This paper presents an algorithm for incorporating of a priori knowledge into data-driven identification for dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modeled process such as its stability mi...
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This paper presents an algorithm for incorporating of a priori knowledge into data-driven identification for dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modeled process such as its stability minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach was successfully applied to the identification of a laboratory liquid level process.
The determination of the right resolution parameter when estimating frequency functions for linear systems is a trade-off between bias and variance. Traditional approaches, like “window-closing” employ a global reso...
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The determination of the right resolution parameter when estimating frequency functions for linear systems is a trade-off between bias and variance. Traditional approaches, like “window-closing” employ a global resolution parameter - the window width - that is tuned by ad hoc methods, usually visual inspection of the results. Here we suggest an adaptive method that tunes such parameters by an automatic procedure. A further benefit is that the tuning can be done locally, i.e., different resolutions can be used in different frequency bands. The ideas are based on local polynomial regression and the “just-in-time”-model concept. The advantages of the method are illustrated in numerical examples.
Guidelines are presented for specifying the design parameters of multi-level pseudo-random sequences in a manner useful for "plant-friendly" nonlinear system identification. These multi-level signals are int...
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Guidelines are presented for specifying the design parameters of multi-level pseudo-random sequences in a manner useful for "plant-friendly" nonlinear system identification. These multi-level signals are introduced into a rapid thermal processing wafer reactor simulation and compared against a well-designed pseudo-random binary sequence (PRBS). The resulting data serves as a database for a "model on demand" (MoD) predictor. MoD estimation is attractive because it requires less engineering effort to model a nonlinear plant, compared to global nonlinear models such as neural networks. The improved fit of multi-level signals over the PRBS signal, as well as the usefulness of the MoD estimator, is demonstrated on validation data.
"Model on demand" (MoD) simulation of the temperature dynamics in a simulated rapid thermal processing (RTP) reactor is compared against various types of global models (ARX, semiphysical, combined semiphysic...
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"Model on demand" (MoD) simulation of the temperature dynamics in a simulated rapid thermal processing (RTP) reactor is compared against various types of global models (ARX, semiphysical, combined semiphysical with neural net). The identification data is generated from an m-level pseudo-random sequence input whose parameters are specified systematically using a priori information readily available to the engineer. The MoD estimator outperforms the ARX model and a two semi-physical models, while matching the performance of a combined semi-physical with neural net model. This makes MoD estimation an appealing alternative to global methods because of its reduced engineering effort and simplified a priori knowledge regarding model structure.
In this note we consider the problem of H ∞ observer design for a class of uncertain linear discrete-time systems with delayed state and parameter uncertainties. The goal of this problem is to design a linear state o...
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In this note we consider the problem of H ∞ observer design for a class of uncertain linear discrete-time systems with delayed state and parameter uncertainties. The goal of this problem is to design a linear state observer such that, for the positive integer state time-delay and all admissible norm-bounded parameter uncertainties, the observation process remains robustly stable and the transfer function from exogenous disturbances to error state outputs meets the prespecified H ∞ norm upper bound constraint. The observer structure does not depend on the parameter uncertainties. A simple, effective algebraic methodology is developed to derive the conditions for the existence of the desired robust H ∞ observers, and the analytical expression of these observers is then characterized in terms of the matrix Riccati-like equations/inequalities. We provide a numerical example to demonstrate the validity and the applicability of the proposed approach.
This note deals with the problem of H ∞ observer design for a class of uncertain linear systems with delayed state and parameter uncertainties. This problem aims at designing the linear state observers such that, for...
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This note deals with the problem of H ∞ observer design for a class of uncertain linear systems with delayed state and parameter uncertainties. This problem aims at designing the linear state observers such that, for all admissible parameter uncertainties, the observation process remains robustly stable and the transfer function from exogenous disturbances to error state outputs meets the prespecified H ∞ on norm upper bound constraint, independently of the time-delay. The time-delay is assumed to be small but unknown, and the parameter uncertainties are allowed to be norm-bounded and appear in all the matrices of the state-space model. An effective matrix inequality methodology is developed to solve the proposed problem. We derive the conditions for the existence of the desired robust H ∞ observers, and then characterize the analytical expression of these observers. A numerical example demonstrates the validity and applicability of the present approach.
This paper has developed a recursive least squares scheme for operating a class of continuous fermentation processes at the optimal steady state productivity. More precisely, the class of continuous fermentation proce...
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This paper examines the problem of high frequency multi-input periodic operation of continuous fermentation process. Based on π-criterion published prevously, it is shown theoretically that a continuous fermentation ...
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The selection of inputs and outputs for control design in plantwide chemical processes is the topic of this paper. The issues concerning the systematic selection of manipulable inputs and measurable outputs are discus...
The selection of inputs and outputs for control design in plantwide chemical processes is the topic of this paper. The issues concerning the systematic selection of manipulable inputs and measurable outputs are discussed. Tools developed to help the design engineer select the "best", in some sense, process configuration are presented. Case studies are used to demonstrate the usefulness of the tools and their limitations.
A general subset selection method, the branch and bound technique, is applied to a control structure selection problem. Using this method globally optimal results can be obtained without exhaustive evaluations. The mi...
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A general subset selection method, the branch and bound technique, is applied to a control structure selection problem. Using this method globally optimal results can be obtained without exhaustive evaluations. The minimum singular value is used as the criterion for the branch and bound method. The monotonicity of the minimum singular value is proven and a power iteration formula is derived for more efficient calculation. This method is applied to a highly integrated chemical plant, the HDA process.
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