This paper addresses the problem of implementing predictive controllers for supervisory level controlsystems. In this configuration the manipulated variables calculated by the Predictive controller are used as comman...
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Recent years has seen much progress in the theory and application of iterative learning control schemes for both linear and (classes of) nonlinear dynamics. In the case of the former, many algorithms based on minimizi...
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The amplitude estimation of a signal whose waveform is known (up to an unknown scaling factor) in the presence of interference and noise is of interest in several applications including using the emerging quadrupole r...
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The amplitude estimation of a signal whose waveform is known (up to an unknown scaling factor) in the presence of interference and noise is of interest in several applications including using the emerging quadrupole resonance (QR) technology for explosive detection. In such applications, a sensor array is often deployed for interference suppression. This paper considers the complex amplitude estimation of a known waveform signal whose array response is also known a priori. Two approaches, viz the Capon and the maximum likelihood (ML) methods, are considered for the signal amplitude estimation in the presence of temporally white but spatially colored interference and noise. A comparative study shows that the ML estimate is strictly unbiased while the Capon estimate is biased downwards even for SNR /spl Gt/ 1. We show that both methods are asymptotically statistically efficient when number of data samples is large but not when the signal-to-noise ratio (SNR) is high.
In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. At a bottleneck node, there are multiple best-effort sources competing with other high priority c...
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In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. At a bottleneck node, there are multiple best-effort sources competing with other high priority cross traffic sources. The goal of congestion control is to achieve high link utilization, low packet loss, low delay, and fairness among the best-effort sources. In this paper, the high priority traffic is described by an autoregressive integrated moving average (ARIMA) process. To deal with the propagation delays associated with the best-effort sources, model predictive control, particularly, generalized predictive control, techniques are proposed to solve the congestion problem here. It is demonstrated that the proposed controller performs well and is robust to delay uncertainties. In addition, in a multiple-nodes configuration, the controller provides max-min fairness.
In this paper discrete-time iterative learning control (ILC) systems are analysed from an algebraic point of view. The algebraic analysis shows that an ILC synthesis problem can be considered as a tracking problem of ...
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In this paper discrete-time iterative learning control (ILC) systems are analysed from an algebraic point of view. The algebraic analysis shows that an ILC synthesis problem can be considered as a tracking problem of a multi-channel step-function. Furthermore, the plant to be controlled is a static multivariable plant. Another major contribution of this paper is a general convergence theory of ILC systems in terms of their closed-loop poles. This convergence theory shows that time-variant ILC control laws should be typically used instead of time-invariant control laws in order to guarantee good transient tracking behaviour. Simulations high-light the different theoretical findings in this paper.
The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of inte...
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The Capon beamformer has better resolution and much better interference rejection capability than the standard (data-independent) beamformer, provided that the array steering vector corresponding to the signal of interest (SOI) is accurately known. However, whenever the knowledge of SOI steering vector is imprecise, the performance of the Capon beamformer may become worse than that of the standard beamformer. In this paper, we present a natural extension of the Capon beamformer to the case of uncertain steering vectors. The proposed robust Capon beamformer can no longer be expressed in a closed form but it can be efficiently computed. Its excellent performance is demonstrated via a number of numerical examples.
This paper shows how the performance of orthogonal space-time block codes can be improved by using a diagonal weighting matrix at the transmitter. The optimal diagonal weighting matrix which minimizes the bit-error-ra...
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This paper shows how the performance of orthogonal space-time block codes can be improved by using a diagonal weighting matrix at the transmitter. The optimal diagonal weighting matrix which minimizes the bit-error-rate is derived. Since the optimal weights depend on the channel, a feedback from the transmitter to the receiver is necessary. However this feedback can be achieved using only log/sub 2/(n/sub t/) bits where n/sub t/ is the number of transmit antennas. Simulations show that for a system with two transmit antennas and one receive antenna, an improvement of 1.5 dB can be achieved with a feedback of only one bit. The effect of errors in the feedback is also analyzed, and an error tolerant weighting scheme is introduced to reduce the adverse effects of feedback errors.
Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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This paper examines two classes of algorithms that estimate a continuous time ARX type of models from discrete data: one is based on infinite impulse response (IIR) filters while the other is based on finite impulse r...
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This paper examines two classes of algorithms that estimate a continuous time ARX type of models from discrete data: one is based on infinite impulse response (IIR) filters while the other is based on finite impulse response (FIR) filters. The IIR filters use continuous time state variable filters, and discretisation is performed on the filtered derivatives. In contrast, the FIR filters are in a discrete form with carefully chosen coefficients to approximate the derivatives of the continuous time variables. The strength and weakness of each approach are discussed and demonstrated by a set of simulation examples.
This paper details the application of a constrained receding horizon control strategy to an indoor vectored-thrust flight experiment known as the Caltech Ducted Fan. The strategy is used to stabilize the experiment ab...
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This paper details the application of a constrained receding horizon control strategy to an indoor vectored-thrust flight experiment known as the Caltech Ducted Fan. The strategy is used to stabilize the experiment about one operating point, and step response and disturbance rejection are examined with different configurations and in comparison to a gain-scheduled LQR controller. Issues related to non-zero computation times, choice of horizon length and terminal cost are discussed.
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