Abstract: A method is provided for tuning an oxygen trim controller during the commissioning of a combustion control system for controlling operation of a boiler combustion system, rather than tuning the oxygen trim controller after the commissioning process has been completed.

Abstract: A method is provided for the automated setup of a metered combustion control system for controlling operation of a boiler combustion system. The automated setup process includes both commissioning and controller tuning, rather than tuning the carbon monoxide and/or oxygen trim controller after the commissioning process has been completed. The oxygen trim controller or the carbon monoxide trim controller is used to identify the air/fuel ratio.

Abstract: A method includes predicting measurements or states to be used by a controller to control a process. The predicted measurements or states are generated using a model of the process. The method also includes providing the predicted measurements or states to the controller such that the controller uses the predicted measurements or states at a sampling rate of the controller. In addition, the method includes updating at least some of the predicted measurements or states using measurements associated with a characteristic of an item from a sensor. The model may represent a discrete time model, and the method may also include generating the discrete time model using a continuous time model of the process. The measurements could be received from a plurality of sensors, where at least two of the sensors have different sampling times.

Abstract: An automated tuning method of a large-scale multivariable model predictive controller for multiple array papermaking machine cross-directional (CD) processes can significantly improve the performance of the controller over traditional controllers. Paper machine CD processes are large-scale spatially-distributed dynamical systems. Due to these systems' (almost) spatially invariant nature, the closed-loop transfer functions are approximated by transfer matrices with rectangular circulant matrix blocks, whose input and output singular vectors are the Fourier components of dimension equivalent to either number of actuators or number of measurements. This approximation enables the model predictive controller for these systems to be tuned by a numerical search over optimization weights in order to shape the closed-loop transfer functions in the two-dimensional frequency domain for performance and robustness.

Abstract: A fast and reliable technique for tuning multivariable model predictive controllers (MPCs) that accounts for performance and robustness is provided. Specifically, the technique automatically yields tuning weights for the MPC based on performance and robustness requirements. The tuning weights are parameters of closed-loop transfer functions which are directly linked to performance and robustness requirements. Automatically searching the tuning parameters in their proper ranges assures that the controller is optimal and robust. This technique will deliver the traditional requirements of stability, performance and robustness, while at the same time enabling users to design their closed-loop behavior in terms of the physical domain. The method permits the user to favor one measurement over another, or to use one actuator more than another.

Abstract: A method includes receiving measurement data from at least one sensor at a first controller. The measurement data is associated with a process. The method also includes receiving a first output signal at the first controller. The first output signal is generated by a second controller and affects the process. The method further includes determining how to control at least a portion of the process using the measurement data and the first output signal. In addition, the method includes providing a second output signal, where the second output signal is operable to control at least the portion of the process. The second controller may be operable to use the second output signal to generate the first output signal.

Abstract: A technique for fast performance prediction of multivariable model predictive controllers (MPC) for large-scale spatially-distributed dynamic systems is provided. When operating an MPC for a two-dimensional sheetmaking process, the controller's performance can be separated into spatial performance and dynamic performance. The steady-state spatial performance for different sheet property modes is first predicted, thereafter, the dynamic performance is predicted on the basis of the steady-state actuator profiles. During the steady-state spatial performance prediction, the original MPC's cost function is approximated to be a steady-state cost function. Then the steady-state measurement profiles and the actuator profiles are calculated through the steady-state cost function. The actuator profiles in time series can be efficiently solved by minimizing the difference between the actuator profiles and the steady-state actuator profiles in the face of constraints.

Abstract: A fast and reliable technique for tuning multivariable model predictive controllers (MPCs) that accounts for performance and robustness is provided. Specifically, the technique automatically yields tuning weights for the MPC based on performance and robustness requirements. The tuning weights are parameters of closed-loop transfer functions which are directly linked to performance and robustness requirements. Automatically searching the tuning parameters in their proper ranges assures that the controller is optimal and robust. This technique will deliver the traditional requirements of stability, performance and robustness, while at the same time enabling users to design their closed-loop behavior in terms of the physical domain. The method permits the user to favor one measurement over another, or to use one actuator more than another.

Abstract: A method includes receiving measurement data from at least one sensor at a first controller. The measurement data is associated with a process. The method also includes receiving a first output signal at the first controller. The first output signal is generated by a second controller and affects the process. The method further includes determining how to control at least a portion of the process using the measurement data and the first output signal. In addition, the method includes providing a second output signal, where the second output signal is operable to control at least the portion of the process. The second controller may be operable to use the second output signal to generate the first output signal.

Abstract: A method includes predicting measurements or states to be used by a controller to control a process. The predicted measurements or states are generated using a model of the process. The method also includes providing the predicted measurements or states to the controller such that the controller uses the predicted measurements or states at a sampling rate of the controller. In addition, the method includes updating at least some of the predicted measurements or states using measurements associated with a characteristic of an item from a sensor. The model may represent a discrete time model, and the method may also include generating the discrete time model using a continuous time model of the process. The measurements could be received from a plurality of sensors, where at least two of the sensors have different sampling times.

Abstract: A technique for fast performance prediction of multivariable model predictive controllers (MPC) for large-scale spatially-distributed dynamic systems is provided. When operating an MPC for a two-dimensional sheetmaking process, the controller's performance can be separated into spatial performance and dynamic performance. The steady-state spatial performance for different sheet property modes is first predicted, thereafter, the dynamic performance is predicted on the basis of the steady-state actuator profiles. During the steady-state spatial performance prediction, the original MPC's cost function is approximated to be a steady-state cost function. Then the steady-state measurement profiles and the actuator profiles are calculated through the steady-state cost function. The actuator profiles in time series can be efficiently solved by minimizing the difference between the actuator profiles and the steady-state actuator profiles in the face of constraints.

Abstract: An automated tuning method of a large-scale multivariable model predictive controller for multiple array papermaking machine cross-directional (CD) processes can significantly improve the performance of the controller over traditional controllers. Paper machine CD processes are large-scale spatially-distributed dynamical systems. Due to these systems' (almost) spatially invariant nature, the closed-loop transfer functions are approximated by transfer matrices with rectangular circulant matrix blocks, whose input and output singular vectors are the Fourier components of dimension equivalent to either number of actuators or number of measurements. This approximation enables the model predictive controller for these systems to be tuned by a numerical search over optimization weights in order to shape the closed-loop transfer functions in the two-dimensional frequency domain for performance and robustness.