Loudspeakers were invented over 150 years ago, but the loudspeakers used todayare still based on the same ideas. Traditionally, good sound quality has been obtainedby using expensive materials in the loudspeakers and by allowing themto be big. However, nowadays loudspeakers are wanted in applications such asmobile phones and tablets where size and weight are very limited and there is aconstant desire to decrease production costs. Special small loudspeakers, knownas micro loudspeakers, have been developed for this purpose but due to the severerestrictions in size and manufacturing costs, the sound quality in the microloudspeakers is relatively poor. One problem is that the nonlinearities of thesystem, present in any loudspeaker, become more evident in the case of microloudspeakers and cause noticeable distortion of the sound.This master’s thesis has been performed in cooperation with Opalum (formerlyActiwave), a company specializing in using digital signal processing to improvethe sound in loudspeakers with poor acoustic properties. The objective of thethesis is to investigate ways to increase the sound quality in micro loudspeakersby using nonlinear control. Focus has been on frequencies below the resonancefrequency since the distortion is more noticeable at low frequencies. First, a nonlinearmodel of the micro loudspeaker has been obtained using system identificationstrategies. The model describes the relationship between the voltage overthe voice-coil and the diaphragm displacement. Subsequently, input-output linearisationhas been used to design a controller for the system and the effect onthe distortion has been investigated through experiments. Two different modelstructures have been tested, a physical model based on the Thiele-Small modeland a black-box model with a Hammerstein-Wiener structure. In both cases, thenonlinearities were modelled as polynomials. The controller was then extendedwith an updating algorithm, making it adaptive.The efficiency of the controllers has been proved by experiments, where distortionwas decreased by up to 60 % compared to the case without control. The effectwas largest for low frequencies, around one third of the resonance frequency,but improvements were noted up to about two thirds of the resonance frequency,depending on the loudspeaker unit. The approach using a physical model andthat using a black-box model have shown similar results. / Högtalaren uppfanns för över 150 år sedan men de högtalare som används idagbygger till stora delar på samma teknik. Högkvalitativt ljud har traditionellt uppnåttsgenom att ge högtalaren goda akustiska egenskaper genom att tillåta den attvara stor och tillverkad av dyra material. Utmaningen idag ligger i att högtalarefinns inbyggda i exempelvis mobiltelefoner, vilket innebär att de behöver görassmå, lätta och billiga att producera. För att möta dessa krav har kompromisserkrävts vilket gör att dessa små högtalare, kallade mikrohögtalare, har sämre ljudkvalitet.Ett problem är att de olinjäriteter som finns i alla högtalare blir extraframträdande i små högtalare vilket leder till distorsion och övertoner i ljudsignalen.Detta examensarbete är gjort i samarbete med Opalum (tidigare Actiwave), vilketär ett företag som specialiserar sig på att med hjälp av digital signalbehandlingförbättra ljudkvaliteten för högtalare med akustiskt dåliga egenskaper. Syftetmed examensarbetet har varit att minska distorsionen i en mikrohögtalaremed hjälp av olinjär reglering. Fokus har legat på den lägre delen av frekvensbandet,under resonansfrekvensen, eftersom det är där distorsionen är mest märkbar.Först har en olinjär modell av högtalaren tagits fram genom systemidentifiering.Modellen förklarar sambandet mellan spänningen över högtalarens talspole ochmembranets utslag. I ett nästa steg har en regulator designats utifrån modellenoch regulatorns effekt på distorsionen har utvärderats genom experiment. Två olikamodellstrukturer har undersökts, dels en fysikalisk modell baserad på Thiele-Smallmodellen och dels en svartlådemodell med Hammerstein-Wienerstruktur.I båda fallen har olinjäriteterna modellerats som polynom. Regulatorn har sedanutökats med en uppdateringsalgoritm som gör den adaptiv.Experiment har visat att regleringen bidrog till att minska distorsionen med upptill 60 % jämfört med då systemet kördes utan reglering. Effekten har varit störstför låga frekvenser, kring en tredjedel av resonsnsfrekvensen, men förbättringarhar kunnat ses upp till frekvenser kring två tredjedelar av resonansfrekvensen.Både metoden med en fysikalisk modellstruktur och med en svartlådestrukturhar visat likartade resultat.

Using recently popularized invertible neural network We predict future video frames from complex dynamic scenes. Our invertible linear embedding (ILE) demonstrates successful learning, prediction and latent state inference. In contrast to other approaches, ILE does not use any explicit reconstruction loss or simplistic pixel-space assumptions. Instead, it leverages invertibility to optimize the likelihood of image sequences exactly, albeit indirectly.Experiments and comparisons against state of the art methods over synthetic and natural image sequences demonstrate the robustness of our approach, and a discussion of future work explores the opportunities our method might provide to other fields in which the accurate analysis and forecasting of non-linear dynamic systems is essential.

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.

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.

Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore, the models constructed in this study cannot be used for scale-up purpose because they are not able to satisfy the rules and criteria for applying dimensional analysis to scale-up.

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