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.Subject: search.filters.subject.predictive control

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Now showing 1 - 5 of 5
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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.