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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1991 - 19946

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Author: search.filters.author.Gattu, GangadharSubject: search.filters.subject.Chemical Process Systems

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Now showing 1 - 6 of 6
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    State Estimation Model Based Algorithm for On-line Optimization and Control of Batch Processes
    (1994) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    Batch/semi-batch processes are highly nonlinear and involve complex reaction mechanisms. Model-plant mismatch always exists. The lack of rapid direct or indirect measurements of the properties to be controlled makes the control task difficult. It is the usual practice to follow the prespecified setpoint profiles for process variables for which measurements are available, in order to obtain desired product properties. Modeling error can be the cause of bad performance when optimal profiles computed for the model, are implemented on the actual plant. In this paper, a state estimation model based algorithm is presented for on-line modification of the optimal profile and control with the goal of obtaining the desired properties at the minimum batch time. The effectiveness of the algorithm is demonstrated by its application to bulk polymerization of styrene.
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    State Estimation Nonlinear QDMC with Input-Output Models
    (1994) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    A State Estimation NLQDMC algorithm is presented for use with nonlinear input-output models. The proposed algorithm extends the state estimation NLQDMC [5] to nonlinear models identified based on input-output information. The algorithm preserves the computational advantages of [5] when compared to the other algorithms based on nonlinear programming techniques. The illustrating example demonstrates the usage of tuning parameters and points out the benefits and shortcomings of the algorithm.
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    Observer Based Nonlinear Quadratic Dynamic Matrix Control for State Space and I/O Models
    (1994) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    Observer based nonlinear QDMC algorithm is presented for use with nonlinear state space and input-output models. The proposed algorithm is an extension of Nonlinear Quadratic Dynamic Matrix Control (NLQDMC) by Garcia (1984) and its extension by Gattu and Zafiriou (1992a). Garcia proposed an extension of linear Quadratic Dynamic Matrix Control (QDMC) to nonlinear processes. Although a nonlinear model is used, only a single Quadratic Program (QP) is solved on-line. Gattu and Zafiriou extended this formulation to open-loop unstable systems, by incorporating a Kalman filter. The requirement of solving only one QP on-line at each sampling time makes this algorithm an attractive option for industrial implementation. This extension of NLQDMC to open-loop unstable systems was ad hoc and did not address the problem of offset free tracking and disturbance rejection in a general state space setting. Independent white noise was added to the model states to handle unstable processes. The approach can stabilize the system but leads to an offset in the presence of persistent disturbances. To obtain offset free tracking Gattu and Zafiriou added a constant disturbance to the predicted output as done in DMC-type algorithms. This addition is ad hoc and does not result from the filtering/prediction theory. The proposed algorithm eliminates the major drawbacks of the algorithm presented by Gattu and Zafiriou and extends that algorithm for nonlinear models identified based on input-output information. An algorithm schematic is presented for measurement delay cases. The algorithm preserves the computational advantages when compared to the other algorithms based on nonlinear programming techniques. The illustrating examples demonstrate the usage of tuning parameters for unstable and stable systems and points out the benefits and short comings of the algorithm.
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    State Estimation Nonlinear QDMC with Input-Output Models
    (1993) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    A State Estimation NLQDMC algorithm is presented for use with nonlinear input-output models. The proposed algorithm extends the state estimation NLQDMC (Gattu and Zafiriou, 1992a) to nonlinear models identified based on input-output information. The algorithm preserves the computational advantages when compared to the other algorithms based on nonlinear programming techniques. The illustrating example demonstrates the usage of tuning parameters and points out the benefits and shortcomings of the algorithm.
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    On the Stability of Nonlinear Quadratic Dynamic Matrix Control
    (1992) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    The extension of Quadratic Dynamic Matrix Control (QDMC) to nonlinear process models is an attractive option for industrial implementation. Although a nonlinear model is utilized, one has to solve only a single Quadratic Program on-line. In this paper, we present the stability properties for the global asymptotic stability of the closed-loop system under NLQDMC law. The conservativeness of these properties is examined by following the steps of the proofs when this algorithm is applied to a simple example. We also demonstrate the application of the nonlinear version of QDMC to processes for which the sign of the system gain changes around the operating point.
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    Nonlinear Quadratic Dynamic Matrix Control with State Estimation
    (1991) Gattu, Gangadhar; Zafiriou, Evanghelos; ISR
    Quadratic Dynamic Matrix Control (QDMC) with state estimation is presented for use with nonlinear process models. This formulation extends Garcia's nonlinear version of QDMC to open- loop unstable nonlinear processes and allows for better disturbance rejection. It also extends Ricker's linear state space formulation with state estimation to nonlinear systems. Stability and better performance is observed when compared to the algorithm without state estimation in rejecting disturbances for processes operating at unstable steady state setpoints, as illustrated with two simple examples. The algorithm requires that only a Quadratic Program be solved on-line. The modest computational requirements make it attractive for industrial implementation. the effectiveness of the approach is demonstrated by its successful application to the temperature control of a semibatch polymerization reactor. A model and related control requirements for this problem were presented at the 1990 AIChE Annual Meeting in a session on "Industrial Challenge Problems in Process Control."