[ES] En muchos campos de la ingeniería los modelos matemáticos son utilizados para describir el comportamiento de los sistemas, procesos o fenómenos. Hoy en día, existen varias técnicas o métodos que pueden ser usadas para obtener estos modelos. Debido a su versatilidad y simplicidad, a menudo se prefieren los métodos de identificación de sistemas. Por lo general, estos métodos requieren la definición de una estructura y la estimación computacional de los parámetros que la componen utilizando un conjunto de procedimientos y mediciones de las señales de entrada y salida del sistema. En el contexto de la identificación de sistemas no lineales, un desafío importante es la selección de la estructura. En el caso de que el sistema a identificar presente una no linealidad de tipo estático, los modelos orientados a bloques, pueden ser útiles para definir adecuadamente una estructura. Sin embargo, el diseñador puede enfrentarse a cierto grado de incertidumbre al seleccionar el modelo orientado a bloques adecuado en concordancia con el sistema real. Además de este inconveniente, se debe tener en cuenta que la estimación de algunos modelos orientados a bloques no es sencilla, como es el caso de los modelos de Wiener-Hammerstein que consisten en un bloque NL en medio de dos subsistemas LTI. La presencia de dos subsistemas LTI en los modelos de Wiener-Hammerstein es lo que principalmente dificulta su estimación. Generalmente, el procedimiento de identificación comienza con la estimación de la dinámica lineal, y el principal desafío es dividir esta dinámica entre los dos bloques LTI. Por lo general, esto implica una alta interacción del usuario para desarrollar varios procedimientos, y el modelo final estimado depende principalmente de estas etapas previas. El objetivo de esta tesis es contribuir a la identificación de los modelos de Wiener-Hammerstein. Esta contribución se basa en la presentación de dos nuevos algoritmos para atender aspectos específicos que no han sido abordados en la identificación de este tipo de modelos. El primer algoritmo, denominado WH-EA, permite estimar todos los parámetros de un modelo de Wiener-Hammerstein con un solo procedimiento a partir de un modelo dinámico lineal. Con WH-EA, una buena estimación no depende de procedimientos intermedios ya que el algoritmo evolutivo simultáneamente busca la mejor distribución de la dinámica, ajusta con precisión la ubicación de los polos y los ceros y captura la no linealidad estática. Otra ventaja importante de este algoritmo es que bajo consideraciones específicas y utilizando una señal de excitación adecuada, es posible crear un enfoque unificado que permite también la identificación de los modelos de Wiener y Hammerstein, que son casos particulares del modelo de Wiener-Hammerstein cuando uno de sus bloques LTI carece de dinámica. Lo interesante de este enfoque unificado es que con un mismo algoritmo es posible identificar los modelos de Wiener, Hammerstein y Wiener-Hammerstein sin que el usuario especifique de antemano el tipo de estructura a identificar. El segundo algoritmo llamado WH-MOEA, permite abordar el problema de identificación como un Problema de Optimización Multiobjetivo (MOOP). Sobre la base de este algoritmo se presenta un nuevo enfoque para la identificación de los modelos de Wiener-Hammerstein considerando un compromiso entre la precisión alcanzada y la complejidad del modelo. Con este enfoque es posible comparar varios modelos con diferentes prestaciones incluyendo como un objetivo de identificación el número de parámetros que puede tener el modelo estimado. El aporte de este enfoque se sustenta en el hecho de que en muchos problemas de ingeniería los requisitos de diseño y las preferencias del usuario no siempre apuntan a la precisión del modelo como un único objetivo, sino que muchas veces la complejidad es también un factor predominante en la toma de decisiones. / [CA] En molts camps de l'enginyeria els models matemàtics són utilitzats per a descriure el comportament dels sistemes, processos o fenòmens. Hui dia, existeixen diverses tècniques o mètodes que poden ser usades per a obtindre aquests models. A causa de la seua versatilitat i simplicitat, sovint es prefereixen els mètodes d'identificació de sistemes. En general, aquests mètodes requereixen la definició d'una estructura i l'estimació computacional dels paràmetres que la componen utilitzant un conjunt de procediments i mesuraments dels senyals d'entrada i eixida del sistema. En el context de la identificació de sistemes no lineals, un desafiament important és la selecció de l'estructura. En el cas que el sistema a identificar presente una no linealitat de tipus estàtic, els models orientats a blocs, poden ser útils per a definir adequadament una estructura. No obstant això, el dissenyador pot enfrontar-se a cert grau d'incertesa en seleccionar el model orientat a blocs adequat en concordança amb el sistema real. A més d'aquest inconvenient, s'ha de tindre en compte que l'estimació d'alguns models orientats a blocs no és senzilla, com és el cas dels models de Wiener-Hammerstein que consisteixen en un bloc NL enmig de dos subsistemes LTI. La presència de dos subsistemes LTI en els models de Wiener-Hammerstein és el que principalment dificulta la seua estimació. Generalment, el procediment d'identificació comença amb l'estimació de la dinàmica lineal, i el principal desafiament és dividir aquesta dinàmica entre els dos blocs LTI. En general, això implica una alta interacció de l'usuari per a desenvolupar diversos procediments, i el model final estimat depén principalment d'aquestes etapes prèvies. L'objectiu d'aquesta tesi és contribuir a la identificació dels models de Wiener-Hammerstein. Aquesta contribució es basa en la presentació de dos nous algorismes per a atendre aspectes específics que no han sigut adreçats en la identificació d'aquesta mena de models. El primer algorisme, denominat WH-EA (Algorisme Evolutiu per a la identificació de sistemes de Wiener-Hammerstein), permet estimar tots els paràmetres d'un model de Wiener-Hammerstein amb un sol procediment a partir d'un model dinàmic lineal. Amb WH-EA, una bona estimació no depén de procediments intermedis ja que l'algorisme evolutiu simultàniament busca la millor distribució de la dinàmica, afina la ubicació dels pols i els zeros i captura la no linealitat estàtica. Un altre avantatge important d'aquest algorisme és que sota consideracions específiques i utilitzant un senyal d'excitació adequada, és possible crear un enfocament unificat que permet també la identificació dels models de Wiener i Hammerstein, que són casos particulars del model de Wiener-Hammerstein quan un dels seus blocs LTI manca de dinàmica. L'interessant d'aquest enfocament unificat és que amb un mateix algorisme és possible identificar els models de Wiener, Hammerstein i Wiener-Hammerstein sense que l'usuari especifique per endavant el tipus d'estructura a identificar. El segon algorisme anomenat WH-MOEA (Algorisme evolutiu multi-objectiu per a la identificació de models de Wiener-Hammerstein), permet abordar el problema d'identificació com un Problema d'Optimització Multiobjectiu (MOOP). Sobre la base d'aquest algorisme es presenta un nou enfocament per a la identificació dels models de Wiener-Hammerstein considerant un compromís entre la precisió aconseguida i la complexitat del model. Amb aquest enfocament és possible comparar diversos models amb diferents prestacions incloent com un objectiu d'identificació el nombre de paràmetres que pot tindre el model estimat. L'aportació d'aquest enfocament se sustenta en el fet que en molts problemes d'enginyeria els requisits de disseny i les preferències de l'usuari no sempre apunten a la precisió del model com un únic objectiu, sinó que moltes vegades la complexitat és també un factor predominant en la presa de decisions. / [EN] In several engineering fields, mathematical models are used to describe the behaviour of systems, processes or phenomena. Nowadays, there are several techniques or methods for obtaining mathematical models. Because of their versatility and simplicity, system identification methods are often preferred. Generally, systems identification methods require defining a structure and estimating computationally the parameters that make it up, using a set of procedures y measurements of the system's input and output signals. In the context of nonlinear system identification, a significant challenge is the structure selection. In the case that the system to be identified presents a static type of nonlinearity, block-oriented models can be useful to define a suitable structure. However, the designer may face a certain degree of uncertainty when selecting the block-oriented model in accordance with the real system. In addition to this inconvenience, the estimation of some block-oriented models is not an easy task, as is the case with the Wiener-Hammerstein models consisting of a NL block in the middle of two LTI subsystems. The presence of two LTI subsystems in the Wiener-Hammerstein models is what mainly makes their estimation difficult. Generally, the identification procedure begins with the estimation of the linear dynamics, and the main challenge is to split this dynamic between the two LTI block. Usually, this implies a high user interaction to develop several procedures, and the final model estimated mostly depends on these previous stages. The aim of this thesis is to contribute to the identification of the Wiener-Hammerstein models. This contribution is based on the presentation of two new algorithms to address specific aspects that have not been addressed in the identification of this type of model. The first algorithm, called WH-EA (An Evolutionary Algorithm for Wiener-Hammerstein System Identification), allows estimating all the parameters of a Wiener-Hammerstein model with a single procedure from a linear dynamic model. With WH-EA, a good estimate does not depend on intermediate procedures since the evolutionary algorithm looks for the best dynamic division, while the locations of the poles and zeros are fine-tuned, and nonlinearity is captured simultaneously. Another significant advantage of this algorithm is that under specific considerations and using a suitable excitation signal; it is possible to create a unified approach that also allows the identification of Wiener and Hammerstein models which are particular cases of the Wiener-Hammerstein model when one of its LTI blocks lacks dynamics. What is interesting about this unified approach is that with the same algorithm, it is possible to identify Wiener, Hammerstein, and Wiener-Hammerstein models without the user specifying in advance the type of structure to be identified. The second algorithm called WH-MOEA (Multi-objective Evolutionary Algorithm for Wiener-Hammerstein identification), allows to address the identification problem as a Multi-Objective Optimisation Problem (MOOP). Based on this algorithm, a new approach for the identification of Wiener-Hammerstein models is presented considering a compromise between the accuracy achieved and the model complexity. With this approach, it is possible to compare several models with different performances, including as an identification target the number of parameters that the estimated model may have. The contribution of this approach is based on the fact that in many engineering problems the design requirements and user's preferences do not always point to the accuracy of the model as a single objective, but many times the complexity is also a predominant factor in decision-making. / Zambrano Abad, JC. (2021). Identification of nonlinear processes based on Wiener-Hammerstein models and heuristic optimization [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171739

<p>In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. </p><p>Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. </p><p>Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.</p>

Modeling of nonlinear systems based on special Hammerstein forms has been considered. In Hammerstein system modeling a static nonlinearity is connected to a dynamic linearity in cascade form. Fundamental contributions of this work are: 1) Introduction of Bezier curve nonlinearity representations / 2) Introduction of B-Spline curve nonlinearity representations instead of polynomials in cascade modeling. As a result, local control in nonlinear system modeling is achieved. Thus, unexpected variations of the output can be modeled more closely. As an important demonstration case, a model is developed and named as Middle East Technical University Neural Networks and Cascade Model (METU-NN-C). Application examples are chosen by considering the Near-Earth space processes, which are important for navigation, telecommunication and many other technical applications. It is demonstrated that the models developed based on the contributions of this work are especially more accurate under disturbed conditions, which are quantified by considering Space Weather parameters. Examples include forecasting of Total Electron Content (TEC), and mapping / estimation of joint angle of simple forced pendulum / estimation of joint angles of spring loaded inverted double pendulum with forced table / identification of Van der Pol oscillator / and identification of speakers. The operation performance results of the International Reference Ionosphere (IRI-2001), METU Neural Networks (METU-NN) and METU-NN-C models are compared qualitatively and quantitatively. As a numerical example, in forecasting the TEC by using the METU-NN-C having Bezier curves in nonlinearity representation, the average absolute error is 1.11 TECu. The new cascade models are shown to be promising for system designers and operators.

In this Master's thesis, Maximum Likelihood-based parametric identification methods for discrete-time SISO Hammerstein models from perturbed observations on both input and output, are investigated. Hammerstein models, consisting of a static nonlinear block followed by a dynamic linear one, are widely applied to modeling nonlinear dynamic systems, i.e., dynamic systems having nonlinearity at its input. Two identification methods are proposed. The first one assumes a Hammerstein model where the input signal is noise-free and the output signal is perturbed with colored noise. The second assumes, however, white noises added to the input and output of the nonlinearity and to the output of the whole considered Hammerstein model. Both methods operate directly in the time domain and their properties are illustrated by a number of simulated examples. It should be observed that attention is focused on derivation, numerical calculation, and simulation corresponding to the first identification method mentioned above.

The world's energy demand has been rapidly increasing and is projected to continue growing for at least the next two decades. With increasing global energy demand and competition from renewable energy, the oil and gas industry is striving for more efficient petroleum production. Many technical breakthroughs have enabled the drilling industry to expand the exploration to more difficult drilling such as deepwater drilling and multilateral directional drilling. For example, managed pressure drilling (MPD) offers ceaseless operation with multiple manipulated variables (MV) and wired drill pipe (WDP) provides two-way, high-speed measurements from bottom hole and along-string sensors. These technologies have maximum benefit when applied in an automation system or as a real-time advisory tool. The objective of this study is to investigate the benefit of nonlinear model-based control and estimation algorithms with various types of models. This work presents a new simplified flow model (SFM) for bottomhole pressure (BHP) regulation in MPD operations. The SFM is embedded into model-based control and estimation algorithms that use model predictive control (MPC) and moving horizon estimation (MHE), respectively. This work also presents a new Hammerstein-Wiener nonlinear model predictive controller for BHP regulation. Hammerstein-Wiener models employ input and output static nonlinear blocks before and after linear dynamics blocks to simplify the controller design. The control performance of the new Hammerstein-Wiener nonlinear controller is superior to conventional PID controllers in a variety of drilling scenarios. Conventional controllers show severe limitations in MPD because of the interconnected multivariable and nonlinear nature of drilling operations. BHP control performance is evaluated in scenarios such as drilling, pipe connection, kick attenuation, and mud density displacement and the efficacy of the SFM and Hammerstein-Wiener models is tested in various control schemes applicable to both WDP and mud pulse systems. Trusted high-fidelity drilling simulators are used to simulate well conditions and are used to evaluate the performance of the controllers using the SFM and Hammerstein-Wiener models. The comparison between non-WDP (semi-closed loop) and WDP (full-closed loop) applications validates the accuracy of the SFM under the set of conditions tested and confirms comparability with model-based control and estimation algorithms. The SFM MPC maintains the BHP within ± 1 bar of the setpoint for each investigated scenario, including for pipe connection and mud density displacement procedures that experience a wider operation range than normal drilling.

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This thesis considers the development of a Hammerstein-bilinear approach to non-linear systems modelling, analysis and control systems design, which builds on and extends the applicability of an existing bilinear approach. The underlying idea of the Hammerstein-bilinear approach is to use the Hammerstein-bilinear system models to capture various physical phenomena of interest and subsequently use these for model based control system designs with the premise being that of achieving enhanced control performance. The advantage of the Hammerstein-bilinear approach is that the well-structured system models allow techniques that have been originally developed for linear systems to be extended and applied, while retaining moderate complexity of the corresponding system identification schemes and nonlinear model based control designs. In recognition of the need to be able to identify the Hammerstein-bilinear models a unified suite of algorithms, being the extensions to the simplified refined instrumental variable method for parameter estimation of linear transfer function models is proposed. These algorithms are able to operate in both the continuous-time and discrete-time domains to reflect the requirements of the intended purposes of the identified models with the emphasis being placed on straightforward applicability of the developed algorithms and recognising the need to be able to operate under realistic practical system identification scenarios. Moreover, the proposed algorithms are also applicable to parameter estimation of Hammerstein and bilinear models, which are special cases of the wider Hammerstein-bilinear model class. The Hammerstein-bilinear approach has been applied to an industrial heating, ventilation and air conditioning (HVAC) system, which has also been the underlying application addressed in this thesis. A unique set of dynamic control design purpose oriented air temperature and humidity Hammerstein-bilinear models of an environmentally controlled clear room manufacturing zone has been identified. The greater insights afforded by the knowledge of the system nonlinearities then allow for enhanced control tuning of the associated commercial HVAC control system leading to an improved overall control performance.

The objective of this research work is to investigate, design and measurement of a digital predistortion linearizer that is able to compensate the dynamic nonlinear distortion of a High Power Amplifier (PA). The effectiveness of the proposed baseband predistorter (PD) on the performance of a WLAN OFDM transmitter utilizing a nonlinear PA with memory effect is observed and discussed. For this purpose, a 10W Class-A/B power amplifier with a gain of 22 dB, operated over the 3.5 GHz frequency band was designed and implemented. The proposed baseband PD is independent of the operating RF frequency and can be used in multiband applications. Its operation is based on the Hammerstein system, taking into account PA memory effect compensation, and demonstrates a noticeable improvement compared to memoryless predistorters. Different types of modelling procedures and linearizers were introduced and investigated, in which accurate behavioural models of Radio Frequency (RF) PAs exhibiting linear and nonlinear memory effects were presented and considered, based on the Wiener approach employing a linear parametric estimation technique. Three new linear methods of parameter estimation were investigated, with the aim of reducing the complexity of the required filtering process in linear memory compensation. Moreover, an improved wiener model is represented to include the nonlinear memory effect in the system. The validity of the PA modelling approaches and predistortion techniques for compensation of nonlinearities of a PA were verified by several tests and measurements. The approaches presented, based on the Wiener system, have the capacity to deal with the existing trade-off between accuracy and convergence speed compared to more computationally complex behavioural modelling algorithms considering memory effects, such as those based on Volterra series and Neural Networks. In addition, nonlinear and linear crosstalks introduced by the power amplifier nonlinear behaviour and antennas mutual coupling due to the compact size of a MIMO OFDM transmitter have been investigated.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-12-23T10:42:58Z No. of bitstreams: 1 Cassio Binkowski_.pdf: 1965327 bytes, checksum: 87b7380f1bab367237fb868e0de20388 (MD5) / Made available in DSpace on 2016-12-23T10:42:59Z (GMT). No. of bitstreams: 1 Cassio Binkowski_.pdf: 1965327 bytes, checksum: 87b7380f1bab367237fb868e0de20388 (MD5) Previous issue date: 2016-09-14 / Nenhuma / A identificação de sistemas está longe de ser uma tarefa nova. Sendo inicialmente proposta na metade do século XX, foi extensamente desenvolvida para sistemas lineares, devido às exigências da época relacionadas à complexidade dos sistemas e também do poder computacional, atingindo excelente resultados. No entanto, com o aumento da complexidade dos sistemas e das exigências de controle, os modelos lineares não mais conseguiam representar os sistemas em toda a faixa de operação exigida, sendo assim requerendo uma aplicação dos modelos não-lineares. Visto que todos os sistemas presentes na natureza possuem certo grau de não linearidade, é correto afirmar que um modelo não-linear é capaz de representar as dinâmicas dos sistemas de forma mais compreensiva que um modelo linear. A identificação de sistemas não lineares foi então estudada e diversos modelos foram propostos, atingindo ótimos resultados. Nesse trabalho foi realizado um estudo de dois modelos não-lineares, NARMAX e Hammerstein-Wiener, aplicando esses modelos a dois processos simulados. Foram então derivados dois algoritmos para realizar a estimação dos parâmetros dos modelos NARMAX e Hammerstein-Wiener, utilizando um estimador ortogonal, e também um algoritmo para geração de sinais de entrada multinível. Os modelos foram então estimados para os sistemas simulados, e comparados utilizando os critérios AIC, FPE, Lipschitz e de correlação cruzada de alta ordem. Os melhores resultados foram obtidos com os modelos Hammerstein-Wiener-OLS e NARMAX-OLS, ao contrário do modelo NARMAX-RLS. No entanto, devido a resultados bastante divergentes entre os modelos, pode-se concluir que essa área ainda carece de desenvolvimento de técnicas precisas para comparação e avaliação de modelos, bem como quanto à quantificação do nível de não-linearidade do sistema em questão. / The task of system identification is far from being a new one. It was initially proposed in the mid of the 20th century, and had then been extensively developed for linear systems, due to the demands of that time concerning computational power, systems complexity and control requirements. It has achieved excellent results in this approach. However, due to the rise of systems complexity and control requirements, linear models were no longer able to meet the desired accuracy and larger operating range, and therefore the usage nonlinear models were pursued. As all systems in nature are nonlinear to some extent, it is correct to state that nonlinear models can represent a whole lot more of systems’ dynamics than linear models. Nonlinear models were then studied, and several techniques were presented, being able to achieve very good results. In this work, two of the available nonlinear models were studied, namely NARMAX and Hammerstein-Wiener, applying these models in two simulated systems. Two algorithms were then derived to estimate parameters for NARMAX and Hammerstein-Wiener models using an orthogonal estimator, and also an algorithm for generating multi-level input signals. The models were then estimated to the simulated systems, and compared using the AIC, FPE, Lipschitz and high-order cross-correlation criteria. The best results were obtained for the Hammerstein-Wiener-OLS and NARMAX-OLS models, as opposed to the NARMAX-RLS model. However, due to divergent observed results between models, it can be concluded that precise methods for model comparison and validation still needs to be developed, as well as a method for nonlinearity quantification for the system in hand.