scholarly journals Parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

2019 ◽  
Vol 28 ◽  
pp. 01031
Author(s):  
Rafal Szczepanski ◽  
Tomasz Tarczewski ◽  
Lech M. Grzesiak
Keyword(s):  
Parallel Computing ◽  
State Feedback ◽  
Computation Time ◽  
The Other ◽  
Speed Controller ◽  
Speed Up ◽  
Tuning Process ◽  
Auto Tuning ◽  
Reduce Computation Time ◽  
Simulation Time

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors can be used to reduce computation time by using parallel computing. The parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive.

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Auto-Tuning Process of State Feedback Speed Controller Applied for Two-Mass System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3067
Author(s):  
Rafal Szczepanski ◽  
Marcin Kaminski ◽  
Tomasz Tarczewski
Keyword(s):  
State Feedback ◽  
Mass System ◽  
Tuning Method ◽  
Optimal Coefficients ◽  
Speed Controller ◽  
Bee Colony ◽  
Tuning Process ◽  
Drive Systems ◽  
The Stability ◽  
Variable Moment

The state feedback controller is increasingly applied in electrical drive systems due to robustness and good disturbance compensation, however its main drawback is related to complex and time consuming tuning process. It is particularly troublesome for designer, if the plant is compound, nonlinear elements are taken into account, measurement noise is considered, etc. In this paper the application of nature-inspired optimization algorithm to automatic tuning of state feedback speed controller (SFC) for two-mass system (TMS) is proposed. In order to obtain optimal coefficients of SFC, the Artificial Bee Colony algorithm (ABC) is used. The objective function is described and discussed in details. Comparison with analytical tuning method of SFC is also included. Additionally, the stability analysis for the control system, optimized using the ABC algorithm, is presented. Synthesis procedure of the controller is utilized in Matlab/Simulink from MathWorks. Next, obtained coefficients of the controller are examined on the laboratory stand, also with variable moment of inertia values, to indicate robustness of the controller with optimal coefficients.

scholarly journals Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2016 ◽  
pp. 1071-1084
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Speeding Up and Parallelizing the GARFIELD++

2018 ◽  
Vol 174 ◽  
pp. 06004
Author(s):  
A. Sheharyar ◽  
O. Bouhali ◽  
A. Castaneda
Keyword(s):  
Parallel Computing ◽  
Electric Field ◽  
Particle Detectors ◽  
Multiple Events ◽  
Enormous Amount ◽  
Speed Up ◽  
Search Operation ◽  
Computing Support ◽  
Simulation Time ◽  
Multiple Machines

Garfield++ is a toolkit for the simulation of particle detectors that use gas and semi-conductors as sensitive medium. It takes enormous amount of time to complete the simulation of complex scenarios such as those involving high detector voltages, gases with large gains, or electric field meshes with large number of elements. We observed that most of the simulation time is being consumed in finding the correct element in the electric field mesh. We optimized the element search operation and achieved significant boost in the speed up. In addition, We added the parallel computing support in the toolkit to simulate multiple events simultaneously over multiple machines. In this paper, we present our approach of speeding up the computations and benchmark results.

Numerical Simulation and Analysis of Compartment Fire in Subway Train

2012 ◽  
Vol 166-169 ◽  
pp. 2726-2730
Author(s):  
Bo Si Zhang ◽  
Shou Xiang Lu
Keyword(s):  
Numerical Simulation ◽  
Transport System ◽  
Fire Safety ◽  
Urban Transport ◽  
The Other ◽  
Compartment Fire ◽  
Fire Simulation ◽  
Subway Train ◽  
Simulation Time

Subway plays an important role in urban transport system. Fire as the major risk of the subway, is gaining increasing concern. In this study, fire simulation is performed to estimate fire safety of different compartments of the subway train. Result shows that the two compartments in the middle become dangerous at 150s and the compartments in the two ends are not safe at 300s approximately. The other two compartments are always safe during the simulation time.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

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