1,105 research outputs found

    A New Variable Regularized Transform Domain NLMS Adaptive Filtering Algorithm-Acoustic Applications and Performance Analysis

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    Optical Beam Jitter Control for NPS HEL Beam Control Testbed

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    In this paper, an optical beam jitter control method for the Naval Postgraduate School HEL beam control testbed is presented. Additional hardware is developed and integrated on the testbed to realize the strap-down IRU jitter compensation architectures. Feedforward control design of the strap-down IRU design is studied and tested on the testbed. An adaptive filtering method for narrow-field-of-view video tracker jitter correction is also presented. In this paper, an optical beam jitter control method for the Naval Postgraduate School HEL beam control testbed is presented. Additional hardware is developed and integrated on the testbed to realize the strap-down IRU jitter compensation architectures. Feedforward control design of the strap-down IRU design is studied and tested on the testbed. An adaptive filtering method for narrow-field-of-view video tracker jitter correction is also presented

    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

    IIR Youla-Kucera parametrized adaptive feedforward compensators for active vibration control with mechanical coupling

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    International audienceAdaptive feedforward broadband vibration (or noise) compensation requires a reliable correlated measurement with the disturbance (an image of the disturbance). The reliability of this measurement is compromised in most of the systems by a "positive" internal feedback coupling between the compensator system and the correlated measurement of the disturbance. The system may become unstable if the adaptation algorithms do not take in account this positive feedback. Instead of using classical IIR or FIR feedforward compensators, the present paper proposes and analyses an IIR Youla - Kucera parametrization of the feedforward compensator. A model based central IIR stabilizing compensator is used and its performance is enhanced by the adaptation of the parameters (Q-parameters) of an IIR Youla-Kucera filter. Adaptation algorithms assuring the stability of the system in the presence of the positive internal feedback are provided. Their performances are evaluated experimentally on an active vibration control (AVC) system. Theoretical and experimental comparisons with FIR Youla-Kucera parametrized feedforward compensators and IIR feedforward compensators are provided

    Iterative analysis of the steady-state weight fluctuations in LMS-type adaptive filters

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    Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters

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    PhD ThesisMany industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems. The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.Iraq Ministry of Higher Educatio

    Development of Urban Electric Bus Drivetrain

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    The development of the drivetrain for a new series of urban electric buses is presented in the paper. The traction and design properties of several drive variants are compared. The efficiency of the drive was tested using simulation calculations of the vehicle rides based on data from real bus lines in Prague. The results of the design work and simulation calculations are presented in the paper

    Digital Filters

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    The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

    Learning algorithms for adaptive digital filtering

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    In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme
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