This thesis presents an approach for identifying critical model parameters in dynamic air-conditioning systems using limited sensor information. The expansion valve model and the compressor model parameters play a crucial role in the system model's accuracy. In the past, these parameters have been estimated using a mass flow meter; however, this is an expensive devise and at times, impractical. In response to these constraints, a novel method to estimate the unknown parameters of the expansion valve model and the compressor model is developed. A gray box model obtained by augmenting the expansion valve model, the evaporator model, and the compressor model is used. Two numerical search algorithms, nonlinear least squares and Simplex search, are used to estimate the parameters of the expansion valve model and the compressor model. This parameter estimation is done by minimizing the error between the model output and the experimental systems output. Results demonstrate that the nonlinear least squares algorithm was more robust for this estimation problem than the Simplex search algorithm. In this thesis, two types of expansion valves, the Electronic Expansion Valve and the Thermostatic Expansion Valve, are considered. The Electronic Expansion Valve model is a static model due to its dynamics being much faster than the systems dynamics; the Thermostatic expansion valve model, however, is a dynamic one. The parameter estimation algorithm developed is validated on two different experimental systems to confirm the practicality of its approach. Knowing the model parameters accurately can lead to a better model for control and fault detection applications. In addition to parameter estimation, this thesis also provides and validates a simple usable mathematical model for the Thermostatic expansion valve.

The analysis of searching optimization tasks and methods at work have been performed, rules of constructing simplex, its motion rules have been presented. The methods of simplex search analysis have been investigated; simplex search methods compared to other optimization methods. The analysis of statistical characteristics of rectangular simplex search was made using multiple Markov chains. Double Markov chains describing properties of rectangular simplex search in the climbing stage were designed, simplex search statistical characteristics calculating methodic was created. Theoretical statistical characteristics – probabilities of search steps, mathematical expectancy of shift of the simplex center towards the aim – which let us evaluate the velocity of rectangular simplex search in the climbing stage and reveal its features were obtained. Rectangular simplex search with two and three controlled variables was analyzed experimentally. Theoretical characteristics are confirmed by the results of modeling the search. The task of filter structure synthesis was solved using simplex search method. The results and conclusions of the research can be used in creating new algorithms of simplex search.

Analysis of simplex search and design of algorithmic system synthesis methods is performed in the current PhD thesis. The main objective is to create algorithmic methods for stochastic automatic control system synthesis, filter synthesis and algorithmic methods of variational calculus. Algorithmic methods allow solving system synthesis problems in cases when mathematical object is complex or it does not exist, when statistical characteristics of operating signals are unknown, i.e. when application of classical system synthesis methods is impossible. / Disertacijoje atliekami simpleksinės paieškos tyrimai ir nagrinėjama algoritminės sistemų sintezės metodų sudarymo problema. Pagrindinis disertacijos tikslas yra sukurti algoritminius stochastinių automatinio valdymo sistemų, filtrų sintezės bei variacinio skaičiavimo metodus. Algoritminiai metodai leidžia spręsti sistemų sintezės uždavinius, kuomet objekto matematinis modelis sudėtingas arba jo išvis nėra, kai nežinomos stochastines sistemas veikiančių signalų statistinės charakteristikos, t. y. kai klasikinių sistemų sintezės metodų taikymas yra neįmanomas.

Disertacijoje atliekami simpleksinės paieškos tyrimai ir nagrinėjama algoritminės sistemų sintezės metodų sudarymo problema. Pagrindinis disertacijos tikslas yra sukurti algoritminius stochastinių automatinio valdymo sistemų, filtrų sintezės bei variacinio skaičiavimo metodus. Algoritminiai metodai leidžia spręsti sistemų sintezės uždavinius, kuomet objekto matematinis modelis sudėtingas arba jo išvis nėra, kai nežinomos stochastines sistemas veikiančių signalų statistinės charakteristikos, t. y. kai klasikinių sistemų sintezės metodų taikymas yra neįmanomas. Disertaciją sudaro septyni skyriai, iš kurių paskutinis – rezultatų apibendrinimas. Pirmajame (įvadiniame) skyriuje nagrinėjamas problemos aktualumas, formuluojamas darbo tikslas bei uždaviniai, aprašomas mokslinis darbo naujumas, pristatomi autoriaus pranešimai ir publikacijos, disertacijos struktūra. Antrasis skyrius skirtas literatūros apžvalgai. Jame analizuojami paieškinio optimizavimo, optimalaus valdymo, AVS optimizavimo uždaviniai, optimizavimo objektų savybės, pateikta paieškinio optimizavimo metodų charakteristika, aprašyti kintamos struktūros valdymo sistemų sintezės principai, variacinio skaičiavimo metodai ir simpleksinės paieškos savybės. Trečiajme disertacijos skyriuje pateiktos stačiakampio simplekso taisyklės, paieškos statistinių charakteristikų skaičiavimo metodika, charakteristikų skaičiavimo teoriniai ir modeliavimo eksperimentiniai rezultatai. Ketvirtajame disertacijos skyriuje pateikti stochastinių... [toliau žr. visą tekstą] / Analysis of simplex search and design of algorithmic system synthesis methods is performed in the current PhD thesis. The main objective is to create algorithmic methods for stochastic automatic control system synthesis, filter synthesis and algorithmic methods of variational calculus. Algorithmic methods allow solving system synthesis problems in cases when mathematical object is complex or it does not exist, when statistical characteristics of operating signals are unknown, i. e. when application of classical system synthesis methods is impossible. The thesis is divided into seven chapters including the conclusion’s chapter. First chapter is dedicated for an introduction to the problem and its topicality. There are also formulated purposes and tasks of the work; the used methods and novelty of solutions are described; author‘s publications and structure of the thesis are presented. Second chapter covers an analysis of existing publications related with problems of the thesis. Search optimization algorithms, optimal control, automatic system optimization problems are analyzed, characteristics of search optimization algorithms are presented, the principles of variable structure system synthesis, variational calculus methods and simplex search characteristics are described. In the third chapter the rules of rectangular simplex search, the methods to calculate statistical characteristics of search, the results of characteristics calculation and experimental results of... [to full text]