CN107390526B - Spacecraft drag-free control method based on feature model - Google Patents
Spacecraft drag-free control method based on feature model Download PDFInfo
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Abstract
The invention provides a spacecraft non-dragging control method based on a characteristic model, which is characterized by receiving a residual acceleration measured value fed back by an accelerometer in each control period; considering the dynamic characteristics of a controlled spacecraft, a thruster and an accelerometer, respectively calculating a golden section adaptive control component, a logical integral control component and a logical double integral control component of the current control period according to a golden section adaptive control law, a logical integral control law and a logical double integral control law on the basis of a characteristic model input from a thruster instruction to the accelerometer for measurement and output; and synthesizing the golden section self-adaptive control component, the logic integral control component and the logic dual integral control component of the current control period to obtain a thruster control instruction, and sending the thruster control instruction to the thruster. The method of the invention fully considers the space environment characteristics, so that the non-gravitational acceleration inhibition precision of the spacecraft is superior to that of the traditional integral control method and embedded model control method.
Description
Technical Field
The invention relates to a drag-free control method for a spacecraft, in particular to a drag-free control method for the spacecraft based on a characteristic model, and belongs to the technical field of drag-free control of the spacecraft.
Background
The drag-free spacecraft at least comprises a test mass, and the test mass is not interfered by external environments such as atmospheric resistance, sunlight pressure and the like due to the closed cavity. The drag-free control system consists of a test mass suspended in a spacecraft cavity, a sensor for detecting the relative displacement between the test mass and the spacecraft, a high-precision proportional thruster and a drag-free controller. Drag-free control refers to counteracting the non-gravitational forces and all moments acting on the spacecraft, so that the spacecraft is free to move under the action of gravitational forces only. One solution to achieve drag-free flight of a spacecraft is to let the spacecraft track the test quality, i.e. the displacement mode. Another solution is to let the test mass track the spacecraft, the test mass being actively controlled and forming an electrostatic levitation accelerometer with the spacecraft, i.e. accelerometer mode. Unlike the displacement mode, the spacecraft and proof mass in the accelerometer mode more closely resemble a space accelerometer, and the residual non-gravitational acceleration acting on the satellite is measured and then fed back to the drag-free controller to achieve resistance cancellation.
Some basic physical experiments in space, such as launched GP-B satellites, GOCE satellites, future LISA plans, celesta plans and the like, require that the spacecraft has a pure gravity orbit. In the engineering practice, the orbit of the spacecraft is interfered by atmospheric resistance, sunlight pressure and the like. The advantage of drag-free control of the spacecraft is that counteracting these non-attractive forces to some acceptable level provides a relatively quiet working environment for the load, especially with very high control accuracy required in the measurement band.
At present, the embedded model control method provided in the article "Drag-free control and attribute control for the GOCE satellite", published in 2008 in the journal of Automatica (vol.44, No.7,2008, p 1766-1780) in the field of Drag-free control of spacecraft, is the most well known, because it is successfully applied to european GOCE satellites and achieves satisfactory control effect. However, the theory system of the embedded model control method is very complex, and the practical engineering application requires a designer to have deep understanding of the relevant theory, so that the engineering is difficult to realize. The embedded model control has no self-adaptability, and the problem is that the model is cut off, the embedded model of the drag-free spacecraft from the thruster to the gradiometer is first order, the embedded model of the atmospheric resistance in the advancing direction is second order, and the reduced model omits the information of a high-order model and inevitably sacrifices the precision of a closed-loop control system. Furthermore, in dealing with the problem of Drag-free control of spacecraft operating in the accelerometer mode, the paper "Drag-free control for functional physics missions" published in the journal of Advances in Space Research (Vol.32, No.7,2003, p 1221-1226, 2003 indicates that the transfer function between the input command from the thruster and the output of the gradiometer can be approximated as a constant, and then the Drag-free control can be applied with the simplest integral control. Although the integral controller has a simple structure and can stabilize the closed-loop control system, it has a poor effect of suppressing the atmospheric resistance in the middle frequency band.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for controlling the non-dragging of the spacecraft based on the characteristic model is provided, so that the non-gravitational acceleration suppression precision of the spacecraft is improved.
The technical scheme of the invention is as follows: a spacecraft drag-free control method based on a feature model comprises the following steps:
(1) considering the dynamic characteristics of a controlled spacecraft, a thruster and an accelerometer in a spacecraft drag-free control system, and defining the characteristic model form from the thruster instruction input to the accelerometer measurement output as follows:
y(k+1)＝f1(k)y(k)+f2(k)y(k-1)+g0(k)u(k)
wherein y (k +1) is the residual acceleration measurement value of the next control period, y (k) represents the residual acceleration measurement value of the current control period, and y (k-1) represents the residual acceleration measurement value of the previous control period; u (k) indicates the thruster input command of the current control period, f1(k)、f2(k) And g0(k) Characteristic coefficients representing a characteristic model of the current control period;
(2) and the definition of θ (k) ═ f1(k),f2(k),g0(k)]T，
Introducing regression vector phi (k) ═ y (k), y (k-1), u (k)]TInitializing k to 0
φ(0)＝[0,0,0]T、ui(0)＝0、uii(0) And (5) after 0, adding 1 to k in each control period to update k, acquiring a residual acceleration measured value y (k) of the current control period in real time, and executing the steps (3) to (7):
(3) calculating the estimated value of the characteristic coefficient of the characteristic model of the current control period by adopting a gradient descent algorithm
(4) According to the residual acceleration measured value y (k) of the current control period, the residual acceleration measured value y (k-1) of the previous control period and the estimated value of the characteristic coefficient of the characteristic model of the current control period
And calculating to obtain a golden section self-adaptive control component u of the current control periodg(k)；
(5) Calculating a logic integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic integral control law of the current control periodi(k)；
(6) Calculating a logic dual integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic dual integral control law of the current control periodii(k)：
uii(k)＝2uii(k-1)-uii(k-2)-Kiiy(k)
In the formula uii(k) Representing the logically doubly integrated control component, u, of the current control periodii(k-1) represents the logical double integral control component, u, of the previous control periodii(K-2) represents the logical double integral control component of the upper control period, KiiA logical double integral gain is shown which is,uii(0)＝0，uii(1)＝-Kiiy(1)；
(7) self-adapting the golden section of the current control period to control the component ug(k) Logical integral control component ui(k) Sum logic dual integral control component uii(k) Synthesizing to obtain the input instruction u (k) of the thruster in the current control period: u (k) ═ ug(k)+ui(k)+uii(k)。
The golden section self-adaptive control component u of the current control periodg(k) Comprises the following steps:
in the formula ug(k) Representing the golden section adaptive control component, l1＝0.382，l2＝0.618，
And
respectively representing the characteristic coefficient estimators of the current control period characteristic model, parameter lambda1Is constant and 0<λ1≤1。
The current control period logic integral control component ui(k) Comprises the following steps:
ui(k)＝ui(k-1)-Kiy(k))
in the formula ui(k) Representing the logically integrated control component, K, of the current control periodiRepresenting the logical integral gain.
Logical integral gain KiThe value-taking principle is as follows: when y (k) -y (k-1)]≤Δ1While, the logic integral gain is set to KiFor a preset logic integral gain minimum threshold Kimin(ii) a When y (k) -y (k-1)]>Δ1Time, logic integral gain KiSet as a preset logic integral gain maximum threshold Kimax，Δ1Has a value range of [0,1 ]]。
Logic double integral gain takingThe value principle is as follows: when y (k) -y (k-1)]≤Δ2Time, logic double integral gain KiiSetting as a preset double integral gain minimum threshold Kiimin(ii) a When y (k) -y (k-1)]>Δ2The logic double integral gain is set as the preset double integral gain maximum value threshold Kiimax，Δ2Has a value range of [0,1 ]]。
Step (3) calculating the estimated value of the characteristic coefficient of the characteristic model of the current control period
Then, the amplitude limiting processing is carried out on the digital signal to make the digital signal within a reasonable range. Compared with the prior art, the invention has the advantages that:
(1) the invention considers the space environment characteristics and introduces logic double integral control to effectively improve the steady state control precision, especially the control precision of middle and low frequency band.
(2) The method considers the dynamic characteristics of the controlled object, and describes the characteristic model from the thruster to the gradiometer by a second-order difference equation with a slowly time-varying coefficient, and the range of the characteristic coefficient can be determined in advance, so that the control precision can be improved;
(3) the drag-free control algorithm provided by the method is easy to realize. In the aspect of control precision, the method and the embedded model control method have equivalent restraining precision of the residual acceleration in the advancing direction in the measuring frequency band (5 mHz-0.1 Hz), and both reach 0.004-0.02 mu m/s2/Hz1/2(ii) a In the high frequency band (>0.1Hz), the drag-free control algorithm provided by the invention is superior to the embedded control algorithm.
(4) The method of the invention requires that the range of the characteristic coefficient is determined in advance through a characteristic modeling theory, and the range of the characteristic coefficient is limited in the parameter estimation process, the initial estimation value can be selected randomly in the range, different from other self-adaptive control methods, the parameter estimation does not need to start from a zero value, so that the closed-loop control system can also ensure the stability in the transition process stage.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of a spacecraft drag-free control system;
FIG. 3 is a PSD curve of the spacecraft forward direction using an integral control method.
FIG. 4 is a PSD curve of the spacecraft forward direction using an embedded model control method.
FIG. 5 is a PSD curve of the spacecraft forward direction using the control method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for controlling a spacecraft without drag based on a feature model includes the following steps: (1) and considering the dynamic characteristics of the controlled spacecraft, the thruster and the accelerometer in the spacecraft drag-free control system, and establishing a second-order characteristic model form from the thruster instruction input to the accelerometer measurement output by using a characteristic modeling theory as follows:
y(k+1)＝f1(k)y(k)+f2(k)y(k-1)+g0(k)u(k)
wherein y (k +1) is the residual acceleration measurement value of the next control period, y (k) represents the residual acceleration measurement value of the current control period, and y (k-1) represents the residual acceleration measurement value of the previous control period; u (k) represents the input command of the thruster in the current control period and the unit is Newton, N, f1(k)、f2(k) And g0(k) And the characteristic coefficient representing the characteristic model of the current control period is a time-varying parameter of the current control period of the second-order characteristic model. f. of1(k)、f2(k) And g0(k) When the discretization step length delta t is 0.001s, the satellite mass m is related to the discretization step length, the natural frequency and the damping coefficient of each second-order link in the non-dragging loop and the likeSCAt 1000kg, the static gain has an uncertainty of 9%, i.e.
In time, the range of the estimated value of the characteristic coefficient of the characteristic model of the current control period can be obtained by utilizing the theoretical derivation of characteristic modeling by combining specific dynamic characteristics
(2) And the definition of θ (k) ═ f1(k),f2(k),g0(k)]T，
Introducing regression vector phi (k) ═ y (k), y (k-1), u (k)]TInitializing k to 0
Initial value of estimated value of characteristic coefficient of characteristic model
May be arbitrarily selected from the above ranges, and phi (0) is [0,0 ]]T、ui(0)＝0、uii(0) And (5) after 0, adding 1 to k in each control period to update k, acquiring a residual acceleration measured value y (k) of the current control period in real time, and executing the steps (3) to (7):
(3) calculating the estimated value of the characteristic coefficient of the characteristic model of the current control period by adopting a gradient descent algorithm
Wherein the content of the first and second substances,
feature model-based residuals representing current control periodResidual acceleration estimator, k (k) ═ phi (k-1)/[ lambda [)2+φ(k-1)Tφ(k-1)]Representing a current control period estimator gain vector;
representing a characteristic model-based residual acceleration estimator for a current control cycle;
(4) according to the residual acceleration measured value y (k) of the current control period, the residual acceleration measured value y (k-1) of the previous control period and the estimated value of the characteristic coefficient of the characteristic model of the current control period
And calculating to obtain a golden section self-adaptive control component u of the current control periodg(k)；
The golden section self-adaptive control component u of the current control periodg(k) Comprises the following steps:
wherein u isg(k) Representing the golden section adaptive control component, l1＝0.382，l2＝0.618，
And
respectively representing the characteristic coefficient estimators of the current control period characteristic model, parameter lambda1Is constant and 0<λ11. ltoreq.1, such as: lambda [ alpha ]1＝0.01。
(5) Calculating a logic integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic integral control law of the current control periodi(k)；
The current control period logic integral control component ui(k) Comprises the following steps:
ui(k)＝ui(k-1)-Kiy(k)
wherein u isi(k) Representing the logically integrated control component, K, of the current control periodiRepresenting the logical integral gain. Logical integral gain KiThe value-taking principle is as follows: when y (k) -y (k-1)]≤Δ1When the logic integral gain is set as the preset logic integral gain minimum value threshold KiminNamely: let Ki＝Kimin(ii) a When y (k) -y (k-1)]>Δ1Time, logic integral gain KiSet as a preset logic integral gain maximum threshold KimaxNamely: let Ki＝Kimax，Kimax>Kimin，KimaxHas a value range of
KiminHas a value range of
The stability and robustness of the closed-loop control system; delta1Is a small positive number, Δ1Has a value range of [0,1 ]]. Such as: when y (k) -y (k-1)]-Δ1When the integral gain is less than or equal to 0, the integral gain Ki100; when y (k) -y (k-1)]-Δ1>Integral gain K at 0i＝400。Δ1＝1×10-15。
(6) Introducing a logic double integral control law according to second-order disturbance characteristics of atmospheric resistance, and calculating a logic double integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic double integral control law of the current control periodii(k)：
uii(k)＝2uii(k-1)-uii(k-2)-Kiiy(k)
Wherein y (k) represents the current control period residual acceleration measurement, uii(k) Representing the logically doubly integrated control component, u, of the current control periodii(k-1) represents the logical double integral control component, u, of the previous control periodii(K-2) represents the logical double integral control component of the upper control period, KiiRepresenting the logical double integral gain, uii(0)＝0，uii(1)＝-Kiiy(1)。
Logic double integral gain KiiThe value-taking principle is as follows: when y (k) -y (k-1)]≤Δ2Time, logic double integral gain KiiSetting as a preset double integral gain minimum threshold KiiminI.e. Kii＝Kiimin(ii) a When y (k) -y (k-1)]>Δ2The logic double integral gain is set as the preset double integral gain maximum value threshold Kiimax(i.e. K)ii＝Kiimax)，Kiimax>Kiimin，KiimaxHas a value range of
KiiminHas a value range of
The stability and robustness of the closed-loop control system; delta2Is a small positive number, Δ2Has a value range of [0,1 ]]. Such as: when y (k) -y (k-1)]-Δ2When the gain is less than or equal to 0, the gain K is doubly integratedii55; when y (k) -y (k-1)]-Δ2>At 0, the gain K is doubly integratedii＝60。Δ2＝1×10-15。
The selection criteria of the logic integral and the logic double integral gain are as follows: first, the gain value of the logical integral is determined, and then the dual integral gain is determined. The specific value range of the gain is different according to different controlled objects. The logic integral gain and logic double integral gain value taking steps of the control method are given in the following aiming at the problem of drag-free control of the spacecraft working in an accelerometer mode:
(a) determining K of integral gain by root trajectory methodiIs 1340. In principle, the integral gain KiValues in the range of 0 to 1340 are feasible, but the higher the integral gain, the higher the low-frequency band control precision, but the worse the robustness of the closed-loop control system, the easy divergence; the integral gain is generally required to be a little smaller. If a robust stability criterion is given, if the H infinite norm of the closed-loop sensitivity function is required to be less than 1.52 to satisfy robustness, the upper bound of the integral gain can be found to be
(b) After the upper bound of the logical integral gain is determined. General selection
Kimin＝0.25×Kimax＝100。
(c) And after the logic integral gain is determined, further determining a logic double integral gain. General experience requires that the logical double integral gain be less than the logical integral gain. When determining the range of the double integral gain, it is assumed that the integral gain does not exactly change, i.e.
Then, a root locus method is utilized to obtain a double integral gain KiiThe upper limit value of (2) is 292. Likewise, the greater the double integral gain, the higher the control accuracy of the low frequency band, but the less robust the closed loop control system. Therefore, engineering practice needs to select double integral gains according to robustness requirements. The H infinite norm of the sensitivity function of the closed-loop system is considered to be less than 1.68, so that the value limit of the logic integral gain is restricted to be
(d) After determining the upper bound of the logical double integral gain. General selection
Kiimin＝0.9×Kiimax＝54。
The key point is to determine the logical integral gain and the logical double productLimit value of sub-gain
And
mainly determined by the robustness of the closed loop system.
(7) Self-adapting the golden section of the current control period to control the component ug(k) Logical integral control component ui(k) Sum logic dual integral control component uii(k) Synthesizing to obtain the input instruction u (k) of the thruster in the current control period: u (k) ═ ug(k)+ui(k)+uii(k)。
As shown in fig. 2, the control system established according to the above-mentioned feature model-based spacecraft non-towing method includes a thruster, an accelerometer, and a non-towing controller, wherein:
the thruster generates thrust acting on the controlled spacecraft along the track direction according to a thruster control instruction sent by the drag-free controller and is used for offsetting atmospheric resistance received by the controlled spacecraft along the track direction;
the accelerometer is used for measuring the residual acceleration of the controlled spacecraft in real time and feeding the residual acceleration back to the drag-free controller;
a drag-free controller receiving a residual acceleration measurement fed back by the accelerometer at each control cycle; considering the dynamic characteristics of a controlled spacecraft, a thruster and an accelerometer, respectively calculating a golden section adaptive control component, a logical integral control component and a logical double integral control component of the current control period according to a golden section adaptive control law, a logical integral control law and a logical double integral control law on the basis of a characteristic model input from a thruster instruction to the accelerometer for measurement and output; and synthesizing the golden section self-adaptive control component, the logic integral control component and the logic dual integral control component of the current control period to obtain a thruster control instruction, and sending the thruster control instruction to the thruster.
The accelerometer can be an electrostatic suspension accelerometer arranged at the center of mass of the spacecraft or six electrostatic suspension accelerometersThe residual acceleration measuring noise of the electrostatic attraction gradiometer consisting of the suspension accelerometer is lower than 2 multiplied by 10 within the measuring frequency band of 5 mHz-0.1 Hz-12m/s2/Hz1/2。
The thruster can be an ion thruster, the output thrust has high-precision continuous regulation capacity, the response time is less than 100ms, the thrust range can cover the peak value of atmospheric resistance, and the low frequency band (a)<2.8mHz) is lower than 5mN/Hz1/2High frequency band of (>0.28Hz) is lower than 0.05mN/Hz1/2. Control period Tc＝0.1s。
Example (b):
the simulation results of integral control, embedded model control and full-coefficient adaptive control with dual integral behavior are given in fig. 3, 4 and 5, respectively. Among the three drag-free control methods: 1) under integral control, embedded model control and full-coefficient adaptive control, the static variances of the residual acceleration are 1.218, 1.906 and 1.004 (multiplied by 10) respectively-13(m/s2)2) It can be seen that the static variance of the method of the invention is superior to integral control and embedded model control; 2) the method and the embedded model control method have equivalent restraining precision of the residual acceleration in the advancing direction in the measuring frequency band (5 mHz-0.1 Hz), and both reach 0.004-0.02 mu m/s2/Hz1/2Are superior to the traditional integral control; 3) full-coefficient adaptive control in the high frequency band (>0.1Hz) is superior to embedded model control and integral control.
In summary, in order to solve the problem of high-precision drag-free control of the spacecraft, the invention performs the following three operations:
(1) the dynamic characteristics of the controlled spacecraft, the thruster and the accelerometer in the spacecraft drag-free control system are considered, the characteristic model from the thruster to the gradiometer is described by a second-order difference equation with slowly time-varying coefficients, the range of the characteristic coefficients can be determined in advance, and the control precision can be improved. .
(2) The invention provides a logic double integral control law, which effectively improves the steady-state control precision, in particular the control precision of a medium-low frequency band, by considering the space environment characteristic of atmospheric resistance in a drag-free control loop of a spacecraft.
(3) The spacecraft full-coefficient self-adaptive drag-free control law designed based on the second-order characteristic model comprises three parts, namely a golden section self-adaptive control law, a logic integral control law and a logic double integral control law. Compared with the existing full-coefficient adaptive control law based on the second-order feature model, which comprises maintenance/tracking control, golden section adaptive control, logic differential control and logic integral control, the non-gravitational acceleration suppression precision of the spacecraft is improved.
Parts of the present invention not described in detail in the specification are within the common general knowledge of those skilled in the art.
Claims (4)
1. A spacecraft drag-free control method based on a feature model is characterized by comprising the following steps:
(1) considering the dynamic characteristics of a controlled spacecraft, a thruster and an accelerometer in a spacecraft drag-free control system, and defining the characteristic model form from the thruster instruction input to the accelerometer measurement output as follows:
y(k+1)＝f1(k)y(k)+f2(k)y(k-1)+g0(k)u(k)
wherein y (k +1) is the residual acceleration measurement value of the next control period, y (k) represents the residual acceleration measurement value of the current control period, and y (k-1) represents the residual acceleration measurement value of the previous control period; u (k) indicates the thruster input command of the current control period, f1(k)、f2(k) And g0(k) Characteristic coefficients representing a characteristic model of the current control period;
(2) and the definition of θ (k) ═ f1(k),f2(k),g0(k)]T，
Introducing regression vector phi (k) ═ y (k), y (k-1), u (k)]TInitializing k to 0
φ(0)＝[0,0,0]T、ui(0)＝0、uii(0) And (5) after 0, adding 1 to k in each control period to update k, acquiring a residual acceleration measured value y (k) of the current control period in real time, and executing the steps (3) to (7):
(3) calculating the estimated value of the characteristic coefficient of the characteristic model of the current control period by adopting a gradient descent algorithm
(4) According to the residual acceleration measured value y (k) of the current control period, the residual acceleration measured value y (k-1) of the previous control period and the estimated value of the characteristic coefficient of the characteristic model of the current control period
And calculating to obtain a golden section self-adaptive control component u of the current control periodg(k)；
The golden section self-adaptive control component u of the current control periodg(k) Comprises the following steps:
in the formula ug(k) Representing the golden section adaptive control component, l1＝0.382，l2＝0.618，
And
respectively representing the characteristic coefficient estimators of the current control period characteristic model, parameter lambda1Is constant and 0<λ1≤1；
(5) Calculating a logic integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic integral control law of the current control periodi(k)；
The current control period logic integral control component ui(k) Comprises the following steps:
ui(k)＝ui(k-1)-Kiy(k))
in the formula ui(k) Representing the logically integrated control component, K, of the current control periodiRepresenting a logical integral gain;
(6) calculating a logic dual integral control component u of the current control period according to the residual acceleration measured value y (k) and the logic dual integral control law of the current control periodii(k)：
uii(k)＝2uii(k-1)-uii(k-2)-Kiiy(k)
In the formula uii(k) Representing the logically doubly integrated control component, u, of the current control periodii(k-1) represents the logical double integral control component, u, of the previous control periodii(K-2) represents the logical double integral control component of the upper control period, KiiRepresenting the logical double integral gain, uii(0)＝0，uii(1)＝-Kiiy(1)；
(7) Self-adapting the golden section of the current control period to control the component ug(k) Logical integral control component ui(k) Sum logic dual integral control component uii(k) Synthesizing to obtain the input instruction u (k) of the thruster in the current control period: u (k) ═ ug(k)+ui(k)+uii(k)。
2. The drag-free control method for the spacecraft based on the feature model according to claim 1, characterized in that: logical integral gain KiThe value-taking principle is as follows: when y (k) -y (k-1)]≤Δ1While, the logic integral gain is set to KiFor a preset logic integral gain minimum threshold Kimin(ii) a When y (k) -y (k-1)]>Δ1Time, logic integral gain KiSet as a preset logic integral gain maximum threshold Kimax，Δ1Has a value range of (0, 1)]。
3. The method of claim 1A spacecraft drag-free control method based on a feature model is characterized by comprising the following steps: the logic double integral gain value principle is as follows: when y (k) -y (k-1)]≤Δ2Time, logic double integral gain KiiSetting as a preset double integral gain minimum threshold Kiimin(ii) a When y (k) -y (k-1)]>Δ2The logic double integral gain is set as the preset double integral gain maximum value threshold Kiimax，Δ2Has a value range of (0, 1)]。
4. The drag-free control method for the spacecraft based on the feature model according to claim 1, characterized in that: step (3) calculating the estimated value of the characteristic coefficient of the characteristic model of the current control period
Then, the amplitude limiting processing is carried out on the digital signal to make the digital signal within a reasonable range.
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| CN110032185A (en) * | 2019-03-27 | 2019-07-19 | 昆明理工大学 | A kind of gondola drive-in unmanned boat bow based on characteristic model is to control method |
| CN111638643B (en) * | 2020-05-07 | 2022-07-05 | 北京控制工程研究所 | Displacement mode drag-free control dynamics coordination condition determination method |
| CN111580388B (en) * | 2020-05-11 | 2022-08-09 | 北京控制工程研究所 | Displacement mode differential golden section self-adaptive drag-free control method |
| CN111505941B (en) * | 2020-05-11 | 2022-07-29 | 北京控制工程研究所 | Acceleration mode self-adaptive drag-free control method based on first-order feature model |
| CN112241122B (en) * | 2020-09-23 | 2021-08-03 | 北京科技大学 | Self-adaptive drag-free control method based on set value identification algorithm |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1290874A (en) * | 2000-11-22 | 2001-04-11 | 中国航天科技集团公司第五研究院第五○二研究所 | Golden-section intelligent control method based on description of object characteristic-model |
| CN101339404A (en) * | 2008-08-07 | 2009-01-07 | 北京航空航天大学 | Aircraft posture kinetics simplified model gain switching proportion-differential control design method |
| CN101364085A (en) * | 2008-09-12 | 2009-02-11 | 北京控制工程研究所 | Non-linear differential calculus golden cut adaptive control method |
| JP2009537926A (en) * | 2006-05-19 | 2009-10-29 | シーメンス エナジー アンド オートメーション インコーポレイテッド | Automated tuning of closed-loop control |
| CN103592844A (en) * | 2012-08-15 | 2014-02-19 | 王景芳 | Incremental PI parameter time varying intelligent optimal control |
| CN105629988A (en) * | 2016-03-31 | 2016-06-01 | 北京航空航天大学 | Anti-interference attitude control method of drag-free satellite |
| CN105912025A (en) * | 2016-06-08 | 2016-08-31 | 北京控制工程研究所 | High-altitude-airship horizontal position control method based on characteristic model |
| CN106200721A (en) * | 2016-07-05 | 2016-12-07 | 上海应用技术学院 | The adaptive temperature controller of feature based model |
-
2017
- 2017-07-31 CN CN201710637329.2A patent/CN107390526B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1290874A (en) * | 2000-11-22 | 2001-04-11 | 中国航天科技集团公司第五研究院第五○二研究所 | Golden-section intelligent control method based on description of object characteristic-model |
| JP2009537926A (en) * | 2006-05-19 | 2009-10-29 | シーメンス エナジー アンド オートメーション インコーポレイテッド | Automated tuning of closed-loop control |
| CN101339404A (en) * | 2008-08-07 | 2009-01-07 | 北京航空航天大学 | Aircraft posture kinetics simplified model gain switching proportion-differential control design method |
| CN101364085A (en) * | 2008-09-12 | 2009-02-11 | 北京控制工程研究所 | Non-linear differential calculus golden cut adaptive control method |
| CN103592844A (en) * | 2012-08-15 | 2014-02-19 | 王景芳 | Incremental PI parameter time varying intelligent optimal control |
| CN105629988A (en) * | 2016-03-31 | 2016-06-01 | 北京航空航天大学 | Anti-interference attitude control method of drag-free satellite |
| CN105912025A (en) * | 2016-06-08 | 2016-08-31 | 北京控制工程研究所 | High-altitude-airship horizontal position control method based on characteristic model |
| CN106200721A (en) * | 2016-07-05 | 2016-12-07 | 上海应用技术学院 | The adaptive temperature controller of feature based model |
Non-Patent Citations (4)
| Title |
|---|
| A new Golden Section method-based maximum power point tracking algorithm for photovoltaic systems;A.Kheldoun等;《Energy Conversion and Management》;20130301;第111卷;第125-136页 * |
| 基于双重积分模型抗扰控制的压电薄壁结构振动控制;张晓宇，等;《西北工业大学学报》;20170228;第35卷(第1期);第82-89页 * |
| 基于特征模型描述的黄金分割智能控制;吴宏鑫;《1996年中国智能自动化学术会议论文集（上册）》;19960831;第409-419页 * |
| 高超声速无人机基于特征模型的机动飞行控制研究;曹智;《中国优秀硕士学位论文全文数据库工程科技II辑》;20130228(第02(2013)期);第C031-69页 * |
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