Honeywell G.m.b.h.

Honeywell International Inc.

Honeywell Asca Inc.

Tongwen Chen

Johan Backstrom

Michael Forbes

Ning He

Xiaotao Liu

Dawei Shi

Danlei Chu

Jiadong Wang

Ghulam Mustafa

A method includes obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays and performing spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding a worst-case cutoff frequency over all output channels for each process input, designing the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.

Method and apparatus for designing model-based control having spatial robustness for multiple array cross-direction (CD) web manufacturing or processing systems or other systems

A method includes obtaining a model associated with a model-based controller in an industrial process having multiple actuator arrays and performing temporal tuning of the controller. The temporal tuning includes adjusting one or more parameters of a multivariable filter used to smooth reference trajectories of actuator profiles of the actuator arrays. The temporal tuning could also include obtaining one or more uncertainty specifications for one or more temporal parameters of the model, obtaining one or more overshoot limits for the actuator profiles, identifying a minimum bound for profile trajectory tuning parameters, and identifying one or more of the profile trajectory tuning parameters that minimize one or more measurement settling times without exceeding the one or more overshoot limits. The controller could be configured to use the adjusted parameter(s) during control of the industrial process such that the adjusting of the parameter(s) alters operation of the controller and the industrial process.

Method and apparatus for designing model-based control having temporally robust stability and performance for multiple-array cross-direction (CD) web manufacturing or processing systems or other systems

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties to reduce variability of the actuator and measurement profiles in the spatial domain is proposed. Decoupling properties of the spatial and temporal frequency components permit separate controller design and parameter tuning. CD-MPC design that explicitly accounts for parametric model uncertainty while finding MPC cost function weighing matrices that prevent actuator picketing and guarantee robust stability of the spatial CD profile. Picketing refers to periodic variation patterns in the actuator array. The inventive technique includes: (i) determining the worst case cutoff frequency of all process models, given parametric uncertainty, (ii) designing a weighing matrix to penalize high frequency actuator variability based on the process model and worst case cutoff frequency, and (iii) finding a multiplier for the spatial frequency weighted actuator variability term in the MPC cost function that assures robust spatial stability.

Method of designing model predictive control for cross directional flat sheet manufacturing processes to guarantee spatial robustness and to prevent actuator picketing

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties are developed. The CD-MPC design explicitly accounts for parametric model uncertainty while finding a value for the CD profile trajectory generation that minimizes the CD settling time of the measurement 2 sigma spread and does not exceed an overshoot limit for actuator 2 sigma spread. The inventive technique includes: (i) providing uncertainty specifications for the temporal parameters of the process model, (ii) specifying 2 sigma overshoot limits for the CD actuator profile, (iii) using robust stability theory to find a minimum bound for the profile trajectory tuning parameter, (iv) using a frequency domain technique to reduce the search range for the profile trajectory tuning parameter, and (v) performing an intelligent search for the tuning parameter that minimizes measurement 2 sigma settling time without exceeding the actuator 2 sigma overshoot limit.

Method of designing model predictive control for cross directional flat sheet manufacturing processes to guarantee temporal robust stability and performance

A method includes obtaining information identifying (i) uncertainties associated with multiple time-domain parameters of a model and (ii) time-domain performance specifications for a model-based industrial process controller. The model mathematically represents a MIMO industrial process. The method also includes generating multiple tuning parameters for the controller based on the uncertainties and the time-domain performance specifications. The tuning parameters include vectors of tuning parameters associated with the controller, and each vector includes values associated with different outputs of the industrial process. The time-domain parameters could include a process gain, a time constant, and a time delay for each input-output pair of the model. The time-domain performance specifications could include requirements related to worst-case overshoots, settling times, and total variations. The uncertainties could be specified as intervals in which the time-domain parameters lie.

Method and apparatus for robust tuning of model-based process controllers used with uncertain multiple-input, multiple-output (MIMO) processes

A method includes identifying an initial solution to a quadratic programming (QP) problem associated with a process. The method also includes performing an iterative procedure having one or more iterations. Each iteration includes determining whether any constraint associated with the process is violated in the solution. Each iteration also includes selecting a violated constraint, determining a step direction and a step length associated with the selected violated constraint, and updating the solution based on the step direction and the step length. Determining the step direction and the step length includes using a Schur complement based on an active set of constraints associated with the solution. The Schur complement is nonsingular during all iterations of the iterative procedure except when the active set is empty.

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Edmonton, Canada

Tongwen Chen