Real-time control method and system for electron cyclotron resonance heating power

Abstract

The invention discloses a real-time control method of electron cyclotron resonance heating power based on model predictive control, and relates to the technical field of electron cyclotron resonance heating control. The method comprises the following steps: a segmented modeling step: dividing the working range of the electron cyclotron resonance heating system into a plurality of power intervals according to output power, and establishing a linear system dynamic model for describing the dynamic characteristics of the system in each interval; and a real-time control cycle step: acquiring real-time output power and a state estimation value in each control period, and determining a currently used target model according to the real-time output power in combination with a hysteresis switching strategy, and based on the target model and a preset power reference trajectory, constructing a quadratic programming problem containing a target function for minimizing a power tracking error and a constraint condition, and solving to obtain an optimal control input and acting on the system. The invention aims to solve the problem that the existing control method is difficult to consider both the regulation speed and the multivariable constraint, realize the rapid and accurate control of the electron cyclotron resonance heating power, and improve the safety and stability of the operation of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electron cyclotron resonance heating control, and particularly relates to a real-time control method and system for electron cyclotron resonance heating power. BACKGROUND

[0002] Electron cyclotron resonance heating is an important auxiliary heating method in nuclear fusion devices, and its principle is to emit microwave energy to the plasma to improve the plasma temperature and density. The power control of the electron cyclotron resonance heating system has the characteristics of strong nonlinearity, multi-variable coupling and rapid dynamic change, and the traditional proportional-integral-derivative control strategy is difficult to meet the control requirements of high precision and fast response.

[0003] In the actual operation process of the electron cyclotron resonance heating system, there are many physical constraint conditions, for example, the anode voltage, cathode voltage, filament power and magnet current of the gyrotron must be maintained within the safe operating range, and the cathode temperature is also strictly limited. The existing proportional-integral-derivative control method cannot effectively handle the above multi-variable coupling problem and strict physical constraints at the same time, and in actual application, there are often defects such as slow regulation speed, large overshoot and poor constraint processing ability, which can easily lead to equipment overvoltage, overcurrent or frequent triggering of shutdown protection due to touching the safety boundary.

[0004] Therefore, how to realize the rapid and accurate control of the electron cyclotron resonance heating power and effectively handle the multi-variable and complex physical constraints under the premise of ensuring system safety has become a technical problem to be solved. SUMMARY

[0005] The main purpose of the present application is to provide a real-time control method and system for electron cyclotron resonance heating power, which aims to realize the rapid and accurate control of the electron cyclotron resonance heating power and effectively handle the multi-variable and complex physical constraints under the premise of ensuring system safety.

[0006] In order to achieve the above purpose, the present application provides a real-time control method for electron cyclotron resonance heating power, comprising the following steps: Segmented modeling step: dividing the working range of the electron cyclotron resonance heating system into multiple power intervals according to the output power, and for each power interval, a linear system dynamic model describing the dynamic characteristics of the system in the interval is established; Real-time control cycle step: in each control period, the following operations are performed: Obtain the real-time output power and state estimation value of the electron cyclotron resonance heating system; Determine the target model used in the current control period according to the real-time output power; the target model is selected from the multiple linear system dynamic models established in the segmented modeling step; constructing a quadratic programming problem of model predictive control based on the target model and a preset power reference trajectory; the quadratic programming problem contains an objective function for minimizing power tracking error and constraint conditions for control input and system state; the constraint conditions at least include amplitude constraint and rate constraint for control input; solving the quadratic programming problem to obtain optimal control input and applying the optimal control input to the electron cyclotron resonance heating system.

[0007] Preferably, in the segment modeling step, the division of the plurality of power intervals is determined based on the nonlinear turning characteristics of the electron cyclotron resonance heating system; the plurality of power intervals at least includes a low power interval, a medium power interval and a high power interval, and adjacent power intervals are divided by a preset power threshold; the linear system dynamic model is established by system excitation experiment and input-output data collection at the geometric center point of each power interval.

[0008] Preferably, the linear system dynamic model is a discrete-time state space model, which has the following form:

[0009] wherein, is a state vector, is an input vector, is an output; the state vector contains cathode temperature, magnet power supply current and gyrotron electron beam current; the input vector contains filament power, anode voltage and cathode voltage; the output is gyrotron output power; , , , are system matrix, input matrix, output matrix and feedforward matrix corresponding to the power interval, respectively.

[0010] Preferably, the target model used in the current control period is determined according to the real-time output power, which includes: at the switching boundary of adjacent power intervals, a hysteresis switching strategy is adopted to determine the target model; the specific process of the hysteresis switching strategy is: setting a switching threshold and a preset hysteresis bandwidth; when the real-time output power of the electron cyclotron resonance heating system crosses the switching threshold and exceeds the hysteresis bandwidth, the target model is updated; otherwise, the currently used target model is kept.

[0011] Preferably, the mathematical expression of the objective function is:

[0012] wherein, is a prediction horizon, is a control horizon, is a predicted output power, is a reference power trajectory, is a control input rate of change, is a soft constraint slack variable, and is a weight matrix, is a penalty coefficient.

[0013] Preferably, the constraint conditions include: input amplitude constraints: limiting the filament power, anode voltage and cathode voltage within their respective preset allowable ranges; input rate of change constraints: limiting the filament power rate of change, anode voltage rate of change and cathode voltage rate of change within their respective preset maximum values; state constraints: limiting the cathode temperature to be less than or equal to a preset maximum temperature, limiting the magnet power supply current to be less than or equal to a preset maximum current, and limiting the gyrotron electron beam current to be less than or equal to a preset maximum electron beam current.

[0014] Preferably, the constraint conditions further include output power constraints; the output power constraints introduce slack variables to achieve soft constraints, and the specific forms include upper limit constraints and lower limit constraints: the upper limit constraint is:

[0015] the lower limit constraint is:

[0016] wherein, is an output power prediction base term, is an input rate of change influence coefficient on output power, is the slack variable, and are preset maximum and minimum output power values, respectively; denotes the control input rate of change at the time.

[0017] Preferably, in the real-time control loop step, the state estimation value is obtained by a Kalman filtering algorithm; the Kalman filtering algorithm includes a prediction step of predicting the state at the next time using the target model, and an update step of correcting the prediction result in combination with the actual measurement value.

[0018] Preferably, the method further includes an online adaptive updating step: real-time recording of input and output data of the electron cyclotron resonance heating system to a ring buffer. When a preset update trigger condition is met, parameters of the linear system dynamic model are updated using the data in the ring buffer and using a recursive least squares method; The update trigger condition includes a timing trigger, a performance degradation trigger, or an operating mode change trigger.

[0019] Preferably, the method further comprises a safe fallback step: After solving the quadratic programming problem, it is determined whether the solving is successful; If the solving fails or times out, or a system trigger interlock signal is detected, a fallback control strategy is executed; The fallback control strategy includes maintaining the last valid control amount, reducing output power according to a maximum allowed slope, or switching to a backup control law.

[0020] The application also discloses a real-time control system for electron cyclotron resonance heating power, comprising: A segmented modeling module is configured to divide the working range of an electron cyclotron resonance heating system into multiple power intervals according to output power, and for each power interval, a linear system dynamic model describing the dynamic characteristics of the system in the interval is established; A state estimation module is configured to obtain real-time output power and state estimation values of the electron cyclotron resonance heating system; A model selection module is configured to determine a target model used in a current control period according to the real-time output power; the target model is selected from the multiple linear system dynamic models established by the segmented modeling module; An optimization solving module is configured to construct a quadratic programming problem of model predictive control based on the target model and a preset power reference trajectory, and to solve the quadratic programming problem to obtain optimal control input; the quadratic programming problem includes an objective function for minimizing power tracking error, and constraint conditions for control input and system state; the constraint conditions at least include amplitude constraint and rate constraint for the control input; An execution interface module is configured to apply the optimal control input to the electron cyclotron resonance heating system.

[0021] The above technical solution has the following advantages: The application effectively reduces the influence of system nonlinearity on control accuracy by dividing the working range of the electron cyclotron resonance heating system into multiple power intervals according to output power and respectively establishing linear system dynamic models, and ensures the prediction accuracy of the model in the whole working domain. Meanwhile, an online adaptive model parameter updating mechanism is introduced to compensate for equipment drift and aging, reduce the need for manual setting, and reduce the steady-state error of the system after long-term operation. In the real-time control process, the target model is dynamically selected according to the real-time output power, and a quadratic programming problem of model predictive control is constructed combined with the preset power reference trajectory. This method not only can directly incorporate the input amplitude constraint, change rate constraint and state constraint of the controlled object into the optimization solving process, thereby maximizing the control performance under the premise of ensuring the safe operation of the system, but also can obtain the optimal control input by solving the quadratic programming problem, realize the fast response and accurate tracking of the output power, and significantly outperform the traditional control strategy in handling multivariable coupling and hard constraints. BRIEF DESCRIPTION OF DRAWINGS

[0022] The application will be described in detail below with specific examples and drawings, in which: Figure 1 The system architecture schematic diagram of the electron cyclotron resonance heating power real-time control method provided by the embodiment of the application is shown.

[0023] Figure 2 The working interval division schematic diagram of the segmented linearization model provided by the embodiment of the application is shown.

[0024] Figure 3 The function module block diagram of the electron cyclotron resonance heating power real-time control system provided by the embodiment of the application is shown.

[0025] Figure 4 The real-time control cycle and model switching flowchart provided by the embodiment of the application is shown.

[0026] Figure 5 The safety interlocking and solving failure fallback control processing flowchart provided by the embodiment of the application is shown.

[0027] 101, power measurement module; 102, state estimation module; 103, model selection module; 104, MPC optimization solving module; 105, constraint and safety monitoring module; 106, fallback control module; 107, execution interface module; 108, electron cyclotron heating power execution object; 109, interlocking signal input. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1 like Figures 1 to 5 As shown, this embodiment provides a real-time control method for electron cyclotron resonant heating power. This method primarily addresses the problem that traditional PID control strategies struggle to balance regulation speed and safety margin in existing electron cyclotron resonant heating systems under nonlinear, multivariable coupling, and stringent safety constraints. By introducing piecewise linearization modeling and model predictive control mechanisms, this embodiment can achieve precise tracking of output power within a millisecond timescale while strictly meeting the operational constraints of the physical equipment.

[0031] The real-time control method mainly relies on a real-time control system, which includes a power measurement module 101, a state estimation module 102, a model selection module 103, an MPC optimization solution module 104, a constraint and safety monitoring module 105, a backoff control module 106, an execution interface module 107, and an electronic gyratory heating power execution object 108 as the controlled object.

[0032] The specific execution flow of this method is as follows: First, the piecewise modeling step is performed. Since the input-output characteristics of the electron cyclotron resonance heating system exhibit significant nonlinearity under wide-range power regulation, a single linear model is difficult to maintain high-precision prediction ability in the full operating range. Therefore, the operating range of the electron cyclotron resonance heating system is first divided into multiple power intervals according to the output power in this embodiment. Specifically, based on the nonlinear turning characteristics of the system at different power levels, the operating range is divided into at least three intervals, namely a low-power zone, a medium-power zone, and a high-power zone. In this embodiment, the low-power zone is set to 100 kW to 300 kW, the medium-power zone is set to 300 kW to 600 kW, and the high-power zone is set to 600 kW to 1000 kW.

[0033] For each of the above power intervals, the geometric center point thereof is selected as a working point to perform system excitation experiments. For example, 200 kW, 450 kW, and 800 kW are selected as the center working points of the low, medium, and high three intervals, respectively. During the experiment, a composite excitation signal is applied to the system, which includes a step signal, a pseudo-random binary sequence (PRBS) signal, and a sweep signal with a frequency range of 0.1 Hz to 10 Hz, and the input data and output data of the system are synchronously collected. Based on the collected data, a linear system dynamic model describing the dynamic characteristics of the system in each power interval is established using system identification methods or physical equation Taylor expansion methods. In this process, the goodness-of-fit index is calculated by comparing the model prediction output with the actual measured data to ensure that the normalized root mean square error (NRMSE) is not less than 85%.

[0034] In this embodiment, the linear system dynamic model established is a discrete-time state-space model, with a sampling period of 10 ms, and the mathematical expression form is as follows:

[0035] wherein, represents different power interval numbers. is a state vector, is an input vector, and an output. Specifically, the state vector contains three key physical quantities, namely the cathode temperature , the magnet power supply current , and the gyrotron electron beam current . The input vector contains three control variables, namely the filament power , the anode voltage , and the cathode voltage . The output is the gyrotron output power The state space model is constructed in such a way that not only the mapping between control input and power output is established, but also key safety indicators such as cathode temperature and magnet current are included in the state variables, thus providing a mathematical basis for directly handling these safety constraints in the subsequent control algorithm.

[0036] After the modeling is completed, the system enters the real-time control loop step. The loop is executed once every control period, i.e., 10 ms.

[0037] Step one, obtain the real-time output power of the electron cyclotron resonance heating system and the state estimation value. The power measurement module 101 real-time collects the output power of the electron cyclotron heating power execution object 108. At the same time, the state estimation module 102 obtains the state estimation value by using the Kalman filtering algorithm. The Kalman filtering algorithm includes a prediction step and an update step. The prediction step uses the currently used target model to predict the state at the next time, and the update step corrects the prediction result by combining the actual measurement value. In this way, even in the case where some state variables such as electron beam current are difficult to measure directly with high precision, accurate state estimation can be obtained, thereby improving the quality of feedback control.

[0038] Step two, determine the target model used in the current control period according to the real-time output power. The model selection module 103 selects the most matched one from the multiple linear system dynamic models established in the segmented modeling step as the target model according to the current real-time output power. In order to avoid frequent switching of the model at the boundary of adjacent power intervals due to measurement noise or small fluctuations, a hysteresis switching strategy is used at the switching boundary of adjacent power intervals in this embodiment. The specific process is as follows: set the switching threshold and the preset hysteresis bandwidth. For example, at the switching boundary of 600 kW between the medium power zone and the high power zone, a hysteresis bandwidth of plus or minus 50 kW is set. When the real-time output power of the electron cyclotron resonance heating system crosses the switching threshold from low to high and exceeds the upper limit of the hysteresis bandwidth, i.e., 650 kW, the target model is updated to the high power zone model; conversely, when the power crosses the switching threshold from high to low and is lower than the lower limit of the hysteresis bandwidth, i.e., 550 kW, the medium power zone model is switched back. Otherwise, the currently used target model remains unchanged. This strategy significantly enhances the stability of the control system during the working condition conversion process.

[0039] Step three, based on the target model and the preset power reference trajectory, construct a quadratic programming problem of model predictive control. The MPC optimization solving module 104 receives the state estimation value from the state estimation module 102 and the model parameters from the model selection module 103, and combines the preset power reference trajectory to construct a quadratic programming problem aimed at minimizing the power tracking error. The quadratic programming problem includes an objective function and constraint conditions for control input and system state.

[0040] where the mathematical expression of the objective function is designed as:

[0041] where, is the prediction horizon, which is set to 10 in this embodiment; is the control horizon, which is set to 3 in this embodiment; is the predicted output power; is the reference power trajectory; is the rate of change of control input; is the soft constraint slack variable; and are the output error weight matrix and the input rate of change weight matrix, respectively; is the penalty coefficient. The first term of the objective function guarantees that the system can track the desired power trajectory quickly and accurately, the second term limits the drastic change of control input, thus protecting the actuator and preventing system oscillation, and the third term is used to handle the soft constraint, allowing a small constraint violation in extreme working conditions to ensure the feasibility of the solution.

[0042] Meanwhile, the constraints and safety monitoring module 105 defines strict constraint conditions, which are converted into linear inequality constraints in the quadratic programming problem. The constraint conditions include input amplitude constraints, input rate of change constraints, and state constraints.

[0043] The input amplitude constraints limit the absolute range of each control variable. For example, the filament power is limited between 0 W and 1200 W, the anode voltage is limited between 18 kV and 24 kV, and the cathode voltage is limited between -50 kV and -40 kV.

[0044] The input rate of change constraints limit the maximum change of each control variable within a single control period. For example, the filament power rate of change is limited to within 0.5 W / ms, the anode voltage rate of change is limited to within 0.1 kV / ms, and the cathode voltage rate of change is limited to within 0.2 kV / ms. Such limitations are crucial for protecting the high-voltage power supply and the expensive gyrotron device, preventing the risk of breakdown due to voltage mutation.

[0045] The state constraints directly limit the safety boundaries of key physical quantities inside the system. For example, the cathode temperature is limited to be less than or equal to 1200°C, the magnet power supply current is limited to be less than or equal to 90 A, and the gyrotron electron beam current is limited to be less than or equal to 40 A. By directly considering these state constraints in the optimization solving stage, the controller can predict and avoid state overruns at future times, which is more advanced and safe than the traditional alarm shutdown mechanism after the overrun.

[0046] In addition, the constraint condition also includes an output power constraint. In order to improve the success rate of solving, the embodiment introduces a slack variable in the output power constraint to realize a soft constraint, which includes an upper limit constraint and a lower limit constraint.

[0047] The upper limit constraint is expressed as:

[0048] The lower limit constraint is expressed as:

[0049] wherein, and are a preset maximum output power, i.e. 995 kW, and a minimum output power, i.e. 95 kW, respectively, is an influence coefficient of the input change rate on the output power. is a predicted basis term of the output power, and the calculation formula is as follows:

[0050] wherein, is a state estimation value, is a control amount at the previous moment.

[0051] Step four, obtaining the optimal control input by solving the quadratic programming problem, and applying the optimal control input to the electron cyclotron resonance heating system. The MPC optimization solving module 104 uses an efficient solver such as qpOASES to solve the quadratic programming problem constructed above. Among a series of future control increment sequences obtained by solving, only the first component is taken as the optimal control input at the current moment. The optimal control input is issued to the electron cyclotron heating power execution object 108 through the execution interface module 107, so as to complete the closed-loop control of one control cycle.

[0052] Through the above implementation process, the embodiment can realize smooth adjustment of the electron cyclotron resonance heating system in the full power range, control the steady-state error within 1.5%, and effectively avoid the occurrence of overvoltage, overcurrent and other faults in the process of rapid power rise and fall, thereby significantly improving the operation efficiency and safety of the system.

[0053] Embodiment two Based on the embodiment one, the embodiment two further elaborates the specific implementation scheme of online adaptive updating and safety rollback in view of the model mismatch problem that may occur in the long-term operation of the electron cyclotron resonance heating system and the control safety problem under extreme working conditions.

[0054] As the core components in the electron cyclotron resonance heating device, such as the cyclotron tube, will produce aging effects with the passage of time, deviations will occur between the actual physical parameters of the system and the linear system dynamic model initially established. In order to overcome this problem, the method of the embodiment introduces an online adaptive updating step.

[0055] Firstly, the system records the input and output data in real time during operation to a ring buffer. The ring buffer is designed to have a fixed capacity of storage space, such as being able to store system operation data within the last 100 seconds. With a sampling period of 10 ms, the buffer capacity is set to 10,000 groups of data. The data writing adopts a circular coverage mode, i.e. the new data will automatically overwrite the earliest written data, so as to ensure that the data samples in the buffer always reflect the latest dynamic characteristics of the system.

[0056] Secondly, when the preset updating trigger condition is met, the system uses the data in the ring buffer to update the parameters of the linear system dynamic model using the recursive least squares method. The design of the updating trigger condition considers three dimensions of time period, performance index and operation mode. Specifically, it includes: timing trigger, i.e. setting to automatically perform parameter updating once every 24 hours to cope with slow aging drift; performance decline trigger, i.e. triggering updating immediately when it is detected that the deviation between the model predicted output and the actual measured value continuously exceeds the preset threshold; operation mode change trigger, i.e. triggering updating when the system switches from one working mode to another significantly different working mode. Through the recursive least squares method, the algorithm continuously corrects the element values in the model parameter matrix using newly collected data, and updates the parameter covariance matrix, so that the control model can track the time-varying characteristics of the system online.

[0057] In addition, in order to ensure absolute safety when the control algorithm fails or the system is abnormal, the method of the embodiment also includes a safety fallback step.

[0058] After the MPC optimization solving module 104 solves the quadratic programming problem, the system will immediately determine whether the solving is successful. The cases of solving failure can include that the algorithm does not converge within the specified time, or that the optimization problem itself has no solution under the current constraints. If the judgment result is solving failure or timeout, or the constraint and safety monitoring module 105 detects that the system triggers an interlock signal, the fallback control strategy is immediately activated.

[0059] The rollback control strategy includes three levels of processing logic. The first level is to maintain the last valid control amount, which is suitable for single-time calculation timeout of the solver occasionally, at which time the output of the last control cycle is directly used to maintain system stability. The second level is to reduce the output power according to the maximum allowed slope, which is suitable for the case of continuous failure or slight abnormality of the solver, and the system will smoothly reduce the power to zero or a safe low power level at a preset safe slope. The third level is to switch to a backup control law, which is suitable for a serious failure of the MPC algorithm, at which time the system seamlessly switches to a pre-designed backup controller based on rules or simple PID to ensure that the system does not lose control. At the same time, if a serious hardware interlocking signal input 109 is detected, such as water cooling failure or vacuum destruction, the above strategy is directly bypassed and an emergency shutdown program is executed.

[0060] Embodiment Three The embodiment provides a real-time control system of electron cyclotron resonance heating power. The system is a hardware and software carrier of the method in the embodiment one and the embodiment two, and is designed to realize high real-time performance and high reliability of industrial control.

[0061] As shown in Figure 3 , the real-time control system mainly includes a segmented modeling module, a state estimation module, a model selection module, an optimization solving module and an execution interface module. These modules rely on a high-performance real-time controller, such as an NI cRIO-9049 platform, in physical implementation, and combine FPGA technology to meet the millisecond-level control cycle requirement.

[0062] The segmented modeling module is mainly used to divide the working range of the electron cyclotron resonance heating system into a low-power zone, a medium-power zone and a high-power zone according to the output power in the system initialization stage or the offline stage, and to establish a linear system dynamic model describing the dynamic characteristics of the system in each power zone interval. The model parameters are stored in the non-volatile memory of the controller for subsequent calling.

[0063] The state estimation module corresponds to Figure 3 the state estimation module 102 in the embodiment one, and the function is to obtain the real-time output power and state estimation value of the electron cyclotron resonance heating system. In hardware, the module acquires voltage, current and power signals through a high-precision analog input module, such as an NI-9223. In the software algorithm, it internally embeds a Kalman filter to reconstruct the unmeasurable state quantities such as the cathode temperature in real time by using the collected noise data.

[0064] The model selection module corresponds to Figure 3The model selection module 103 in the model library module is used to determine the target model used in the current control period according to the real-time output power. The module internally runs a logic judgment program with hysteresis, compares the current power value with the preset switching threshold in real time, and sends the corresponding parameter matrix to the optimization layer from the model library established by the segmented modeling module.

[0065] The optimization solving module corresponds to Figure 3 The MPC optimization solving module 104 in the model library module is the core calculation unit of the system. It constructs a quadratic programming problem of model predictive control based on the selected target model and the preset power reference trajectory. The module uses the floating-point operation capability of the processor to run the qpOASES solver to calculate the optimal control input that minimizes the power tracking error and satisfies all constraint conditions in each control period.

[0066] The execution interface module corresponds to Figure 3 The execution interface module 107 in the model library module is used to apply the calculated optimal control input to the electron cyclotron resonance heating system. In hardware, the module converts digital control quantities into analog voltage signals through an analog output module, such as NI-9269, to drive the filament power supply, high-voltage power supply, and other execution mechanisms. At the same time, the module also integrates a hardware-level amplitude limiting protection circuit as the last safety line in addition to software control.

[0067] In addition, the system also integrates data management functions to record running data to local storage or upload to the upper computer for subsequent fault analysis and offline calibration of model parameters. The real-time control program written by LabVIEW runs under the RTLinux operating system to ensure that the data flow and calculation tasks of all the above modules are completed strictly within the deterministic timing of 10ms.

[0068] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0069] The above only describes the embodiments of the present application and is not used to limit the present application. The present application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for real-time control of electron cyclotron resonance heating power, characterized in that, Includes the following steps: Segmented modeling steps: Divide the working range of the electron cyclotron resonance heating system into multiple power intervals according to the output power, and for each power interval, establish a linear system dynamic model describing the dynamic characteristics of the system within that interval. Real-time control cycle steps: Perform the following operations within each control cycle: Obtain the real-time output power and state estimate of the electron cyclotron resonance heating system; The target model used in the current control cycle is determined based on the real-time output power; the target model is selected from multiple linear system dynamic models established in the piecewise modeling step. Based on the target model and the preset power reference trajectory, a quadratic programming problem for model predictive control is constructed. The quadratic programming problem includes an objective function that minimizes the power tracking error, as well as constraints on the control input and system state. The constraints include at least the magnitude and rate of change constraints on the control input. The optimal control input is obtained by solving the quadratic programming problem and applied to the electron cyclotron resonance heating system.

2. The method according to claim 1, characterized in that, In the segmented modeling step, the division of the multiple power intervals is determined based on the nonlinear transition characteristics of the electron cyclotron resonance heating system; the multiple power intervals include at least a low power interval, a medium power interval, and a high power interval, and adjacent power intervals are divided by a preset power threshold; the linear system dynamic model is established by conducting system excitation experiments and collecting input and output data at the geometric center point of each power interval.

3. The method according to claim 1, characterized in that, The dynamic model of the linear system is a discrete-time state-space model, and its form is as follows: in, For state vectors, For the input vector, For output; the state vector Includes cathode temperature, magnet power supply current, and gyrotron electron beam current; the input vector Includes filament power, anode voltage, and cathode voltage; the output This is the output power of the gyrotron; , , , These are the system matrix, input matrix, output matrix, and feedforward matrix for the corresponding power ranges.

4. The method according to claim 1, characterized in that, The step of determining the target model used in the current control cycle based on the real-time output power includes: At the switching boundary between adjacent power ranges, a hysteresis switching strategy is used to determine the target model; The specific process of the hysteresis switching strategy is as follows: set a switching threshold and a preset hysteresis bandwidth; when the real-time output power of the electron cyclotron resonance heating system crosses the switching threshold and exceeds the hysteresis bandwidth, update the target model; otherwise, keep the currently used target model.

5. The method according to claim 1, characterized in that, The mathematical expression for the objective function is: in, To predict the time domain, To control the time domain, For the predicted output power, For reference power trajectory, To control the rate of change of the input, For soft-constrained slack variables, and This is the weight matrix. This is the penalty coefficient.

6. The method according to claim 1, characterized in that, The constraints include: Input amplitude constraints: Limit filament power, anode voltage, and cathode voltage to their respective preset allowable ranges; Input rate of change constraint: Limits the rate of change of filament power, anode voltage, and cathode voltage to their respective preset maximum values; State constraints: limit the cathode temperature to be less than or equal to the preset maximum temperature, limit the magnet power supply current to be less than or equal to the preset maximum current, and limit the gyrotron electron beam current to be less than or equal to the preset maximum electron beam current.

7. The method according to claim 6, characterized in that, The constraints also include output power constraints; these output power constraints introduce slack variables to achieve soft constraints, specifically including upper bound constraints and lower bound constraints. The upper limit constraint is: The lower bound constraint is: in, This is a fundamental term for output power prediction. This is the coefficient representing the influence of the input rate of change on the output power. For the slack variable, and These are the preset maximum and minimum output power values, respectively; Indicates the first The rate of change of the control input at any given time.

8. The method according to claim 1, characterized in that, In the real-time control loop step, the state estimate is obtained through a Kalman filter algorithm; the Kalman filter algorithm includes a prediction step that uses the target model to predict the state at the next moment, and an update step that combines the actual measurement value to correct the prediction result.

9. The method according to claim 1, characterized in that, The method also includes an online adaptive update step: The input and output data of the electron cyclotron resonance heating system are recorded in real time to a ring buffer. When the preset update trigger condition is met, the parameters of the linear system dynamic model are updated using the data in the circular buffer and the recursive least squares method. The update triggering conditions include timed triggering, performance degradation triggering, or operation mode change triggering.

10. The method according to claim 1, characterized in that, The method also includes a safe rollback step: After solving the quadratic programming problem, determine whether the solution was successful; If the solution fails or times out, or if a system interlock signal is detected, a rollback control strategy is executed. The fallback control strategy includes maintaining the previously effective control variable, reducing the output power at the maximum permissible slope, or switching to a standby control law.

11. A real-time control system for electron cyclotron resonance heating power, characterized in that, include: The segmented modeling module is used to divide the working range of the electron cyclotron resonance heating system into multiple power intervals according to the output power, and to establish a linear system dynamic model describing the dynamic characteristics of the system in each power interval. The state estimation module is used to obtain the real-time output power and state estimation value of the electron cyclotron resonance heating system; The model selection module is used to determine the target model to be used in the current control cycle based on the real-time output power; the target model is selected from multiple linear system dynamic models established by the piecewise modeling module. The optimization solution module is used to construct a quadratic programming problem for model predictive control based on the target model and a preset power reference trajectory, and solve it to obtain the optimal control input. The quadratic programming problem includes an objective function that minimizes the power tracking error, as well as constraints on the control input and system state. The constraints include at least magnitude constraints and rate of change constraints on the control input. An execution interface module is used to apply the optimal control input to the electronic cyclotron resonance heating system.

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