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IEEE Conference on Decision and control

作者： D.E. Rivera S.V. Gaikwad Department of Chemical Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ USA

The use of prefiltered ARX (autoregressive with exogenous input) estimation to obtain reduced-order models that satisfy the Prett-Garcia (1988) digital PID (proportional plus integral plus derivative) tuning rules is described. The design of the prefilter is performed systematically using the engineer's desired control requirements and the setpoint/disturbance characteristics of the problem. The benefits of this method are shown for a fourth-order system.< >

关键词： Three-term control Digital control Reduced order systems Design engineering systems engineering and theory control systems

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Modified output error method with parameter mismatch compensation for improved performance model reference adaptive control

Modified output error method with parameter mismatch compens...

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IEEE Conference on Decision and control

作者： I.N.M. Papadakis S.C.A. Thomopoulos Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A method for model reference adaptive control (MRAC) with improved transient performance is introduced. It is shown that with this method the zero-state output error can be made arbitrarily small. For the linear system case, the value of the high frequency gain, k/sub p/, of the plant does not have to be known a priori, i.e., some uncertainty on k/sub p/ is allowed. The structure of the proposed controller allows for existing convergence results such as exponential convergence of output and parameter errors in the presence of sufficiently rich reference inputs to remain valid. It is also shown that the proposed controller demonstrates improved robustness in the presence of bounded disturbances and/or modeled dynamics, as well as in the case in which adaptation is switched off.< >

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Modeling for control Design in Combined Feedback/Feedforward control

Modeling for Control Design in Combined Feedback/Feedforward...

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American control Conference (ACC)

作者： Daniel E. Rivera Sujit V. Gaikwad Department of Chemical Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ USA

This paper presents control-relevant parameter estimation as a means to address the problem of modeling requirements for a combined feedback-feedforward control system. The key element in the estimation procedure is prefiltering of the input and output time series obtained from the plant. This insures that the estimated model retains those plant characteristics that are most significant with regards to the user's control requirements. An example model reduction problem is presented which uses the IMC design procedure to design a reduced-order feedback-feedforward control system. Its performance is compared to both full-order IMC and model-predictive control via DMC.

关键词： control design control system synthesis Parameter estimation Linear feedback control systems control systems Design engineering systems engineering and theory Process control Transfer functions Predictive models

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Robustness and Identification Issues in Horizon Predictive control With Application to a Binary Distillation Column

Robustness and Identification Issues in Horizon Predictive C...

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American control Conference (ACC)

作者： D.E. Rivera K.S. Jun E. Elisante V.E. Sater B. C. Horn Department of Chemical Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ USA Honeywell Industrial Automation and Control Phoenix AZ USA

This paper analyzes the robustness properties and modeling requirements for model-predictive control via Horizon Predictive control (HPC). The theory of Structured Singular Values is used to determine optimal values for the correction horizon in HPC given user-provided uncertainty intervals and performance weights. Regarding system identification, control-relevant identification principles are used to provide guidelines for input signal design, prefiltered estimation, and uncertainty modeling. These results are tested experimentally using data from a methanol-isopropanol distillation column.

关键词： Robust control Predictive control Distillation equipment Predictive models Uncertainty System identification control system synthesis Guidelines Signal design Testing

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Optimal Non-Parametric System Identification From Arbitrary Corrupt Finite Time Series: A control-Oriented Approach

Optimal Non-Parametric System Identification From Arbitrary ...

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American control Conference (ACC)

作者： Jie Chen Carl N. Nett Michael K. H. Fan Schools of Aerospace and Electrical Engineering Georgia Institute of Technology Atlanta GA USA Laboratory for Identification and Control of Complex Highly Uncertain Systems (LICCHUS) School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Computer-Aided Control Systems Engineering Laboratory (CACSEL) School of Electrical Engineering Georgia Institute of Technology Atlanta GA USA

In this paper we formulate and solve a control-oriented system identification problem for single-input, single-output, linear, shift-invariant, distributed parameter plants. In this problem the available apriori information is minimal, consisting only of worst-case/deterministic, time dependent, upper and lower bounds on the plant impulse response and the additive output noise. The available aposteriori information consists of a corrupt finite output time series obtained in response to a known, non-zero but otherwise arbitrary, applied input. A novel system identification method is presented for this problem. This method maps the available apriori and aposteriori information into an "uncertain model" of the plant. The resulting uncertain plant model is comprised of a nominal plant model, a bounded additive output noise, and a bounded additive model uncertainty. The upper bound on the model uncertainty is explicit, worst-case/deterministic in nature, and expressed in terms of both the l 1 and H ∞ system norms. Under the assumption that the available apriori information is "correct" for the underlying physical plant, the resulting uncertain plant model has the property that it not only "explains" the available aposteriori information, but will also explain all aposteniori information observed in the future. Because this property hinges on the correctness of the available apriori information, a method is also presented for developing confidence that the available apriori information is in fact correct.

关键词： System identification Optimal control control systems Additive noise Robust control Uncertainty Noise level Aerospace engineering Upper bound Fasteners

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Joint detection/estimation for modal order selection in Gaussian and nonGaussian noise

Joint detection/estimation for modal order selection in Gaus...

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MILCOM, Military Communications Conference

作者： S.C.A. Thomopoulos T.W. Hilands Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA Applied Research Laboratory Pennsylvania State University University Park PA USA

The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of maximum a posteriori detection and nonlinear estimation using either the extended Kalman filter when the noise is Gaussian, or the extended high order filter when the noise is nonGaussian. The problem is formulated as a multiple hypothesis testing problem with assumed known a priori probabilities for each hypothesis. The advantage of the approach lies in the potential to accommodate time-varying as well as time-invariant parameters in the measurement model. Experimental evaluation of the approach demonstrates excellent performance in selecting the correct model order and estimating the system parameters even for signal-to-noise ratios as low as -5 dB.< >

关键词： Gaussian noise State estimation Noise measurement Autocorrelation Matrix decomposition Recursive estimation control systems Additive noise Additive white noise Application software

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Nonlinear filtering methods for harmonic retrieval

Nonlinear filtering methods for harmonic retrieval

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MILCOM, Military Communications Conference

作者： T.W. Hilands S.C.A. Thomopoulos Applied Research Laboratory Pennsylvania State University University Park PA USA Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

The authors address the problem of high-resolution parameter estimation of superimposed sinusoids using nonlinear filtering techniques. Six separate nonlinear filters are evaluated for the estimation of the parameters of sinusoids in white and colored Gaussian noise. Experimental evaluation demonstrates that the nonlinear filters perform very satisfactorily (close to the Cramer-Rao bound) for reasonable values of the initial estimation error. A major advantage of using nonlinear filtering methods for harmonic retrieval is that the filters can be applied to time-varying process models as well.< >

关键词： Filtering Power harmonic filters Nonlinear filters Noise measurement Parameter estimation Equations Gaussian noise Frequency estimation Damping White noise

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Nonlinear adaptive detection/estimation for single and multiple radar processing

Nonlinear adaptive detection/estimation for single and multi...

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MILCOM, Military Communications Conference

作者： S.C.A. Thomopoulos T.W. Hilands Department of Electrical and Computer Engineering Decision and Control Systems Laboratory Pennsylvania State University University Park PA USA Applied Research Laboratory Pennsylvania State University University Park PA USA

A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/estimation problem on the unknown parameters in the signal return. The problems of detecting the target and estimating its parameters are considered jointly. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori likelihood of the existence of the target in a given space-time resolution cell. Within a given cell, the localized filters are used to produce refined spatial estimates of the target parameters. A decision logic is used to decide on the existence of a target within any given resolution cell based on the a posteriori estimates obtained from the likelihood functions. Simulation results show excellent detection capabilities and excellent resolution in target parameter estimation for both single and multiple sensor data.< >

关键词： Radar detection Spatial resolution Parameter estimation Detectors Data processing Object detection Nonlinear filters Signal resolution Refining Logic

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Sufficient Conditions for the Robust Stability of systems with Multiaffine Parameter Dependence

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IFAC Proceedings Volumes 1992年第21期25卷 168-171页

作者： B.D.O. Anderson F.J. Kraus M. Mansour S. Dasgupta Department of Systems Engineering Research School of Physical Sciences and Engineering Australian National University GPO Box 4 Canberra ACT 2601 Australia. Automatic Control Laboratory ETH Zurich 8092 Zürich Switzerland Department of Electrical & Computer Engineering University of Iowa Iowa City lA 52242 USA.

A number of robust stability problems take the following form: A polynomial has real coefficients wvhich are multiaffine in real parameters that are confined to a box in parameter space. An efficient method is required for checking the stability of this set of polynomials. We present two sufficient conditions in this paper. They involve: checking certain properties at the corners and edges of the parameter space box.

关键词： Robust Stability Multiaffine Parameter Dependence

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Visual pattern recognition using morphological methods

Visual pattern recognition using morphological methods

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： I.N.M. Papadakis J.G. Reisman S.C.A. Thomopoulos Dept. of Electr. & Comput. Eng. Pennsylvania State Univ. University Park PA USA Decision and Control Systems Laboratory Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

An effective method for visual pattern recognition using morphological techniques is presented. It is shown that it can be successfully used for the recognition of deformed letters. The method extracts morphological information by the successive dilation of an idealized letter set. At each stage a properly defined similarity index is computed. The maximum values of the similarity index for each stored letter are compared and are used for the classification decision. Classification results are presented for a set of deformed capital English letters with a realistic level of deformation. Skeletonization of the deformed pattern is shown to improve the performance of the classification method. The described method can be easily implemented using a parallel architecture.< >

关键词： Pattern recognition Artificial neural networks Feature extraction Clocks control systems Laboratories Data mining Parallel architectures Character recognition Tiles

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