WO2000041045A1 - Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems - Google Patents
Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems Download PDFInfo
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- WO2000041045A1 WO2000041045A1 PCT/US1999/027726 US9927726W WO0041045A1 WO 2000041045 A1 WO2000041045 A1 WO 2000041045A1 US 9927726 W US9927726 W US 9927726W WO 0041045 A1 WO0041045 A1 WO 0041045A1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
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- the present invention is related to that disclosed in (i) United States Patent No. 5,351,184 entitled “METHOD OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL;" (ii) United States Patent No. 5,561,599 entitled “METHOD OF INCORPORATING INDEPENDENT FEEDFORWARD CONTROL IN A MULTIVARIABLE PREDICTIVE CONTROLLER;” (iii) United States Patent No. 5,574,638 entitled “METHOD OF OPTIMAL SCALING OF VARIABLES IN A MULTIVARIABLE PREDICTIVE CONTROLLER UTILIZING RANGE CONTROL;” (iv) United States Patent No.
- the present invention is directed, in general, to control systems for process facilities and, more specifically, to systems for generating and using lookup tables with process facility control systems and models of the same, and methods of operating such systems, all for use to optimize process facilities.
- process facilities e.g. , a manufacturing plant, a mineral or crude oil refinery, etc.
- Contemporary control systems include numerous modules tailored to control or monitor various associated processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
- Process facility management providers such as HONEYWELL, INC., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, refining, etc.).
- a primary objective of such providers is to centralize control of as many processes as possible to improve an overall efficiency of the facility.
- Each process, or group of associated processes has certain input (e.g., flow, feed, power, etc.) and output (e.g., temperature, pressure, etc.) characteristics associated with it.
- model predictive control MPC
- One technique uses algorithmic representations to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes.
- the controller is designed to control a "worst case” process.
- An optimal controller for the process is achieved and, if the actual process is not a “worst case process,” the performance of the controller is better than anticipated.
- Controller performance deteriorates for many reasons, although the most common cause is changing dynamics of the process. Since PID controller performance has been related to the accuracy of the process model chosen, a need exists for PID controllers that allows for such uncertainty by accounting for changing system dynamics. Further, the requirement for ever-higher performance control systems demands that system hardware maximize software performance.
- Conventional control system architectures are made up of three primary components: (i) a processor, (ii) a system memory and (iii) one or more input/output devices.
- the processor controls the system memory and the input output (“I/O") devices.
- the system memory stores not only data, but also instructions that the processor is capable of retrieving and executing to cause the control system to perform one or more desired functions.
- the I/O devices are operative to interact with an operator through a graphical user interface, and with the facility as a whole through a network portal device and a process interface.
- processor throughput An alternate approach to improve process control system performance is to increase the number of instructions executed per clock cycle by the system processor ("processor throughput").
- One technique for increasing processor throughput calls for the processor to be divided into separate processing stages. Instructions are processed in an "assembly line” fashion in the processing stages. Each processing stage is optimized to perform a particular processing function, thereby causing the processor as a whole to become faster. There is again a practical maximum to the clock rate that is acceptable to conventional system hardware.
- the principles of the present invention may be used to define and populate a lookup table in response to the needs of a global controller.
- the lookup table is populated with a range of possible values of at least one measurable characteristic associated with one or more processes of the process facility and in accordance with a model of at least a portion of the same.
- the present invention introduces a data structure capable of maintaining a range of possible values of one or more of such certain characteristics.
- Use of the lookup table in lieu of execution and re-execution of the instructions for performing characteristic calculations decreases the number of instructions required to preform the functions of the process control system.
- the lookup table once suitably populated, accounts for process uncertainty by maintaining the range of possible values, thereby accounting for changing process dynamics.
- An exemplary computer system for use with a process facility that is capable of populating a data structure in accordance with the principles of the present invention includes both a memory and a processor.
- the memory is capable of maintaining (i) the data structure, which has a plurality of accessible fields, and (ii) a model of at least a portion of at least one process of a plurality of associated processes of the process facility.
- the model may advantageously include a mathematical representation of at least a portion of the at least one process, defining certain relationships among inputs and outputs of the at least one process.
- the processor is capable of populating ones of the plurality of accessible fields of the data structure using the model iteratively with a range of possible values of the at least one measurable characteristic.
- the computer system is capable of using the range of possible values of the at least one measurable characteristic to predict an unforced response associated with the at least one process.
- the data structure may be populated and maintained on-line (e.g., at a controller, distributed through a process control system, etc.), off-line (e.g., standalone computer, computer network, etc.), or through some suitable combination of the same.
- the data structure may remain static upon population, be dynamic, or be modifiable, at least in part.
- controllers may be implemented in hardware, software, or firmware, or some suitable combination of the same, and, in general, that the use of computing systems in control systems for process facilities is known.
- the phrase "associated with” and derivatives thereof, as used herein, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, be a property of, be bound to or with, have, have a property of, or the like; the term “include” and derivatives thereof, as used herein, are defined broadly, meaning inclusion without limitation; and the term "or,” as used herein, means and/or.
- FIGURE la illustrates a simple block diagram of an exemplary process facility with which the present invention may be used
- FIGURE lb illustrates a detailed block diagram of one of the exemplary local controllers introduced in FIGURE la;
- FIGURE 2 illustrates a flow diagram of an exemplary method for populating a data structure in accordance with the principles of the present invention.
- FIGURE 3 illustrates an exemplary two-dimensional graphical representation of MV and PV curves in accordance with the principles of the present invention.
- FIGURE la wherein a simple block diagram of such a process facility (generally designated 100) is illustrated.
- Exemplary process facility 100 is operative to process raw materials, and includes a control center
- Exemplary control center 105 illustrates a central area that is commonly operator manned (not shown) for centrally monitoring and for centrally controlling the three exemplary process stages.
- a first process stage includes three raw material grinders
- the second process stage includes a washer 11 Od that operates to receive the ground raw materials and clean the same to remove residue from the first stage.
- the third process stage includes a pair of separators 11 Oe and 11 Of that operate to receive the ground and washed raw materials and separate the same, such as into desired minerals and any remaining raw materials.
- Exemplary control system 115 illustratively includes a global controller 120 and six local controllers 125a to 125f, each of which is implemented in software and executable by a suitable conventional computer system (e.g., standalone, network, etc.), such as any of HONEYWELL, INC.'S AM K2LCN, AM K4LCN, AM HMPU, AxM or like systems.
- a suitable conventional computer system e.g., standalone, network, etc.
- suitable conventional computer system e.g., standalone, network, etc.
- Global controller 120 is associated with each of local controllers 125, directly or indirectly, to allow communication of information between the same.
- the phrase "associated with” and derivatives thereof, as used throughout this patent document, may mean to include within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, be a property of, be bound to or with, have, have a property of, or the like.
- Global controller 120 monitors measurable characteristics (e.g., status, temperature, utilization, efficiency, cost and other economic factors, etc.) of associated processes 110, either directly or indirectly (as shown, through local controllers 125 associated with processes 110). Depending upon the implementation, such monitoring may be of an individual process, group of processes, the facility as a whole, or otherwise. Similarly, local controllers 125 monitor associated processes 110, and, more particularly, monitor certain characteristics of associated processes 110. Global controller 120 generates, in response to such monitoring efforts, control data that may be communicated via local controllers 125 to associated processes 110 to optimize process facility 100.
- measurable characteristics e.g., status, temperature, utilization, efficiency, cost and other economic factors, etc.
- control data is defined as any numeric, qualitative or other value generated by global controller 120 to globally control (e.g., direct, manage, modify, recommend to, regulate, suggest to, supervise, cooperate, etc.) a particular process, a group of processes, a facility, a process stage, a group of process stages, a sequence of processes or process stages, or the like to optimize the facility.
- Local controllers 125 operate to varying degrees in accordance with the control data to control the associated processes, and, more particularly, to modify one or more processes and improve the monitored characteristics and the facility.
- control data may be dynamically generated using a lookup table defined and populated in accordance with the principles hereof, and such control data generation is based, at least in part, upon a given facility's efficiency, production or economic cost, and, most preferably, all three.
- the lookup table may be populated and maintained on-line (e.g., at global controller 120, at local controller 125, distributed within control system 115, etc.), off-line (e.g., standalone computer, network computer, etc.), or through some suitable combination of the same; likewise, the lookup table may be static upon population, be dynamic, or be modifiable, at least in part.
- the global controller 120 and the local controllers 125 may suitably use one or more such lookup tables to control processes 110 to conserve processing resources and increase the overall speed of control system 115.
- Control system 115 achieves a high level of both global and local monitoring, and cooperative control of associated processes 110 among controllers 120 and 125, by allowing the local controllers 125 to vary their individual or respective compliance with the control data. Varying degrees of compliance by local controllers 125 may range between full compliance and noncompliance.
- the relationship between global controller 120 and various ones of local controllers 110 may be master-slave (full compliance), cooperative (varying compliance, e.g., using control data as a factor in controlling the associated processes), complete disregard (noncompliance), as well as anywhere along that range.
- the relationship between global controller 120 and specific local controllers 125 may be static ( i.e., always only one of compliance, cooperative, or noncompliance), dynamic (i.e., varying over time, such as within a range between compliance and noncompliance, some lesser range therebetween, or otherwise), or varying between the same.
- One or more specific processes 110, and facility 100 as a whole may be dynamically and cooperatively controlled as a function of local and global optimization efforts, and such dynamic and cooperative control is independent of the relationship between global controller 120 and specific local controllers 125, as described above.
- FIGURE lb illustrated is a more detailed block diagram of one of the exemplary local controllers 125 that is associated with one or group of associated processes 110.
- Local controller 125 uses a single loop model predictive control (“SL-MPC") structure that uses an efficient matrix prediction form in accordance with the principles of the present invention, as well as an analytical control solution map to reduce utilization of processing resources relative to conventional MPC technology.
- SL-MPC single loop model predictive control
- local controller 125 receives as inputs, control/optimization specifications 130 (e.g., bounds, ranges, tolerances, control points, etc.) and feedback data 135 (e.g., output of associated process 110).
- Control/optimization specifications 130 may be received from any of a number of sources depending upon the associated process or group of associated processes 110, an associated process facility or any other factor.
- any of control/optimization specifications 130 may be received from an operator of a control center for the associated process facility, retrieved from a database or data repository, received from another associated controller (e.g., one or more local controllers 125, global controller 120, or a suitable combination thereof), etc.
- Control/optimization specifications 130 include two types of variables: (1) a first variable (“MV”) that may be manipulated, such as flow, feed, air blower, etc; and (2) a second variable (“DV”) that cannot be manipulated and is a disturbance variable, such as burn rate, fuel quality per unit, etc.
- Feedback data 135 is a third variable (“CV") that is responsive to MVs and DVs, and is an output of associated process 110, such as pressure, temperature, etc.
- a sub-variable (“PV”) of Feedback data 135 is indicative of the iterative response of the associated process 110 to monitoring and control by the local controller 125. Many, if not all, of such MVs, DVs and CVs represent measurable characteristics of associated process 110 that may be suitably monitored by local controller 125.
- Local controller 125 includes a dynamic prediction task with state estimation 150, a local linear program/quadratic program (“LP/QP") optimization task 155, a dynamic control/optimization augmented range control algorithm (“RCA”) 160 and a lookup table 165.
- Exemplary dynamic prediction task 150 receives CVs and operates to generate an array of multiple predictions (or dynamic unforced predictions) and, at 5 tau (response time close to end), an unforced prediction for values associated with associated process 110.
- the CVs represent feedback data 135 (e.g., inputs, outputs, etc.) associated with process 105, and dynamic prediction task 150 operates to accesses lookup table 165 and selects one or more values from the range of possible values, such selection being responsive, at least in part, to the received feedback data 135.
- a preferred method of using data structures, such as lookup table 165, or functionally equivalent dedicated circuitry, to maintain a range of possible values for one or more measurable characteristics associated with a process is disclosed and described in
- Exemplary local LP/QP optimization task 155 receives optimization specifications 140a and, in response to the unforced prediction, operates to generate, at 5 tau, optimal values associated with associated process 110.
- a preferred method of performing the foregoing task is disclosed and described in United States Patent No. 5,758,047, entitled “METHOD OF PROCESS CONTROLLER OPTIMIZATION IN A MULTIVARIABLE PREDICTIVE CONTROLLER,” which is commonly owned by the assignee of this patent document and related invention, the disclosure of which has previously been incorporated herein by reference for all purposes as if fully set forth herein.
- optimization specifications 140a are associated, directly or indirectly, with an economic value of the output of associated process 110.
- the unforced prediction may suitably be represented as a single variable and the LP/QP optimization task may be a linear determination of a minimum value or a maximum value, or a quadratic determination of a desired value.
- Exemplary dynamic control/optimization augmented RCA 160 receives control specifications 140b and, in response to receiving the array of multiple predictions (from dynamic prediction task 150) and the optimal values (from local LP/QP optimization task 155), operates to generate control values, the MVs, that are input to associated process 110.
- An important aspect of exemplary local controller 125 is the use of control/optimization specifications 140 and feedback data 135 to locally unify economic/operational optimization with MPC dynamically for a specific process or group of processes.
- FIGURE 2 illustrated is a flow diagram of an exemplary method (generally designated 200) for populating a data structure 165, shown as a lookup table, in accordance with the principles of the present invention (this discussion of FIGURE 2 makes concurrent reference to FIGURES la and lb).
- data structure is defined broadly as any syntactic structure of expressions, data or other values or indicia, including both logical and physical structures.
- a data structure may therefore be any array (i.e., any arrangement of objects into one or more dimensions, e.g., a matrix, a table, etc.), or other like grouping, organization, or categorization of objects in accordance herewith.
- a processor 205 and a memory 210 are introduced.
- Exemplary memory 210 is operative to store, or to maintain, lookup table 165, along with the various tasks/ instructions (generally designated 215) comprising method 200.
- Exemplary processor 205 is operative to select and execute tasks/ instructions 215 which, in turn, cause processor 205 to perform the functions of method 200.
- processor 205 is directed through the execution of method 200 (e.g. , manually (i.e., through interaction with an operator), automatically, or partially- automatically) to define a model 220 of at least a portion of at least one of the associated processes 110 (process step 225).
- Processor 205 is directed to store model 220 in memory 210, preferably representing at least a portion of process 110 mathematically.
- the mathematical representation defines one or more relationships among inputs and outputs of process 110.
- Processor 205 is directed to define a data structure, such as lookup table 165, having a plurality of accessible fields (process step 230).
- An exemplary source code embodiment for performing this definition is attached as APPENDIX A, and incorporated herein by reference as if fully set forth herein, and that is written in Pascal.
- the contents of such accessible fields may suitably be nulled, defaulted, or otherwise initialized or used.
- Memory 210 directed by processor 205, maintains lookup table 165, preferably representing, at least in part, an AB0I matrix 235 and a feedback vector 240.
- ABOI matrix 235 and feedback vector 240 have the following respective definitions:
- I and 0 respectively and illustratively represent an identity matrix and an null matrix, for the purpose of this illustrative model, to maintain, or hold, MV constant (illustrated with respect to FIGURE 3).
- Processor 205 is directed to delineate mathematically a relationship among the above-given matrix 235 and vector 240 (process step 245), which according to an advantageous embodiment, has the following form:
- Processor 205 is directed to delineate mathematically a relationship among the above- given discrete state space model form and the Z vector 240 (process step 250), which, according to an advantageous embodiment, gives the following prediction form for any p interval, or point in the future:
- Z vector 240 represents, or defines, mathematically, the relationship among the one or more inputs and outputs of modeled process 110.
- process 110 For a variety of purposes, as above-stated, for monitoring and for control of process 110, it is desirable to decrease utilization of processing resources. This may be accomplished, in part, through a recognition that certain characteristics of process 110 are measurable (e.g., appraising, assessing, gauging, valuating, estimating, comparing, t . computing, rating, grading, synchronizing, analyzing, etc.), whether or not such characteristics are dependent, independent, interdependent, or otherwise effected by other characteristics of the same process, a group of processes, a facility, a process stage, a group of process stages, a sequence of processes or process stages, or the like. Many of these measurable characteristics have a range of possible values, which may or may not change, or vary, over time. It is desirable, in the present example, to determine an efficient prediction form ("EPF"), the range of values of which may suitably be maintained in lookup table 165.
- EPF efficient prediction form
- Processor 205 is directed to populate ones of the accessible fields 255 of lookup table 165 with a range of possible values of at least one measurable characteristic associated with at least process 110 (process step 260).
- An exemplary source code embodiment for performing this population is attached as APPENDIX B, and incorporated herein by reference as if fully set forth herein, and that is written in Pascal.
- EPF may be given by:
- epf is independent from the feed back information contained in the Z vector and may therefore be calculated in advance and given by:
- exemplary processor 205 uses model 220 iteratively, or incrementally, to populate lookup table 165 with k possible values, thereby defining a range of values.
- a Vk vector is formulated to conveniently calculate both Zk and for different incremental k, which has the following form:
- FIGURE 3 illustrated is an exemplary two-dimensional graphical representation of MV and PV curves in accordance with a use of lookup table 165 in accordance with the control system 100 of FIGURES la and lb and the principles of the present invention.
- FIGURES la, lb, 2, and 3 along with the various embodiments used to describe the principles of the present invention in this patent document are illustrative only.
- alternate embodiments of model 220 may define any particular process, a group of processes, a facility, a process stage, a group of process stages, an interrelationship among, or a sequence of, processes or process stages, or some suitable portion or combination of any of the same.
- a matrix structure was chosen for the EPF in this embodiment, however, alternate embodiments may use any appropriate data structure or dedicated circuitry to create a suitably arranged lookup array, or table, or the like.
- Such data structures and dedicated circuitry may be populated off-line, on-line or through some suitable combination of the same; likewise, such populated data structures and dedicated circuitry may be static, dynamic, modifiable, centralized, distributed, or any suitable combination of the same.
- computer system 105 described using processor 205 and memory 210 may be any suitably arranged hand- held, laptop/notebook, mini, mainframe or super computer, as well as network combination of the same.
- alternate embodiments of computer system_ 205 may include, or be replaced by, or combined with, any suitable circuitry, including programmable logic devices, such as programmable array logic ("PALs”) and programmable logic arrays (“PLAs”), digital signal processors (“DSPs”), field programmable gate arrays (“FPGAs”), application specific integrated circuits (“ASICs”), very large scale integrated circuits (“VLSIs”) or the like, to form the processing systems described and claimed herein.
- PALs programmable array logic
- PLAs programmable logic arrays
- DSPs digital signal processors
- FPGAs field programmable gate arrays
- ASICs application specific integrated circuits
- VLSIs very large scale integrated circuits
- ALLOC_S_Z_ERR 206;
- sp_pool_data_ptr ⁇ sp_pool_data
- sp_cds_data_ptr ⁇ sp_cds_data
- sp_cds_data record ⁇
- G_s sp__pin_arr_s; ⁇ Steady-state gains, used by controller ⁇
- Three_Tau single; ⁇ Three Tau (continuous) ⁇ ⁇ used as integer ⁇
- T_con single; ⁇ Control execution interval (minutes) ⁇ ⁇ spid_cds:NUMSTIL ⁇
- Section 2 Variables used by Pascal/CL/LCN GUI ⁇
- ⁇ spid_cds:YY_L ⁇ y_L_ent single; ⁇ Entered CV low limit.
- ⁇ spid_cds:YY_H ⁇ y_H_ent single; ⁇ Entered CV high limit.
- ⁇ spid_cds:YLDTGT ⁇ y_Filt_Ramp2 single; ⁇ spid_cds:YIELD ⁇ Filt_opt_Const : single ; ⁇ spid_cds:YIELDINT ⁇
- Filt_opt_Const2 single ;
- ⁇ spid_cds TYPE ⁇ y_type : single; ⁇ 0: stable, 1 integrator,
- ⁇ spid_cds:DIS_NAME ⁇ ss_display single; ⁇ PC use only, use in simulation.
- Flag: 0 means display ss values ⁇ ⁇ spid_cds:STP ⁇ saved_3t_pr : single; ⁇ saved Three_Tau ( for integrator ) or
- n sJL single; ⁇ No. of intervals for J.L.'s mods ⁇ ⁇ used as integer ⁇
- ⁇ spid_cds:ZOOMDEV(1..5) z coef_arr; ⁇ used in spidScalc ⁇ ⁇ spid_cds:NUMSAMP ⁇ n_mvblk : single ; ⁇ mv block sampling time ⁇ ⁇ used as integer ⁇
- ⁇ KYS - save the whole QMap or QMapl which is the 1st row of QMap ?
- ⁇ sp_pool_data record y_end : sp_block_arr_ptr; n_s : integer; s_z : heap_array_s_ptr; epfl : Matrix_Type; epf2 : Matrix_Type;
- G_s single;
- T_con single;
- Icnunit integer
- n_order integer
- procedure new_alloc_urv (var urv : urv_set; tot_num_cv, ⁇ will include num of combined constraints for DQP ⁇ num mv : integer; callerlD : integer; ⁇ see URV_DATA.PAS for definition ⁇ Icnunit ⁇ :LCN_USE: : $unit_identifier; : ⁇ ⁇ :HP_USE: : integer; : ⁇
- ⁇ NAXJJSE integer; : ⁇
- ⁇ NAXJJSE integer; : ⁇
- sp_ctfcheck ( model no : integer; n_dv : integer; var n_num : single; var n_den : single; var num : coef_arrs; var den : coef arrs; var G_s : single; delay : single; var max delay : sp_pin_arr_s; var max_d_int : sp_pin_arr_s; T con : single; var setup_flag single; Three_Tau : single; perf_rat : single; y ype : single; var saved_3t_pr single; var call setup single; var status : single); var i, j, total_max_d_int,
- call_setup FORCE then begin ⁇ call_setup ⁇
- Var loc_y array [-n_F_max_dim..O] of single; totalJB, terml, term2 : single; i, i_kp : integer;
- n_sJL single ; ⁇ Number of intervals for J.L.'s mods. ⁇ perf at : single ; ⁇ FF cntl/FB cntl response ratio. ⁇
- n_numi, n deni integer; sqtau2, taulmul4, tmidl, tmid2, tmid3, tmid4, tmid5, tmid ⁇ , tmid7, tmid ⁇ , a, b : single; numptr, denptr, b_z_ptr, f_z__ptr : coef_arr __ptr;
- T_con end; ⁇ Tau2 ⁇ 2 greater then ( 4 * Taul ), ab ⁇ end ⁇ l/( tl * S ⁇ 2 + 12 * S + 1) ⁇
- ⁇ status 100; ⁇ end;
- delay_int single; Icnunit integer;
- n order integer;
- callerlD integer; ⁇ see URV DATA.PAS for definition ⁇
- ⁇ NAXJJSE integer; : ⁇
- n mini(max_row_size, n); ⁇ double check MC_Control/OptSS later, what if n > max_row_size??? ⁇
- R.mat[i] : (*:loophole:*) Row_ptr ( new_mc_getmem(m sizeof(URV_sd),
- V.mat[i] : (*:loophole:*) Row_ptr ( new_mc_getmem(m sizeof(URV_sd),
- ⁇ :VAX_USE : integer; : ⁇
- ⁇ :PC_USE ⁇ : integer; ⁇ : ⁇ ⁇ Point's unit ⁇ var status : single); (*:LOCAL:*) var n,m,i, get_amount : integer; gen_ptr : r anyptr;
- ⁇ delay_int integer, value is dead time for each block ⁇
- value is number of values in
Abstract
Description
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
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AU19193/00A AU1919300A (en) | 1998-12-31 | 1999-11-22 | Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems |
EP99962834A EP1141791B1 (en) | 1998-12-31 | 1999-11-22 | Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems |
CA002358401A CA2358401C (en) | 1998-12-31 | 1999-11-22 | Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems |
DE69926014T DE69926014T2 (en) | 1998-12-31 | 1999-11-22 | SYSTEMS FOR GENERATING AND USING A REFERENCE TABLE IN CONTROL SYSTEMS AND MODELS OF A MACHINING SYSTEM AND METHOD FOR OPERATING SUCH SYSTEMS |
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US09/224,439 | 1998-12-31 | ||
US09/224,439 US6542782B1 (en) | 1998-12-31 | 1998-12-31 | Systems for generating and using a lookup table with process facility control systems and models of the same, and methods of operating such systems |
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US (2) | US6542782B1 (en) |
EP (1) | EP1141791B1 (en) |
AU (1) | AU1919300A (en) |
CA (1) | CA2358401C (en) |
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CA2358401C (en) | 2009-02-17 |
US20020107585A1 (en) | 2002-08-08 |
EP1141791A1 (en) | 2001-10-10 |
DE69926014T2 (en) | 2006-05-11 |
US6542782B1 (en) | 2003-04-01 |
CA2358401A1 (en) | 2000-07-13 |
AU1919300A (en) | 2000-07-24 |
EP1141791B1 (en) | 2005-06-29 |
DE69926014D1 (en) | 2005-08-04 |
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