Device, method, controller, cell culture system and storage medium

Abstract

The present invention provides an apparatus, a method, a controller, a cell culture system and a storage medium, wherein the apparatus comprises: an acquisition unit for acquiring control variables and a control target of a control object in a process controlled by a controller; a derivation unit for deriving a closed-loop transfer function near an action point based on a control variable model representing the behavior of the control variables in the process and a control function used in the control of the controller; and a setting parameter generation unit for generating setting parameters used in the control of the controller in a manner that makes the closed-loop transfer function suitable for a control target function derived from the control target.

Description

Technical Field

[0001] The invention relates to a device, a method, a controller, a cell culture system and a storage medium. Background Art

[0002] Patent documents 1 to 12 and non-patent documents 1 to 2 describe the following methods, etc., which provide cell culture process specifications for optimizing the cell culture process in a reactor system based on culture data of the cell culture process, and the method comprises: ... a step of obtaining culture data of the cell culture process; ... a step of applying a digital twin obtained by the method described in any one of claims 1 to 6 to adjust or generate at least one optimized cell culture process specification from the obtained culture data (claim 7 of patent document 4). Prior art literature Patent Literature Patent Document 1: International Publication No. 2020 / 252442 Patent Document 2: U.S. Patent Application Publication No. 2019 / 153381 Patent Document 3: International Publication No. 2020 / 238918 Patent Document 4: Japanese Patent Application No. 2022-537799 Patent Document 5: Japanese Patent Application Publication No. 2019-041656 Patent Document 6: International Publication No. 2020 / 039683 Patent Document 7: International Publication No. 2021 / 166824 Patent Document 8: Japanese Patent Application Publication No. 2007-202500 Patent Document 9: Japanese Patent Application Publication No. 2018-117567 Patent Document 10: Japanese Patent Application No. 2009-510606 Patent Document 11: Japanese Patent Application Laid-Open No. 59-045872 Patent Document 12: Japanese Patent Application Publication No. 2012-141791 Non-patent literature Non-patent document 1: Murakami Seiji, "Subcommittee 2, Current Status and Issues of Continuous Cultivation Processes", Journal of the Society of Bioengineering, Vol. 97, No. 6 Non-patent literature 2: Kano, M., Tasaka, K., Ogawa, M., Takinami, A., Takahashi, S., & Yoshii, S. (2011). Extended fictitious reference iterative tuning and its application to chemical processes. Proceedings of the 2011 4th International Symposium on Advanced Control of Industrial Processes, 379-384 Summary of the invention

[0003] In a first aspect of the present invention, there is provided an apparatus comprising: an acquisition unit for acquiring control variables and a control target of a control object in a process controlled by a controller; a derivation unit for deriving a closed-loop transfer function near an action point based on a control variable model representing the behavior of the control variables in the process and a control function used in the control of the controller; and a setting parameter generation unit for generating setting parameters used in the control of the controller in such a way that the closed-loop transfer function is suitable for a control target function derived from the control target.

[0004] The above-mentioned device may further include an approximate transformation unit that transforms the control variable model into an approximate control variable model that linearly approximates near the operating point of the process, and the derivation unit derives a closed-loop transfer function near the operating point based on the approximate control variable model and the control function.

[0005] Any of the above-mentioned devices may further include an adjustment unit configured to set a feedforward adjustment amount that cancels out at least one variable in the process in the controlled variable model.

[0006] In any of the above-mentioned devices, the control function may be a function corresponding to a PID control method.

[0007] Any of the above-mentioned devices may further include an updating unit configured to update a setting parameter based on an operating condition of the process.

[0008] In any of the above-mentioned devices, the setting parameter generating unit may generate the setting parameter using a local model matching method so that the closed-loop transfer function is adapted to the control target function.

[0009] Any of the above-mentioned devices may further include a control target function setting unit configured to set a control target function based on the control target.

[0010] In the above device, the control objective function setting unit may set the control objective function of a binomial coefficient standard form, a Butterworth standard form, or an ITAE (Integral of Time multiplied by Absolute Error) minimum standard form.

[0011] In a second aspect of the present invention, there is provided a method in which a controller controls at least one of process values ​​of a cell culture process using setting parameters generated by any one of the above-mentioned devices.

[0012] In a third aspect of the present invention, there is provided a controller for performing feedback control on at least one of process values ​​of a cell culture process using a setting parameter generated by any one of the above-mentioned devices.

[0013] In a fourth aspect of the present invention, a cell culture system is provided, comprising: a culture tank for culturing cells or microorganisms during a cell culture process; a sensor for measuring a process value managed during the cell culture process; an actuator for applying an operation to the culture tank; and the above-mentioned controller for controlling the actuator according to the measurement results of the sensor.

[0014] In a fifth aspect of the present invention, a method is provided, which includes: a step of obtaining a control variable and a control target of a control object in a process controlled by a controller; a step of deriving a closed-loop transfer function near an action point based on a control variable model representing the behavior of the control variable in the process and a control function used in the control of the controller; and a step of generating setting parameters used in the control of the controller in a manner that makes the closed-loop transfer function fit the control target function derived from the control target.

[0015] In a fifth aspect of the present invention, a storage medium is provided, wherein the storage medium stores a program, and a computer functions as an acquisition unit, a derivation unit, and a setting parameter generation unit by executing the program, wherein the acquisition unit acquires a control variable and a control target of a control object in a process controlled by a controller, wherein the derivation unit derives a closed-loop transfer function near an action point based on a control variable model representing the behavior of the control variable in the process and a control function used in the control of the controller, and wherein the setting parameter generation unit generates setting parameters used in the control of the controller in such a way that the closed-loop transfer function fits the control target function derived from the control target.

[0016] The above summary of the invention does not list all the essential features of the present invention. In addition, sub-combinations of these feature groups may also constitute other inventions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A cell culture system 1 according to an embodiment is shown. Figure 2 A control loop 15 of the cell culture system 1 is shown. Figure 3 The setting device 14 of the embodiment is shown. Figure 4 The operation of the setting device 14 is shown. Figure 5 An example of a computer 2200 that can implement all or part of the various aspects of the present invention is shown. Description of Reference Numerals 1 Cell culture system 10 Culture Tanks 11 Actuator 12 Sensors 13 Controller 14 Setting device 15 Control loop 300 Acquisition 310 Control variable model setting unit 320 Adjustment Department 330 Approximate Transformation Unit 340 Export Department 350 Control target function setting unit 360 Setting parameter generation unit 370 Output 380 Update Department 101 Sampling device 102 Cell removal device 103 Flow Cell 120 Online Sensors 121 Dielectric Spectrometer 122 Near infrared / infrared / Raman spectrometer 124 Analysis device 131 stirring device 1210 Electrode 1220 Luminescence Sensor 2200 Computer 2201DVD-ROM 2210 Main Controller 2212CPU 2214RAM 2216 Graphics Controller 2218 Display Device 2220 Input / Output Controller 2222 Communication Interface 2224 Hard Drive 2226DVD-ROM drive 2230ROM 2240 Input / Output Chip 2242 Keyboard DETAILED DESCRIPTION

[0018] The present invention will be described below by way of the embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, all combinations of features described in the embodiments are not necessarily essential to the solution of the invention.

[0019] Figure 1 The cell culture system 1 of this embodiment is shown. The cell culture system 1 performs culture to produce a target substance through a bioreaction. The cell culture system 1 can produce, for example, biopharmaceuticals such as antibody drugs, and can culture animal cells such as CHO cells. In addition, the cell culture system 1 can culture cells of vertebrates other than CHO cells, cells of shellfish and insects, human cells, plant cells, or microbial cells, and can also perform yeast fermentation.

[0020] The cell culture system 1 may include a culture tank 10, one or more actuators 11, one or more sensors 12, a controller 13, and a setting device 14. The culture tank 10, one or more sensors 12, the controller 13, and one or more actuators 11 may form one or more control loops (for example, a closed-loop control loop or an open-loop control loop), and the cell culture system 1 may control the actuator 11 through the controller 13 based on the measurement result of the sensor 12.

[0021] The culture tank 10 is a container for containing culture medium and cells and culturing cells or microorganisms during the culture process. In order to proliferate cells, the temperature, pH, and dissolved oxygen (DO) of the culture solution in the culture tank 10 can be appropriately managed by the actuator 11. As an example, in the culture tank 10, perfusion culture can be performed, and the culture medium can be continuously supplied to the culture tank 10 at a stage where the cells proliferate to a certain extent (for example, from the second day to the third day after the start of culture), and the cell-free solution (harvest solution) of the same amount as the supply amount can be recovered from the culture tank 10. In this way, the volume of the culture solution in the culture tank 10 can be kept constant.

[0022] In addition, a manually operated sampling device 101 and a feeding device (not shown) may be provided in the culture tank 10. The sampling device 101 may sample the culture solution in the culture tank 10, and the feeding device may feed a feeding agent for supplementing the nutrient source into the culture tank 10. These sampling devices 101 and the feeding device may also be automatically operated actuators 11.

[0023] Each actuator 11 is a driving device that performs physical movement and applies operation to the culture tank 10. Each actuator 11 can control at least one of the dissolved oxygen concentration, pH, temperature, substrate concentration (glucose concentration), cell density, culture solution volume, stirring speed, pressure, weight, volume, and flow rate in the culture solution in the culture tank 10. Each actuator 11 can be any one of a valve, a pump, a heater, a fan, a motor, and a switch.

[0024] In this embodiment, as an example, the cell culture system 1 can have a pump P1 for supplying culture medium to the culture tank 10, a pump P2 for supplying glucose to the culture tank 10, a pump P3 for supplying alkali to the culture tank 10, a stirring device 131 for stirring the culture liquid in the culture tank 10, a pump P4 for discharging cells and culture liquid from the culture tank 10, and a pump P5 for discharging harvest liquid from the culture tank 10, as an actuator 11.

[0025] Among these actuators 11, the pump P5 can discharge the harvested liquid in the culture tank 10 through the cell removal device 102 and the flow cell 103. The cell removal device 102 can have a filtering action called TFF (tangential flow filtration) or ATF (alternating tangential flow filtration), and can maintain the cells in the culture tank 10 and discharge the harvested liquid from the culture tank 10.

[0026] Each sensor 12 measures the state of the culture tank 10. Each sensor 12 can measure a process value managed during the cell culture process. Each sensor 12 can measure a process value (also referred to as a process parameter, PV (Process Value)) indicating dissolved oxygen concentration, pH, temperature, substrate concentration (glucose concentration), cell density, culture solution volume, stirring speed, pressure, weight, volume, or flow rate in the culture solution.

[0027] In this embodiment, as an example, the cell culture system 1 may include: a sensor (not shown) for measuring the weight of the culture tank 10; a sensor (not shown) for measuring the volume and weight of the fluid supplied by pumps P1 to P3; a sensor (not shown) for measuring the density of cells; a dielectric spectrometer 121 for measuring the dielectric constant of the culture solution; a near-infrared / infrared / Raman spectrometer 122 for measuring the nutrients and metabolic components in the culture solution; an analyzer 124 for analyzing the components of the culture solution; and an online sensor 120 immersed in the culture solution to measure the state of the culture solution, as the sensor 12.

[0028] Among these sensors 12, the dielectric spectrometer 121 can measure the dielectric constant of the culture medium based on the current flowing between the electrodes 1210 inserted into the culture medium, and can also further measure the number of cells, survival rate, etc. in the culture medium based on the measured dielectric constant.

[0029] The near infrared / infrared / Raman spectrometer 122 can irradiate the culture solution with various measurement lights (near infrared light, infrared light, monochromatic light) by inserting the light-receiving sensor 1220 in the culture solution, and measure the near infrared spectrum, infrared spectrum, Raman spectrum, etc. of the light transmitted through the culture solution or reflected by the culture solution, and can also further measure glucose, lactic acid, ammonia, various amino acids, etc. in the culture solution based on the measured spectrum. In addition, in the measurement performed by the near infrared / infrared / Raman spectrometer 122, a calibration model can be constructed using a solution with a pre-adjusted component concentration, and the spectrum obtained from the light-receiving sensor 1220 immersed in the culture tank 10 can be converted into a component concentration using the calibration model to perform the measurement. The light-receiving sensor 1220 can also be set in the flow cell 103.

[0030] The analysis device 124 may be, for example, a high-speed liquid chromatography device (HPLC), a cell culture analysis device, etc. The analysis device 124 may analyze the components of the culture solution collected from the culture tank 10 by the sampling device 101 at a reference interval (for example, 24 hours) in an offline manner, i.e., away from the culture tank 10.

[0031] The online sensor 120 can be immersed in the culture solution to measure the state of the culture solution during the cell culture. As an example, the online sensor 120 can measure at least one of the basic physical / chemical parameters of the culture solution, such as pH, dissolved oxygen content (DO), temperature, gas partial pressure (oxygen partial pressure, carbon dioxide partial pressure, etc.), osmotic pressure, nutrients, metabolic components, and target product concentration. The electrode 1210 of the dielectric spectrometer 121 and the luminescent sensor 1220 of the near-infrared / infrared / Raman spectrometer 122 can be the online sensor 120. In addition, the measurement results of the online sensor 120 may also drift due to changes in the culture environment. As the main reasons for the change in the culture environment, there can be cited the change in mechanical stress generated in the culture solution due to stirring and gas ventilation, the depletion of nutrients and oxygen in the culture solution, and the accumulation of waste products (also called debris, impurities) such as lactic acid and ammonia produced by cells.

[0032] At least one sensor 12 among the one or more sensors 12 included in the cell culture system 1 may be provided with a noise removal filter for removing noise from the measurement result. The noise removal filter may cause a delay from when the measurement target of the sensor 12 changes until when the measurement result of the sensor 12 changes.

[0033] Each sensor 12 can perform measurement at a preset cycle. Each sensor 12 can supply a process value as a measurement result to the controller 13. Each sensor 12 can be calibrated at an arbitrary timing.

[0034] In addition, the process value may be an analysis value related to at least one of the culture solution components, the culture environment, and the biomass state obtained by sampling the culture solution at a reference frequency (as an example, once a day) and analyzing it by the analysis device 124. Here, the analysis value related to the culture solution components may be a value representing glucose, glutamine, glutamic acid, lactic acid, ammonium ions, other components that affect cell proliferation or the quality and production of the target product, or metabolites. The analysis value related to the culture environment may be the temperature, pH, dissolved oxygen concentration, dissolved carbon dioxide concentration, osmotic pressure, culture solution volume, or perfusion rate of the culture solution. The analysis value related to the biomass state may be the bacterial concentration (viable cell density, total cell density) of the microorganism, the proliferation rate, the consumption rate of nutrients such as glucose, the generation rate of the target product or by-product, or the cell survival rate. The cell density measured by the dielectric spectrometer 121 or the like may also be calculated by staining the culture solution and analyzing the captured image. The nutrients and metabolic components measured by the near infrared / infrared / Raman spectrometer 122 or the like may also be measured using an enzyme sensor or the like.

[0035] The controller 13 can control one or more actuators 11 based on the measurement results of one or more sensors 12. For example, the controller 13 can control the actuator 11 by supplying a control signal representing an operation amount calculated based on the measurement results of the sensor 12 to the actuator 11. The controller 13 can use the measurement results of one or more sensors 12 for calculation of the operation amount after scaling (normalization as an example).

[0036] The controller 13 can control one or more actuators 11 according to the measurement results of one or more sensors 12, thereby controlling the cell culture process of the cell culture system 1. For example, the controller 13 can monitor the state of the culture solution in the culture tank 10, perform various controls related to the perfusion culture process (operation of various pumps, motors, etc., temperature control, etc.), and control the supply amount, recovery amount, ventilation amount, outflow (bleed) flow rate, perfusion rate, etc. of the culture tank 10. As an example, the controller 13 can control basic process values ​​such as dissolved oxygen concentration, pH, temperature, substrate concentration (glucose concentration), cell density, culture solution volume, stirring speed, etc. in the culture solution, and supplement the nutrients contained in the culture medium and enhancers that increase the rate of cell proliferation or production.

[0037] The controller 13 can set a target value (also referred to as a set value, SV (Set Value)) of the process value. When setting the target value, the controller 13 can calculate the operation amount so that the process value becomes the target value (or within a reference range from the target value) to control the actuator 11. When the cell culture system 1 has a plurality of control loops 15, the target value can be set for the controller 13 for each control loop 15. The target value of the process value can be set by the operator so as to achieve an arbitrary control target.

[0038] The controller 13 is set with setting parameters. The controller 13 uses the setting parameters to perform feedback control on at least one of the process values ​​of the cell culture process. The setting parameter may be a parameter indicating a control condition for the actuator 11, for example, a parameter for feedback control. As an example, the setting parameter for feedback control may be at least one of a proportional gain, an integral time, and a differential time. The setting parameter may also be a parameter indicating an output frequency or a control period of a control signal for the actuator 11 (also referred to as a frequency parameter), or may be an upper and lower limit value of an operation amount indicated to the actuator 11 (also referred to as an operation amount for the actuator 11). As an example, the upper and lower limit values ​​of the operation amount may be set at a ratio of 0 to 100%, -50% to 50%, or the like.

[0039] The controller 13 can discharge the cells and the culture solution from the culture tank 10 through the pump P4 when the cell density in the culture solution reaches a reference value, thereby bringing the cell density in the culture tank 10 into an appropriate range. The controller 13 can also include a setting screen for a target value of a process value as a user interface, a trend display function, a data storage function, and an output function.

[0040] The setting device 14 is an example of a device, and generates setting parameters for the controller 13 in the control of the cell culture system 1. The setting device 14 can generate setting parameters based on information related to the control of the cell culture system 1 and set the setting parameters in the controller 13.

[0041] Figure 2 1 shows a control loop 15 of the cell culture system 1 . The controller 13 , the actuator 11 , the culture tank 10 and the sensor 12 of the cell culture system 1 may form one or more closed control loops 15 .

[0042] Figure 3 The configuration example of the setting device 14 of this embodiment is shown. The setting device 14 includes an acquisition unit 300, a control variable model setting unit 310, an adjustment unit 320, an approximate transformation unit 330, a derivation unit 340, a control target function setting unit 350, a setting parameter generation unit 360, an output unit 370, and an update unit 380.

[0043] The acquisition unit 300 can acquire information related to the setting parameters of the controller 13 from the operator 400 and the controller 13. The acquisition unit 300 acquires the control variables and control targets of the control object in the cell culture process controlled by the controller 13. The acquisition unit 300 can acquire one or more control targets and one or more control variables. The acquisition unit 300 can acquire information (for example, the type of process value and target value, etc.) that specifies process values ​​such as dissolved oxygen concentration, pH, temperature, substrate concentration, cell density, culture solution amount, stirring speed, pressure, weight, volume, or flow rate in the culture solution as the control variable.

[0044] Here, the control target obtained by the acquisition unit 300 may be the control performance to be achieved during the cell culture process (e.g., target value tracking characteristics or interference suppression characteristics, etc.), or the target value of the control performance, etc. The control target may be a target related to the stability characteristics. For example, it may be that the process value becomes within the reference range from the target value when the state of the culture tank 10 becomes a stable state. The control target may also be a target related to the transient characteristics. For example, it may be that the adjustment time (also called response delay time) until the process value reaches the target value (or within the reference range from the target value) becomes less than the reference time. The control target may also be a target related to the tracking performance with respect to the target value. For example, it may be that the ratio (also called tracking ratio) of the process value reaching the target value (or within the reference range from the target value) when the target value is changed for any process value and cell culture is performed exceeds the reference ratio.

[0045] The control variable model setting unit 310 is connected to the acquisition unit 300. The control variable model setting unit 310 sets a control variable model representing the behavior of the control variable in the cell culture process. The control variable model setting unit 310 can set a control variable model corresponding to the control variable acquired by the acquisition unit 300.

[0046] The adjustment unit 320 is connected to the control variable model setting unit 310. The adjustment unit 320 sets a feedforward adjustment amount that offsets at least one variable in the cell culture process in the control variable model.

[0047] The approximate conversion unit 330 is connected to the adjustment unit 320. The approximate conversion unit 330 converts the control variable model into an approximate control variable model that linearly approximates the vicinity of the operating point of the cell culture process.

[0048] The derivation unit 340 is connected to the approximate transformation unit 330. The derivation unit 340 derives a closed-loop transfer function near the operating point based on the control variable model and the control function used for the control of the controller 13.

[0049] The control target function setting unit 350 is connected to the acquisition unit 300. The control target function setting unit 350 sets the control target function according to the control target. The control target function setting unit 350 can set the control target function that satisfies the control target acquired by the acquisition unit 300.

[0050] The setting parameter generation unit 360 is connected to the derivation unit 340 and the control target function setting unit 350. The setting parameter generation unit 360 generates setting parameters for control of the controller 13 in such a manner that the closed-loop transfer function fits the control target function derived from the control target. The setting parameter generation unit 360 may generate the setting parameters using a local model matching method in such a manner that the closed-loop transfer function fits the control target function.

[0051] The output unit 370 is connected to the controller 13 , and outputs the generated setting parameters to the controller 13 .

[0052] The updating unit 380 is connected to the setting parameter generating unit 360. The updating unit 380 updates the setting parameters according to the operation conditions of the cell culture process. The updating unit 380 can update the setting parameters according to the operation conditions of the cell culture process received from the controller 13.

[0053] Figure 4 1 shows a setting parameter generation flow of the setting device 14. The setting device 14 determines setting parameters that achieve a desired control target by performing the processing of steps S11 to S18.

[0054] In step S11, the acquisition unit 300 acquires control variables and control targets. The acquisition unit 300 can acquire a combination of control variables and control targets input by the operator 400 through a keyboard, a mouse, a touch pad, etc. The acquisition unit 300 can acquire a combination of one or more control variables and one or more control targets. The acquisition unit 300 can store a combination of control variables and control targets, and can acquire a combination of control variables and control targets according to information related to the cell culture process (e.g., the type of culture tank 10, the type of cells cultured in the culture tank 10, etc.) input by the operator 400. Thus, the operator 400 can set the control variables and the control targets for the control variables to the setting device 14.

[0055] In step S12, the control variable model setting unit 310 can set a control variable model (for example, a nonlinear differential equation) representing the behavior in the cell culture process for the control variable acquired by the acquisition unit 300. The control variable model setting unit 310 can set the control variable model based on the operating conditions (type of process value, target value, etc.) of the cell culture process acquired by the acquisition unit 300. The control variable model setting unit 310 can set a control variable model representing the relationship between the control variable and the process value (operated variable, etc.) other than the control variable. The control variable model setting unit 310 can acquire the control variable model from the operator 400 through the acquisition unit 300 and set the acquired control variable model. The control variable model setting unit 310 can store one or more control variable models in advance in correspondence with the control variables, respectively, or set a control variable model corresponding to the control variable acquired by the acquisition unit 300 among the stored control variable models.

[0056] As an example, in the control of the live cell density in perfusion culture, the control variable model set by the control variable model setting unit 310 is shown. In this example, the acquisition unit 300 acquires the live cell density as a control variable, and the control variable model setting unit 310 sets the control variable model of the live cell density. The behavior of cell proliferation in the culture tank 10 is described by the time change of the exponential function. In addition, in perfusion culture, in order to maintain a constant culture environment for a long period of time, an operation is implemented to manage the live cell density to be constant, and a method of discharging a part of the cell culture solution in the culture tank 10 to the outside of the system is used as an operating means. Based on these, the control variable model setting unit 310 can define the control variable model of the live cell density in the cell culture process of perfusion culture as shown in formula (1).

[0057] [Mathematical formula 1]

[0058] Here, in formula (1), [X v ] represents the viable cell density, V represents the volume of culture medium, μ represents the specific proliferation rate (a model parameter representing the cell proliferation per unit cell and per unit time), q B represents the outflow rate (the discharge rate of the cell culture medium), q Samp represents the discharge flow rate for sampling the culture fluid for analysis. In formula (1), the specific growth rate μ can be set to a representative value determined based on prior knowledge of the cell line used, such as data of seed culture (subculture during amplification). In formula (2), q in is the total flow rate on the inflow side connected to the culture tank 10, q outIt is the flow rate of the entire outflow side connected to the culture tank 10. The control variable model setting unit 310 can combine equation (1) and equation (2) to set a control variable model of live cell density such as equation (3).

[0059] [Mathematical formula 2]

[0060] In the formula (3), q' is the flow rate of the pump other than the outflow pump that affects the live cell density, and represents the effect of the time change dv / dt of the culture solution volume on the time change of the live cell density.

[0061] In addition, the control variable model setting unit 310 can set other control variable models of live cell density, and can also set control variable models of other control variables. In order to make the control loop 15 of the cell culture system 1 a control algorithm based on the mathematical model of the control object, the control variable model setting unit 310 can set a control variable model in a richly descriptive expression form such as a first principle model of a physical phenomenon. In addition, the control variable model setting unit 310 can set a black box model based on a statistical method as a control variable model. In addition, from the perspective of setting parameter adjustment, the model parameters of the control variable model are preferably model parameters whose representative values ​​are known. In addition, regarding the dead time element, the control variable model setting unit 310 can be grasped based on sensor characteristics, etc., and can be set in the control variable model when the response delay of the cell culture process cannot be ignored. However, in the biological cell culture process accompanied by biological reactions (cell proliferation, matrix consumption), the response delay (rise time, adjustment time, etc.) is more dominant than the dead time. Therefore, the control variable model setting unit 310 may not be set when the dead time is unknown.

[0062] In step S13, the adjustment unit 320 can set a feedforward adjustment amount for the control variable model to offset at least one variable other than the control variable obtained by the acquisition unit 300. The adjustment unit 320 can set the feedforward adjustment amount for the feedforward control performed by the controller 13 during the cell culture process. When the control variable model includes interference terms of other control loops and other known external inputs, the adjustment unit 320 can set the feedforward adjustment amount for interference feedforward control. In addition, interference feedforward control is a control that eliminates the influence of interference before the output of the control system is disturbed by known external effects (such as the influence of interference, etc.). In the case of the controller 13, especially a SISO (1 input 1 output) controller such as a PID (Proportional-Integral-Differential) controller, the adjustment unit 320 can set the feedforward adjustment amount. The adjustment unit 320 can set the feedforward adjustment amount to eliminate items other than the control variable and the manipulated variable, thereby making the adjustment of the setting parameters in the subsequent steps more efficient.

[0063] Here, the operation variable may be, for example, the supply amount, recovery amount, outflow rate (discharge rate of the culture solution), ventilation rate, or perfusion rate of the culture tank 10 controlled by the controller 13 .

[0064] As an example, the adjustment unit 320 can set a feedforward adjustment amount for the control variable model of the live cell density set as shown in formula (3). The control variable model of formula (3) includes an interference term related to the flow rate q' of the pump other than the outflow pump. In the cell culture process of perfusion culture, there are effects on the cell culture process caused by the discharge flow rate (harvest flow rate) of the cell fluid and the input flow rate of the perfusion culture medium for the purpose of product recovery and liquid volume management. Therefore, in the live cell density control system, these flows are determined as interference. On the other hand, these flows are the operated variables (operation items) controlled in the cell culture process, and therefore are also known process values ​​that the operator 400 can grasp and use. Therefore, the time changes in the live cell density that accompany the changes in these flows can be offset by interference feedforward control. The adjustment unit 320 can generate formula (4) by setting the feedforward adjustment amount corresponding to these flows for the control variable model.

[0065] [Mathematical formula 3] q B =q B,FF +q B,FB (5) q B,FF =q′ (6)

[0066] In formula (6), q B,FF represents the feedforward adjustment item (feedforward adjustment amount) of the outflow flow, qB,FB Indicates the feedback adjustment item (feedback adjustment amount) of the outflow flow.

[0067] In step S14, the approximate transformation unit 330 transforms the control variable model into an approximate control variable model that linearly approximates near the operating point of the cell culture process. The approximate transformation unit 330 can linearly approximate the nonlinear terms related to the control variables and the manipulated variables in the control variable model within a range predetermined based on the target value of at least one process value in the cell culture process (for example, the range of the target value ±α), thereby transforming the control variable model into the approximate control variable model. When the control variable model has nonlinearity, the approximate transformation unit 330 can facilitate the processing of the control variable model in the subsequent steps by focusing on the vicinity of the operating point and locally linearly transforming the dynamic characteristics of the nonlinear complex feedback loop. That is, step S14 is implemented in order to apply the model optimal control system design method (local model matching method) to the control object represented by the nonlinear control variable model.

[0068] As an example, the approximate conversion unit 330 approximates the control variable model in which the feedforward adjustment amount is set as shown in equation (4) and converts it into an approximate control variable model. The control variable model of equation (4) includes the control variable [X v ] and feedback adjustment amount (manipulated variable) q B,FB The multiplied terms (nonlinear terms) describe the dynamic characteristics of the control system in a complex relationship. The approximate conversion unit 330 linearly approximates the nonlinear terms in the control variable model of equation (4) near the operating point and converts it into an approximate control variable model of equation (7).

[0069] [Formula 4] In formula (7), X V ￣ (Here, X V ￣ The upper line X in expression (7) is V ) is the approximate live cell density near the operating point, and may be, for example, the same value as the target value of the process value of the live cell density. V ￣ It can also be regarded as a gain multiplied by the outflow flow rate, so in the actual cell culture process it can be regarded as the live cell density of the outflow channel (the live cell density in the discharged cell culture fluid). Therefore, the approximate control variable model of formula (7) becomes an approximate representation of the stable state of live cell density control. In addition, [X v ] is the density of viable cells in the culture tank 10 to be managed, and X V ￣Difference.

[0070] In step S15, the deriving unit 340 derives a closed-loop transfer function near the operating point based on the approximate control variable model and the control function used for the control of the controller 13. The deriving unit 340 derives the closed-loop transfer function when the feedback control rule is applied. The closed-loop transfer function can represent the overall mathematical model of the control loop 15 that describes the input-output characteristics between at least one of the external inputs such as the target value of the process value and the disturbance and the controlled variable (output).

[0071] The derivation unit 340 may store the control function in advance, obtain the information of the controller 13 through the acquisition unit 300, and use the control function corresponding to the control side of the controller 13. In addition, the derivation unit 340 may obtain the control function from the operator 400 through the acquisition unit 300. For example, when the controller 13 is a PID controller widely used in the cell culture process industry including the cell culture process, the manipulated variable is calculated by the control function (PID control rule) corresponding to the PID control method. In this case, as an example, the derivation unit 340 may use the control function of formula (8).

[0072] [Formula 5] In equation (8), u(t) represents the manipulated variable (MV), and e(t) represents the deviation (the difference between the process value (PV) and the target value (SV)). P 、T I and T D is the setting parameter, K P represents the proportional gain, T I Indicates the integration time, T D The adjustment of these PID parameters has a great influence on the control performance and is therefore extremely important in the setting operation of the controller 13 of the cell culture system 1 .

[0073] The derivation unit 340 can derive equation (9) representing the input-output relationship between PV (output) and the external signal (input) by combining the approximate controlled variable model and the control function of equation (8) corresponding to the PID control method.

[0074] [Mathematical formula 6] y=W r (θ C ,θ PM )·r+W d (θ C ,θ PM )·d (9) In formula (9), y represents the process value (PV), r represents the target value (SV), and d represents the disturbance. CIt represents a vector summarizing the setting parameters. In the case of designing a control system of a PID controller, it is represented by θ C =[K P , T I , T D ]. Similarly, θ PM is a vector summarizing the parameters of the approximate control variable model. In addition, W r is a function that represents the input-output relationship between the target value r and the control variable y as the process value (target value response transfer function), W d is a function that represents the input-output relationship between disturbance d and control variable y (disturbance response transfer function), which is represented by θ C With θ PM function.

[0075] As an example, the deriving unit 340 derives a closed-loop transfer function when a proportional differential advance type PID control rule (I-PD control rule) is applied to the outflow operation of the living cell density control system. In addition, the I-PD control rule is more stable than the standard PID control rule in calculating the manipulated variable for a sudden target value change, which is expressed as formula (10). [Formula 7] In equation (10), u(t) represents the manipulated variable. In the live cell density control system of outflow operation, the feedback adjustment amount q of the outflow flow rate is B,FB Therefore, the deriving unit 340 can derive the closed-loop transfer function by substituting equation (10) into the approximate controlled variable model of the living cell density control system shown in equation (7).

[0076] The derivation unit 340 transforms the approximate control variable model of equation (7) and the calculation equation (controller 13 model) of equation (10) as shown in equation (11) and equation (12) respectively, and derives the closed-loop transfer function. In this example, the expression of differential integral is replaced by an algebraic description based on Laplace transform.

[0077] [Mathematical formula 8] In equations (11) and (12), s represents the Laplace operator (equivalent to differential calculation in the time domain). Xv 、r Xv They represent the process value and target value of live cell density control respectively.

[0078] The deriving unit 340 can derive the closed-loop transfer function (target value response transfer function) W of the live cell density control near the operating point as shown in equation (14) by substituting equation (12) into equation (11): Xv.

[0079] [Mathematical formula 9] θ C =[K P ,T 1 ,T D ] (16)

[0080] In step S16, the control objective function setting unit 350 sets the control objective function according to the control target obtained by the acquisition unit 300. The control objective function setting unit 350 can set the control objective function of the binomial coefficient standard form, the Butterworth standard form, or the ITAE (Integral of Time multiplied by Absolute Error) minimum standard form. If the controller 13 is adjusted with a focus on one control characteristic, the other control characteristics become a design guideline for a compromise selection of specifications that are subordinately determined. Therefore, the control objective function setting unit 350 can set the control objective function so that the one control target obtained by the acquisition unit 300 becomes the control characteristic that is desired to be obtained preferentially during the cell culture process. For example, in the case of live cell density control in perfusion culture, the setting parameter adjustment of the target value tracking is studied from the perspective of closed-loop stabilization of cell proliferation, followability of sequential condition studies (target value changes), and improvement of adaptability. In this case, the target value response transfer function W can be used. r , calculate the setting parameters.

[0081] The control objective function setting unit 350 may obtain and store one or more control objective functions in correspondence with the control objective respectively in advance, and may set the control objective function from the stored control objective functions according to the control objective obtained by the obtaining unit 300. In addition, the control objective function setting unit 350 may receive the structure of the control objective function and the specification of the parameters input by the operator 400 through the obtaining unit 300, and set the control objective function representing the ideal performance for the control objective according to the received structure and parameters of the control objective function. The control objective function setting unit 350 may set a closed-loop transfer function as the control objective function.

[0082] As an example, the control objective function setting unit 350 sets the control objective function R related to the target value response characteristic (e.g., the achievement target of the adjustment time, etc.) recorded in the non-patent document 2 as shown in the binomial coefficient standard form formula (18). r M . [Formula 10] In equations (18) and (19), T 99 The target time for the process value to respond to a step-like target value change is 99% (equivalent to the adjustment time after removing the process waste time). M represents the estimated process dead time, and n represents the order of the control objective function. In addition, θ RM Represents a vector summarizing the parameters of the control objective function, in this case θ RM =[T 99 , L M , n].

[0083] The control objective function setting unit 350 may set the control objective function with a structure similar to the closed-loop transfer function derived in step S15. The control objective function setting unit 350 may set the control objective function shown in equation (20) in a manner similar to the structure of the closed-loop transfer function of equation (14) derived in step S15. [Mathematical formula 11] Formula (20) is the parameter L of the control objective function shown in Formula (18): M and n is L M =0, n=2. In addition, the control objective function setting unit 350 can obtain the operator 400's response to T in equation (19) through the acquisition unit 300. 99 The parameter τ of the control objective function of equation (20) is determined by specifying (adjustment time).

[0084] In step S17, the setting parameter generation unit 360 generates setting parameters so that the closed-loop transfer function derived by the deriving unit 340 is compatible with the control target function. The setting parameter generation unit 360 can generate a conditional expression for making the closed-loop transfer function compatible with the control target function by performing a compatible calculation between the closed-loop transfer function and the control target function.

[0085] For example, the setting parameter generation unit 360 may transform the closed-loop transfer function derived by the deriving unit 340 so as to be expressed in a numerical expression similar to the control target function, and determine the coefficient part of the closed-loop transfer function (θ shown in equation (14) C With θ PM As an example, the parameter generation unit 360 focuses on the closed-loop transfer function of equation (9) and the control objective function of equation (18), as shown below. First, in order to easily compare equation (9) and equation (18), the parameter generation unit 360 can be transformed into denominator series expressions such as equation (21) and equation (22), respectively.

[0086] [Mathematical formula 12] In formula (21) and formula (22), c n With γ n are the coefficients of the closed-loop transfer function and the control objective function derived from the denominator series, c n Denoted as θ C With θ PM The function of γ n Denoted as θ RM function.

[0087] The setting parameter generation unit 360 can derive c according to equations (21) and (22). i =γ i Such simultaneous equations to be solved satisfy the necessary conditions for the control target function as a closed-loop transfer function for representing the dynamic characteristics of the control loop 15. As an example, since the structure of the equation is already similar, the setting parameter generation unit 360 can derive equations (23) and (24) as conditional equations for making the closed-loop transfer function of equation (14) fit the control target function of equation (20).

[0088] [Mathematical formula 13] Here, K in equation (23) and equation (24) P ' is the variable represented in formula (15).

[0089] The setting parameter generation unit 360 can generate setting parameters from the obtained conditional expressions. The setting parameter generation unit 360 can combine the obtained conditional expressions to uniquely express the setting parameter θ C and the parameters θ of the approximate control variable model PM And the parameter θ that controls the objective function RM The relationship between the control parameters and the control objective function is used to derive the adjustment rules for each setting parameter. In this way, the adjustment rules for the setting parameters that satisfy the control objective function can be derived.

[0090] Based on the closed-loop transfer function and the control target function representing the dynamic characteristics of the control loop 15, conditional expressions exceeding the number of setting parameters of the controller 13 are sometimes obtained, and the performance related to the setting parameters varies depending on the combination of the conditional expressions. In this case, the setting parameter generation unit 360 can perform local model matching to compare the coefficients of the number of setting parameters starting from the low-order terms (terms with a small order of s) of the control target function. For example, when the setting parameter generation unit 360 uses equations (21) and (22) to adjust the parameters of the control function (PID control rule) shown in equation (8), the setting parameters (PID parameters) are 3 (KP , T I , T D ), so according to c 1 =γ 1 、c 2 =γ 2 , and c 3 =γ 3 The three conditional expressions of derive the adjustment rules of setting parameters. Setting parameter θ C As shown in formula (25), the parameter θ of the approximate control variable model can be expressed as PM and the parameter θ of the control objective function RM function.

[0091] [Formula 14] θ C =f Control (θ PM ,θ PM ) (25)

[0092] In formula (25), f Control It is a function that represents the combination of the parameters of the control objective function (the realization target of the control characteristic) and the parameters of the approximate control variable model (the characteristic value of the cell culture process) to express the setting parameters that meet the control target. Formula (25) indicates that the setting parameters can be adjusted according to the characteristics of the cell culture process and the implementation requirements of the control, and trial and error is not required in the establishment of the setting parameters. The setting parameter generation unit 360 can generate setting parameters according to the derived adjustment rules.

[0093] As an example, the following is an example of deriving an adjustment rule using the conditional expressions of equations (23) and (24). In this example, there are three setting parameters for the adjustment object (proportional gain, integral time, differential time), and only two conditional expressions are given. In this case, since the setting parameter generation unit 360 uses two setting parameters to derive a unique solution that satisfies the control objective function (there is a degree of freedom in the adjustment of the setting parameters), the differential element of the controller 13 can be omitted to derive the adjustment rule. That is, the target value tracking characteristic of the living cell density control is limited to the case where equation (20) is given as the realization target of the closed-loop transfer function, but when the differential time T is set, the target value tracking characteristic of the living cell density control is limited to the case where equation (20) is given as the realization target of the closed-loop transfer function. D The desired performance can also be obtained in the I-PD control law (proportional action first type PI control law, IP control law) with a value of 0. Therefore, T in equation (24) can be D =0 is treated as a new conditional expression.

[0094] [Mathematical formula 15]

[0095] The setting parameter generation unit 360 can uniquely derive the proportional gain K of the control objective function satisfying equation (20) as shown in equation (27) by sorting equation (23), equation (26) and equation (16). P and integration time T I .

[0096] [Formula 16] T 1 =(2+μτ)τ (29)

[0097] According to the example of the above-mentioned mathematical expansion, it can be confirmed that the adjustment rule of each setting parameter obtained in the present invention is expressed as in equation (25) using the parameter θ of the control target function representing the achievement target of the control characteristic. RM =[T 99 , L M , n] and the parameters θ of the approximate control variable model PM =[μ, X V ￣ The setting parameter generation unit 360 can generate setting parameters corresponding to the adjustment rule derived in this way. The setting parameter generation unit 360 can generate setting parameters corresponding to the action conditions such as the target value of the process value in the cell culture process by using the adjustment rule.

[0098] The output unit 370 can output the setting parameters generated by the setting parameter generating unit 360 to the controller 13, and set the setting parameters to the controller 13. Thus, the controller 13 performs feedback control on the process value of the cell culture process according to the set setting parameters.

[0099] In step S18, the updating unit 380 may determine whether to update the setting parameters according to the operating conditions of the cell culture process. The updating unit 380 may obtain the operating conditions of the cell culture process controlled by the controller 13 using the setting parameters generated by the setting parameter generating unit 360 in step S17 from the operator 400 or the controller 13 periodically or in real time. The updating unit 380 may obtain at least one of the process value of the cell culture process and the target value of the process value as the operating condition of the cell culture process. The updating unit 380 may determine to update the setting parameters when the operating conditions of the cell culture process exceed a predetermined threshold value or when the change in the operating conditions from the time when the setting parameters used in the control of the cell culture process are generated (for example, the difference between the operating conditions when the setting parameters are generated and the operating conditions obtained this time) exceeds a predetermined threshold value (for example, α). The updating unit 380 may also determine to update the setting parameters according to the update instruction of the operator 400.

[0100] When the updating unit 380 decides to update the setting parameters ( Figure 4 In step S18 of the embodiment of the present invention, if the user selects the control parameter generation unit 360 and the control parameter generation unit 360 determines whether the control parameter generation unit 360 is a control variable model (or an approximate control variable model), the control parameter generation unit 360 may request the setting parameter generation unit 360 to update the setting parameter. The setting parameter generation unit 360 may generate new setting parameters based on the request using the action conditions obtained this time, and set the controller 13. As an example, the setting parameter generation unit 360 may use the specific proliferation rate μ in the action conditions obtained this time to update the parameters of the control variable model (or the approximate control variable model), thereby generating new setting parameters. The specific proliferation rate μ is often treated as a complex function with time-varying properties, so it is preferable to be able to appropriately update it using real-time process values.

[0101] When the updating unit 380 decides not to update the setting parameters ( Figure 4 The updating unit 380 can obtain new operating conditions during the cell culture process and repeat the step S18 to determine whether to update the setting parameters. As shown in formula (25), the setting parameters and the parameters of the approximate control variable model are derived in an associated form, so the updating unit 380 can appropriately update and adjust the setting parameters according to the operating conditions at all times. Even when the dynamic characteristics of the control loop 15 change according to the culture conditions, it is possible to use the controller 13 that meets the desired control target throughout the entire culture interval.

[0102] According to the present embodiment, the setting device 14 can set the setting parameters with high accuracy according to the mechanism of the cell culture process, thereby eliminating the trouble of adjusting the setting of the controller of the culture system by trial and error during the test operation, and reducing the huge amount of man-hours and costs required to find the setting parameters that meet the management range of the cell culture process. In addition, even if the operating conditions of the cell culture process (specific proliferation rate, perfusion rate (ratio of the amount of culture medium input to the volume of culture fluid per day)) change over time, the setting parameters suitable for the operating conditions at the time can be quickly updated through the derived adjustment rules.

[0103] In addition, when, for example, the acquisition unit 300 newly acquires at least one of the control variables and control targets of different control systems related to the culture control process, the setting device 14 can execute steps S11 to S18 to generate new setting parameters.

[0104] In addition, when the setting device 14 obtains a plurality of control variables, it can generate setting parameters for a plurality of control loops 15 in a single setting parameter generation process. When multivariate disturbance is observed in the control loop 15 of interest, it is easier to summarize the design requirements of each loop by focusing on the specifications of the control system rather than performing engineering design individually.

[0105] In addition, although the perfusion culture is described with the cell culture system 1, it is also possible to carry out culture using other methods such as fedbatch culture and continuous culture. In addition, the setting device 14 may not include the adjustment unit 320. In this case, the setting device 14 may not set the feedforward adjustment amount, but simply generate the setting parameters of the feedback control system. In addition, when the control variable model is not nonlinear, the setting device 14 may not include the approximate transformation unit 330.

[0106] In addition, various embodiments of the present invention may be described with reference to flow charts and block diagrams, where a module may represent (1) a stage of a process for performing an operation or (2) a portion of a device having the function of performing an operation. Specific stages and portions may be implemented by dedicated circuits, programmable circuits supplied together with computer-readable instructions stored on a computer-readable medium, and / or processors supplied together with computer-readable instructions stored on a computer-readable medium. Dedicated circuits may include digital and / or analog hardware circuits, and may also include integrated circuits (ICs) and / or discrete circuits. Programmable circuits may include reconfigurable hardware circuits, which include logical AND, logical OR, logical XOR, logical NAND, logical NOR and other logical operations, flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs) and other memory elements, etc.

[0107] Computer-readable media may include any tangible device capable of storing instructions executed by an appropriate device, and as a result, a computer-readable medium having instructions stored therein includes a product containing instructions that can be executed in order to make a means for performing the operations specified by the flowchart or block diagram. Examples of computer-readable media may include: electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include: floppy (registered trademark) disks, magnetic disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), electrically erasable programmable read-only memories (EEPROM), static random access memories (SRAM), compact disk read-only memories (CD-ROM), digital versatile disks (DVD), Blu-ray (RTM) disks, memory sticks, integrated circuit cards, etc.

[0108] Computer readable instructions include any of source code and object code described by any combination of one or more programming languages ​​including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or object-oriented programming languages ​​such as Smalltalk (registered trademark), JAVA (registered trademark), C++, and existing procedural programming languages ​​such as the "C" programming language or similar programming languages.

[0109] The computer readable instructions may be provided to a processor or programmable circuit of a general purpose computer, a special purpose computer or other programmable processing device via a local or local area network (LAN), a wide area network (WAN) such as the Internet, and the computer readable instructions are executed to produce a means for performing the operations specified by the flowchart or block diagram. Examples of processors include: computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0110] Figure 5 The computer 2200 is an example of a computer that can implement various modes of the present invention in whole or in part. The computer 2200 can perform the operation associated with the device of the embodiment of the present invention or the function of one or more parts of the device through the program installed in the computer 2200, or perform the operation or the one or more parts, and / or the computer 2200 can perform the process of the embodiment of the present invention or the stage of the process. In order to make the computer 2200 perform specific operations associated with some or all of the modules in the flowcharts and block diagrams described in this specification, the CPU 2212 can execute such a program.

[0111] The computer 2200 of this embodiment includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are connected to each other through a main controller 2210. The computer 2200 also includes an input / output unit such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the main controller 2210 via an input / output controller 2220. The computer also includes a conventional input / output unit such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0112] The CPU 2212 controls each unit by operating according to the program stored in the ROM 2230 and the RAM 2214. The graphic controller 2216 acquires image data generated by the CPU 2212 in a frame buffer or the like provided in the RAM 2214 or in itself, and displays the image data on the display device 2218.

[0113] The communication interface 2222 can communicate with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0114] The ROM 2230 stores therein a boot program or the like executed by the computer 2200 upon activation and / or a program that depends on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0115] The program is provided by a computer-readable medium such as DVD-ROM 2201 or an IC card. The program is read from the computer-readable medium and installed in the hard disk drive 2224, RAM 2214 or ROM 2230, which is also an example of a computer-readable medium, and is executed by the CPU 2212. The information processing described in these programs is read to the computer 2200, thereby bringing about cooperation between the program and the various types of hardware resources described above. The device or method can be constituted by realizing the operation or processing of information with the use of the computer 2200.

[0116] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 can execute a communication program loaded in the RAM 2214, and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads the transmission data stored in the transmission buffer processing area provided in the storage medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or the IC card, and transmits the read transmission data to the network, or writes the reception data received from the network to the reception buffer processing area provided on the storage medium, etc.

[0117] In addition, the CPU 2212 can read all or a necessary part of a file or database stored in an external storage medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), or an IC card into the RAM 2214, and perform various types of processing on the data on the RAM 2214. Then, the CPU 2212 writes the processed data back to the external storage medium.

[0118] Various types of information such as various types of programs, data, tables, and databases can be stored in storage media and receive information processing. CPU2212 performs various types of processing recorded in various places of this disclosure on the data read from RAM2214 and writes the results back to RAM2214. The various types of processing include various types of operations specified by the instruction sequence of the program, information processing, conditional judgment, conditional branching, unconditional branching, information retrieval / replacement, etc. In addition, CPU2212 can retrieve information from files, databases, etc. in the storage medium. For example, in the case where multiple entries having attribute values ​​of the first attribute associated with the attribute values ​​of the second attribute are stored in the storage medium, CPU2212 can retrieve an entry consistent with the condition specifying the attribute value of the first attribute from the multiple entries, and read the attribute value of the second attribute stored in the entry, thereby obtaining the attribute value of the second attribute associated with the first attribute that meets the predetermined condition.

[0119] The program or software module described above may be stored in a computer-readable medium on or near the computer 2200. In addition, a storage medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the program to the computer 2200 via the network.

[0120] The present invention has been described above using the embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. According to the description of the claims, the method of making such changes or improvements can also be included in the technical scope of the present invention.

[0121] The execution order of each process such as actions, processes, steps and stages in the apparatus, system, program and method shown in the claims, specifications and drawings is not specifically indicated as "earlier", "before", etc. In addition, it should be noted that as long as the output of the previous process is not used in the subsequent process, it can be implemented in any order. Even if the action flow in the claims, specifications and drawings is described using "first," "next," etc. for the sake of convenience, it does not mean that it must be implemented in this order.

Claims

1. A device, characterized in that: have: An acquisition unit that acquires a control variable and a control target of a control object in a process controlled by the controller; a deriving unit that derives a closed-loop transfer function near an operating point of the process based on a control variable model representing the behavior of the control variable in the process and a control function used in control by the controller; as well as A setting parameter generating unit generates setting parameters used in control of the controller so that the closed-loop transfer function is adapted to a control target function set according to the control target.

2. The device according to claim 1, characterized in that The device further includes an approximate conversion unit that converts the control variable model into an approximate control variable model that linearly approximates the vicinity of an operating point of the process. The derivation unit derives a closed-loop transfer function in the vicinity of the operating point based on the approximate controlled variable model and the control function.

3. The device according to claim 1, characterized in that The device further includes an adjustment unit that sets a feedforward adjustment amount that cancels out at least one variable in the process in the controlled variable model.

4. The device according to claim 1, characterized in that The control function is a function corresponding to the PID control method.

5. The device according to claim 1, characterized in that The apparatus further includes an updating unit configured to update the setting parameter based on an operation condition of the process.

6. The device according to claim 1, characterized in that The setting parameter generation unit generates the setting parameter using a local model matching method so that the closed-loop transfer function is adapted to the control target function.

7. The device according to any one of claims 1 to 6, characterized in that The device further includes a control target function setting unit configured to set the control target function based on the control target.

8. The device according to claim 7, characterized in that The control objective function setting unit sets the control objective function of a binomial coefficient standard form, a Butterworth standard form, or an ITAE minimum standard form.

9. A method, characterized in that The controller controls at least one of the process values ​​of the cell culture process using the set parameters generated by the apparatus of claim 1.

10. A controller, characterized in that: The controller uses the setting parameters generated by the device of claim 1 to feedback control at least one of the process values ​​of the cell culture process.

11. A cell culture system, characterized in that: have: Culture tanks, where cells or microorganisms are cultured in the cell culture process; a sensor for measuring a process value to be managed during said cell culture process; an actuator for applying an operation to the culture tank; and The controller according to claim 10 controls the actuator based on the measurement result of the sensor.

12. A method, characterized in that include: The step of obtaining a control variable and a control target of a control object in a process controlled by a controller; A step of deriving a closed-loop transfer function near an operating point of the process based on a control variable model representing the behavior of the control variable in the process and a control function used in control by the controller; as well as A step of generating setting parameters used in control by the controller so that the closed-loop transfer function is adapted to a control target function derived from the control target.

13. A storage medium, characterized in that: The storage medium stores a program, The computer functions as an acquisition unit, a derivation unit, and a setting parameter generation unit by executing the program. The acquisition unit acquires a control variable and a control target of a control object in a process controlled by the controller. The derivation unit derives a closed-loop transfer function near an operating point of the process based on a control variable model representing the behavior of the control variable in the process and a control function used in control by the controller. The setting parameter generating unit generates setting parameters used in control of the controller so that the closed-loop transfer function is adapted to a control target function derived from the control target.

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