proposes a two-loop position/speed control system for linear DC motors. The control sy... more This paper proposes a two-loop position/speed control system for linear DC motors. The control system is based on the parameterization of lead compensator using the genetic algorithm (GA) optimization method. A performance criterion including the information of overshoot, rise time, settling time, and steady-state error is proposed as the objective function. The linear DC motor is modeled using Simscape toolbox in MATLAB/Simulink platform. The simulation shows that the results successfully demonstrate the effectiveness and good dynamic performance of the proposed two-loop control system under specific constraints and various test conditions. In addition, the results show that the adopted method can perform an efficient search for the optimal parameters of the used controller.

2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019

There are numerous algorithms for designing lead compensators, some of which are graphical wherea... more There are numerous algorithms for designing lead compensators, some of which are graphical whereas others are analytical. When designing a lead compensator, the parameters of the compensator are considered as an optimization problem which aims at getting the required time and frequency specifications. This paper presents a comparison between lead compensators designed by nature-inspired algorithms against those designed by conventional algorithms for various types of systems. The nature-inspired algorithms considered in this paper are the genetic algorithm (GA), which is built on the concept of natural selection process which imitates biological evolution, and the particle swarm optimization (PSO), which is stimulated by social behavior of fish schooling or bird flocking. In this paper, two different examples are considered to demonstrate the comparison between the design methods. The simulation results of these examples show that the nature-inspired algorithms provided better transient response due to reduced settling and rise times and provided better relative stability due to zero overshoot and higher phase margin.

The European Physical Journal Plus, 2019

Abstract.Finding an accurate model to present the hysteresis nonlinearities behavior of the smart... more Abstract.Finding an accurate model to present the hysteresis nonlinearities behavior of the smart actuator has attracted the attention of the researchers in recent years, since an accurate model has an essential role in the position control application of these actuators. Different models have been developed to describe the hysteresis nonlinearities, the generalized Prandtl-Ishlinskii (GPI) model is one of the most popular used models. This model uses the play operators represented by the threshold values and weights integrated with the odd envelope functions to characterize the hysteresis nonlinearities of smart actuators. The contribution of this paper proposes three different approaches using the Real-Coded Genetic Algorithm (RCGA) for the parameters identification of the Generalized Prandtl-Ishlinskii (GPI) model. In Approach 1, the thresholds and the values of the weights are calculated based on the proposed formulas with the unknown parameters to be identified using RCGA. In Approach 2, the thresholds values are calculated based on the proposed formula with the unknown parameters to be identified using RCGA and the values of the weights are identified directly using RCGA. In Approach 3, the thresholds and the values of the weights are identified directly using RCGA. Also, RCGA was used to identify the values of the coefficients of the envelope functions for all approaches. All approaches are tested through four different examples. Two examples are simulated examples that have linear and tangent hyperbolic envelope functions. Moreover, the other two examples represent experimental data obtained for a piezoelectric actuator and a shape alloy memory (SMA) actuator. The simulation results are carried through by the statistical and convergence analysis of the proposed approaches. The comparison and analysis show that three different approaches can be employed for modeling hysteresis nonlinearities with minimum differences between them.

SSRN Electronic Journal, 2018

Applied Mathematics & Information Sciences, 2016

World Scientific proceedings series on computer engingeering and information science, Oct 1, 2012

Communications in Computer and Information Science, 2010

This paper addresses a novel model order reduction (MOR) technique with dominant substructure pre... more This paper addresses a novel model order reduction (MOR) technique with dominant substructure preservation. This process leads to cost minimization of the considered physical system which could be of any type from motors to circuitry packaging to software design. The new technique is formulated based on an artificial neural network (ANN) transformation along with the linear matrix inequality (LMI) optimization method. The proposed method is validated by comparing its performance with the following well-known reduction techniques Balanced Schur Decomposition (BSD) and state elimination via balanced realization.

In past, Gutowski proposed a special type of real-coded genetic algorithms, which is suited for c... more In past, Gutowski proposed a special type of real-coded genetic algorithms, which is suited for continuous optimization problems with continuous and/or smooth solution curves. The algorithm uses smooth operators throughout the evolution process and results in smooth solution curves. However, Gutowski's algorithm is restricted to problems involving single solution curve and suffers from slow convergence rates. In this work, an advanced continuous genetic algorithm (ACGA) has been developed based on Gutowski's algorithm by incorporating new initialization functions and a novel performance enhancement scheme. ACGA is designed to deal with single as well as multiple solution curves. It can also handle problems with bounded variables. ACGA has been applied for the solution of two problems that are of great importance in the engineering field in order to demonstrate its efficiency. The problems include the Cartesian path generation of robot manipulators and the second- order, two-...

Transactions of the Institute of Measurement and Control, 2011

Solution of the chemical reactor problem, as an optimal control problem, using continuous genetic... more Solution of the chemical reactor problem, as an optimal control problem, using continuous genetic algorithms (CGAs) is presented in this paper. The proposed approach overcomes the drawbacks of the traditional approaches in terms of lack of efficiency, lack of accuracy and lack of robustness. The solution is based on the value of the performance index and the final system state constraints. Simulation results show clearly that the new technique outperforms the existing direct and indirect methods. Based on the convergence analysis, the solution of the optimal control problem is achieved without any limitation on the nature of the problem and regardless of the CGA tuning parameters.

Electric Power Components and Systems, 2014

ABSTRACT A new substructure preservation Sylvester-based model order reduction technique with app... more ABSTRACT A new substructure preservation Sylvester-based model order reduction technique with application to power systems is presented in this article. The new approach is intended for multiple-input–multiple-output linear time invariant systems, given in the form of state-space realization with the objective of obtaining a proper reduced-order model (complexity reduction), preserving the dominant eigenvalues of the full-order model as a subset in the reduced model, and maintaining a minimum steady-state error. The proposed reduction method is performed based on transforming the system state matrix into a special form, taking into account the dominant eigenvalues, while the rest of the model transformation is derived utilizing the Sylvester equation formula. Once the system is transformed, the reduced-order model is obtained by truncating the less dominant eigenvalues using the singular perturbation technique. To evaluate the potential of the new approach, results of the proposed technique are compared to some of the well-known methods for model order reduction and relatively recently published work. Results comparison shows the superiority of the new method especially in terms of time convergence.

Applied Mathematical Modelling, 2013

In this research, we propose a numerical scheme to solve the system of second-order boundary valu... more In this research, we propose a numerical scheme to solve the system of second-order boundary value problems. In this way, we use the Local Radial Basis Function Differential Quadrature (LRBFDQ) method for approximating the derivative. The LRBFDQ method approximates the derivatives by Radial Basis Functions (RBFs) interpolation using a small set of nodes in the support domain of any node. So the new scheme needs much less computational work than the globally supported RBFs collocation method. We use two techniques presented by Bayona et al. (2011, 2012) [29,30] to determine the optimal shape parameter. Some examples are presented to demonstrate the accuracy and easy implementation of the new technique. The results of numerical experiments are compared with the analytical solution, finite difference (FD) method and some published methods to confirm the accuracy and efficiency of the new scheme presented in this paper.

Journal of Applied Mathematics and Decision Sciences

Intelligent Automation & Soft Computing, 2015

AbstractAs the mathematical procedure of system modelling often leads to a comprehensive descript... more AbstractAs the mathematical procedure of system modelling often leads to a comprehensive description, which causes significant difficulty in both analysis and control synthesis, it is necessary to find lower order models, which maintain the dominant characteristics of the original system. In this paper, different soft computing (named as artificial intelligence (AI)) techniques are presented, applied, and analysed for model order reduction (MOR) of multi time scale systems with the objective of substructure preservation. In addition to that, we investigate the firefly optimization technique for MOR with substructure preservation. The analysis is concerned with the optimization approach and quality of method performance.

Computational Intelligence and Neuroscience, 2015

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete sys... more A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements ofB,C, andDmatrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method...

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS), 2013

ABSTRACT In this paper, Continuous Genetic Algorithms (CGAs) are used as an intelligent computati... more ABSTRACT In this paper, Continuous Genetic Algorithms (CGAs) are used as an intelligent computational technique to provide a problem optimal solution. The problem is formulated as an optimization problem by the direct minimization of the performance index subject to constraints, and is then solved using a CGA. The presented approach has some advantages over the other existing direct and indirect methods which either suffer from low accuracy or lack of robustness. One advantage is that our method can be applied without any limitation on the nature of the problem (number of control signals and mesh points). Another advantage is that high accuracy can be achieved in that the performance index is globally minimized.