Recent developments in the practice control field have heightened the need for performance enhancement. The designed controller should not only guarantee the variables to follow their set point values, but also ought to focus on the performance of systems like quality, efficiency, etc. Hence, with the fact that the inevitable noises are widely existing during industry processes, the randomness of the tracking errors can be considered as a critical performance to improve further. In addition, due to the fact that some controllers for industrial processes cannot be changed once the parameters are designed, it is crucial to design a control algorithm to minimise the randomness of tracking error without changing the existing closed-loop control. In order to achieve the above objectives, a class of novel algorithms are proposed in this thesis for different types of systems with unmeasurable states. Without changing the existing closed-loop proportional integral(PI) controller, the compensative controller is extra added to reduce the randomness of tracking error. That means the PI controller can always guarantee the basic tracking property while the designed compensative signal can be removed any time without affecting the normal operation. Instead of just using the output information as PI controller, the compensative controller is designed to minimise the randomness of tracking error using estimated states information. Since most system states are unmeasurable, proper filters are employed to estimate the system states. Based on the stochastic system control theory, the criterion to characterise the system randomness are valid to different systems. Therefore a brief review about the basic concepts of stochastic system control contained in this thesis. More specifically, there are overshoot minimisation for linear deterministic systems, minimum variance control for linear Gaussian stochastic systems, and minimum entropy control for non-linear and non-Gaussian stochastic systems. Furthermore, the stability analysis of each system is discussed in mean-square sense. To illustrate the effectiveness of presented control methods, the simulation results are given. Finally, the works of this thesis are summarised and the future work towards to the limitations existed in the proposed algorithms are listed.

Yes / Probability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense. / De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136]

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-03-14T21:19:45Z No. of bitstreams: 1 PAULO GUILHERME SILVA DE GOÉS - DISSERTAÇÃO PPGEQ 2016..pdf: 2059035 bytes, checksum: 422a69768c36f09a757f3e56b6dd7eb0 (MD5) / Made available in DSpace on 2018-03-14T21:19:45Z (GMT). No. of bitstreams: 1 PAULO GUILHERME SILVA DE GOÉS - DISSERTAÇÃO PPGEQ 2016..pdf: 2059035 bytes, checksum: 422a69768c36f09a757f3e56b6dd7eb0 (MD5) Previous issue date: 2016-11-07 / CNPq / As aplicações de novas metodologias para análise e otimização de sistemas reativos podem ser consideradas como fatores decisivos para o crescimento e a consolidação de um dado processo industrial. Vários estudos demonstram que quando fundamentações termodinâmicas, especialmente a segunda lei, são inseridas na metodologia de análise e otimização de processos químicos, melhores resultados são obtidos. Tal fato ocorre devido a capacidade da segunda lei da termodinâmica de mensurar, através da entropia, a tendência de a energia fluir em uma direção particular para que uma distribuição de energia mais uniforme seja alcançada. Tornando-se, assim, a entropia, uma propriedade de fundamental importância na análise de tais processos. Entretanto, apesar do crescente desenvolvimento destas metodologias, observa-se uma carência nos indicadores que comprovam a melhoria do sistema em estudo, isto é, falta um indicador que consiga de maneira simples e objetiva identificar a direção do estado ótimo de operação. Portanto, este trabalho teve por objetivo o desenvolvimento de um índice de desempenho entrópico para aplicação em sistemas reacionais, o qual está fundamentado na termodinâmica clássica, em especial no conceito da máxima entropia. Tal indicador utiliza o conceito básico da máxima entropia para indicar o quão eficientemente se processou uma determinada reação química, de modo a atingir a máxima produtividade do produto de principal interesse econômico, e consequentemente, a mínima entropia do sistema final. Para ilustrar o desempenho do índice desenvolvido, utilizou-o como parâmetro para a escolha do melhor estado de operação em três estudos de casos. Através do índice desenvolvido, pode-se de maneira simples afirmar qual é a melhor condição de operação, para comprovar tal premissa um conjunto de indicadores clássicos são utilizados de maneira auxiliar. Os resultados indicam ser o índice consistente, eficiente e relevando ainda o quão afastado o processo se encontra da sua condição ótima, além de ser de fácil aplicação. / The applications of new methodologies for analysis and optimization of reactive systems can be considered as decisive factor for the growth and consolidation of a given industrial process. Several studies demonstrate that when thermodynamics fundamentals, especially the second law, are inserted in the methodology of analysis and optimization of chemical process, better results are obtained. This is due to the ability of the second law of thermodynamics to measure, through entropy, the tendency for energy to flow in a particular direction so that a more uniform energy distribution is achieved. Thus, entropy becomes a property of fundamental importance in the analysis of such process. However, despite the growing development of these methodologies, there is a lack indicators that prove the improvement of the system under study, that is, an indicator that simply and objectively identifies the direction of the optimal operating state is lacking. Therefore, the objective of this work was the development of an entropic performance index for application in reactional systems, which is based on classical thermodynamics, especially in the concept of maximum entropy. This indicator uses the basic concept of maximum entropy to indicate how efficiently a given chemical reaction has been processed in order to achieve the maximum productivity of the product of primary economic interest and consequently, the minimum entropy of the final system. To illustrate the performance of the develop index, it was used as a parameter for choosing the best state of operation in three case studies. Through the developed index, one can easily state which is the best operating condition, to prove this premise a set of classic indicators are used in a auxiliary way. The results indicate that the index is consistent, efficient and also reveals how far the process is from its optimal condition, besides being easy to apply.

Yes / This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.

Yes / In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm. / This work was supported in part by the PNNL Control of Complex Systems Initiative and in part by the National Natural Science Foundation of China under Grants 61621004,61573022 and 61333007.

This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / School of Environmental Sciences / M. Sc. (Astronomy)

This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / Environmental Sciences / M. Sc. (Astronomy)

Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data. / <p>QC 20131128</p> / ACORNS: Acquisition of Communication and Recognition Skills / LISTA – The Listening Talker