Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Zafiriou, E.

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    Model Reduction for RTCVD Optimization
    (1996) Theodoropoulou, A.; Adomaitis, Raymond A.; Zafiriou, E.; ISR
    A model of a three-zone Rapid Thermal Chemical Vapor Deposition (RTCVD) system is developed to study the effects of spatial wafer temperature patterns on polysilicon deposition uniformity. A sequence of simulated runs is performed, varying the lamp power profiles so that different wafer temperature modes are excited. The dominant spatial wafer thermal modes are extracted via Proper Orthogonal Decomposition and subsequently used as a set of trial functions to represent both the wafer temperature and deposition thickness. A collocation formulation of Galerkin's method is developed to discretize the original modeling equations, giving a low-order model which looses little of the original, high-order model's fidelity. We make use of the excellent predictive capabilities of the reduced model to optimize power inputs to the lamp banks to achieve a desired polysilicon deposition thickness at the end of a run with minimal deposition spatial nonuniformity.
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    A Local Small Gain Theorem and Its Use for Robust Stability of Uncertain Feedback Volterra Systems
    (1993) Zheng, Q.; Zafiriou, E.; ISR
    The requirement to evaluate a gain over the whole signal space is one of the restrictions in the well-known small gain theorem. Using the concepts of local gain and strict causality a local form of small gain theorem is proposed, which can be used to analyze input magnitude dependent stability problems of feedback nonlinear systems, such as a Volterra system. Since only finite order Volterra series can be handled in practice, an uncertainly model is derived to address the robustness issue of approximating a nonlinear system by a finite Volterra series in the context of closed-loop control. The local small gain theorem is then used to analyze the feedback properties of the uncertain Volterra system and a sufficient condition for robust stability is obtained.
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    Stability Analysis of Inverse Volterra Series
    (1993) Zhang, Q.; Zafiriou, E.; ISR
    Among various nonlinear control methods, the one based on the Volterra series expansion is a promising approach for chemical process control. Almost all compensator design methods based on Volterra series system models utilize the inverse or some type of pseudo-inverse of the models. It is well known that this inverse is usually stable only for a limited amplitude of input signals, and this limited range is not understood quantitatively. Traditional input-output stability analysis methods cannot be used to analyze such an input amplitude dependent stability problem. Under the assumption of the open-loop system being strictly causal, Local Small Gain Theorem (LSGT) is first developed in the paper, which states a sufficient condition for the stability of the closed-loop nonlinear system. Using the new theorem, not only can one determined the local stability of the closed-loop system but also obtain a bound on the external input signal which guarantees BIBO stability. Then, this theorem is used to analyze the stability problem of inverse Volterra series. It so happens that for the Volterra series models an approximation of the local system gain can be easily obtained. By solving a simple single-variable optimization problem, a bound on the external input signal can be obtained, which guarantees the stability of the inverse Volterra series. Both mathematical analysis and simulation results are presented.
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    Optimization-based Tuning of Nonlinear Model Predictive Control with State Estimation
    (1993) Ali, Emad; Zafiriou, E.; ISR
    Nonlinear Model Predictive Controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for on-line prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithm presented in this paper incorporates an Extended Kalman Filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi- objective optimization package off-line to determine parameter values that satisfy these criteria. Three reactor examples are used to demonstrate the effectiveness of the proposed on-line algorithm and off-line procedure.
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    On the Tuning of Nonlinear Model Predictive Control Algorithms
    (1993) Ali, Emad; Zafiriou, E.; ISR
    Nonlinear Model Predictive Controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for on-line prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithm presented in this paper incorporates an Extended Kalman Filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi- objective optimization package off-line to determine parameter values that satisfy these criteria. A reactor example is used to demonstrate the effectiveness of the proposed on-line algorithm and off-line tuning procedure.
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    On the Effect of Constraint Softening on the Stability and Performance of Model Predictive Controllers
    (1992) Zafiriou, E.; Chiou, Hung-Wen; ISR
    The presence of constraints in the on-line optimization problem solved by Model Predictive Control algorithms results in a nonlinear control system, even if the plant and model dynamics are linear. This is the case both for physical constraints, like saturation constraints, as well for performance or safety constraints on outputs or other variables of the process. Performance constraints can usually be softened by allowing violation if necessary. This is advisable, as hard constraints can lead to stability problems. The determination of the necessary degree of softening is usually a trial-and-error matter. This paper utilizes a theoretical framework that allows to relate hard as well as soft constraints to closed-loop stability. The problem of determining the appropriate degree of softening is addressed by treating the parameters (weights) affecting the amount of softening as one-sided real-valued uncertainty and solving a robust stability problem.
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    On the Closed-Loop Stability of Constrained QDMC
    (1991) Zafiriou, E.; ISR
    The presence of constraints in the on-line optimization problem solved by Model Predictive Control algorithms results in a nonlinear control system, even if the plant and model dynamics are linear. This is the case both for physical constraints, like saturation constraints, as well for performance or safety constraints on outputs or other variables of the process. This paper discusses how constraints affect the stability properties of the closed-loop nonlinear system. In particular we concentrate on presenting a formulation that allows one to relate hard as well as soft constraints to stability. The degree of softening can be determined to guarantee stability.
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    Internal Model Control: Robust Digital Controller Synthesis for Multivariable Open-Loop Stable or Unstable Processes
    (1990) Zafiriou, E.; Morari, M.; ISR
    The two-step Internal Model Control (IMC) procedure is presented for the synthesis of multivariable discrete controllers. This paper adds the following features to the IMC design methodology: (i) Extension to open-loop unstable plants. (ii) Design of the first-step (no model error) IMC controller so that the L2-error (sum of squared errors) is minimized for every setpoint or disturbance vector in a designer-specified set and their linear combinations. (iii) The second-step (model-plant mismatch) multivariable low-pass filter is designed for robust stability and performance by minimizing a non-conservative robustness measure, the Structured Singular Value. (iv) The potential problem in intersample rippling is avoided by introducing a modification in the first-step controller and formulating the robust performance objective for the continuous plant output.
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    Control System Sensor Failure Detection via Networks of Localized Receptive Fields
    (1990) Yao, S.C.; Zafiriou, E.; ISR
    This paper investigates the use of local receptive field networks (LRFN) in detecting sensor failures of a control system in the presence of model-plant mismatch. Simulation results indicate that LRFNs hold significant promise in sensor failure detection. Another issue discussed in this paper is a method to prune redundant nodes. A simple scheme which uses singular value decomposition (SVD) is developed to identify and remove excess nodes. Comparable classification performance is obtained using reduced and standard LRFN.
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    Optimal Control of Semi-Batch Processes in the Presence of Modeling Error
    (1990) Zafiriou, E.; Zhu, J.M.; ISR
    Batch processes are usually complex and highly nonlinear systems. Modeling error can be the cause of bad performance when optimal input profiles computed for a particular model are applied to the actual plant. The approach followed in this paper uses the available model and actual plant measurements to modify the operation of the next batch, without requiring the remodeling of the process. The effect of model error on the convergence of the iterative batch to batch input profile determination is investigated. The method is applied through computer simulations to the determination of the optimal feedrate profile for a cell mass production process. A model parameter update scheme is also proposed, based on the convergence analysis. This is applied to the determination of the optimal temperature profile of bulk polymerication of the optimal temperature profile of styrene.