201 resultados para Dynamic control


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This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

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Directional cell migration requires force generation that relies on the coordinated remodeling of interactions with the extracellular matrix (ECM), which is mediated by integrin-based focal adhesions (FAs). Normal FA turnover requires dynamic microtubules, and three members of the diverse group of microtubule plus-end-tracking proteins are principally involved in mediating microtubule interactions with FAs. Microtubules also alter the assembly state of FAs by modulating Rho GTPase signaling, and recent evidence suggests that microtubule-mediated clathrin-dependent and -independent endocytosis regulates FA dynamics. In addition, FA-associated microtubules may provide a polarized microtubule track for localized secretion of matrix metalloproteases (MMPs). Thus, different aspects of the molecular mechanisms by which microtubules control FA turnover in migrating cells are beginning to emerge.

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Cross-talk between microtubule networks and sites of cell-matrix and cell-cell adhesion has profound impact on these structures and is essential for proper cell organization, polarization and motility. Components of adhesion sites can interact directly with microtubules or with proteins that specifically associate with microtubule plus ends and minus ends and in this way capture, stabilize or destabilize microtubules. In their turn, microtubules can serve as routes for delivery of structural and regulatory factors that control adhesion site turnover. In addition, the microtubule lattice or growing microtubule plus ends can serve as diffusional sinks that accumulate and scaffold regulatory molecules, thereby affecting their activity in the vicinity of adhesions. Combination of these mechanisms underlies the functional co-operation between microtubules and adhesion sites and defines their dynamic behavior.

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Injection velocity has been recognized as a key variable in thermoplastic injection molding. Its closed-loop control is, however, difficult due to the complexity of the process dynamic characteristics. The basic requirements of the control system include tracking of a pre-determined injection velocity curve defined in a profile, load rejection and robustness. It is difficult for a conventional control scheme to meet all these requirements. Injection velocity dynamics are first analyzed in this paper. Then a novel double-controller scheme is adopted for the injection velocity control. This scheme allows an independent design of set-point tracking and load rejection and has good system robustness. The implementation of the double-controller scheme for injection velocity control is discussed. Special techniques such as profile transformation and shifting are also introduced to improve the velocity responses. The proposed velocity control has been experimentally demonstrated to be effective for a wide range of processing conditions.

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Electric distribution networks are now in the era of transition from passive to active distribution networks with the integration of energy storage devices. Optimal usage of batteries and voltage control devices along with other upgrades in network needs a distribution expansion planning (DEP) considering inter-temporal dependencies of stages. This paper presents an efficient approach for solving multi-stage distribution expansion planning problems (MSDEPP) based on a forward-backward approach considering energy storage devices such as batteries and voltage control devices such as voltage regulators and capacitors. The proposed algorithm is compared with three other techniques including full dynamic, forward fill-in, backward pull-out from the point of view of their precision and their computational efficiency. The simulation results for the IEEE 13 bus network show the proposed pseudo-dynamic forward-backward approach presents good efficiency in precision and time of optimization.

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Extant models of decision making in social neurobiological systems have typically explained task dynamics as characterized by transitions between two attractors. In this paper, we model a three-attractor task exemplified in a team sport context. The model showed that an attacker–defender dyadic system can be described by the angle x between a vector connecting the participants and the try line. This variable was proposed as an order parameter of the system and could be dynamically expressed by integrating a potential function. Empirical evidence has revealed that this kind of system has three stable attractors, with a potential function of the form V(x)=−k1x+k2ax2/2−bx4/4+x6/6, where k1 and k2 are two control parameters. Random fluctuations were also observed in system behavior, modeled as white noise εt, leading to the motion equation dx/dt = −dV/dx+Q0.5εt, where Q is the noise variance. The model successfully mirrored the behavioral dynamics of agents in a social neurobiological system, exemplified by interactions of players in a team sport.

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Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.

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This paper presents a trajectory-tracking control strategy for a class of mechanical systems in Hamiltonian form. The class is characterised by a simplectic interconnection arising from the use of generalised coordinates and full actuation. The tracking error dynamic is modelled as a port-Hamiltonian Systems (PHS). The control action is designed to take the error dynamics into a desired closed-loop PHS characterised by a constant mass matrix and a potential energy with a minimum at the origin. A transformation of the momentum and a feedback control is exploited to obtain a constant generalised mass matrix in closed loop. The stability of the close-loop system is shown using the close-loop Hamiltonian as a Lyapunov function. The paper also considers the addition of integral action to design a robust controller that ensures tracking in spite of disturbances. As a case study, the proposed control design methodology is applied to a fully actuated robotic manipulator.

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This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.

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Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

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One of the objectives of this study was to evaluate soil testing equipment based on its capability of measuring in-place stiffness or modulus values. As design criteria transition from empirical to mechanistic-empirical, soil test methods and equipment that measure properties such as stiffness and modulus and how they relate to Florida materials are needed. Requirements for the selected equipment are that they be portable, cost effective, reliable, a ccurate, and repeatable. A second objective is that the selected equipment measures soil properties without the use of nuclear materials.The current device used to measure soil compaction is the nuclear density gauge (NDG). Equipment evaluated in this research included lightweight deflectometers (LWD) from different manufacturers, a dynamic cone penetrometer (DCP), a GeoGauge, a Clegg impact soil tester (CIST), a Briaud compaction device (BCD), and a seismic pavement analyzer (SPA). Evaluations were conducted over ranges of measured densities and moistures.Testing (Phases I and II) was conducted in a test box and test pits. Phase III testing was conducted on materials found on five construction projects located in the Jacksonville, Florida, area. Phase I analyses determined that the GeoGauge had the lowest overall coefficient of variance (COV). In ascending order of COV were the accelerometer-type LWD, the geophone-type LWD, the DCP, the BCD, and the SPA which had the highest overall COV. As a result, the BCD and the SPA were excluded from Phase II testing.In Phase II, measurements obtained from the selected equipment were compared to the modulus values obtained by the static plate load test (PLT), the resilient modulus (MR) from laboratory testing, and the NDG measurements. To minimize soil and moisture content variability, the single spot testing sequence was developed. At each location, test results obtained from the portable equipment under evaluation were compared to the values from adjacent NDG, PLT, and laboratory MR measurements. Correlations were developed through statistical analysis. Target values were developed for various soils for verification on similar soils that were field tested in Phase III. The single spot testing sequence also was employed in Phase III, field testing performed on A-3 and A-2-4 embankments, limerock-stabilized subgrade, limerock base, and graded aggregate base found on Florida Department of Transportation construction projects. The Phase II and Phase III results provided potential trend information for future research—specifically, data collection for in-depth statistical analysis for correlations with the laboratory MR for specific soil types under specific moisture conditions. With the collection of enough data, stronger relationships could be expected between measurements from the portable equipment and the MR values. Based on the statistical analyses and the experience gained from extensive use of the equipment, the combination of the DCP and the LWD was selected for in-place soil testing for compaction control acceptance. Test methods and developmental specifications were written for the DCP and the LWD. The developmental specifications include target values for the compaction control of embankment, subgrade, and base materials.

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Purpose To test the effectiveness of static and dynamic orthoses using them as an exclusive treatment for proximal interphalangeal (PIP) joint flexion contracture compared with other hand therapy conservative treatments described in the literature. Methods 60 patients who used orthoses were compared with a control group that received other hand therapy treatments. Clinical assessments were measured before the experiment and 3 months after and included active PIP joint extension and function. Results A significant improvement in the extension active range of motion at the PIP joint in the second measurement was found in both groups, but it was significantly greater in the experimental group. Improvement in function (Disabilities of the Arm, Shoulder, and Hand score) between the first and second assessment was similar in the control and experimental groups. Conclusions Using night progressive static and daily dynamic orthoses as an exclusive treatment during the proliferative phase led to significant improvements in the PIP joint active extension, but the improvement did not correlate with increased function as perceived by the patient.

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There are currently 23,500 level crossings in Australia, broadly divided into one of two categories: active level crossings which are fully automatic and have boom barriers, alarm bells, flashing lights, and pedestrian gates; and passive level crossings, which are not automatic and aim to control road and pedestrianised walkways solely with stop and give way signs. Active level crossings are considered to be the gold standard for transport ergonomics when grade separation (i.e. constructing an over- or underpass) is not viable. In Australia, the current strategy is to annually upgrade passive level crossings with active controls but active crossings are also associated with traffic congestion, largely as a result of extended closure times. The percentage of time level crossings are closed to road vehicles during peak periods increases with the rise in the frequency of train services. The popular perception appears to be that once a level crossing is upgraded, one is free to wipe their hands and consider the job done. However, there may also be environments where active protection is not enough, but where the setting may not justify the capital costs of grade separation. Indeed, the associated congestion and traffic delay could compromise safety by contributing to the risk taking behaviour by motorists and pedestrians. In these environments it is important to understand what human factor issues are present and ask the question of whether a one size fits all solution is indeed the most ergonomically sound solution for today’s transport needs.

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The aim of this project was to evaluate the cost-effectiveness of hand hygiene interventions in resource-limited hospital settings. Using data from north-east Thailand, the research found that such interventions are likely to be very cost-effective in intensive care unit settings as a result of reduced incidence of methicillin-resistant Staphylococcus aureus bloodstream infection alone. This study also found evidence showing that the World Health Organization's (WHO) multimodal intervention is effective and when adding either goal-setting, reward incentives, or accountability strategies to the WHO intervention, compliance could be further improved.

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This paper presents an unmanned aircraft system (UAS) that uses a probabilistic model for autonomous front-on environmental sensing or photography of a target. The system is based on low-cost and readily-available sensor systems in dynamic environments and with the general intent of improving the capabilities of dynamic waypoint-based navigation systems for a low-cost UAS. The behavioural dynamics of target movement for the design of a Kalman filter and Markov model-based prediction algorithm are included. Geometrical concepts and the Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of a target, thus delivering a new waypoint for autonomous navigation. The results of the application to aerial filming with low-cost UAS are presented, achieving the desired goal of maintained front-on perspective without significant constraint to the route or pace of target movement.