4,372 research outputs found

    The design of an analogue RF front end for a multi-role radio

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    Active vibration control techniques for flexible space structures

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    Two proposed control system design techniques for active vibration control in flexible space structures are detailed. Control issues relevant only to flexible-body dynamics are addressed, whereas no attempt was made to integrate the flexible and rigid-body spacecraft dynamics. Both of the proposed approaches revealed encouraging results; however, further investigation of the interaction of the flexible and rigid-body dynamics is warranted

    Adaptive Feedforward Compensation Algorithms for Active Vibration Control with Mechanical Coupling and Local Feedback - a unified approach

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    Adaptive feedforward broadband vibration (or noise) compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. Most of the active vibration control systems feature an internal "positive" mechanical feedback between the compensation system and the reference source (a correlated measurement with the disturbance). Such systems have often also a feedback control loop for reducing the effect of disturbances. Therefore the adaptive feedforward compensation algorithms should take into account this structure. For stability reasons the adaptation algorithms requires the implementation of a filter on observed data or a filtering of the residual acceleration in order to satisfy some passivity conditions. The paper proposes new algorithms for the adaptive feedforward compensation in this context with both filtering of data and of the residual acceleration and using an "Integral + Proportional" (IP) adaptation as a means for accelerating the transients as well as for relaxing the positive real conditions required by the stability analysis. The paper also shows that the main interest in filtering the residual acceleration is to shape in the frequency domain the power spectral density (PSD) of the residual acceleration. The algorithms have been applied to an active vibration control (AVC) system and real time results illustrating the advantages of the proposed algorithms are presented

    Architecture of a network-in-the-Loop environment for characterizing AC power system behavior

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    This paper describes the method by which a large hardware-in-the-loop environment has been realized for three-phase ac power systems. The environment allows an entire laboratory power-network topology (generators, loads, controls, protection devices, and switches) to be placed in the loop of a large power-network simulation. The system is realized by using a realtime power-network simulator, which interacts with the hardware via the indirect control of a large synchronous generator and by measuring currents flowing from its terminals. These measured currents are injected into the simulation via current sources to close the loop. This paper describes the system architecture and, most importantly, the calibration methodologies which have been developed to overcome measurement and loop latencies. In particular, a new "phase advance" calibration removes the requirement to add unwanted components into the simulated network to compensate for loop delay. The results of early commissioning experiments are demonstrated. The present system performance limits under transient conditions (approximately 0.25 Hz/s and 30 V/s to contain peak phase-and voltage-tracking errors within 5. and 1%) are defined mainly by the controllability of the synchronous generator

    Incremental construction of LSTM recurrent neural network

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    Long Short--Term Memory (LSTM) is a recurrent neural network that uses structures called memory blocks to allow the net remember significant events distant in the past input sequence in order to solve long time lag tasks, where other RNN approaches fail. Throughout this work we have performed experiments using LSTM networks extended with growing abilities, which we call GLSTM. Four methods of training growing LSTM has been compared. These methods include cascade and fully connected hidden layers as well as two different levels of freezing previous weights in the cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five controllers of the Central Nervous System control has to be modelled. We have compared growing LSTM results against other neural networks approaches, and our work applying conventional LSTM to the task at hand.Postprint (published version
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