PARTICLE SWARM OPTIMIZATION FOR MOR OF SINGULARLY PERTURBED SYSTEMS WITH CRITICAL FREQUENCY PRESERVATION AND APPLICATION TO POWER SYSTEMS SIMPLIFIED MODELING

2014 ◽  
Vol 23 (05) ◽  
pp. 1450073 ◽  
Author(s):  
OTHMAN M. K. ALSMADI ◽  
DIA I. ABU-AL-NADI ◽  
ZAER. S. ABO-HAMMOUR
Keyword(s):  
Particle Swarm Optimization ◽  
Critical Frequency ◽  
Mimo Systems ◽  
Particle Swarm ◽  
Reduction Process ◽  
Reduced Order Model ◽  
Reduced Model ◽  
Order Model ◽  
Swarm Optimization ◽  
Reduced Order

In this paper, a model-order reduction (MOR) technique with the advantage of critical frequency preservation capability is proposed using the particle swarm optimization (PSO). The new approach is capable of simplifying single-input single-output (SISO) systems as well as multi-input multi-output (MIMO) systems. If critical frequency preservation is desired, then this objective is achieved by retaining the exact critical frequencies of the full-order model as a subset in the reduced-order model. Otherwise, the reduction process is proceeded without such restriction. The reduction process is performed using the PSO technique to determine all of the necessary parameters in the reduced model. Determining the reduced-order model is performed based on minimizing the mean square error between the outputs of the original full-order model and the outputs of the reduced model. For method evaluation and validation, the proposed technique was applied to different models and compared with some of the well-known methods and recently published work for MOR. Results' comparison shows clearly the superiority of the proposed technique in terms of quality performance and accuracy of substructure preservation.

scholarly journals Optimal Block Diagonalization precoding scheme for MU-MIMO Downlink channel Using Particle Swarm Optimization

2018 ◽  
Vol 7 (2.17) ◽  
pp. 90
Author(s):  
Shivangini Saxena ◽  
Dr R.P. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Mimo Systems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Base Station ◽  
Block Diagonalization ◽  
Swarm Optimization ◽  
Data Rates ◽  
Mimo Downlink ◽  
The Impact

As wireless communication turns out to be more common, the interest for higher rates of data transfer and continuous availability is expanding. Future wireless systems are provisioned to be very heterogeneous and interconnected. Higher data rates and Quality of Service (Qos) are two major expectations from any wireless technology. Fading is the main phenomenon which restricts the realization of Qos demand and higher data rates in wireless technologies. Fading is caused by obstacles in signal path which degrades the received signal’s quality. To mitigate the impact of fading on communication system the application of precoding techniques can be used. In this regard, this paper presents optimization of Block-Diagonalization (BD) based linear precoding scheme for multi-user multiple-input multiple output (MU-MIMO) systems. Simulation environment consists of a MIMO downlink scenario where a single base station (BS) with  antennas transmits to K receivers each with  antenna. The application of Particle Swarm Optimization (PSO) is used to find the optimal number of received antennas so as to reduce system complexity while maintaining Bit Error Rate (BER) performance of the system. MATLAB based simulation scenario is presented and evaluated over Rayleigh fading environment. Simulation results validate that the performance of Block– Diagonalization scheme can be improved up to 5dB with the application of Particle Swarm Optimization technique. 

A Multivariable Coupling Design for Variable Structure Control Using Particle Swarm Optimization

2006 ◽  
Author(s):  
Leandro dos Santos Coelho ◽  
Viviana Cocco Mariani
Keyword(s):  
Particle Swarm Optimization ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Mimo Systems ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Design Tool ◽  
Link Length ◽  
Swarm Optimization ◽  
Mode Control

This paper presents a new discrete-time sliding-mode control design for multiple-input multi-output (MIMO) systems with tuning parameters by particle swarm optimization (PSO). PSO is a kind of evolutionary algorithm based on a population of individuals and motivated by the simulation of social behavior instead of the survival of the fittest individual. Several control algorithms are presented, two decoupling design and six new approaches of the coupling design of sliding-mode control without the necessity of calculate the process interactor matrix. SMC needs a design tool for parameter configuration and efficient practically to deal with multivariable processes. Simulations are carried out using both decoupling and coupling discrete-time SMC designs. Results shown that the new proposed approach for designing the discrete-time coupling SMC is a powerful tool and it performs better than the decoupling design, usually utilized in MIMO process. The simulations are assessed on a robotic manipulator of two degree-of-freedom (2-DOF), that constitute a MIMO nonlinear coupling dynamic system, with treatment of payload mass and link length variations. Simulation results show that the application of this control strategy effectively improve the trajectory tracking precision of position and velocity variables.

scholarly journals Particle Swarm Optimization Based Beamforming in Massive MIMO Systems

2020 ◽  
Vol 14 (05) ◽  
pp. 176
Author(s):  
Thaar A. Kareem ◽  
Maab Alaa Hussain ◽  
Mays Kareem Jabbar
Keyword(s):  
Particle Swarm Optimization ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Particle Swarm ◽  
Optimal Performance ◽  
Achievable Rate ◽  
Swarm Optimization ◽  
Transmit Precoding

<p>This research puts forth an optimization- based analog beamforming scheme for millimeter-wave (mmWave) massive MIMO systems. Main aim is to optimize the combination of analog precoder / combiner matrices for the purpose of getting near-optimal performance. Codebook-based analog beamforming with transmit precoding and receive combining serves the purpose of compensating the severe attenuation of mmWave signals. The existing and traditional beamforming schemes involve a complex search for the best pair of analog precoder / combiner matrices from predefined codebooks. In this research, we have solved this problem by using Particle Swarm Optimization (PSO) to find the best combination of precoder / combiner matrices among all possible pairs with the objective of achieving near-optimal performance with regard to maximum achievable rate. Experiments prove the robustness of the proposed approach in comparison to the benchmarks considered. <strong></strong></p><p class="IndexTerms"> </p>

Reduced Order Model for Efficient Engineering Analysis

2020 ◽  
Author(s):  
Allan X. Zhong ◽  
Haoyue Zhang
Keyword(s):  
Finite Element ◽  
Design Of Experiments ◽  
Reduced Order Model ◽  
Reduced Model ◽  
Complex Structures ◽  
Engineering Analysis ◽  
Order Model ◽  
Element Analysis ◽  
Reduced Order ◽  
Numerical Design

Abstract Engineering analysis of complex structures or mechanical systems typically involves contact with multiple components, large deformation, and material nonlinearity, which requires the application of nonlinear finite element methods. Despite the advancement of commercial software for finite element analysis (FEA), nonlinear FEA of a multi-component mechanical assembly will take hours to days, and even weeks to complete. It is highly desired to develop a reduced-order model for a family of complex structures that can reduce an original problems’ complexity and degree of freedom but has a reasonably small discrepancy with the full model and significantly reduces the computation time. The typical approach to construct a reduced model includes 1) the response surface method via numerical design of experiments and, 2) the simplified physics approach. In this paper, it is proposed to develop a reduced model through the combination of simplified physics, dimensional analysis [1], and numerical design of experiments. The approach is applied to the construction of a reduced model for the analysis of a downhole plug [2]. The developed reduced model is verified by full-scale FEA models and validated through physical tests. The reduced model is implemented in a spreadsheet and takes only seconds to complete a calculation in contrast to hours using a full FEA model, enabling engineers’ quick evaluation of the corresponding designs.

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