A dual control loop charge management method and system

Abstract

The present invention relates to the field of charge management technology, and in particular to a dual control loop charge management method and system. It includes: establishing a nonlinear charge and discharge mathematical model, designing a model predictive controller; through the output of the charge management system at the current moment, feeding back the output of the charge management system at the current moment together with the control input at the previous moment into the model predictive controller; constructing an objective function, calculating the optimal control quantity according to the objective function and the constraint conditions of the ultraviolet light power, and using it as the reference light power of the mixed gain fuzzy PID controller; adjusting the light power of the ultraviolet lamp through the mixed gain fuzzy PID controller of the inner loop to match the reference light power; using an optical power meter to measure the actual output light power, and using it as the feedback signal of the model predictive controller; using the actual output light power as the control input, updating the current state of the system, and using it as the input for the next prediction after the update, to form a closed-loop control. The advantages are: precise control of ultraviolet light and stable transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of charge management, and in particular to a dual-control loop charge management method and system. Background Art

[0002] Charge management technology is an extremely important part of the space gravitational wave detection mission. The basic principle of space gravitational wave detection is to use laser interferometer to detect the relative position change of the test mass in two satellites far apart, so as to detect gravitational waves. The high-energy particles that are widely present in space will bombard the spacecraft and accumulate on the surface of the test mass through direct or indirect charging effects. This will cause additional coupling between the test mass and the surrounding frame structure, affecting the accuracy of gravitational wave detection. Therefore, a charge management system is needed to ensure the stability of the residual charge on the surface of the test mass and eliminate the Coulomb force generated by the interaction between the accumulated charge on the test mass and the conductor, as well as the Lorentz force generated by the coupling with the magnetic field. Currently, most research teams use a charging and discharging method that uses ultraviolet light to irradiate sensitive structures. The basic principle is as follows: Figure 1 As shown. When ultraviolet light irradiates the test mass surface, if the energy of the photon exceeds the work function of the metal, the free electrons in the metal will absorb the energy of the photon and escape as photoelectrons from the test mass. In order to allow the test mass to emit charges under the irradiation of ultraviolet light, it is necessary to use high-energy (short-wavelength) light to irradiate the test mass surface.

[0003] According to the discharge principle, the current charge management discharge methods can be divided into DC discharge method and AC discharge method. The DC discharge method is to use a DC-driven ultraviolet light source to continuously irradiate the surface of the test mass. By controlling the polarity and magnitude of the bias voltage between the test mass and the electrode cage, the test mass can be charged or discharged. The AC discharge method no longer controls the bias voltage between the test mass and the electrode cage, but directly drives the ultraviolet lamp with alternating current. This discharge method no longer needs to introduce an additional bias voltage signal, and can implement a more complex discharge strategy.

[0004] Depending on the current driving the UV lamp and the irradiation time, the charge management can also be performed by continuous discharge or rapid discharge. That is, the test mass can be continuously charged and discharged using a low current, low power output UV lamp; or the test mass can be rapidly charged and discharged using a high current, high power output UV lamp.

[0005] There is no feedback loop in the implementation process of the existing charge management method, that is, the test mass is directly charged and discharged using an open-loop control method, which cannot adjust the surface potential of the test mass in real time and is highly dependent on the accurate calibration of the photoelectric characteristics inside the inertial sensor; and it is impossible to quickly and accurately adjust the surface potential of the test mass to a specified level. At present, there is an urgent need for a charge management system with a closed-loop control function that can adjust the relative potential of the test mass and the electrode cage surface to a specified size, so as to carry out subsequent research. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a dual control loop charge management method and system.

[0007] The first object of the present invention is to provide a dual control loop charge management method, which specifically comprises the following steps:

[0008] S1. Establish a nonlinear charge management system charging and discharging mathematical model;

[0009] S2. Design of model predictive controller based on nonlinear charge management system charging and discharging mathematical model;

[0010] S3. The model predictive controller uses the charge management system output at the current moment, feeds the charge management system output at the current moment together with the charge management system control input at the previous moment into the model predictive controller, and obtains the future system output sequence;

[0011] S4. construct an objective function, and calculate the optimal control quantity according to the objective function and the constraint condition of the ultraviolet light power;

[0012] S5. The optimal control quantity solved is used as the current control input, and the control input is used as the reference optical power of the mixed gain fuzzy PID controller; the optical power of the ultraviolet lamp is adjusted by the mixed gain fuzzy PID controller of the inner loop so that the optical power of the ultraviolet lamp matches the reference optical power;

[0013] S6. Use an optical power meter to measure the actual output optical power of the UV lamp, and use the actual output optical power as a feedback signal of the model predictive controller;

[0014] S7. Use the actual output light power of the UV lamp as the control input to update the current state of the charge management system; the updated state will be used as the input for the next prediction to form a closed-loop control.

[0015] Preferably, step S1 specifically includes the following sub-steps:

[0016] S101. Through simulation experiments, the test mass and the number of photoelectrons escaping from the electrode cage under different light powers are obtained, and a linear function is used for fitting;

[0017] S102. Consider the electrode cage as a reference ground and set the potential to 0 V; use the transformed Sigmoid function to fit the UV light power irradiating the test mass and the electrode cage and the bias voltage between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage; establish a nonlinear charge management system charging and discharging mathematical model.

[0018] Preferably, in step S102, the transformed Sigmoid function includes a transformed Sigmoid function when irradiating the test mass and a transformed Sigmoid function when irradiating the electrode cage;

[0019] The transformation Sigmoid function when irradiating the test quality is:

[0020] ;

[0021] The transformation Sigmoid function when irradiating the electrode cage is:

[0022] ;

[0023] In the formula, The optical power of the UV lamp representing the quality of the irradiation test, represents the light power of the UV lamp irradiating the electrode cage; represents the number of photoelectrons that escape from the surface of the test mass when the test mass is irradiated, Represents the number of photoelectrons escaping from the surface of the electrode cage when irradiating the electrode cage; represents the bias between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage; for The mapping of

[0024] The relationship between the optical power and the number of photoelectrons emitted per second during the irradiation test is as follows:

[0025] ;

[0026] The relationship between the optical power and the number of photoelectrons emitted per second when irradiating the electrode cage is as follows:

[0027] ;

[0028] The expression is as follows:

[0029] ;

[0030] In the formula, To test the potential carried by the mass surface, is the potential carried by the surface of the electrode cage;

[0031] The expression is as follows:

[0032] .

[0033] Preferably, step S1 further includes sub-step S103: in the case of taking into account the external charge accumulation rate, rewriting the nonlinear charge management system charge and discharge mathematical model into the following formula:

[0034] ;

[0035] In the formula, Represents the transformation Sigmoid function when irradiating the test quality, represents the transformation Sigmoid function when irradiating the electrode cage, Represents external charge accumulation noise.

[0036] Preferably, the mixed gain fuzzy PID controller is expressed as:

[0037] ;

[0038] In the formula, Represents the current error value, representing the difference between the target value and the actual value; the gain of the mixed gain fuzzy PID controller P m , I m , D m See the following formulas respectively:

[0039] ;

[0040] In the formula, the fuzzy PID The output gain is , the fuzzy logic controller will and error rate of change Output , and its adjustment formula is:

[0041] ;

[0042] The gain factor is , The size of is determined by the following formula:

[0043] ;

[0044] In the formula, is the difference between the current optical power and the target optical power, is the preset threshold.

[0045] Preferably, the objective function is expressed as follows:

[0046] ;

[0047] In the formula, k represents the control step, represents the reference potential, prediction step size for model predictive control;

[0048] The constraints are: ;

[0049] ;

[0050] in, represents the predicted potential at time k+i+1 calculated at time k, st represents the constraints, represents the unit time, k represents the control step, represents the actual potential, represents the reference potential, represents the residual charge at time k, represents the charge constant, represents the capacitance between the test mass and the electrode cage, is the prediction step size for model predictive control, , They represent the minimum and maximum optical power that can be output when irradiating the test quality. , They represent the minimum and maximum light powers that can be output when irradiating the electrode cage. Represents external charge accumulation noise.

[0051] Preferably, the ultraviolet lamp is an AlGaN-based ultraviolet lamp with a rated wavelength of 250-260 nm.

[0052] The second object of the present invention is to provide a dual control loop charge management system, comprising: a model predictive controller, a mixed gain fuzzy PID controller, an ultraviolet lamp, and an optical power meter;

[0053] The inner loop uses a mixed gain fuzzy PID controller to precisely control the optical power of the UV lamp, so that the UV lamp outputs stably and realizes quantitative control of the surface potential of the test mass. An optical power meter is used to measure the actual output optical power of the UV lamp, and the actual output optical power is used as the feedback signal of the model predictive controller.

[0054] The outer loop monitors and provides feedback on the charge through a model predictive controller, uses the feedback signal to predict and optimize the charge management system, and updates the state of the charge management system. The updated state will serve as input for the next prediction, forming a feedback loop.

[0055] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0056] The present invention establishes a nonlinear model of test mass charging and discharging, which has high accuracy and can reflect the test mass charging and discharging process; designs a dual control loop charge management method, which introduces a closed-loop feedback link and can accurately adjust the surface potential of the test mass in the inertial sensor. The outer loop introduces a charge control link based on model predictive control to monitor and feedback the charge, predict and optimize the system behavior of charge management, and achieve quantitative control of the surface potential of the test mass by accurately regulating the ultraviolet light source system; based on the characteristics of UV LED's fast response speed and good dynamic performance, the inner loop accurately controls the light power of UV LED based on a mixed gain fuzzy PID control method, so that the UV LED stably outputs a given light power. And when the UV LED is output at low power, it also has good stability and responds to system requirements in a timely manner. The control inner loop is used to ensure the stable output state of the core actuator UV LED of the charge management system and the reliable transmission of ultraviolet light. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the basic principle of the charging and discharging method using ultraviolet light (UV LED) to irradiate sensitive structures.

[0058] Figure 2 It is a technical roadmap of a dual control loop charge management method provided according to an embodiment of the present invention.

[0059] Figure 3 This is a graph showing the change in the number of photoelectrons emitted when the quality is tested under 1-60 nW optical power and different bias voltages according to an embodiment of the present invention.

[0060] Figure 4 This is a graph showing the variation in the number of photoelectrons emitted when the electrode cage is irradiated at different bias voltages and with a light power of 1 to 60 nW according to an embodiment of the present invention.

[0061] Figure 5 This is a graph showing the change in the number of photoelectrons emitted when the quality is tested under different light powers and -2.5 to 2.5 V bias voltages according to an embodiment of the present invention.

[0062] Figure 6 This is a graph showing the variation in the number of photoelectrons emitted when the electrode cage is irradiated at different light powers and a bias voltage of -2.5 to 2.5 V according to an embodiment of the present invention.

[0063] Figure 7 It is a function fitting diagram of the illumination test quality provided according to an embodiment of the present invention.

[0064] Figure 8is a function fitting diagram of the irradiated electrode cage provided according to an embodiment of the present invention.

[0065] Fig. 9 is a block diagram of a model predictive controller provided according to an embodiment of the present invention.

[0066] Fig.10 4 is a control block diagram of a mixed gain fuzzy PID controller provided according to an embodiment of the present invention.

[0067] Fig.11 Schematic diagram of the performance of a mixed gain fuzzy PID controller provided according to an embodiment of the present invention.

[0068] Fig.12 It is the actual potential change of the test mass surface at +20e / s provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0070] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0071] The present invention provides a dual control loop charge management method, the technical roadmap is as follows Figure 2 As shown; specifically includes the following steps:

[0072] S1. Establish a nonlinear charge management system charging and discharging mathematical model; obtain a mathematical model based on the inertial sensor charging and discharging method of the ultraviolet light charge management technology designed in the early stage; specifically include the following sub-steps:

[0073] S101. Through simulation experiments, the test mass and the number of photoelectrons escaping from the electrode cage under different light powers are obtained, and a linear function is used for fitting;

[0074] The simulation experiment is as follows: the number of photoelectrons collected when applying different bias voltages to the electrode cage and irradiating the test mass and the electrode cage with 1~60nW optical power is as follows: Figure 3 , Figure 4 As shown; the relationship between the optical power and the number of photoelectrons at 0V is fitted, and the relationship between the optical power and the number of photoelectrons emitted per second in the irradiation test quality is obtained as follows:

[0075] ;

[0076] In the formula, represents the number of photoelectrons that escape when irradiating the test mass, Indicates the optical power of the UV lamp irradiating the test quality;

[0077] The relationship between the optical power and the number of photoelectrons emitted per second when irradiating the electrode cage is as follows:

[0078] ;

[0079] In the formula, represents the number of photoelectrons that escape when irradiating the electrode cage; represents the optical power of the UV lamp irradiating the electrode cage;

[0080] To evaluate the fit, the coefficient of determination was used And relative root mean square error Relative RMSE to evaluate the fitting effect, the calculation formula is:

[0081] ;

[0082] The calculated results are shown in Table 1;

[0083] Table 1 Evaluation index of the fitting effect of the relationship between optical power and the number of escaped photoelectrons

[0084]

[0085] and The coefficient of determination They are all close to 1, which shows that the fitting effect is very good.

[0086] In addition to the optical power, the bias voltage between the test mass and the electrode cage also affects the number of photoelectrons emitted from the test mass per second.

[0087] S102. Consider the electrode cage as a reference ground and set the potential to 0V; use the transformed Sigmoid function to fit the UV light power irradiating the test mass and the electrode cage and the bias voltage between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage; establish a nonlinear charge management system charge and discharge mathematical model;

[0088] The transformation Sigmoid function when irradiating the test quality is:

[0089] ;

[0090] The transformation Sigmoid function when irradiating the electrode cage is:

[0091] ;

[0092] In the formula, The optical power of the UV lamp representing the quality of the irradiation test, represents the light power of the UV lamp irradiating the electrode cage; represents the number of photoelectrons that escape from the surface of the test mass when it is irradiated, and Represents the number of photoelectrons escaping from the surface of the electrode cage when irradiating the electrode cage; The bias voltage (i.e., potential difference) between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage is expressed as follows:

[0093] ;

[0094] In the formula, To test the potential carried by the mass surface, is the potential carried by the surface of the electrode cage;

[0095] make The size ranges from -2.5V to 2.5V, and -2.5V~2.5V is mapped to 0~1000, resulting in the following formula:

[0096] .

[0097] Based on the designed model, the optical power output of the UV lamp is set to , the test quality under different light powers and the number of photoelectrons escaping from the electrode cage per second were obtained, such as Figure 5 , Figure 6 shown.

[0098] Principle: According to the test results of David DAL Bosco of University of Trento, the maximum initial kinetic energy of photoelectrons escaping from the surface of the test mass is 0.98eV. When , the test mass only discharges; and when When the test mass is only charged, considering the impact of the above factors on the charge management system, in order to establish a complete test mass charge and discharge mathematical model, let The size ranges from -2.5V to 2.5V.

[0099] The conventional Sigmoid function is , the transformed Sigmoid function is used to fit the optical power and bias voltage, as follows:

[0100] ;

[0101] In the formula, P is or , The optical power of the UV lamp representing the quality of the irradiation test, represents the light power of the UV lamp irradiating the electrode cage; n is or , represents the number of photoelectrons that escape from the surface of the test mass when the test mass is irradiated, Represents the number of photoelectrons escaping from the surface of the electrode cage when irradiating the electrode cage; and are the fitted coefficients, is a natural constant, It is a correction item.

[0102] At the same time, in order to facilitate fitting, the value of the horizontal axis during simulation is mapped from -2.5V~2.5V to 0~1000, and the mapping relationship is:

[0103] ;

[0104] The fitting result shows that the transformation Sigmoid function when irradiating the test quality is:

[0105] ;

[0106] The transformation Sigmoid function when irradiating the electrode cage is:

[0107] ;

[0108] The obtained function fitting diagram is as follows Figure 7 , Figure 8 shown.

[0109] Using the coefficient of determination The fitting effect is evaluated by using the relative root mean square error (RMSE). The results are shown in Table 2.

[0110] Table 2 Bias voltage Evaluation index of fitting effect of relationship with the number of escaped photoelectrons

[0111]

[0112] By fitting the simulation model data, we get a and The nonlinear function of the number of photoelectrons escaping from the test mass surface shows a good fitting effect through the evaluation indicators.

[0113] S103. Taking into account the external charge accumulation rate, the nonlinear charge management system charge and discharge mathematical model is rewritten as follows:

[0114] ;

[0115] In the formula, Represents the transformation Sigmoid function when irradiating the test quality, represents the transformation Sigmoid function when irradiating the electrode cage, Represents external charge accumulation noise.

[0116] S2. Design a model predictive controller (MPC) based on the nonlinear charge management system charging and discharging mathematical model; the established model predictive controller block diagram is as follows Fig. 9 shown.

[0117] S3. The model predictive controller uses the charge management system output at the current moment, feeds the charge management system output at the current moment together with the charge management system control input at the previous moment into the model predictive controller to obtain the future system output sequence.

[0118] S4. Construct an objective function and calculate the optimal control quantity according to the objective function and the constraint conditions of the ultraviolet light power; ensure that the system is always close to the target value in a dynamic environment;

[0119] The expression of the objective function is as follows: ;

[0120] In the formula, k represents the control step, represents the reference potential, prediction step size for model predictive control;

[0121] The constraints are: ;

[0122]

[0123] In the above formula, represents the predicted potential at time k+i+1 calculated at time k, st represents the constraint condition (the abbreviation of subject to), represents the unit time, k represents the control step, represents the actual potential, represents the reference potential, represents the residual charge at time k, represents the charge constant, represents the capacitance between the test mass and the electrode cage, is the prediction step size for model predictive control, , They represent the minimum and maximum optical power that can be output when irradiating the test quality. , They represent the minimum and maximum light powers that can be output when irradiating the electrode cage. Represents external charge accumulation noise.

[0124] S5. The solved optimal control quantity is used as the current control input, and the control input is used as the reference optical power of the mixed gain fuzzy PID controller; the optical power of the ultraviolet lamp is adjusted by the mixed gain fuzzy PID controller of the inner loop to make the optical power of the ultraviolet lamp match the reference optical power.

[0125] S6. Use an optical power meter to measure the actual output optical power of the UV lamp, and use the actual output optical power as a feedback signal of the model predictive controller.

[0126] S7. Use the actual output light power of the UV lamp as the control input to update the current state of the charge management system; the updated state will be used as the input for the next prediction to form a closed-loop control.

[0127] Specifically, in the optimization process of the model predictive controller, the optimization problem can be defined as The difference between the reference potential and the actual potential must be minimized, and the dynamic equations of the system and the input constraints must be satisfied at the same time.

[0128] The specific optimization problem is as follows:

[0129]

[0130] In the formula, represents the unit time, k represents the control step, represents the actual potential, represents the reference potential, represents the residual charge at time k, represents the charge constant, represents the capacitance between the test mass and the electrode cage, is the prediction step size for model predictive control, , , , They represent the minimum and maximum optical power that can be output when irradiating the test quality and electrode cage, Represents external charge accumulation noise.

[0131] Specifically, the UV lamp selected is the AlGaN-based UVTOP250-HL-TO39 series UV LED. Because the surface of the test mass is plated with gold-platinum alloy, its surface work function is about 3.9eV after being contaminated by hydrocarbons in the air. After calculation, the wavelength of the UV lamp needs to be less than 270nm to cause the photoelectric effect on the surface and escape photoelectrons. The maximum operating current of the above-mentioned model of UV lamp is 20mA, and the rated wavelength is 255nm, which can meet the requirements of the charge management system. At present, the research on AlGaN-based UV LED is still in its infancy. Due to its component characteristics, its rated luminous efficiency is only , which highlights the necessity of high-precision modeling and control of UV LEDs during their application.

[0132] The low-power photoelectric thermal model of AlGaN-based UV LED can be expressed as follows:

[0133] ;

[0134] Where P v represents the actual optical power of the UV lamp; c is the correction constant; b and d are constant correction items introduced to correct the difference between the calibration working point and the actual working point; is the thermal characteristic coefficient, which represents the linear coefficient of the UV LED luminous efficiency decreasing as the temperature increases; Represents the measured temperature of the UV LED; , where k is the Boltzmann constant, n is a constant, T is the thermodynamic temperature, q is the electron charge, is the reverse saturation current, and i is the current input current.

[0135] Specifically, the mixed gain fuzzy PID controller is described as:

[0136] ;

[0137] In the formula, Represents the current error value, representing the difference between the target value and the actual value; the gain of the mixed gain fuzzy PID controller P m , I m , D m See the following formulas respectively:

[0138] ;

[0139] In the formula, the fuzzy PID The output gain is , the fuzzy logic controller will and error rate of change Output , and its adjustment formula is:

[0140] ;

[0141] The gain factor is , The size of is determined by the following formula:

[0142] ;

[0143] In the formula, is the difference between the current optical power and the target optical power, is the preset threshold.

[0144] Principle Description: Control Output of Fuzzy PID Controller The basic calculation formula is:

[0145] ;

[0146] In the formula, Represents the current error value, which represents the difference between the target value and the actual value; is the integral value of the error, representing the accumulation of the error; It is the differential of the error, reflecting the speed of error change; P, I, and D are the gains of the three adjustment links of proportion, integration, and differentiation respectively.

[0147] Assume that fuzzy PID The output gain is , the fuzzy logic controller will and error rate of change Output , and its adjustment formula is:

[0148] ;

[0149] In the charge control of the dual control closed loop, the inner loop needs to have good dynamic performance, timely track the optical power output by the model prediction controller, and maintain high optical power tracking accuracy after reaching the final state. Based on the above requirements, the hybrid gain factor method is introduced to improve the controller on the basis of the fuzzy PID controller; the hybrid gain fuzzy PID gain size is determined by the following formula:

[0150] ;

[0151] The mixed gain fuzzy PID gain number used in the present invention is conditioned based on the error size, assuming that the gain factor size is , The size of is determined by the following formula:

[0152] ;

[0153] In the formula, is the difference between the current optical power and the target optical power, is the preset threshold.

[0154] Therefore, the above controller can be described as:

[0155] .

[0156] like Fig.10 The control block diagram of the designed mixed gain fuzzy PID controller is shown in the figure. For a given optical power, is the actual output optical power, and I is the given current of the controller. In the mixed gain fuzzy PID, the input of the controller is the optimization data result obtained by the outer loop model predictive controller. The controller will calculate the error and error change rate between the reference optical power and the actual optical power at the current moment, and input it into the fuzzy rule inference system. The output result of the fuzzy PID controller is obtained by inference. . and combine it with the mixing gain coefficient The final output gain P, I, D is obtained by hybrid calculation, which makes the controller have better dynamic performance in the adjustment stage and better steady-state performance in the stabilizer.

[0157] The corresponding mixed gain fuzzy PID controller is established in MATLAB. Assuming that the UV light source system is applied with thermal management technology, its shell temperature can be maintained at 25°C. The current is output by a current source that is approximated as a first-order inertial unit. The specific parameters are calibrated through experiments. The controller performance is as follows: Fig.11 shown.

[0158] After simulation, the mixed gain PID used can achieve better control effect and output a stable UV light power signal.

[0159] The present invention provides a dual control loop charge management system, comprising: a model predictive controller, a mixed gain fuzzy PID controller, an ultraviolet lamp, and an optical power meter;

[0160] The inner loop uses a mixed gain fuzzy PID controller to precisely control the optical power of the UV lamp, so that the UV lamp outputs stably and realizes quantitative control of the surface potential of the test mass. An optical power meter is used to measure the actual output optical power of the UV lamp, and the actual output optical power is used as the feedback signal of the model predictive controller.

[0161] The outer loop monitors and provides feedback on the charge through a model predictive controller, uses the feedback signal to predict and optimize the charge management system, and updates the state of the charge management system. The updated state will serve as input for the next prediction, forming a feedback loop.

[0162] Application examples:

[0163] In order to verify the performance of the designed dual control loop charge controller, the charge management system under normal working conditions is simulated, assuming that the charge accumulation rate of the charge management system remains stable. The entire simulation process includes the potential rise and potential fall periods, and the charging performance and discharging performance of the dual control loop charge controller we designed are tested respectively. The reference potential is set U It is -0.08~0.08V, and the single charge and discharge time is 100s. The 0V specified potential represents that the charge accumulated on the surface of the test mass is completely removed; The specified potential represents the residual charge just below 34.2 pF when the capacitance between the test mass and the electrode cage is 34.2 pF. The potential at The specified potential of 0.08V is used to test the ability of the charge control technology to output the specified potential. The above potential selections respectively verify that the dual control loop charge management method keeps the test mass residual charge at 0. , the residual charge is kept at 10 7 e The potential rises by 0.04 V in each cycle from 100s to 400s and drops by 0.04 V in each cycle from 500s to 900s. In addition, according to the literature, the one-sided spectral density of the shot noise of the particle charging in the real model is set to , the charge measurement noise is .

[0164] Perform simulation according to the above parameter simulation settings, such as Fig.12 The figure shows the dynamic performance of the dual control loop charge manager when tracking the potentials of -0.08V, -0.04V, 0V, 0.04V, and 0.08V. It can be seen that under normal working conditions, the designed dual control loop charge controller can track the reference potential very well and has high output stability.

[0165] Brief description of the concept of the present invention: In the process of the charge management system tracking the reference potential, first obtain the current system output of the nonlinear mathematical model based on the test mass charge and discharge simulation model, and feed it back into the test mass charge and discharge prediction model together with the control input of the previous moment to obtain the future system output sequence, and then calculate the optimal control quantity through the constructed objective function and the constraint conditions on the ultraviolet light power; use the input quantity of the first step as the control action of the next step to the control input of the test mass charge and discharge. In addition, due to external interference errors and other factors, after obtaining the residual potential of the test mass surface by obtaining the test mass charge and discharge simulation model with added noise, use the residual potential value as the current state of the system to perform the optimal input calculation for the next step.

[0166] The signal transmission process of the model predictive control starts with the state estimation of the system. By obtaining the current system state and predicting the state of multiple moments in the future through the dynamic model of the system, the present invention uses the established charge management charge and discharge simulation model based on ultraviolet discharge to fit a set of nonlinear dynamic models that conform to the test quality charge and discharge law. On this basis, the model predictive controller will calculate the target error according to the set reference potential, and construct an optimization problem to minimize the error and the change of the control input, and use the optimization function to solve the optimal control input. In the control input sequence obtained by the solution, the first control input is selected, and this control input is used as the reference light power of the mixed gain fuzzy PID. The light power of the UV LED is controlled by the mixed gain fuzzy PID of the inner loop, and finally the actual light power of the UV LED is read out by the optical power meter. The actual light power output by the UV LED is used as the control input of the charge management charge and discharge simulation model based on ultraviolet discharge, thereby updating the state of the system. The updated state will be used as the input for the next prediction to form a feedback loop. This process is continuously carried out in each time step to ensure that the system state is always close to the target value in a dynamic environment.

[0167] In summary, the closed-loop control requirements of the charge management system, as well as the requirements for improving the control accuracy of the surface potential of the test mass, maintaining the stability of the surface potential of the test mass, and outputting the specified relative potential are met. Firstly, a nonlinear model of the test mass charge and discharge is established based on the test mass charge and discharge simulation method of the ultraviolet charge management technology designed in the early stage, and a dual-control loop charge management method is designed. This method aims to introduce a closed-loop feedback link to accurately adjust the surface potential of the test mass in the inertial sensor. The outer loop introduces a charge control link based on model predictive control to monitor and feedback the charge, predict and optimize the system behavior of the charge management, and achieve quantitative control of the surface potential of the test mass through precise regulation of the ultraviolet light source system; based on the characteristics of UV LED's fast response speed and good dynamic performance, the inner loop accurately controls the light power of UV LED based on the mixed gain fuzzy PID control method, so that the UV LED can stably output the given light power. And when the UV LED has good stability at low power output, it can respond to system requirements in a timely manner. The control inner loop is used to ensure the stable output state of the core actuator UV LED of the charge management system and the reliable transmission of ultraviolet light.

[0168] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the disclosure of the present invention can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0169] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A dual control loop charge management method, characterized in that: The specific steps include: S1. Establish a nonlinear charge management system charging and discharging mathematical model; S2. Design of model predictive controller based on nonlinear charge management system charging and discharging mathematical model; S3. The model predictive controller uses the charge management system output at the current moment, feeds the charge management system output at the current moment together with the charge management system control input at the previous moment into the model predictive controller, and obtains the future system output sequence; S4. construct an objective function, and calculate the optimal control quantity according to the objective function and the constraint condition of the ultraviolet light power; S5. The optimal control quantity solved is used as the current control input, and the control input is used as the reference optical power of the mixed gain fuzzy PID controller; the optical power of the ultraviolet lamp is adjusted by the mixed gain fuzzy PID controller of the inner loop so that the optical power of the ultraviolet lamp matches the reference optical power; S6. Use an optical power meter to measure the actual output optical power of the UV lamp, and use the actual output optical power as a feedback signal of the model predictive controller; S7. Use the actual output light power of the UV lamp as the control input to update the current state of the charge management system; the updated state will be used as the input for the next prediction to form a closed-loop control.

2. A dual control loop charge management method according to claim 1, characterized in that: The step S1 specifically includes the following sub-steps: S101. Through simulation experiments, the test mass and the number of photoelectrons escaping from the electrode cage under different light powers are obtained, and a linear function is used for fitting; S102. Consider the electrode cage as a reference ground and set the potential to 0 V; use the transformed Sigmoid function to fit the UV light power irradiating the test mass and the electrode cage and the bias voltage between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage; establish a nonlinear charge management system charging and discharging mathematical model.

3. A dual control loop charge management method according to claim 2, characterized in that: In the step S102, the transformed Sigmoid function includes a transformed Sigmoid function when irradiating the test mass and a transformed Sigmoid function when irradiating the electrode cage; The transformation Sigmoid function when irradiating the test quality is: ； The transformation Sigmoid function when irradiating the electrode cage is: ； In the formula, The optical power of the UV lamp representing the quality of the irradiation test, represents the light power of the UV lamp irradiating the electrode cage; represents the number of photoelectrons that escape from the surface of the test mass when the test mass is irradiated, Represents the number of photoelectrons escaping from the surface of the electrode cage when irradiating the electrode cage; represents the bias between the potential carried on the surface of the test mass and the potential carried on the surface of the electrode cage; for The mapping of The relationship between the optical power and the number of photoelectrons emitted per second during the irradiation test is as follows: ； The relationship between the optical power and the number of photoelectrons emitted per second when irradiating the electrode cage is as follows: ； The expression is as follows: ； In the formula, To test the potential carried by the mass surface, is the potential carried by the surface of the electrode cage; The expression is as follows: 。 4. A dual control loop charge management method according to claim 2 or 3, characterized in that: The step S1 further includes a sub-step S103: in the case of taking into account the external charge accumulation rate, rewriting the nonlinear charge management system charge and discharge mathematical model into the following formula: ； In the formula, Represents the transformation Sigmoid function when irradiating the test quality, represents the transformation Sigmoid function when irradiating the electrode cage, Represents external charge accumulation noise.

5. The dual control loop charge management method according to claim 1, characterized in that: The mixed gain fuzzy PID controller is expressed as: ； In the formula, Represents the current error value, representing the difference between the target value and the actual value; the gain of the mixed gain fuzzy PID controller P m , I m , D m See the following formulas respectively: ； In the formula, the fuzzy PID The output gain is , the fuzzy logic controller will and error rate of change Output , and its adjustment formula is: ； The gain factor is , The size of is determined by the following formula: ； In the formula, is the difference between the current optical power and the target optical power, is the preset threshold.

6. A dual control loop charge management method according to claim 3, characterized in that: The expression of the objective function is as follows: ； In the formula, k represents the control step, represents the reference potential, prediction step size for model predictive control; The constraints are: ; ； in, represents the predicted potential at time k+i+1 calculated at time k, st represents the constraints, represents the unit time, k represents the control step, represents the actual potential, represents the reference potential, represents the residual charge at time k, represents the charge constant, represents the capacitance between the test mass and the electrode cage, is the prediction step size for model predictive control, , They represent the minimum and maximum optical power that can be output when irradiating the test quality. , They represent the minimum and maximum light powers that can be output when irradiating the electrode cage. Represents external charge accumulation noise.

7. A dual control loop charge management method according to claim 1, characterized in that: The ultraviolet lamp is an AlGaN-based ultraviolet lamp with a rated wavelength of 250-260nm.

8. A dual control loop charge management system, used to implement a dual control loop charge management method according to any one of claims 1 to 7, characterized in that: include: Model predictive controller, mixed gain fuzzy PID controller, UV lamp, optical power meter; The inner loop uses a mixed gain fuzzy PID controller to precisely control the optical power of the UV lamp, so that the UV lamp outputs stably and realizes quantitative control of the surface potential of the test mass. An optical power meter is used to measure the actual output optical power of the UV lamp, and the actual output optical power is used as the feedback signal of the model predictive controller. The outer loop monitors and provides feedback on the charge through a model predictive controller, uses the feedback signal to predict and optimize the charge management system, and updates the state of the charge management system. The updated state will serve as input for the next prediction, forming a feedback loop.

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