Relevant bibliographies by topics / Probabilistic robust controller synthesis

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Probabilistic robust controller synthesis'

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Published: 25 January 2025

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Journal articles on the topic "Probabilistic robust controller synthesis"

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Badings, Thom, Licio Romao, Alessandro Abate, David Parker, Hasan A. Poonawala, Marielle Stoelinga, and Nils Jansen. "Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions." Journal of Artificial Intelligence Research 76 (January 21, 2023): 341–91. http://dx.doi.org/10.1613/jair.1.14253.

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Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target, while also avoiding unsafe regions of the state space. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP and compute a controller for which these guarantees carry over to the original control system. In addition, we develop a tailored computational scheme that reduces the complexity of the synthesis of these guarantees on the iMDP. Benchmarks on realistic control systems show the practical applicability of our method, even when the iMDP has hundreds of millions of transitions.
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Kanev, S., B. De Schutter, and M. Verhaegen. "An ellipsoid algorithm for probabilistic robust controller design." Systems & Control Letters 49, no. 5 (August 2003): 365–75. http://dx.doi.org/10.1016/s0167-6911(03)00115-4.

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Bakumenko, N. S., V. Y. Strilets, M. L. Ugryumov, R. O. Zelenskyi, K. M. Ugryumova, V. P. Starenkiy, S. V. Artiukh, and A. M. Nasonova. "COMPUTATIONAL INTELLIGENCE METHODS TO PATIENTS STRATIFICATION IN THE MEDICAL MONITORING SYSTEMS." Radio Electronics, Computer Science, Control, no. 1 (February 24, 2023): 24. http://dx.doi.org/10.15588/1607-3274-2023-1-3.

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Context. In modern medical practice the automation and information technologies are increasingly being implemented for diagnosing diseases, monitoring the condition of patients, determining the treatment program, etc. Therefore, the development of new and improvement of existing methods of the patient stratification in the medical monitoring systems is timely and necessary. Objective. The goal of intelligent diagnostics of patient’s state in the medical monitoring systems – reducing the likelihood of adverse states based on the choice of an individual treatment program: − reducing the probability of incorrectly determining the state of the patients when monitoring patients; − obtaining stable effective estimates of unknown values of treatment actions for patients (corresponding to the found state); − the choice of a rational individual treatment program for the patients, identified on the basis of the forecasted state. Method. Proposed methodology, which includes the following computational intelligence methods to patient’s stratification in the medical monitoring systems: 1) method of cluster analysis based on the agent-based approach – the determination of the possible number of patient’s states using controlled variables of state; 2) method of robust metamodels development by means artificial neuron networks under a priori data uncertainty (only accuracy of measurements is known) in the monitoring data: a) a multidimensional logistic regression model in the form of analytical dependences of the posterior probabilities of different states of the patients on the control and controlled variables of state; b) a multidimensional diagnostic model in the form of analytical dependences of the objective functions (quality criteria of the patient’s state) on the control and controlled variables of state; 3) method of estimating informativeness controlled variables of state at a priori data uncertainty; 4) method of robust multidimensional models development for the patient’s state control under a priori data uncertainty in the monitoring data in the form of analytical dependencies predicted from the measured values of the control and controlled variables of state in the monitoring process; 5) method of reducing the controlled state variables space dimension based on the analysis of the variables informativeness of the robust multidimensional models for the patient’s state control; 6) method of patient’s states determination based on the classification problem solution with the values of the control and forecasted controlled variables of state with using the probabilistic neural networks; 7) method of synthesis the rational individual patient’s treatment program in the medical monitoring system, for the state identified on the basis of the forecast. Proposed the structure of the model for choosing the rational individual patient’s treatment program based on IT Data Stream Mining, which implements the «Big Data for Better Outcomes» concept. Results. The developed advanced computational intelligence methods for forecast states were used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment. Conclusions. Experience in the implementation of “Big Data for Better Outcomes” concept for the solution of the problem of computational models for new patient stratification strategies is presented. Advanced methodology, computational methods for a patient stratification in the medical monitoring systems and applied information technology realizing them have been developed. The developed methods for forecast states can be used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment.
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Bao, Jie, Peter L. Lee, Fuyang Wang, and Weibiao Zhou. "New robust stability criterion and robust controller synthesis." International Journal of Robust and Nonlinear Control 8, no. 1 (January 1998): 49–59. http://dx.doi.org/10.1002/(sici)1099-1239(199801)8:1<49::aid-rnc309>3.0.co;2-h.

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Bernstein, D. S., and W. M. Haddad. "Robust controller synthesis using Kharitonov's theorem." IEEE Transactions on Automatic Control 37, no. 1 (1992): 129–32. http://dx.doi.org/10.1109/9.109648.

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Oya, Hidetoshi, Daisuke Yamasaki, Shunya Nagai, and Kojiro Hagino. "Synthesis of Adaptive Gain Robust Controllers for Polytopic Uncertain Systems." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/854306.

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We present a new adaptive gain robust controller for polytopic uncertain systems. The proposed adaptive gain robust controller consists of a state feedback law with a fixed gain and a compensation input with adaptive gains which are tuned by updating laws. In this paper, we show that sufficient conditions for the existence of the proposed adaptive gain robust controller are given in terms of LMIs. Finally, illustrative examples are presented to show the effectiveness of the proposed adaptive gain robust controller.
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Mihaly, Vlad, Mircea Şuşcă, Dora Morar, Mihai Stănese, and Petru Dobra. "μ-Synthesis for Fractional-Order Robust Controllers." Mathematics 9, no. 8 (April 20, 2021): 911. http://dx.doi.org/10.3390/math9080911.

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The current article presents a design procedure for obtaining robust multiple-input and multiple-output (MIMO) fractional-order controllers using a μ-synthesis design procedure with D–K iteration. μ-synthesis uses the generalized Robust Control framework in order to find a controller which meets the stability and performance criteria for a family of plants. Because this control problem is NP-hard, it is usually solved using an approximation, the most common being the D–K iteration algorithm, but, this approximation leads to high-order controllers, which are not practically feasible. If a desired structure is imposed to the controller, the corresponding K step is a non-convex problem. The novelty of the paper consists in an artificial bee colony swarm optimization approach to compute the nearly optimal controller parameters. Further, a mixed-sensitivity μ-synthesis control problem is solved with the proposed approach for a two-axis Computer Numerical Control (CNC) machine benchmark problem. The resulting controller using the described algorithm manages to ensure, with mathematical guarantee, both robust stability and robust performance, while the high-order controller obtained with the classical μ-synthesis approach in MATLAB does not offer this.
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Field, R. V., P. G. Voulgaris, and L. A. Bergman. "Methods to Compute Probabilistic Measures of Robustness for Structural Systems." Journal of Vibration and Control 2, no. 4 (October 1996): 447–63. http://dx.doi.org/10.1177/107754639600200405.

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Model uncertainty, if ignored, can seriously degrade the performance of an otherwise well-designed control system. If the level of this uncertainty is extreme, the system may even be driven to instability. In the context of structural control, performance degradation and instability imply excessive vibration or even structural failure. Robust control has typically been applied to the issue of model uncertainty through worst- case analyses. These traditional methods include the use of the structured singular value (μ-analysis), as applied to the small gain condition, to provide estimates of controller robustness. However, this emphasis on the worst-case scenario has not allowed a probabilistic understanding of robust control. Because of this, an attempt to view controller robustness as a probability measure is presented. As a result, a much more intuitive insight into controller robustness can be obtained. In this context, the joint probability distribution is of dimension equal to the number of uncertain parameters, and the failure hypersurface is defined by the onset of instability of the closed-loop system in the eigenspace. A first-order reliability measure (FORM) of the system is computed and used to estimate controller robustness. It is demonstrated via an example that this FORM method can provide accurate results on the probability of failure despite the potential complexity of the closed-loop. In addition to the FORM method, a probabilistic measure of robustness is developed based on the fundamentals of μ-analysis. It is shown that the μ-analysis based method is inferior to the FORM method and can only have qualitative value when assessing control system robustness in a probabilistic framework.
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Li, Ying, and Xue Gong Ding. "Robust μ-Synthesis Control for Parameter Variations." Advanced Materials Research 546-547 (July 2012): 850–55. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.850.

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The robust ASAC(active structure acoustic control) model for the system of structural acoustical coupling is established and the μ-synthesis design is presented in the paper. The main idea is as follows: First, the robust performance problem of this system is transformed into the robust stability problem of an augmented system. Through the robust stability controller for this augmented system is solved by the standard D-K iteration. Simulation results show that μ-controller can provide good disturbance rejection and is more robust to parameter variations.
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Amin, Rooh ul, and Aijun Li. "Modelling and robust attitude trajectory tracking control of 3-DOF four rotor hover vehicle." Aircraft Engineering and Aerospace Technology 89, no. 1 (January 3, 2017): 87–98. http://dx.doi.org/10.1108/aeat-11-2015-0236.

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Purpose The purpose of this paper is to present μ-synthesis-based robust attitude trajectory tracking control of three degree-of-freedom four rotor hover vehicle. Design/methodology/approach Comprehensive modelling of hover vehicle is presented, followed by development of uncertainty model. A μ-synthesis-based controller is designed using the DK iteration method that not only handles structured and unstructured uncertainties effectively but also guarantees robust performance. The performance of the proposed controller is evaluated through simulations, and the controller is also implemented on experimental platform. Simulation and experimental results validate that μ-synthesis-based robust controller is found effective in: solving robust attitude trajectory tracking problem of multirotor vehicle systems, handling parameter variations and dealing with external disturbances. Findings Performance analysis of the proposed controller guarantees robust stability and also ensures robust trajectory tracking performance for nominal system and for 15-20 per cent variations in the system parameters. In addition, the results also ensure robust handling of wind gusts disturbances. Originality/value This research addresses the robust performance of hover vehicle’s attitude control subjected to uncertainties and external disturbances using μ-synthesis-based controller. This is the only method so far that guarantees robust stability and performance simultaneously.
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Dissertations / Theses on the topic "Probabilistic robust controller synthesis"

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Somers, Franca Maria Emma. "Nouveaux outils probabilistes pour améliorer la vérification et la validation des systèmes de contrôle spatiaux." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0054.

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Les activités actuelles de vérification et validation (V&V) dans l'industrie aérospatiale reposent principalement sur des outils de simulation qui prennent beaucoup de temps. Ces approches classiques de type Monte-Carlo sont largement utilisées depuis des décennies pour évaluer les performances des systèmes de guidage, de navigation et de contrôle (GNC) et des systèmes de contrôle d'attitude et d'orbite (SCAO) contenant de multiples paramètres incertains. Elles permettent de quantifier la probabilité d'occurrence de phénomènes suffisamment fréquents, mais peuvent échouer dans la détection de combinaisons rares, mais critiques, de paramètres. Au fur et à mesure que la complexité des systèmes spatiaux modernes augmente, cette limitation joue un rôle de plus en plus important. Ces dernières années, les méthodes d'analyse des pires cas basées sur des modèles ont atteint un bon niveau de maturité. Sans avoir recours à des simulations, ces outils peuvent explorer l'espace de toutes les combinaisons possibles de paramètres incertains et fournir des limites mathématiques garanties sur les marges de stabilité robustes et les niveaux de performance pire-cas. Les configurations problématiques, identifiées à l'aide de ces méthodes, peuvent être utilisées pour guider les campagnes Monte-Carlo finales, ce qui raccourcit considérablement le processus V&V standard. L'une des limites des méthodes classiques d'analyse pire-cas basées sur des modèles est qu'elles supposent que les paramètres incertains peuvent prendre n'importe quelle valeur dans un intervalle donné avec une probabilité égale. La probabilité d'occurrence d'une combinaison de paramètres pire-cas n'est donc pas mesurée et la conception d'un système peut ainsi être rejetée sur la base d'un scénario très rare et extrêmement improbable. Cette recherche se concentre sur μ-analyse probabiliste pour développer de nouveaux outils efficaces et fiables afin d'améliorer la caractérisation d'événements rares mais néanmoins possibles. Ceci permet de resserrer l'écart d'analyse V&V ci-dessus entre les méthodes basées sur la simulation et les approches pire-cas déterministes basées sur des modèles
Current verification and validation (V&V) activities in aerospace industry mostly rely on time-consuming simulation-based tools. These classical Monte Carlo approaches have been widely used for decades to assess performance of Guidance, Navigation and Control (GNC) algorithms and Attitude and Orbit Control Systems (AOCS) containing multiple uncertain parameters. They are able to quantify the probability of sufficiently frequent phenomena, but they may fail in detecting rare but critical combinations of parameters. As the complexity of modern space systems increases, this limitation plays an ever more important role. In recent years, model-based worst-case analysis methods have reached a good level of maturity. Without the need of simulations, these tools can fully explore the space of all possible combinations of uncertain parameters and provide guaranteed mathematical bounds on robust stability margins and worst-case performance levels. Problematic parameter configurations, identified using these methods, can be used to guide the final Monte Carlo campaigns, thereby drastically shortening the standard V&V process. A limitation of classical model-based worst-case analysis methods is that they assume the uncertain parameters can take any value within a given range with equal probability. The probability of occurrence of a worst-case parameter combination is thus not measured and a control architecture can be rejected based on a very rare and extremely unlikely scenario. This PhD research makes advances in probabilistic μ-analysis to develop new efficient and reliable tools to improve the characterization of rare but nonetheless possible events. This to tighten the aforementioned V&V analysis gap between simulation-based methods and deterministic model-based worst-case approaches
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Corrado, Joseph R. "Robust fixed-structure controller synthesis." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/12945.

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White, David Michael. "Robust controller synthesis utilizing Kharitonov's Theorem." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0015/MQ47113.pdf.

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Ujma, Mateusz. "On verification and controller synthesis for probabilistic systems at runtime." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:9433e3ed-ad05-4f4e-8dbb-507a09283a02.

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Probabilistic model checking is a technique employed for verifying the correctness of computer systems that exhibit probabilistic behaviour. A related technique is controller synthesis, which generates controllers that guarantee the correct behaviour of the system. Not all controllers can be generated offline, as the relevant information may only be available when the system is running, for example, the reliability of services may vary over time. In this thesis, we propose a framework based on controller synthesis for stochastic games at runtime. We model systems using stochastic two-player games parameterised with data obtained from monitoring of the running system. One player represents the controllable actions of the system, while the other player represents the hostile uncontrollable environment. The goal is to synthesize, for a given property specification, a controller for the first player that wins against all possible actions of the environment player. Initially, controller synthesis is invoked for the parameterised model and the resulting controller is applied to the running system. The process is repeated at runtime when changes in the monitored parameters are detected, whereby a new controller is generated and applied. To ensure the practicality of the framework, we focus on its three important aspects: performance, robustness, and scalability. We propose an incremental model construction technique to improve performance of runtime synthesis. In many cases, changes in monitored parameters are small and models built for consecutive parameter values are similar. We exploit this and incrementally build a model for the updated parameters reusing the previous model, effectively saving time. To address robustness, we develop a technique called permissive controller synthesis. Permissive controllers generalise the classical controllers by allowing the system to choose from a set of actions instead of just one. By using a permissive controller, a computer system can quickly adapt to a situation where an action becomes temporarily unavailable while still satisfying the property of interest. We tackle the scalability of controller synthesis with a learning-based approach. We develop a technique based on real-time dynamic programming which, by generating random trajectories through a model, synthesises an approximately optimal controller. We guide the generation using heuristics and can guarantee that, even in the cases where we only explore a small part of the model, we still obtain a correct controller. We develop a full implementation of these techniques and evaluate it on a large set of case studies from the PRISM benchmark suite, demonstrating significant performance gains in most cases. We also illustrate the working of the framework on a new case study of an open-source stock monitoring application.
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Schrödel, Frank [Verfasser], Dirk [Akademischer Betreuer] Abel, and Steffen [Akademischer Betreuer] Leonhardt. "Stability region based robust controller synthesis / Frank Schrödel ; Dirk Abel, Steffen Leonhardt." Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/112591162X/34.

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Wong, Wallace Shung Hui. "Improved direct torque control and robust adaptive control of induction motor drives." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271811.

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Alper, Akmese. "Aeroservoelastic Analysis And Robust Controller Synthesis For Flutter Suppression Of Air Vehicle Control Actuation Systems." Phd thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607310/index.pdf.

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Flutter is one of the most important phenomena in which aerodynamic surfaces become unstable in certain flight conditions. Since the 1930&
#8217
s many studies were conducted in the areas of flutter prediction in design stage, research of design methods for flutter prevention, derivation and confirmation of flutter flight envelopes via tests, and in similar subjects for aircraft wings. With the use of controllers in 1960&
#8217
s, studies on the active flutter suppression began. First the classical controllers were used. Then, with the improvement of the controller synthesis methods, optimal controllers and later robust controllers started to be used. However, there are not many studies in the literature about fully movable control surfaces, commonly referred to as fins. Fins are used as missile control surfaces, and they can also be used as a horizontal stabilizer or as a canard in aircraft. In the scope of this thesis, controllers satisfying the performance and flutter suppression requirements of a fin are synthesized and compared. For this purpose, H2, Hinf, and mu controllers are used. A new flutter suppression method is proposed and used. In order to assess the performance of this method, results obtained are compared with the results of another flutter suppression method given in the literature. or the purpose of implementation of the controllers developed, aeroelastic model equations are derived by using the typical section wing model with thin airfoil assumption. The controller synthesis method is tested for aeroelastic models that are veloped for various flow regimes
namely, steady incompressible subsonic, unsteady incompressible subsonic, nsteady compressible subsonic, and unsteady compressible supersonic.
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Hosoe, Yohei. "Discrete-Time Noncausal Linear Periodically Time-Varying Scaling for Robustness Analysis and Controller Synthesis." 京都大学 (Kyoto University), 2013. http://hdl.handle.net/2433/180502.

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Henter, Gustav Eje. "Probabilistic Sequence Models with Speech and Language Applications." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134693.

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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.

QC 20131128


ACORNS: Acquisition of Communication and Recognition Skills
LISTA – The Listening Talker
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Saleh, Saad Jamil. "Robust controller synthesis for linear systems with structured uncertainty." 1991. http://catalog.hathitrust.org/api/volumes/oclc/24578456.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1991.
Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 114-116).
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Books on the topic "Probabilistic robust controller synthesis"

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Joshi, Suresh M. Application of LQG/LTR technique to robust controller synthesis for a large flexible space antenna. Hampton, Va: Langley Research Center, 1986.

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S, Armstrong Ernest, Sundararajan N, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. Application of LQG/LTR technique to robust controller synthesis for a large flexible space antenna. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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Ji, Baowei. Robust stability analysis and controller synthesis for systems with parametric uncertainties. 2004.

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Book chapters on the topic "Probabilistic robust controller synthesis"

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Curtain, Ruth F., and Hans Zwart. "Robust Finite-Dimensional Controller Synthesis." In Texts in Applied Mathematics, 457–563. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4224-6_9.

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Bacci, Giovanni, Patricia Bouyer, Uli Fahrenberg, Kim Guldstrand Larsen, Nicolas Markey, and Pierre-Alain Reynier. "Optimal and Robust Controller Synthesis." In Formal Methods, 203–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95582-7_12.

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Dräger, Klaus, Vojtěch Forejt, Marta Kwiatkowska, David Parker, and Mateusz Ujma. "Permissive Controller Synthesis for Probabilistic Systems." In Tools and Algorithms for the Construction and Analysis of Systems, 531–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54862-8_44.

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Andriushchenko, Roman, Ezio Bartocci, Milan Češka, Francesco Pontiggia, and Sarah Sallinger. "Deductive Controller Synthesis for Probabilistic Hyperproperties." In Quantitative Evaluation of Systems, 288–306. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43835-6_20.

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Sankur, Ocan, Patricia Bouyer, Nicolas Markey, and Pierre-Alain Reynier. "Robust Controller Synthesis in Timed Automata." In CONCUR 2013 – Concurrency Theory, 546–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40184-8_38.

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Dole, Kalyani, Ashutosh Gupta, and Shankara Narayanan Krishna. "Robust Controller Synthesis for Duration Calculus." In Automated Technology for Verification and Analysis, 429–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59152-6_24.

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Tsai, Mi-Ching, and Da-Wei Gu. "A CSD Approach to H-Infinity Controller Synthesis." In Robust and Optimal Control, 267–302. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-6257-5_9.

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Datta, Aniruddha, Ming-Tzu Ho, and Shankar P. Bhattacharyya. "Robust and Non-fragile PID Controller Design." In Structure and Synthesis of PID Controllers, 125–39. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-3651-4_6.

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Ebihara, Yoshio, Dimitri Peaucelle, and Denis Arzelier. "Multiobjective Controller Synthesis for LTI Systems." In S-Variable Approach to LMI-Based Robust Control, 139–64. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6606-1_5.

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Henrion, Didier. "Positive Polynomial Matrices for LPV Controller Synthesis." In Robust Control and Linear Parameter Varying Approaches, 87–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36110-4_4.

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Conference papers on the topic "Probabilistic robust controller synthesis"

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Caverly, Ryan J., and Vibhor L. Bageshwar. "State Feedback Synthesis for Robust Performance with Probabilistic Parametric Uncertainty." In 2024 American Control Conference (ACC), 1885–90. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644577.

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Schafer, Lukas, and Matthias Althoff. "Computing Robust Control Invariant Sets of Nonlinear Systems Using Polynomial Controller Synthesis." In 2024 American Control Conference (ACC), 4162–69. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644939.

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Lu, Bei. "Probabilistic Design of Networked Control Systems With Uncertain Time Delay." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42829.

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Networked control systems (NCSs), where control loops are closed through a real-time network, have been adopted in many application areas. Examples include manufacturing plants, automobiles, aircraft, and spacecraft. However, the insertion of a real-time network introduces time delays due to time-sharing of the communication media. The network-induced delay can degrade the performance of an NCS, and can even destabilize the system. Due to its random nature, in this paper, we apply the promising probability robust control approach to handle the network-induced delay, which is modeled as an uncertainty governed by a probability distribution function. With considering both stability and performance of NCSs in the stage of control design, we propose the synthesis condition of ℋ∞ state-feedback control of NCSs. It is formulated as a set of linear matrix inequalities with uncertain parameter present in the the state-space data. The ellipsoid randomized algorithm is applied to solve the matrix variables and design a probabilistic robust controller. A numerical example is given to demonstrate the probabilistic design method for NCSs.
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Mahani, Maziar Fooladi, and Yue Wang. "Trust-Based Runtime Verification for Multi-Quad-Rotor Motion Planning With a Human-in-the-Loop." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9174.

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In this paper, we propose a trust-based runtime verification (RV) framework for deploying multiple quad-rotors with a human-in-the-loop (HIL). By bringing together approaches from runtime verification, trust-based decision-making, human-robot interaction (HRI), and hybrid systems, we develop a unified framework that is capable of integrating human cognitive skills with autonomous capabilities of multi-robot systems to improve system performance and maximize the intuitiveness of the human-robot-interaction. On top of the RV framework, we utilize a probabilistic trust inference model as the key component in forming the HRI, designed to maintain the system performance. A violation avoidance controller is designed to account for the unexpected/unmodeled environment behaviors e.g. collision with static/moving obstacles. We also use the automata theoretic approaches to generate motion plans for the quad-rotors working in a partially-known environment by automatic synthesis of controllers enforcing specifications given in temporal logic languages. Finally, we illustrated the effectiveness of this framework as well as its feasibility through a simulated case study.
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Mehr, Negar, Dorsa Sadigh, and Roberto Horowitz. "Probabilistic controller synthesis for freeway traffic networks." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7525023.

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Chamanbaz, Mohammadreza, Mario Sznaier, Constantino Lagoa, and Fabrizio Dabbene. "Probabilistic Discrete Time Robust H2 Controller Design." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9304278.

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Bernstein, D. S., and W. M. Haddad. "Robust controller synthesis using Kharitonov's theorem." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203802.

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Veenman, Joost, Martin Lahr, and Carsten W. Scherer. "Robust controller synthesis with unstable weights." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798620.

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Hashimoto, Kazumune, Shuichi Adachi, and Dimos V. Dimarogonas. "Robust safety controller synthesis using tubes." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263718.

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Collins, E. G., D. Sadhukhan, and L. T. Watson. "Robust controller synthesis via nonlinear matrix inequalities." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.611756.

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