890 resultados para Discrete-time sliding mode control


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The challenge of detecting a change in the distribution of data is a sequential decision problem that is relevant to many engineering solutions, including quality control and machine and process monitoring. This dissertation develops techniques for exact solution of change-detection problems with discrete time and discrete observations. Change-detection problems are classified as Bayes or minimax based on the availability of information on the change-time distribution. A Bayes optimal solution uses prior information about the distribution of the change time to minimize the expected cost, whereas a minimax optimal solution minimizes the cost under the worst-case change-time distribution. Both types of problems are addressed. The most important result of the dissertation is the development of a polynomial-time algorithm for the solution of important classes of Markov Bayes change-detection problems. Existing techniques for epsilon-exact solution of partially observable Markov decision processes have complexity exponential in the number of observation symbols. A new algorithm, called constellation induction, exploits the concavity and Lipschitz continuity of the value function, and has complexity polynomial in the number of observation symbols. It is shown that change-detection problems with a geometric change-time distribution and identically- and independently-distributed observations before and after the change are solvable in polynomial time. Also, change-detection problems on hidden Markov models with a fixed number of recurrent states are solvable in polynomial time. A detailed implementation and analysis of the constellation-induction algorithm are provided. Exact solution methods are also established for several types of minimax change-detection problems. Finite-horizon problems with arbitrary observation distributions are modeled as extensive-form games and solved using linear programs. Infinite-horizon problems with linear penalty for detection delay and identically- and independently-distributed observations can be solved in polynomial time via epsilon-optimal parameterization of a cumulative-sum procedure. Finally, the properties of policies for change-detection problems are described and analyzed. Simple classes of formal languages are shown to be sufficient for epsilon-exact solution of change-detection problems, and methods for finding minimally sized policy representations are described.

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This study presents a proposal of speed servomechanisms without the use of mechanical sensors (sensorless) using induction motors. A comparison is performed and propose techniques for pet rotor speed, analyzing performance in different conditions of speed and load. For the determination of control technique, initially, is performed an analysis of the technical literature of the main control and speed estimation used, with their characteristics and limitations. The proposed technique for servo sensorless speed induction motor uses indirect field-oriented control (IFOC), composed of four controllers of the proportional-integral type (PI): rotor flux controller, speed controller and current controllers in the direct and quadrature shaft. As the main focus of the work is in the speed control loop was implemented in Matlab the recursive least squares algorithm (RLS) for identification of mechanical parameters, such as moment of inertia and friction coefficient. Thus, the speed of outer loop controller gains can be self adjusted to compensate for any changes in the mechanical parameters. For speed estimation techniques are analyzed: MRAS by rotóricos fluxes MRAS by counter EMF, MRAS by instantaneous reactive power, slip, locked loop phase (PLL) and sliding mode. A proposition of estimation in sliding mode based on speed, which is performed a change in rotor flux observer structure is displayed. To evaluate the techniques are performed theoretical analyzes in Matlab simulation environment and experimental platform in electrical machinery drives. The DSP TMS320F28069 was used for experimental implementation of speed estimation techniques and check the performance of the same in a wide speed range, including load insertion. From this analysis is carried out to implement closed-loop control of sensorless speed IFOC structure. The results demonstrated the real possibility of replacing mechanical sensors for estimation techniques proposed and analyzed. Among these, the estimator based on PLL demonstrated the best performance in various conditions, while the technique based on sliding mode has good capacity estimation in steady state and robustness to parametric variations.

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Given that landfills are depletable and replaceable resources, the right approach, when dealing with landfill management, is that of designing an optimal sequence of landfills rather than designing every single landfill separately. In this paper we use Optimal Control models, with mixed elements of both continuous and discrete time problems, to determine an optimal sequence of landfills, as regarding their capacity and lifetime. The resulting optimization problems involve splitting a time horizon of planning into several subintervals, the length of which has to be decided. In each of the subintervals some costs, the amount of which depends on the value of the decision variables, have to be borne. The obtained results may be applied to other economic problems such as private and public investments, consumption decisions on durable goods, etc.

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The horticultural sector has become an increasingly important sector of food production, for which greenhouse climate control plays a vital role in improving its sustainability. One of the methods to control the greenhouse climate is Model Predictive Control, which can be optimized through a branch and bound algorithm. The application of the algorithm in literature is examined and analyzed through small examples, and later extended to greenhouse climate simulation. A comparison is made of various alternative objective functions available in literature. Subsequently, a modidified version of the B&B algorithm is presented, which reduces the number of node evaluations required for optimization. Finally, three alternative algorithms are developed and compared to consider the optimization problem from a discrete to a continuous control space.

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Doctor of Philosophy in Mathematics

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Modern power networks incorporate communications and information technology infrastructure into the electrical power system to create a smart grid in terms of control and operation. The smart grid enables real-time communication and control between consumers and utility companies allowing suppliers to optimize energy usage based on price preference and system technical issues. The smart grid design aims to provide overall power system monitoring, create protection and control strategies to maintain system performance, stability and security. This dissertation contributed to the development of a unique and novel smart grid test-bed laboratory with integrated monitoring, protection and control systems. This test-bed was used as a platform to test the smart grid operational ideas developed here. The implementation of this system in the real-time software creates an environment for studying, implementing and verifying novel control and protection schemes developed in this dissertation. Phasor measurement techniques were developed using the available Data Acquisition (DAQ) devices in order to monitor all points in the power system in real time. This provides a practical view of system parameter changes, system abnormal conditions and its stability and security information system. These developments provide valuable measurements for technical power system operators in the energy control centers. Phasor Measurement technology is an excellent solution for improving system planning, operation and energy trading in addition to enabling advanced applications in Wide Area Monitoring, Protection and Control (WAMPAC). Moreover, a virtual protection system was developed and implemented in the smart grid laboratory with integrated functionality for wide area applications. Experiments and procedures were developed in the system in order to detect the system abnormal conditions and apply proper remedies to heal the system. A design for DC microgrid was developed to integrate it to the AC system with appropriate control capability. This system represents realistic hybrid AC/DC microgrids connectivity to the AC side to study the use of such architecture in system operation to help remedy system abnormal conditions. In addition, this dissertation explored the challenges and feasibility of the implementation of real-time system analysis features in order to monitor the system security and stability measures. These indices are measured experimentally during the operation of the developed hybrid AC/DC microgrids. Furthermore, a real-time optimal power flow system was implemented to optimally manage the power sharing between AC generators and DC side resources. A study relating to real-time energy management algorithm in hybrid microgrids was performed to evaluate the effects of using energy storage resources and their use in mitigating heavy load impacts on system stability and operational security.

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During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.

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This paper is on modeling and simulation for an offshore wind system equipped with a semi-submersible floating platform, a wind turbine, a permanent magnet synchronous generator, a multiple point clamped four level or five level full-power converter, a submarine cable and a second order filter. The drive train is modeled by three mass model considering the resistant stiffness torque, structure and tower in deep water due to the moving surface elevation. The system control uses PWM by space vector modulation associated with sliding mode and proportional integral controllers. The electric energy is injected into the electric grid either by an alternated current link or by a direct current link. The model is intend to be a useful tool for unveil the behavior and performance of the offshore wind system, especially for the multiple point clamped full-power converter, under normal operation or under malfunctions.

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Power electronic circuits are moving towards higher switching frequencies, exploiting the capabilities of novel devices to shrink the dimension of passive components. This trend demands sensors capable enough to operate at such high frequencies. This thesis aims to demonstrate through experimental characterization, the broadband capability of a fully integrated CMOS X-Hall current sensor in current mode interfaced with a transimpedance amplifier (TIA), chip CH09, realized in CMOS technology for power electronics applications such as power converters. The system exploits a common-mode control system to operate the dual supply system, 5-V for the X-Hall probe and 1.2-V for the readout. The developed prototype achieves a maximum acquisition bandwidth of 12 MHz, a power consumption of 11.46 mW, resolution of 39 mArms, a sensitivity of 8 % /T, and a FoM of 569-MHz/A2mW, significantly higher than current state-of-the-art. Further enhancements were proposed to CH09 as a new chip CH100, aiming for accuracy levels prerequisite for a real-time power electronic application. The TIA was optimized for a wider bandwidth of 26.7 MHz with nearly 30% reduction of the integrated input referred noise of 26.69 nArms at the probe-AFE interface in the frequency band of DC-30 MHz, and a 10% improvement in the dynamic range. The expected input range is 5-A. The chip incorporates a dual sensing chain for differential sensing to overcome common mode interferences. A novel offset cancellation technique is proposed that would require switching of polarity of bias currents. Thermal gain drift was improved by a factor of 8 and will be digitally calibrated utilizing a new built-in temperature sensor with a post calibration measurement accuracy greater than 1%. The estimated power consumption of the entire system is 55.6 mW. Both prototypes have been implemented through a 90-nm microelectronic process from STMicroelectronics and occupy a silicon area of 2.4 mm2.

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Consider a random medium consisting of N points randomly distributed so that there is no correlation among the distances separating them. This is the random link model, which is the high dimensionality limit (mean-field approximation) for the Euclidean random point structure. In the random link model, at discrete time steps, a walker moves to the nearest point, which has not been visited in the last mu steps (memory), producing a deterministic partially self-avoiding walk (the tourist walk). We have analytically obtained the distribution of the number n of points explored by the walker with memory mu=2, as well as the transient and period joint distribution. This result enables us to explain the abrupt change in the exploratory behavior between the cases mu=1 (memoryless walker, driven by extreme value statistics) and mu=2 (walker with memory, driven by combinatorial statistics). In the mu=1 case, the mean newly visited points in the thermodynamic limit (N >> 1) is just < n >=e=2.72... while in the mu=2 case, the mean number < n > of visited points grows proportionally to N(1/2). Also, this result allows us to establish an equivalence between the random link model with mu=2 and random map (uncorrelated back and forth distances) with mu=0 and the abrupt change between the probabilities for null transient time and subsequent ones.

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Survival or longevity is an economically important trait in beef cattle. The main inconvenience for its inclusion in selection criteria is delayed recording of phenotypic data and the high computational demand for including survival in proportional hazard models. Thus, identification of a longevity-correlated trait that could be recorded early in life would be very useful for selection purposes. We estimated the genetic relationship of survival with productive and reproductive traits in Nellore cattle, including weaning weight (WW), post-weaning growth (PWG), muscularity (MUSC), scrotal circumference at 18 months (SC18), and heifer pregnancy (HP). Survival was measured in discrete time intervals and modeled through a sequential threshold model. Five independent bivariate Bayesian analyses were performed, accounting for cow survival and the five productive and reproductive traits. Posterior mean estimates for heritability (standard deviation in parentheses) were 0.55 (0.01) for WW, 0.25 (0.01) for PWG, 0.23 (0.01) for MUSC, and 0.48 (0.01) for SC18. The posterior mean estimates (95% confidence interval in parentheses) for the genetic correlation with survival were 0.16 (0.13-0.19), 0.30 (0.25-0.34), 0.31 (0.25-0.36), 0.07 (0.02-0.12), and 0.82 (0.78-0.86) for WW, PWG, MUSC, SC18, and HP, respectively. Based on the high genetic correlation and heritability (0.54) posterior mean estimates for HP, the expected progeny difference for HP can be used to select bulls for longevity, as well as for post-weaning gain and muscle score.

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In order to model the synchronization of brain signals, a three-node fully-connected network is presented. The nodes are considered to be voltage control oscillator neurons (VCON) allowing to conjecture about how the whole process depends on synaptic gains, free-running frequencies and delays. The VCON, represented by phase-locked loops (PLL), are fully-connected and, as a consequence, an asymptotically stable synchronous state appears. Here, an expression for the synchronous state frequency is derived and the parameter dependence of its stability is discussed. Numerical simulations are performed providing conditions for the use of the derived formulae. Model differential equations are hard to be analytically treated, but some simplifying assumptions combined with simulations provide an alternative formulation for the long-term behavior of the fully-connected VCON network. Regarding this kind of network as models for brain frequency signal processing, with each PLL representing a neuron (VCON), conditions for their synchronization are proposed, considering the different bands of brain activity signals and relating them to synaptic gains, delays and free-running frequencies. For the delta waves, the synchronous state depends strongly on the delays. However, for alpha, beta and theta waves, the free-running individual frequencies determine the synchronous state. (C) 2011 Elsevier B.V. All rights reserved.

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The discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions.

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Importin-alpha is the nuclear import receptor that recognizes cargo proteins which contain classical monopartite and bipartite nuclear localization sequences (NLSs), and facilitates their transport into the nucleus. To determine the structural basis of the recognition of the two classes of NLSs by mammalian importin-alpha, we co-crystallized an N-terminally truncated mouse receptor protein with peptides corresponding to the monopartite NLS from the simian virus 40 (SV40) large T-antigen, and the bipartite NLS from nucleoplasmin. We show that the monopartite SV40 large T-antigen NLS binds to two binding sites on the receptor, similar to what was observed in yeast importin-alpha. The nucleoplasmin NLS-importin-alpha complex shows, for the first time, the mode of binding of bipartite NLSs to the receptor. The two basic clusters in the NLS occupy the two binding sites used by the monopartite NLS, while the sequence linking the two basic clusters is poorly ordered, consistent with its tolerance to mutations. The structures explain the structural basis for binding of diverse NLSs to the sole receptor protein. (C) 2000 Academic Press.

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It is well accepted that the Americas were the last continents reached by modern humans, most likely through Beringia. However, the precise time and mode of the colonization of the New World remain hotly disputed issues. Native American populations exhibit almost exclusively five mitochondrial DNA (mtDNA) haplogroups (A-D and X). Haplogroups A-D are also frequent in Asia, suggesting a northeastern Asian origin of these lineages. However, the differential pattern of distribution and frequency of haplogroup X led some to suggest that it may represent an independent migration to the Americas. Here we show, by using 86 complete mitochondrial genomes, that all Native American haplogroups, including haplogroup X, were part of a single founding population, thereby refuting multiple-migration models. A detailed demographic history of the mtDNA sequences estimated with a Bayesian coalescent method indicates a complex model for the peopling of the Americas, in which the initial differentiation from Asian populations ended with a moderate bottleneck in Beringia during the last glacial maximum (LGM), around similar to 23,000 to similar to 19,000 years ago. Toward the end of the LGM, a strong population expansion started similar to 18,000 and finished similar to 15,000 years ago. These results support a pre-Clovis occupation of the New World, suggesting a rapid settlement of the continent along a Pacific coastal route.