| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 700030000 | Comparison with model (e.g., model reference) | 65 |
| 20080208371 | CONTROL SYSTEM FOR A PLANT - A system for controlling a plant process wherein the plant is over-actuated with N+1 actuators and provides N performance variables is disclosed. The system includes an outer-loop controller operable to compare N performance variable values from the plant with a reference set point and provide a desired virtual control input to an inner-loop controller. The inner-loop controller receives the desired virtual control input from the outer-loop controller and provides real control inputs to the at least N+1 actuators. The N+1 actuators receive the real control inputs and subsequently provide an actual virtual control input to the plant. Upon receiving the actual virtual control input from the N+1 actuators the plant is operable to produce N updated performance variable values in an optimized manner. | 08-28-2008 |
| 20080221709 | Control method, control system, and program - By repeatedly executing a predetermined measurement at set intervals, data on a predetermined performance (a best focus position) of a predetermined apparatus and data on variation factors of the performance are obtained (Steps | 09-11-2008 |
| 20080255682 | Online Fault Detection and Avoidance Framework for Distributed Factory Control Systems - An on-line fault detection and avoidance method is provided for industrial control systems that include multiple interacting process controllers. The method addresses the problem that not all faults can be determined and removed at the time of system design and testing. When a fault translates into a time-out condition in one or more controllers, symptoms are identified, persistence is measured, other involved controllers are identified, the fault condition is identified and control laws are reconfigured to avoid the fault condition in the future. | 10-16-2008 |
| 20090043406 | System and Method for Planning the Operation of, Monitoring Processes in, Simulating, and Optimizing a Combined Power Generation and Water Desalination Plant - The disclosure relates to a system and a method for planning the operation of, monitoring processes in, simulating, and/or optimizing a technical installation comprising several units that can be combined with each other. Said system comprises at least one process planning module, at least one process simulation module, and at least one process optimization module. Components for modeling, simulating, and optimizing the technical installation are stored in said modules. The interrelated modules cooperate with a data management layer via at least one interface, said data management layer making available actual measured and/or historical process data for determining parameters and/or operational data for the modules in order to plan operations as well as simulate and optimize processes. The parameters and/or operational data determined in the modules can be fed to the data management layer for further processing by taking into account the stored components. | 02-12-2009 |
| 20090088871 | HISTORIAN INTEGRATED WITH MES APPLIANCE - A simulation that integrates historical data and real-time data as a test or simulation tool can capture an entry that relates to a desired output as function points. A determination can intellectually be made as to which activities can achieve the desired output. The activities can be process steps that can represent a workflow that can be automatically implemented by an MES Appliance or other enterprise components. If a simulation reveals that the desired output might not be achieved, a change to one or more function points can be analyzed in an attempt to achieve the desired result. This change can be input into a simulation tool through a feedback loop, for example. Another simulation can performed on the modified data until a determination is made that the desired output can be achieved. | 04-02-2009 |
| 20090093893 | System and method for recognizing and compensating for invalid regression model applied to abnormal situation prevention - A system for preventing abnormal situations in process plants is provided. A polynomial regression model is employed to predict values of a monitored variable based on measured samples of a load variable. An abnormal situation is detected when a predicted value of the monitored variable differs from a measured value of the monitored variable by more than a predetermined. The system recognizes when a data model is invalid and takes steps to compensate for the invalid model. | 04-09-2009 |
| 20090118841 | Virtual sensor network (VSN) system and method - A method is used for providing sensing data to a control system of a machine. The method may include providing a plurality of virtual sensors, each of which may have a model type, at least one input parameter, and at least one output parameter. The method may also include integrating the plurality of virtual sensors into a virtual sensor network; determining interdependencies among the plurality of virtual sensors; and obtaining operational information of the plurality of virtual sensors. Further, the method may include determining a first condition under which the virtual sensor network is unfit to provide one or more virtual sensor output parameter to the control system based on the determined interdependencies and the operational information; and presenting the determined first condition to the control system. | 05-07-2009 |
| 20090143872 | On-Line Adaptive Model Predictive Control in a Process Control System - A method of creating and using an adaptive DMC type or other MPC controller includes using a model switching technique to periodically determine a process model, such as a parameterized process model, for a process loop on-line during operation of the process. The method then uses the process model to generate an MPC control model and creates and downloads an MPC controller algorithm to an MPC controller based on the new control model while the MPC controller is operating on-line. This technique, which is generally applicable to single-loop MPC controllers and is particularly useful in MPC controllers with a control horizon of one or two, enables an MPC controller to be adapted during the normal operation of the process, so as to change the process model on which the MPC controller is based to thereby account for process changes. The adaptive MPC controller is not computationally expensive and can therefore be easily implemented within a distributed controller of a process control system, while providing the same or in some cases better control than a PID controller, especially in dead time dominant process loops, and in process loops that are subject to process model mismatch within the process time to steady state. | 06-04-2009 |
| 20090177292 | CONTROL SYSTEM ACTUATION FAULT MONITORING - Methods and apparatus for monitoring and detecting failures in the actuation of a control system, such as a flight control system for an aircraft, include defining a nominal model of the control system in terms of the state variables of the control system, defining a model of an asymmetric “actuation monitoring envelope” that dynamically bounds a range that measured state variables of the system are allowed to take during operation of the system as a function of the nominal system state model, monitoring a signal corresponding to a state variable of the system during operation thereof, and detecting a failure in the actuation of the control system when the monitored signal exceeds the bounds of the monitoring envelope. | 07-09-2009 |
| 20090198350 | ROBUST ADAPTIVE MODEL PREDICTIVE CONTROLLER WITH TUNING TO COMPENSATE FOR MODEL MISMATCH - An MPC adaptation and tuning technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC adaptation/tuning technique that performs better than traditional MPC techniques in the presence of process model mismatch. The MPC controller performance is enhanced by adding a controller adaptation/tuning unit to an MPC controller, which adaptation/tuning unit implements an optimization routine to determine the best or most optimal set of controller design and/or tuning parameters to use within the MPC controller during on-line process control in the presence of a specific amount of model mismatch or a range of model mismatch. The adaptation/tuning unit determines one or more MPC controller tuning and design parameters, including for example, an MPC form, penalty factors for either or both of an MPC controller and an observer and a controller model for use in the MPC controller, based on a previously determined process model and either a known or an expected process model mismatch or process model mismatch range. A closed loop adaptation cycle may be implemented by performing an autocorrelation analysis on the prediction error or the control error to determine when significant process model mismatch exists or to determine an increase or a decrease in process model mismatch over time. | 08-06-2009 |
| 20090248174 | CONTROL METHOD OF REFRIGERATION SYSTEMS IN GAS PLANTS WITH PARALLEL TRAINS - An optimization method based on statistical modeling relating NGL plant process variables. The modeling may rely on input data from process history and modeled data. The method identifies process scenarios when a compressor from an associated propane/propylene refrigeration system may be deactivated and still allow the NGL plant to achieve product specification. | 10-01-2009 |
| 20090248175 | PLANT CONTROL SYSTEM AND THERMAL POWER GENERATION PLANT CONTROL SYSTEM - The plant control system has a measurement signal data database, a model to estimate the value of measurement signal data used at a time when an operation signal is given to the plant, an operation signal learning unit to learn a method of generating a model input, which is equivalent to an operation signal, so that a model output, which is equivalent to the measurement signal data, attains a target value. The plant control system also has an evaluation function calculating unit to calculate an evaluation function value from the model output obtained as a result of an operation carried out by the operation signal learning unit for the model, and an evaluation function adjusting unit to adjust evaluation function parameters used in calculation of an evaluation function. | 10-01-2009 |
| 20090281640 | BASEPOINT ESTIMATOR - A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, receiving a plurality of sensor feedback signals from a controlled system, and receiving a plurality of predicted output values of the controlled system from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values. The desired change is modified as necessary to hold the limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate. | 11-12-2009 |
| 20090281641 | MULTIVARIABLE CONTROL SYSTEM - A method for controlling a multivariable system according to one non-limiting embodiment includes receiving a plurality of limits, receiving a first quantity of goals each having a desired value, and receiving sensor feedback. The method further includes estimating a basepoint in response to the first quantity of goals, the plurality of limits, and the sensor feedback, wherein the basepoint includes a set of values corresponding to an equilibrium point at which a predetermined amount of enabled limits are met and a second quantity of goals are fulfilled according to a goal prioritization scheme. Predicted values from a mathematical model are compared to the sensor feedback, and the estimated basepoint is selectively adjusted in response to a difference between the predicted values and the sensor feedback in order to reduce the difference. | 11-12-2009 |
| 20090299498 | METHOD AND SIMULATOR FOR REAL-TIME CALCULATION OF STATE VARIABLES OF A PROCESS MODEL - A method for real-time calculation of state variables (x | 12-03-2009 |
| 20090312851 | System and Method for Bioprocess Control - A system and method for controlling a bioprocess equipment (FIG. | 12-17-2009 |
| 20090319059 | APPARATUS AND METHOD FOR MODEL PREDICTIVE CONTROL (MPC) OF A NONLINEAR PROCESS - A method includes obtaining a nonlinear process model modeling a nonlinear process to be controlled. The method also includes obtaining an objective function defining how the process is controlled. The method further includes obtaining a control model defining a dynamic feasible region associated with a controlled variable, where the controlled variable is associated with the process. In addition, the method includes controlling the nonlinear process by solving a control problem that includes the process model, control model, and objective function. The dynamic feasible region defined by the control model could represent a funnel region. The objective function could include terms for minimizing and optimizing adjustments to one or more manipulated variables associated with the process. Controlling the nonlinear process could include performing simultaneous control and optimization, where adjustments to the one or more manipulated variables are chosen to meet the control objectives and possibly to optimize and minimize the adjustments. | 12-24-2009 |
| 20090319060 | Continuously Scheduled Model Parameter Based Adaptive Controller - An adaptive process controller performs continuously scheduled process model parameter interpolation to determine a particular set of process model parameters which are used to develop controller tuning parameters for controller tuning. More particularly, a state-based, adaptive PID controller described herein uses a new technique to determine an appropriate process model to be used to perform adaptive tuning over the various operating regions of the plant, and in particular, uses a process model parameter determination technique that enables continuously scheduled process model parameter update over the various plant operating regions or points. The use of this continuously scheduled process model parameter update method provides for smoother transitions between tuning parameters used in the PID controller during adaptive tuning procedures which are implemented based on changes in the operating region or the operating point of the process, thereby providing for better overall control. | 12-24-2009 |
| 20100010645 | BASEPOINT ESTIMATOR - A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate. | 01-14-2010 |
| 20100082121 | VALIDATION OF LABORATORY TEST DATA - The present invention provides novel techniques for validating laboratory data values for properties of interest of products produced by a process system. In particular, samples of the product may be sent to a laboratory testing facility, where laboratory testing procedures may be used to obtain the laboratory data values for the property of interest. The laboratory data values may be sent to a control system which includes a laboratory data validation module. The laboratory data validation module may be capable of validating the laboratory data values of the property of interest by comparing the laboratory data values of the property of interest with predicted values generated by a model. The model may be created using inputs such as laboratory and measured data values of the property of interest as well as laboratory and measured data values of other properties of the product. In particular, the laboratory data validation module may, in certain embodiments, include a laboratory data validation model, which may aid the validation of the laboratory data values of the property of interest. | 04-01-2010 |
| 20100082122 | PROCESS CONTROL SYSTEM HAVING ON-LINE AND OFF-LINE TEST CALCULATION FOR INDUSTRIAL PROCESS TRANSMITTERS - Methods and systems for assessing transmitter electronics in an industrial process control system comprise generating a process condition reference equation signal, a process condition approximation equation signal, and an accuracy output signal. The process condition reference equation signal is generated using a process condition reference equation and process control inputs. The process condition approximation equation signal is generated using a process condition approximation equation that approximates the reference equation using the process control inputs, and approximation equation coefficients based on the approximation equation and the process control inputs. The approximation equation signal is compared to the reference equation signal at a control room workstation such that the industrial process control system can be adjusted. In one embodiment, the approximation equation coefficients are adjusted and transmitted to process transmitter electronics over a control network. In another embodiment, a parameter of the industrial process control system, such as a primary element or transmitter, is adjusted. | 04-01-2010 |
| 20100087933 | TWO-STAGE MODEL PREDICTIVE CONTROL TECHNIQUE - A two-stage model predictive control (MPC) controller uses a process model and two separate MPC control modules, including a feedfoward MPC control module and a feedback MPC control module, to determine a set of control signals for use in controlling a process. The feedforward MPC control module uses the process model to determine a feedforward control component for each of a set of control signals and the feedback MPC control module uses the process model and one or more measured process outputs to determine a feedback control component for each of the set of control signals. The two-stage MPC controller combines the feedforward control components with the feedback control components to form the final control signals used to control the process. The two different control modules may receive separate and different inputs from the process to determine the feedforward control components and the feedback control components and may be tuned separately, to thereby enable a control operator or other user to perform more standardized and stabilized tuning within an MPC controller environment. | 04-08-2010 |
| 20100131082 | Inversion Loci Generator and Criteria Evaluator for Rendering Errors in Variable Data Processing - Reduction deviations are rendered as dependent coordinate mappings of two-dimensional displacements which characterize restraints associated with deviations of observation sampling measurements from a fitting function. The mappings are considered to be represented by both projections and path coincident deviations. Data inversions are generated as loci and discriminated by criteria corresponding to deviations associated with alternate forms for representing essential weighting. Deficiencies related to nonlinearities and heterogeneous precision are compensated by essential weight factors. | 05-27-2010 |
| 20100204808 | MODEL PREDICTIVE CONTROLLER WITH TUNABLE INTEGRAL COMPONENT TO COMPENSATE FOR MODEL MISMATCH - An MPC controller technique integrates feedback control performance better than methods commonly used today in MPC type controllers, resulting in an MPC controller that performs better than traditional MPC techniques in the presence of process model mismatch. In particular, MPC controller performance is enhanced by adding a tunable integration block to the MPC controller that develops an integral component indicative of the prediction or other control error, and adds this component to the output of an MPC controller algorithm to provide for faster or better control in the presence of model mismatch, which is the ultimate reason for the prediction error in the first place. This technique enables the MPC controller to react more quickly and to provide better set point change and load disturbance performance in the presence of model mismatch, without decreasing the robustness of the MPC controller. | 08-12-2010 |
| 20100241250 | Feedback and feedforward control of a semiconductor process without output values from upstream processes - The present invention discloses a feedback and feedforward process control system, comprising the steps:
| 09-23-2010 |
| 20110040392 | Measurement and Management Technology Platform - Techniques for implementing system best practices are provided. In one aspect, a method for monitoring, modeling and managing a physical system is provided. The method includes the following steps. A physical data model of the physical system is provided. Real time data is obtained from the physical system. The physical data model is updated based on the real time data. An analytic model of the physical system is created based on the updated physical data model. Operation of the physical system is controlled based on output from the analytic model. | 02-17-2011 |
| 20110087341 | SYSTEM FOR PREDICTING THE BEHAVIOR OF A TRANSDUCER - A system for compensating and driving a loudspeaker includes an open loop loudspeaker controller that receives and processes an audio input signal and provides an audio output signal. A dynamic model of the loudspeaker receives the audio output signal, and models the behavior of the loudspeaker and provides predictive loudspeaker behavior data indicative thereof. The open loop loudspeaker controller receives the predictive loudspeaker behavior data and the audio input signal, and provides the audio output signal as a function of the audio input signal and the predictive loudspeaker behavior data. | 04-14-2011 |
| 20110106277 | INTEGRATED OPTIMIZATION AND CONTROL FOR PRODUCTION PLANTS - The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time. In particular, the parametric multifaceted models may convert the first set of parameters into the second set of parameters in a time horizon allowing for control of the process plant by a control system based on the second set of parameters. | 05-05-2011 |
| 20110125293 | FAST ALGORITHM FOR MODEL PREDICTIVE CONTROL - An improved process and corresponding controller provide a model predictive control approach that can be implemented with less computational resources and/or with greater speed than conventional MPC, while at the same time retaining all or a substantial portion of the robustness and advantages of conventional MPC. According to one aspect of the invention, the process provides an improved initial estimate of the MPC model trajectory to reduce the number of iterations to find the optimal one. The improved trajectory is obtained by applying a correction to the computed MPC manipulated value trajectory, and using the corrected manipulated value trajectory as the starting point for the next iteration of MPC manipulated value trajectory computation. As set forth in more detail below, the correction is determined from the LQR feedback control strategy. Since the sequence of control laws for the LQR feedback control strategy can be computed off-line and stored, the real time part of the LQR control strategy needed to determine the correction can be retrieved with relatively little computational resources. | 05-26-2011 |
| 20110144774 | SYSTEM AND METHOD FOR CONTROLLING A MACHINE - A system for controlling a machine includes a first controller, a second controller, and a comparator. During a first cycle, the first controller generates a control signal to the machine while the second controller generates a predicted parameter signal. During the first cycle, the comparator transmits a feedback signal to the second controller if a predetermined threshold is not met. A method for controlling a machine includes transmitting a control signal from a first controller to the machine and generating a predicted parameter value in a second controller. The method further includes transmitting a feedback signal to the second controller if a predetermined threshold is not met. | 06-16-2011 |
| 20110144775 | Method and apparatus for adapting a process instance - A method and an apparatus are disclosed for adapting a process instance, which guarantee that process instances are conform to a specific process meta model. Therefore constraint violations are detected and furthermore remedied according to a derived violation meta model. In at least one embodiment, the present invention finds application in process modeling, system process optimization and/or controlling of machines or technical devices. | 06-16-2011 |
| 20110178610 | SYSTEMS AND METHODS FOR MANAGING UTILITY CONSUMPTION - A system and method for monitoring and managing utility consumption includes use of (A) one or more sensors arranged to monitor an operating condition and/or utility consumption of at least one segment or appliance of a first facility; (B) a memory including at least one of (i) stored utility consumption information correlated or normalized to at least one designated parameter affecting utility consumption or (ii) hypothetical utility consumption; (C) a processor to compare the monitored information with the stored information, and (D) an output medium to store and/or display the comparison information. Stored consumption information may relate to the first facility or a second facility. Hypothetical utility consumption information may relate to a segment or appliance of the first facility operated according to optimized conditions, or an upgraded segment or appliance. | 07-21-2011 |
| 20110230981 | DESIGN AND CONTROL OF ENGINEERING SYSTEMS UTILIZING COMPONENT-LEVEL DYNAMIC MATHEMATICAL MODEL WITH MULTIPLE-INPUT MULTIPLE-OUTPUT ESTIMATOR - A control system comprises an actuator, a control law and a processor. The actuator positions a control surface and the control law controls the actuator. The processor comprises an open loop module, a corrector, a comparator, and an estimator, and generates an output vector to direct the control law. The open loop module generates the output vector as a function of a state vector and an input vector. The corrector generates a corrector vector as a function of the output vector. The comparator generates an error vector by comparing the corrector vector to the input vector. The estimator generates the state vector as a function of the error vector, such that the error vector is minimized. | 09-22-2011 |
| 20110238189 | Method and Apparatus for Controlling a Plant Using Feedback Signals - A method and apparatus controls a plant using feedback signals. A procedural description of a feedback control process for the plant is translated into a set of objects, wherein each object is a portion of the procedural description, wherein each object is a strictly-encapsulated and autonomous software module, wherein the objects execute in a platform, and wherein the platform includes a set of nodes embedded in the plant and each node includes a processor. A feedback signals is generated by passing messages between the set of the objects in response to an operation of the plant. | 09-29-2011 |
| 20110264243 | Method of Control for a Process Control System, and Control System for Controlling an Industrial Process - A method of control for a process control system and an appropriate control system is provided. Model calculations are integrated into the process control system such that the setpoint values calculated using the model calculations are processed further like measured values in the process control system, and control commands are derived from the measured values and/or setpoint values. The model calculations are integrated into the process control system using an adaptation program. The adaptation program includes program code and associated data from the model calculations and, in comparison with the other program components of the process control systems, is designed like a program component of the process control system. Within the adaptation program, the interfaces of the adaptation program match those of the model calculations. | 10-27-2011 |
| 20110270422 | Feedback Control Method and Device Using the Same - The present invention provides a method for feedback control and a device using the same, wherein the device comprising a sensing layer for generating a plurality of sensing signals with respect to the at least one kind of characteristics on the sensing layer, and a driving layer for changing the surface status of the sensing layer. The control method is started a step of acquiring the plurality of sensing signals within a first specific time interval and establishing a first prediction model accordingly, then predicting a distribution status with respect to the at least one kind of characteristic on the sending layer at a specific time point according to the first prediction model, and finally, determining whether to change the surface status of the sensing layer according to the distribution status. | 11-03-2011 |
| 20110288660 | ON-LINE ALIGNMENT OF A PROCESS ANALYTICAL MODEL WITH ACTUAL PROCESS OPERATION - A batch modeling and analysis system uses a simple and computationally inexpensive technique to align data collected from an on-going, currently running or on-line batch process with a batch model formed for the batch process so as to enable the reliable determination of the current operational state of the on-line batch process with respect to the batch model. This data alignment technique enables further statistical processing techniques, such as projection to latent sources (PLS) and principle component analysis (PCA) techniques, to be applied to the on-line batch data to perform analyses on the quality of the currently running batch. These analyses, in turn, provide useful information to a user, such as a batch operator, that enables the user to determine the quality of the batch at the present time, based on the batch model, and the likelihood that the desired batch output quality metrics will be reached at the end of the batch run. | 11-24-2011 |
| 20110295390 | APPARATUS AND METHOD FOR MODELING AND CONTROL OF CROSS-DIRECTION FIBER ORIENTATION PROCESSES - A method includes generating a model associated with cross-directional fiber orientation of a web, which includes identifying spatial frequency characteristics of a fiber orientation (FO) process. The method also includes providing the model for control of the FO process. Generating the model could include performing a spatial impulse test of the FO process, and long wavelength responses of the FO process can be identified by performing a spatial long wavelength test of the FO process or by retrieving information from a historical database. Actuator edge padding can be applied to the model in order to generate a controller model. A controller can be used to control the process based on the controller model. At least one parameter of the controller model can be dynamically adjusted during operation of the controller. The controller can change average fiber orientation angle profiles and twist profiles by only adjusting slice lip actuators in a headbox. | 12-01-2011 |
| 20120078390 | SYSTEM AND METHOD FOR CONTROLLING A MACHINE - A system for controlling a machine includes a first model, a second model, a controller, and a comparator. During a first cycle, the first model generates a response signal to the controller while the second model generates a predicted parameter signal. During the first cycle, the comparator transmits a feedback signal to the second model if a predetermined threshold is not met. A method for controlling a machine includes transmitting a response signal from a first model to a controller, generating a control signal to the machine, and generating a predicted parameter value in a second model. The method further includes transmitting a feedback signal to the second model if a predetermined threshold is not met. | 03-29-2012 |
| 20120083904 | METHOD AND SYSTEM FOR OFFLINE CODE VALIDATION - A method of offline code validation and a process control system are provided. The system includes a plurality of sensors each configured to generate a sensor output value representative of a respective sensed parameter in a process plant, at least one process controller configured to receive one or more of the sensor output values and to generate a controller output value using the one or more of the sensor output values and a control algorithm associated with the at least one process controller, a plurality of controlled components configured to receive an associated controller output value, and an offline computing environment including a selectable model controller configurable to represent the at least one process controller, the model controller selectable by a user from code representing the at least one process controller and displayed on a user interface. | 04-05-2012 |
| 20120095574 | EQUIPMENT CONDITION AND PERFORMANCE MONITORING USING COMPREHENSIVE PROCESS MODEL BASED UPON MASS AND ENERGY CONSERVATION - A method and apparatus capable of monitoring performance of a process and of the condition of equipment units effecting such process is disclosed. A process model predicated upon mass and energy balancing is developed on the basis of a plurality of generally nonlinear models of the equipment units. At least one or more of such equipment models are characterized by one or more adjustable maintenance parameters. Data relating to mass and energy transfer within the process is collected and is reconciled with the mass and energy characteristics of the process predicted by the model. The condition of the equipment units and process performance may then be inferred by monitoring the values of the maintenance parameters over successive data reconciliation operations. | 04-19-2012 |
| 20120109341 | METHOD FOR ASCERTAINING PROCESS VALUES FOR A PROCESS CONTROL - A method for ascertaining process values for a process control is provided. The method includes detecting a measured value, providing a model that simulates the process, and, on the basis of the model, calculating a calculated real value and a calculated measured value. The method also includes comparing the calculated real value with the calculated measured value to obtain a delay compensation value, and adding the delay compensation value to the measured value to obtain an accelerated value indicative of the process value to be ascertained. | 05-03-2012 |
| 20120116545 | CONTROL APPARATUS - A control apparatus capable of improving the control accuracy and stability when controlling a controlled object with a predetermined restraint condition between a plurality of model parameters, or a controlled object having a lag characteristic, using a control target model of a discrete-time system. The control apparatus has an ECU which arranges a control target model including two model parameters such that terms not multiplied by the model parameters and terms multiplied by the same are on different sides of the model, respectively. Assuming the different sides represent a combined signal value and an estimated combined signal value, respectively, the ECU calculates onboard identified values of the model parameters such that an identification error between the signal values is minimized, and calculates an air-fuel ratio correction coefficient using the identified values and a control algorithm derived from the control target model. | 05-10-2012 |
| 20120130509 | Method for Adjusting a Measuring Device - A method and a system for adjusting a measuring device. The measuring device, determines the physical or chemical process variable based on measurement data; an analytical tool, ascertains the analytical data from the measurement data; a database, in which data sets with analytical data for different process conditions and associated parameter sets for adjusting the measuring device are stored, or in which a plurality of models with associated calculational specifications are stored, which produce the analytical data; and a calculation/control unit, which compares the ascertained analytical data with the stored analytical data, ascertains that data set of the stored analytical data, which has maximum agreement with the ascertained analytical data and adjusts the measuring device corresponding to the associated parameter set. | 05-24-2012 |
| 20120283849 | SENSOR SYSTEM HAVING TIME LAG COMPENSATION - A sensor system for use with a machine is disclosed. The sensor system may have a sensor associated with the machine and configured to generate a signal indicative of an actual value for a parameter of the machine, and a controller in communication with the sensor. The controller may be configured to model behavior of the machine under particular conditions and responsively generate a first predicted value for the parameter, determine a time lag coefficient for the sensor based on the signal, model behavior of the sensor based on the time lag coefficient and the first predicted value, and responsively generate a second predicted value for the parameter. The controller may also be configured to determine an error value based on the actual value and the second predicted value, and determine a compensated value for the parameter based on the first predicted value and the error value. | 11-08-2012 |
| 20120310375 | A NONLINEAR INTELLIGENT PULSE-CONTROLLER - An embedded nonlinear cooperative pulse-controller (ENCPC), is characterized in that its control algorithm module comprises: a comparison module, an identification control unit, a dynamic control unit, a steady-state control unit and a cooperative control unit; wherein, the comparison module plays the major role of generating the control errors; the identification control unit mainly through the step response identifies the model parameters such as the amplification gain, the time constant and the delay time; the dynamic control unit plays the major role rapidly reducing the control errors in the dynamic change process, improving the rise time of the control system and decreasing the overshoot of the control system by rapidly outputting a regulation pulse, which can make the process variable rapidly approach the desired steady-state value; the steady-state control unit plays the major role of further eliminating the control errors in a steady change process and improving the control precision, according to a future steady-state output of the ENCPC and in conjunction with proportional and integral control laws; the cooperative control unit, in accordance to the real-time operation status of the control system, is responsible for coordinating the operation of the identification control unit, the dynamic control unit and the steady-state control unit, and generating the final control output signal; and the control algorithm module of the ENCPC enables that the ENCPC can quickly and stably eliminate the control errors with short rise time, small overshoot and short settling time. | 12-06-2012 |
| 20120323343 | VIRTUAL SENSOR SYSTEM AND METHOD - A control system is disclosed. The control system may have a physical sensor configured to measure physical parameter values of a machine. The control system may also have a virtual sensor network system configured to receive the physical parameter values measured by the physical sensor as input parameter values, and generate output parameter values based on the input parameter values. Further, the control system may have an electronic control module configured to store an output parameter value default rate of change and an output parameter value threshold rate of change, compare a rate of change of the output parameter values generated by the virtual sensor network system to the output parameter value threshold rate of change, and control the machine based on the output parameter value default rate of change if the rate of change of the output parameter values exceeds the output parameter value threshold rate of change. | 12-20-2012 |
| 20130096698 | SELF-ORGANIZING QUANTUM ROBUST CONTROL METHODS AND SYSTEMS FOR SITUATIONS WITH UNCERTAINTY AND RISK - Control systems, apparatus, and methods can apply quantum algorithms to control a control object in the presence of uncertainty and/or information risk. A self-organizing controller can include a quantum inference unit that can generate a set of robust control gains for a controller that can meet the control objectives for the particular realization of the control object. In one embodiment, the quantum inference unit can include a quantum correlator configured to generate a plurality of quantum states based on a plurality of controller parameters and a correlation type. In this embodiment, the quantum inference unit can also include a quantum optimizer configured to select the correlation type of the quantum correlator and to select a quantum state from the plurality of the quantum states. The self-organizing controller can control the control object with one or more controller gains that are based on the selected quantum state. | 04-18-2013 |
| 20130116802 | TRACKING SIMULATION METHOD - A tracking simulator models an industrial process simultaneously and in parallel with the industrial process. The simulator receives control inputs provided by an automation system to control the industrial process. Based on these inputs, the simulator with its process model(s) provides simulated process outputs. In order to avoid divergence of the simulation models from the real process, the tracking simulator receives process measurements from the real process and is able to correct, i.e. update, its models based on these real process measurements and the simulator outputs. One or more of the updated or adjusting parameters for the simulation models are generated by PI or PID controller. Additionally, some of the updated parameters can be generated by an NM or SE method. The PI or PID controller can be an automatic controller tuning tool of the automation system. Additionally, some of the updated parameters can be generated by NM. | 05-09-2013 |
| 20130173026 | NUMERICAL CONTROL METHOD - In this numerical control method that drives the feed shaft of a machine tool ( | 07-04-2013 |
| 20130325147 | Method and System for Complex Smart Grid Infrastructure Assessment - An infrastructure assessment system integrates with a smart grid infrastructure at all layers of the infrastructure. Data may be collected across layers. Performance metrics may be monitored and simulations may be performed. Action items may be decided upon based on actual behavior of the infrastructure determined from the collected data and on predicted behavior from simulations of the infrastructure. The action items may then be dispatched to be performed on the infrastructure. The effect of the management actions can then be “acquired” by the system via detailed monitoring and can be used, for example, to measure the effectiveness of the decisions or recalibration of the whole system. | 12-05-2013 |
| 20140128997 | IDENTIFYING MODELS OF DYNAMIC SYSTEMS - Identifying models of dynamic systems is described herein. One method for identifying a model of a dynamic system includes estimating a number of parameters for each of a number of models of the dynamic system, predicting an output using the estimated number of parameters for each of the number of models, calculating a rate of error of the predicted output for each of the number of models compared to an observed output, and identifying a best model among the number of models of the dynamic system based on the calculated rate of errors. | 05-08-2014 |
| 20140297002 | SYSTEM AND METHOD FOR IMPLEMENTING MODEL PREDICTIVE CONTROL IN PLC - A Model Predictive Control (MPC) framework is implemented as part of the run-time system function features of a Programmable Logic Controller (PLC) system. Optimal control calculations are performed in the run-time MPC function block of the PLC. The optimal control function is determined by an MPC block in an engineering tool of the PLC, using a system dynamic matrix containing measurements from a unit step response test performed by the PLC. | 10-02-2014 |
| 20140343694 | SYSTEM AND METHOD FOR ONLINE AUTOMATION - A changepoint detector for modeling data received from at least one sensor in a process in the hydrocarbon industry. The data is segmented into a plurality of segments and for each segment a model is assigned and the data corresponding to the segment fit to that model. A plurality of segmentations are thus provided and these segmentations are evaluated and assigned weights indicative of the fit of the models of the segmentation to the underlying data. The segmentation models are further used to calculate a result that may be input to a process control program. | 11-20-2014 |
| 20150081045 | METHOD OF OFF-LINE HYBRID SYSTEM ASSESSMENT FOR TEST MONITORING AND MODIFICATION - A method and an arrangement of controlling simulation of a coupled hybrid dynamic system comprising a component under test and a virtual model includes driving the physical component under test of the system on a test rig over a period of time to conduct a test by applying an initial test drive signal input to the test rig to generate a test rig response. At least a portion of the test rig response is inputted into the virtual model of the system to obtain a model response of the system. A condition of the physical component under test is assessed during at least a portion of the period of time to conduct the test based on comparing another portion of the test rig response with the model response where an output relating to the assessment is recorded or rendered. | 03-19-2015 |
| 20150081046 | APPARATUS AND METHOD FOR MODELING AND CONTROL OF CROSS-DIRECTION FIBER ORIENTATION PROCESSES - A method includes generating a model associated with cross-directional fiber orientation of a web, which includes identifying spatial frequency characteristics of a fiber orientation (FO) process. The method also includes providing the model for control of the FO process. Generating the model could include performing a spatial impulse test of the FO process, and long wavelength responses of the FO process can be identified by performing a spatial long wavelength test of the FO process or by retrieving information from a historical database. Actuator edge padding can be applied to the model in order to generate a controller model. A controller can be used to control the process based on the controller model. At least one parameter of the controller model can be dynamically adjusted during operation of the controller. The controller can change average fiber orientation angle profiles and twist profiles by only adjusting slice lip actuators in a headbox. | 03-19-2015 |
| 20150342389 | CODE TRANSLATION PROGRAM FOR PRECISION SOUS VIDE COOKER DEVICE - A method for translating cooking time and temperatures for prepackaged food products. The sous-vide program allows users to cook to a food manufacturer recommended doneness based on the size, shape and fat content of the food. The program also lets user adjust setting if the users modified the food product like cut it in half. The program can be located on the sous vide cooking device or on person computing device. | 12-03-2015 |
| 20160003165 | COMPACT AERO-THERMO MODEL BASED CONTROL SYSTEM - Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode, wherein the open loop model generates a current state model as a function of the dynamic states and the model input, wherein a constraint on the current state model is based a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control system and including a series of utilities, the utilities are based on mathematical abstractions of physical properties associated with the component. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model. | 01-07-2016 |
| 20160011572 | INTEGRATED OPTIMIZATION AND CONTROL FOR PRODUCTION PLANTS | 01-14-2016 |
| 20160048112 | CLOUD COMPUTING SYSTEM AND METHOD FOR ADVANCED PROCESS CONTROL - A system and method for performing management and diagnostic functions in a cloud computing system for advanced process control (APC). A cloud based APC management computer retrieves operating process data from an APC control computer and performs an iterative step test on the APC system. The iterative step test modifies at least one test parameter of the operating process data and identifies changes to a set of remaining parameters of the operating process data resulting from modification of the test parameter. The APC management computer determines at least one process variable from the iterative step test and generates at least one process model based on the process variable. The APC management computer transmits the process model to the APC control computer. | 02-18-2016 |
| 20160147203 | Model Predictive Control with Uncertainties - A method controls iteratively the operation of the machine with control inputs determined using the model of the machine based on an optimization of a cost function subject to constraints on the control inputs. A current iteration of the method includes determining a current state of the machine after the controlling with a previous control input determined for a previous iteration by optimizing a previous cost function using a previous model of the machine and determining a current model of the machine to reduce a difference between the current state and a state estimated using the previous model of the machine. The cost function is updated during the current iteration based on a difference between the previous model and the current model to produce a current cost function. A current control input for the controlling at the current iteration is determined using the current model and the current cost function. | 05-26-2016 |
| 20160187863 | CALIBRATION METHOD AND AUTOMATION APPARATUS USING THE SAME - A calibration method applicable for an automation apparatus includes building a first stereoscopic characteristic model corresponding to an object, obtaining a stereoscopic image of the object, building a second stereoscopic characteristic model corresponding to the object based on the stereoscopic image, obtaining at least one error parameter corresponding to the second stereoscopic characteristic model by comparing the second stereoscopic characteristic model with the first stereoscopic characteristic model, and calibrating a processing parameter of the automation apparatus based on the at least one error parameter. | 06-30-2016 |
| 20160378078 | Triggering an Auto-Tuning Function of a PID Controller - A method for initializing an optimization function for parameters of a controller controlling a controlled system is provided. The controlled system includes at least one sensory component and at least one actuator component. The method includes receiving process data over time of the controller, determining a value of at least one prior specified key performance indicator (KPI) of the controller, and identifying a model of the controlled system. The method also includes comparing the at least one determined KPI value with at least one provided former KPI value, and comparing the identified model with at least one provided former model in the case of any significant difference between said values. The method includes initializing an optimization function of the controller parameters by using the determined KPI values as a cost function in the case of any significant difference between the models. | 12-29-2016 |
| 20170236262 | SIMULATOR, SIMULATION METHOD, AND SIMULATION PROGRAM | 08-17-2017 |
| 20190146470 | Methods and Apparatus to Generate a Predictive Asset Health Quantifier of a Turbine Engine | 05-16-2019 |
