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1

Constrained optimization of the brushless DC motor using the salp swarm algorithm

Knypiński Łukasz, Devarepalli Ramesh, Le Menach Yvonnick

Archives of Electrical Engineering

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2022

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Vol. 71, nr 3

775--787

EN

This paper presents an algorithm and optimization procedure for the optimization of the outer rotor structure of the brushless DC (BLDC) motor. The optimization software was developed in the Delphi Tiburón development environment. The optimization procedure is based on the salp swarm algorithm. The effectiveness of the developed optimization procedurewas compared with genetic algorithm and particle swarmoptimization algorithm. The mathematical model of the device includes the electromagnetic field equations taking into account the non-linearity of the ferromagnetic material, equations of external supply circuits and equations of mechanical motion. The external penalty function was introduced into the optimization algorithm to take into account the non-linear constraint function.

2

Maximizing performance of linear model predictive control of glycemia for T1DM subjects

Dodek Martin, Miklovicová Eva

Archives of Control Sciences

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2022

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Vol. 32, No. 2

305--333

EN

The primary objective of this paper is the custom design of an effective, yet relatively easy-to-implement, predictive control algorithm to maintain normoglycemia in patients with type 1 diabetes. The proposed patient-tailorable empirical model featuring the separated feedback dynamics to model the effect of insulin administration and carbohydrate intake was proven to be suitable for the synthesis of a high-performance predictive control algorithm for artificial pancreas. Within the introduced linear model predictive control law, the constraints were applied to the manipulated variable in order to reflect the technical limitations of insulin pumps and the typical nonnegative nature of the insulin administration. Similarly, inequalities constraints for the controlled variable were also assumed while anticipating suppression of hypoglycemia states during the automated insulin treatment. However, the problem of control infeasibility has emerged, especially if one uses too tight constraints of the manipulated and the controlled variable concurrently. To this end, exploiting the Farkas lemma, it was possible to formulate the helper linear programming problem based on the solution of which this infeasibility could be identified and the optimality of the control could be restored by adapting the constraints. This adaptation of constraints is asymmetrical, thus one can force to fully avoid hypoglycemia at the expense of mild hyperglycemia. Finally, a series of comprehensive in-silico experiments were carried out to validate the presented control algorithm and the proposed improvements. These simulations also addressed the control robustness in terms of the intersubject variability and the meal announcements uncertainty.

3

Constrained optimization of line-start PM motor based on the gray wolf optimizer

Knypińskia Łukasz

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 1

1--10

EN

This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next, the result obtained for the developed procedure (together with the gray wolf algorithm) was compared with results obtained using: (a) the particle swarm optimization algorithm, (b) the bat algorithm and (c) the genetic algorithm. The developed optimization algorithm is characterized by good convergence, robustness and reliability. Selected results of the computer simulation are presented and discussed.

4

A design framework for rigorous constrained EM-driven optimization of miniaturized antennas with circular polarization

Bekasiewicz Adrian, Koziel Slawomir

Metrology and Measurement Systems

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2019

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Vol. 26, nr 1

41--52

EN

Compact radiators with circular polarization are important components of modern mobile communication systems. Their design is a challenging process which requires maintaining simultaneous control over several performance figures but also the structure size. In this work, a novel design framework for multi-stage constrained miniaturization of antennas with circular polarization is presented. The method involves sequential optimization of the radiator in respect of selected performance figures and, eventually, the size. Optimizations are performed with iteratively increased number of design constraints. Numerical efficiency of the method is ensured using a fast local-search algorithm embedded in a trust-region framework. The proposed design framework is demonstrated using a compact planar radiator with circular polarization. The optimized antenna is characterized by a small size of 271 mm2 with 37% and 47% bandwidths in respect of 10 dB return loss and 3 dB axial ratio, respectively. The structure is benchmarked against the state-of-the-art circular polarization antennas. Numerical results are confirmed by measurements of the fabricated antenna prototype.

5

Constraints in allocation of thrusters in a DP simulator

Zalewski P.

Zeszyty Naukowe Akademii Morskiej w Szczecinie

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2017

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nr 52 (124)

45--50

EN

Vessels conducting dynamic positioning (DP) operations are usually equipped with thruster configurations that enable the generation of force and torque. Some thrusters in these configurations are deliberately redundant to minimize consequences of thruster failures, enable overactuated control and increase the safety in operation. On such vessels, a thrust allocation system must be used to distribute the control actions determined by the DP controller among the thrusters. The optimal allocation of the thrusters’ settings in DP systems is a problem that can be solved by convex optimization methods depending on the criteria and constraints used. This paper presents a quadratic programming (QP) method, adopted in a DP control model, which is being developed in Maritime University of Szczecin for ship simulation purposes.

6

Application of adaptive multivariable Generalized Predictive Control to a HVAC system in real time

Gulan M., Salaj M., Rohal’-Ilkiv B

Archives of Control Sciences

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2014

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Vol. 24, no. 1

67--84

EN

This paper presents the application of a Multivariable Generalized Predictive Controller (MGPC) for simultaneous temperature and humidity control in a Heating, Ventilating and Air- Conditioning (HVAC) system. The multivariable controlled process dynamics is modeled using a set of MISO models on-line identified from measured input-output process data. The controller synthesis is based on direct optimization of selected quadratic cost function with respect to amplitude and rate input constraints. Efficacy of the proposed adaptive MGPC algorithm is experimentally demonstrated on a laboratory-scale model of HVAC system. To control the airconditioning part of system the designed multivariable predictive controller is considered in a cascade dual-rate control scheme with PID auxiliary controllers.

7

A Hybrid Invasive Weed Optimization with Feasibility-Based Rule for Constrained Optimization Problem

Chen H., Zhou P., Guo P., Ouyang X., He S., Zheng H.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 4

160-167

EN

During the past decade, hybrid algorithms combining evolutionary computation and constraint-handling techniques is one of the most popular method to solve constrained optimization problems. Usually, penalty functions are often used in constrained optimization. But it is difficult to strike the right balance between objective and penalty functions. As a novel population-based algorithm, invasive weed optimization (IWO) algorithm has gained wide applications in a variety of fields, especially for unconstrained optimization problems. In this paper, a hybrid IWO (HIWO) with a feasibility-based rule is proposed to solve constrained optimization problems. The feasibility-based rule does not need additional parameters, which is different from penalty functions. In addition, the complex method is used to provide direction for weed evolution, which can accelerate the convergence speed. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed HIWO.

PL

W artykule przedstawiono opracowaną metodę optymalizacji z funkcją kosztu, bazującą na hybrydowej metodzie IWO (ang. Hybrid Invasive Weed Optimizastion) oraz regułach związanych z wykonalnością. Zasady wykonalności, w przeciwieństwie do funkcji kar, nie wymagają dodatkowych parametrów. Dodatkowo zastosowano kompleksową metodę określania kierunki ewolucji trawy w algorytmie IWO, co pozwala na przyspieszenie konwergencji. Przeprowadzone badania symulacyjne i porównawcze dowodzą skuteczności i sprawności proponowanej metody HIWO.

8

New optimization based method for estimating priority weights

Grzybowski A. Z.

Journal of Applied Mathematics and Computational Mechanics

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2013

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Vol. 12, nr 1

33--44

EN

The estimation of priority vectors from pairwise comparison matrices is a core of the Analytic Hierarchy Process. Perhaps the most popular approach for deriving the priority weights is the right eigenvalue method (EM). Despite its popularity, various shortcomings of the EM have been described in literature. In this paper a new method for deriving priority vectors is proposed. This method makes use of the idea underlying the EM but in difference to the latter, the new one is optimization based. Important features of this new technique are studied via computer simulations and illustrated by some numerical examples.

9

Optymalizacja transformacji wyników pomiarów bloków kadłuba statku z uwzględnieniem ograniczeń

Kniat A.

Pomiary Automatyka Kontrola

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2010

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R. 56, nr 6

597-601

PL

Artykuł przedstawia algorytm optymalizujący transformację wyników pomiarów bloków i sekcji kadłuba statku, która jest wykonywana w celu oceny przydatności montażowej bloków i sekcji podczas ostatniej fazy montażu kadłuba w suchym doku lub na pochylni. Ponieważ każdy blok jest mierzony w innym układzie odniesienia, istnieje konieczność transformacji wyników pomiarów do wspólnego układu przyjętego w modelu CAD. Zakłada się, że transformacja nie pogarsza dokładności wyników pomiarów i zachowuje odległości pomiędzy zmierzonymi punktami. Zaproponowany algorytm optymalizacji transformacji wyników pomiarów minimalizuje sumy odległości pomiędzy mierzonymi punktami, a korespondującymi punktami w modelu CAD, uwzględniając jedno z dwóch możliwych ograniczeń: wspólna płaszczyzna, albo wspólna oś. W artykule zaprezentowano zarówno opis metody optymalizacji, jak i przykład transformacji punktów zmierzonych na styku dwóch sekcji dna podwójnego statku. We wnioskach opisano zalety i wady przedstawionego algorytmu, jak również kierunki dalszych badań.

EN

The paper presents the constrained optimisation algorithm for transformation of measurements taken on ship blocks and sections to asses their feasibility for the final stage of assembly in a dry dock or on a slipway. As each block is measured in different coordinate system it is necessary to make transformations and bring results to a common CAD model. Transformation must keep distances between the measured points and must not lose accuracy. The analysis of transformed measurements requires first to check if blocks were manufactured within tolerances assumed in the design and then to compare if the two neighbour blocks can be joined. The optimisation is aimed at minimising the sum of distances between the transformed and corresponding points in a CAD model. The constraint is alignment of chosen axes or planes. The paper contains both description of the transformation algorithms and an example of transformation of points measured on two neighbour sections of a ship double bottom. In the summary advantages and disadvantages of the algorithm are described and directions for further research are given.

10

Optimality and duality for nonsmooth multiobjective optimization problems with generalized V-r-invexity

Mishra S. K., Singh V., Wang S. Y., Lai K. K.

Journal of Applied Analysis

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2010

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Vol. 16, nr 1

49-58

EN

In this paper, a new concept of invexity for locally Lipschitz vector-valued functions is introduced, called V-r-type I functions. The generalized Karush-Kuhn-Tucker sufficient optimality conditions are proved and duality theorems are established for a non-smooth multiobjective optimization problems involving K-r-type I functions with respect to the same function η.

11

Local stability conditions for discrete-time cascade locally recurrent neural networks

Patan K.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 1

23-34

EN

The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments.

12

Unilateral Contact Applications Using Fem Software

Stavroulaki M. E., Stavroulakis G. E.

International Journal of Applied Mathematics and Computer Science

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2002

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Vol. 12, no 1

115-125

EN

Nonsmooth analysis, inequality constrained optimization and variational inequalities are involved in the modelling of unilateral contact problems. The corresponding theoretical and algorithmic tools, which are part of the area known as nonsmooth mechanics, are by no means classical. In general purpose software some of these tools (perhaps in a simplified way) are currently available. Two engineering applications, a rubber-coated roller contact problem and a masonry wall, solved with MARC, are briefly presented, together with elements of the underlying theory.

13

Markov random fields and constrained optimization for textured image segmentation

Mościńska K.

Machine Graphics and Vision

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1998

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Vol. 7, No. 1/2

259-268

EN

Classical methods of image segmentation , like discontinuity detection or region growing concepts, are not satisfactory in case in textured images. The alternative is the application of stochastic models like Markov Random Fields (MRF) for image modelling and segmentation. Stochastic model may be described in terms of energy function that should be minimized during a relaxation procedure. Instead of doubly-stochastic model, in which boty the intensity and the label process are modelled by the set of deterministic features. Local texture properties are evaluated using local linear transforms or results from the first order histogram. We measure the disparity between spatial freatures on the basis of the Kolmogorov-Smirnov statistics. Stochastic relaxation algorithms is applied for the minimization of the global energy function. The forbidden label configuration task are given. The examples presented in the paper confirm the usefulness of proposed models and the efficiency of the designed algorithms. Parallel implementation of the constrained optimization can be considered due to the local computation.