CN108388135A - A kind of Mars landing track optimized controlling method based on convex optimization - Google Patents
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Abstract
The present invention relates to a kind of Mars landing track optimized controlling methods based on convex optimization, and this method comprises the following steps：(1) it establishes Mars landing kinetic model and carries out convex optimization；(2) with fuel is optimal nominal trajectory is obtained for target progress single offline optimization；(3) Model Predictive Control of rolling time horizon is used to complete Landing Control into line trace to nominal trajectory.Compared with prior art, the present invention effectively combines the Model Predictive Control of convex optimization and rolling time horizon, effectively reduces cumulative errors caused by modeling error and interference, improves landing precision.
Description
Technical Field
The invention relates to a Mars landing trajectory optimization control method, in particular to a Mars landing trajectory optimization control method based on convex optimization.
Background
With the continuous improvement of science and technology in recent years, people meet the demand of space resources at a peak, the interest of Mars exploration is renewed, and the problems of accurate landing of manned aircrafts and the like become more and more important under the background. In the field of aviation, the problem that an aerospace lander accurately lands on a given target place with an error less than several hundred meters under guidance is called an accurate landing problem, and trajectory optimization is a key technology in accurate landing.
In order to ensure safe landing on mars, the scientific research value of each place and the overall terrain conditions (such as gradient and roughness) need to be comprehensively considered. For tasks of natural Exploration nature, the specific landing site within a predetermined error range is not particularly important, such as in the Mars application roads task by NASA, where the landing error can be as large as 35 km. On the other hand, in the demands of some research tasks, it may be necessary to land accurately on dangerous terrain or land at a designated ground location such as a fuel supply point. For example, with the continuous deepening of Chang' e engineering and manned aerospace engineering in China, future lunar exploration is an important research direction in China, and under the background, it is expected that a detector with trajectory optimization and high-precision fixed-point soft landing capability is indispensable
However, the difficulty in the task of Mars landing is that the constraint conditions are numerous, and a large amount of nonlinear time-varying terms exist, so that the system requirements are difficult to achieve by using a common optimization method. An analytical solution of the optimal thrust of a general three-dimensional problem with additional states and control constraints is difficult to obtain, so that a rapid trajectory optimization algorithm suitable for an airborne system is particularly important.
In related researches at home and abroad, common methods include a numerical solving method using a direct configuration method, a direct multiple-shooting method and a Legendre pseudo-spectrum method and a traditional guidance method based on a polynomial, wherein a track based on a quartic polynomial in time is applied to an Apollo task. However, in the case where convergence of the correlation algorithm is unclear, a real-time on-board nonlinear programming solution obtained by a general iterative algorithm may not be a desired result. Since accurate Mars landing requires airborne real-time calculation of the optimal trajectory, an algorithm ensuring convergence to global optimum with deterministic convergence should be designed according to the structure of the problem. Convex optimization has the property of global optimality, so trajectory optimization based on convex optimization will be the direction of research.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a Mars landing trajectory optimization control method based on convex optimization.
The purpose of the invention can be realized by the following technical scheme:
a Mars landing trajectory optimization control method based on convex optimization is characterized by comprising the following steps:
(1) establishing a Mars landing dynamic model and performing convex optimization;
(2) carrying out single off-line optimization by taking the fuel optimization as a target to obtain a nominal track;
(3) and tracking the nominal track by adopting model prediction control of a rolling time domain to finish landing control.
The Mars landing dynamic model in the step (1) is specifically as follows:
Tc＝(nTcosφ)e，
wherein r ∈ IR3，v∈IR3，Tc∈IR3R is the position vector of the spacecraft relative to the target point, v is the velocity vector of the spacecraft in the ground fixed coordinate system, TcThrust vector, IR, to which the spacecraft is subjected3Representing an xyz three-axis coordinate system,is the component of the derivative of the spacecraft with respect to the target point position vector in the direction of the i-axis, viIs the component of the velocity vector of the spacecraft in the fixed coordinate system on the ground in the direction of the i axis, i is x, y, z, m is the mass of the spacecraft, mu is the gravitational constant of mars, α is the constant of the fuel consumption rate, g belongs to IR3G is the gravitational acceleration constant of Mars, IspFor the specific impulse of the propeller, e ∈ IR3E is the instantaneous thrust direction vector, geIs the earth gravity constant, phi is the included angle between the propeller and the instantaneous thrust direction vector e, and n is the number of the sub-propellers.
The convex optimization in the step (1) is specifically as follows:
(11) establishing thrust amplitude constraint:
ρ1＝nT1cosφ，
ρ2＝nT2cosφ，
where ρ is1And ρ2Lower and upper limits of thrust amplitude, tfThe final time of the power descent landing process, n is the number of the sub-propellers, T1Is the non-zero minimum thrust, T, of the sub-thruster2The maximum thrust of the sub-thruster is obtained;
displacement non-negative constraint:
rx＞0，
wherein r isxIs the component of the spacecraft in the direction of the x axis relative to the position vector of the target point;
height angle constraint:
wherein,for a given safety angle, θalt(t) is the altitude of the spacecraft to the target point;
(12) introducing a relaxation variable Γ (T) and an additional constraint | Tc(t) | < Γ (t), variable substitutionAnd further obtaining thrust constraint conditions after convex optimization:
ρ1and ρ2Lower and upper bounds for thrust amplitude, z and z0Representing the quality variable after the variable replacement and the initial value thereof, and sigma representing the new variable after the relaxation variable transformation.
The optimization model after single off-line optimization by taking the fuel optimization as a target in the step (2) is as follows:
0＜ρ1≤‖Tc(t)‖≤ρ2，
‖SX‖+cTX≤0，
m(0)＝mwet，
r(0)＝r0r(tf)＝0，
wherein S and c are both coefficient matrices:
mwetindicating the initial mass at full lander fuel,representing the final mass of the lander when landed and X representing a state vector containing spacecraft position and velocity information.
The step (2) of obtaining the nominal track specifically comprises the following steps: and solving the optimization model by adopting a CVX tool box with Mosek as a solver to obtain a state vector X representing the position and speed information of the spacecraft.
The step (3) is specifically as follows:
(31) establishing a prediction model:
X(k+i|k)＝A(k+i-1|k)X(k+i-1|k)+B(k+i-1|k)U(k+i-1|k)，
wherein X (k + i | k) represents a predicted value of state information at the time k to the time k + i, U (k + i-1| k) represents an input quantity at the time k-1 obtained in an optimization result at the time k, A (k + i-1| k) represents a predicted value of a state matrix A at the time k to the time k + i-1, B (k + i-1| k) represents a predicted value of an input matrix B at the time k to the time k + i-1, the state information comprises spacecraft position and speed information, and the input quantity is thrust;
(32) establishing an optimization objective function:
wherein, Xopt(k + i) represents the target value of the trajectory tracking at the time k + i, Q is a symmetric positively determined parameter matrix, p is the step size of the rolling optimization, X (N | k) represents the predicted value of the state information at the time k to the final time, and Xopt(N) represents the target value of the track tracking at the final moment, and R is a symmetrical positive definite parameter matrix;
(33) taking the prediction model established in the step (31) as a constraint condition, taking the objective function in the step (32) as an optimization index, performing finite time domain online rolling optimization with the step length of p at any time k, and obtaining p optimized control sequences U (k + i | k) at the time k;
(34) and at each current moment, substituting the control quantity and the position information of the current moment in the optimization result into the Mars landing dynamics model to obtain the real position information of the next moment, taking the real position information as the optimization initial information of the next moment, performing rolling optimization on the whole time domain until the optimization is finished, and finishing the control work of the lander by taking the optimization control quantity of the current moment as input each time.
Compared with the prior art, the invention has the following advantages:
(1) according to the method, the result obtained by solving can be guaranteed to be globally optimal through the optimization algorithm based on convex optimization, the defect that the traditional method can be converged to be locally optimal is avoided, and therefore the obtained fuel optimal track is the globally optimal result;
(2) the nominal track is tracked through model prediction control of a rolling time domain, so that accumulated errors caused by modeling errors or actual disturbance can be effectively avoided, the drift between a real track and an optimized track is reduced, and the control method has higher precision compared with a single off-line optimization control method;
(3) convex optimization and rolling time domain model prediction control are effectively combined, so that modeling errors and accumulated errors caused by interference are effectively reduced, and landing accuracy is improved.
Drawings
FIG. 1 is a flow chart of a Mars landing trajectory optimization control method based on convex optimization according to the present invention;
FIG. 2 is a schematic view of the altitude constraint cone during landing;
FIG. 3 is a flow chart of a model predictive control algorithm.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a mars landing trajectory optimization control method based on convex optimization includes the following steps:
(1) establishing a Mars landing dynamic model and performing convex optimization;
(2) carrying out single off-line optimization by taking the fuel optimization as a target to obtain a nominal track;
(3) and tracking the nominal track by adopting model prediction control of a rolling time domain to finish landing control.
The Mars landing dynamic model in the step (1) is specifically as follows:
Tc＝(nTcosφ)e，
wherein r ∈ IR3，v∈IR3，Tc∈IR3R is the position vector of the spacecraft relative to the target point, v is the velocity vector of the spacecraft in the ground fixed coordinate system, TcThrust vector, IR, to which the spacecraft is subjected3Representing an xyz three-axis coordinate system,is the component of the derivative of the spacecraft with respect to the target point position vector in the direction of the i-axis, viIs the component of the velocity vector of the spacecraft in the fixed coordinate system on the ground in the direction of the i axis, i is x, y, z, m is the mass of the spacecraft, mu is the gravitational constant of mars, α is the constant of the fuel consumption rate, g belongs to IR3G is the gravitational acceleration constant of Mars, IspFor the specific impulse of the propeller, e ∈ IR3E is the instantaneous thrust direction vector, geIs the earth gravity constant, phi is the included angle between the propeller and the instantaneous thrust direction vector e, and n is the number of the sub-propellers.
The convex optimization in the step (1) is specifically as follows:
(11) establishing thrust amplitude constraint:
ρ1＝nT1cosφ，
ρ2＝nT2cosφ，
where ρ is1And ρ2Lower and upper limits of thrust amplitude, tfThe final time of the power descent landing process, n is the number of the sub-propellers, T1Is the non-zero minimum thrust, T, of the sub-thruster2The maximum thrust of the sub-thruster is obtained;
displacement non-negative constraint:
rx＞0，
wherein r isxIs the component of the spacecraft in the direction of the x axis relative to the position vector of the target point;
height angle constraint:
wherein,for a given safety angle, θalt(t) is the altitude angle of the spacecraft to the target point, and the altitude angle constraint cone schematic diagram is shown in FIG. 2;
(12) introducing a relaxation variable Γ (T) and an additional constraint | Tc(t) | < Γ (t), variable substitutionAnd further obtaining thrust constraint conditions after convex optimization:
ρ1and ρ2Lower and upper bounds for thrust amplitude, z and z0Representing the quality variable after the variable replacement and the initial value thereof, and sigma representing the new variable after the relaxation variable transformation.
The optimization model after single off-line optimization by taking the fuel optimization as a target in the step (2) is as follows:
0＜ρ1≤‖Tc(t)‖≤ρ2，
‖SX‖+cTX≤0，
m(0)＝mwet，
r(0)＝r0r(tf)＝0，
wherein S and c are both coefficient matrices:
mwetindicating the initial mass at full lander fuel,representing the final mass of the lander when landed and X representing a state vector containing spacecraft position and velocity information.
The step (2) of obtaining the nominal track specifically comprises the following steps: and solving the optimization model by adopting a CVX tool box with Mosek as a solver to obtain a state vector X representing the position and speed information of the spacecraft.
The step (3) is specifically as follows:
(31) establishing a prediction model:
X(k+i|k)＝A(k+i-1|k)X(k+i-1|k)+B(k+i-1|k)U(k+i-1|k)，
wherein X (k + i | k) represents a predicted value of state information at the time k to the time k + i, U (k + i-1| k) represents an input quantity at the time k-1 obtained in an optimization result at the time k, A (k + i-1| k) represents a predicted value of a state matrix A at the time k to the time k + i-1, B (k + i-1| k) represents a predicted value of an input matrix B at the time k to the time k + i-1, the state information comprises spacecraft position and speed information, and the input quantity is thrust;
(32) establishing an optimization objective function:
wherein, Xopt(k + i) represents the target value of the trajectory tracking at the time k + i, Q is a symmetric positively determined parameter matrix, p is the step size of the rolling optimization, X (N | k) represents the predicted value of the state information at the time k to the final time, and Xopt(N) represents the target value of the track tracking at the final moment, and R is a symmetrical positive definite parameter matrix;
(33) taking the prediction model established in the step (31) as a constraint condition, taking the objective function in the step (32) as an optimization index, performing finite time domain online rolling optimization with the step length of p at any time k, and obtaining p optimized control sequences U (k + i | k) at the time k;
(34) and at each current moment, substituting the control quantity and the position information of the current moment in the optimization result into the Mars landing dynamics model to obtain the real position information of the next moment, taking the real position information as the optimization initial information of the next moment, performing rolling optimization on the whole time domain until the optimization is finished, and finishing the control work of the lander by taking the optimization control quantity of the current moment as input each time.
In specific implementation, the system mainly comprises three parts, namely a sensor module, a guidance and control module and an actuator module.
The sensor module comprises a radar detector arranged at the bottom of the Mars lander and an IMU inertia measuring element for measuring the real-time flight state of the lander. The guidance control module is a calculation and control unit with an embedded processor as a core and has stronger operational capability. The actuating mechanism consists of n sub-thrusters symmetrically arranged at the bottom of the lander, and the thrust is adjusted by the opening and closing of the throttle valve.
Furthermore, the core module where guidance and control are located can be designed according to the actual task requirements under the system framework.
The Mars landing trajectory optimization and control scheme based on convex optimization comprises the following specific implementation steps:
the first step is as follows: and (4) designing an embedded controller. Under the conditions that the lander is only under the action of gravity and counter-thrust and other forces caused by wind are ignored and the assumption of a uniform gravity field, a translational motion model of the Mars lander in the landing process is established.
After the motion model of the research object is established, a safe altitude angle is set according to physical factors, engineering limitations and actual task requirements, the constraints of the lander are analyzed and embossed under the condition of considering the altitude angle constraints, the embossed optimization and control model is obtained, and the embossed optimization and control model is written into an embedded controller of a calculation control module before each task is executed.
The second step is that: and (5) performing single off-line optimization. And calling corresponding CPU (central processing unit) operation to perform single off-line optimization through a CVX (composite variable X) tool box built in the embedded system according to the optimization problem established in the first step to obtain the optimal thrust and the optimal track with optimal fuel consumption, and storing the optimal thrust and the optimal track as a nominal track and a reference input in a microcomputer unit.
The third step: and (4) state measurement and input. The method comprises the steps of measuring the speed and the acceleration of a lander in the landing process in real time according to an IMU (inertial measurement Unit) in a sensor module, measuring the current relative position information of the lander and a target point through a radar detector, and connecting the measured value of the state information at the moment into an embedded type of a guidance and control module as input.
The fourth step: and (5) model prediction. And taking the current state as initial information at each moment, predicting the flight track in the next period of time by using the established motion control model, and solving the optimal control problem in the period of time by optimization. For the accuracy of the model, at each moment, only the control information of the current moment in a plurality of control quantities in the optimization result is adopted, and the state of the next moment is obtained according to the current state information and the control quantities and is used as the initial information of the next rolling time domain optimization. Each sub-optimization is only a small section on the optimization time domain axis, the optimization is repeated continuously, namely, the optimization is performed in a rolling mode on the time domain until the complete optimization of the whole process is realized, and the specific algorithm flow is shown in an attached figure 3.
The concrete model is as follows:
X(k+i|k)＝A(k+i-1|k)X(k+i-1|k)+B(k+i-1|k)U(k+i-1|k)，
in the optimization of the rolling time domain, the main consideration factor for selecting the performance index is the tracking condition of a nominal track, and the corresponding optimization objective function is as follows:
the fifth step: and (4) rolling optimization and real-time control.
(1) The problem is translated into a form of model prediction in the rolling time domain. And determining a good rolling time domain value P, and adjusting the optimization time to k equal to 0 given initial state information.
(2) And determining the value of Q in the target function, solving the optimal control problem in the rolling time domain by using a CVX tool box, and solving a series of state information and control information in the rolling time domain.
(3) And at the current moment, bringing the control quantity and the current position information of the current moment in the optimization result into the aircraft motion model to obtain the real position information of the next moment, and taking the real position information as the initial information of the next moment optimization.
(4) Repeating the steps to carry out repeated optimization, stopping the optimization when k is equal to N, obtaining a complete optimization result, and ending the real-time control process.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (6)
1. A Mars landing trajectory optimization control method based on convex optimization is characterized by comprising the following steps:
(1) establishing a Mars landing dynamic model and performing convex optimization;
(2) carrying out single off-line optimization by taking the fuel optimization as a target to obtain a nominal track;
(3) and tracking the nominal track by adopting model prediction control of a rolling time domain to finish landing control.
2. The Mars landing trajectory optimization control method based on convex optimization according to claim 1, wherein the Mars landing dynamics model in the step (1) is specifically as follows:
Tc＝(nTcosφ)e，
wherein r ∈ IR3，v∈IR3，Tc∈IR3R is the position vector of the spacecraft relative to the target point, v is the velocity vector of the spacecraft in the ground fixed coordinate system, TcThrust vector, IR, to which the spacecraft is subjected3Representing an xyz three-axis coordinate system,is the component of the derivative of the spacecraft with respect to the target point position vector in the direction of the i-axis, viIs the component of the velocity vector of the spacecraft in the fixed coordinate system on the ground in the direction of the i axis, i is x, y, z, m is the mass of the spacecraft, mu is the gravitational constant of mars, α is the constant of the fuel consumption rate, g belongs to IR3G is the gravitational acceleration constant of Mars, IspFor the specific impulse of the propeller, e ∈ IR3E is the instantaneous thrust direction vector, geIs the earth gravity constant, phi is the included angle between the propeller and the instantaneous thrust direction vector e, and n is the number of the sub-propellers.
3. The Mars landing trajectory optimization control method based on convex optimization according to claim 2, characterized in that the convex optimization in the step (1) is specifically:
(11) establishing thrust amplitude constraint:
ρ1＝nT1cosφ，
ρ2＝nT2cosφ，
where ρ is1And ρ2Lower and upper limits of thrust amplitude, tfThe final time of the power descent landing process, n is the number of the sub-propellers, T1Is the non-zero minimum thrust, T, of the sub-thruster2The maximum thrust of the sub-thruster is obtained;
displacement non-negative constraint:
rx＞0，
wherein r isxIs the component of the spacecraft in the direction of the x axis relative to the position vector of the target point;
height angle constraint:
wherein,for a given safety angle, θalt(t) is the altitude of the spacecraft to the target point;
(12) introducing a relaxation variable Γ (T) and an additional constraint | Tc(t) | < Γ (t), variable substitutionAnd further obtaining thrust constraint conditions after convex optimization:
ρ1and ρ2Lower and upper bounds for thrust amplitude, z and z0Representing the quality variable after the variable replacement and the initial value thereof, and sigma representing the new variable after the relaxation variable transformation.
4. The Mars landing trajectory optimization control method based on convex optimization according to claim 3, wherein the optimization model of the step (2) after single off-line optimization with the fuel optimization as the target is as follows:
0＜ρ1≤‖Tc(t)‖≤ρ2，
‖SX‖+cTX≤0，
m(0)＝mwet，
r(0)＝r0r(tf)＝0，
wherein S and c are both coefficient matrices:
mwetindicating the initial mass at full lander fuel,representing the final mass of the lander when landed and X representing a state vector containing spacecraft position and velocity information.
5. The Mars landing trajectory optimization control method based on convex optimization according to claim 4, wherein the step (2) of obtaining the nominal trajectory specifically comprises: and solving the optimization model by adopting a CVX tool box with Mosek as a solver to obtain a state vector X representing the position and speed information of the spacecraft.
6. The Mars landing trajectory optimization control method based on convex optimization according to claim 2, wherein the step (3) is specifically as follows:
(31) establishing a prediction model:
X(k+i|k)＝A(k+i-1|k)X(k+i-1|k)+B(k+i-1|k)U(k+i-1|k)，
wherein X (k + i | k) represents a predicted value of state information at the time k to the time k + i, U (k + i-1| k) represents an input quantity at the time k-1 obtained in an optimization result at the time k, A (k + i-1| k) represents a predicted value of a state matrix A at the time k to the time k + i-1, B (k + i-1| k) represents a predicted value of an input matrix B at the time k to the time k + i-1, the state information comprises spacecraft position and speed information, and the input quantity is thrust;
(32) establishing an optimization objective function:
wherein, Xopt(k + i) represents the target value of the trajectory tracking at the time k + i, Q is a symmetric positively determined parameter matrix, p is the step size of the rolling optimization, X (N | k) represents the predicted value of the state information at the time k to the final time, and Xopt(N) represents the final time trace trackingR is a symmetric positive definite parameter matrix;
(33) taking the prediction model established in the step (31) as a constraint condition, taking the objective function in the step (32) as an optimization index, performing finite time domain online rolling optimization with the step length of p at any time k, and obtaining p optimized control sequences U (k + i | k) at the time k;
(34) and at each current moment, substituting the control quantity and the position information of the current moment in the optimization result into the Mars landing dynamics model to obtain the real position information of the next moment, taking the real position information as the optimization initial information of the next moment, performing rolling optimization on the whole time domain until the optimization is finished, and finishing the control work of the lander by taking the optimization control quantity of the current moment as input each time.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130332065A1 (en) * | 2012-06-12 | 2013-12-12 | Trx Systems, Inc. | Fusion Of Sensor And Map Data Using A Contraint Based Optimization |
| CN104267734A (en) * | 2014-08-01 | 2015-01-07 | 北京理工大学 | Mars complex terrain region safe landing trajectory generation method with minimum fuel consumption |
| CN105867402A (en) * | 2016-05-10 | 2016-08-17 | 北京航空航天大学 | Anti-interference composite online guiding method of atmosphere entering period of Mars lander |
| CN105929844A (en) * | 2016-04-26 | 2016-09-07 | 哈尔滨工业大学 | Obstacle avoidance method for soft landing of object outside earth under multi-obstacle constraint environment |
| CN106778012A (en) * | 2016-12-29 | 2017-05-31 | 北京理工大学 | A kind of small feature loss attachment detection descending trajectory optimization method |
| CN107202584A (en) * | 2017-07-06 | 2017-09-26 | 北京理工大学 | A kind of planet precision landing anti-interference method of guidance |
| CN107300386A (en) * | 2017-06-05 | 2017-10-27 | 西北工业大学 | A kind of convex optimization Optimal Rendezvous method of guidance of closed loop navigated based on only angle measurement |
-
2018
- 2018-03-30 CN CN201810279467.2A patent/CN108388135B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130332065A1 (en) * | 2012-06-12 | 2013-12-12 | Trx Systems, Inc. | Fusion Of Sensor And Map Data Using A Contraint Based Optimization |
| CN104267734A (en) * | 2014-08-01 | 2015-01-07 | 北京理工大学 | Mars complex terrain region safe landing trajectory generation method with minimum fuel consumption |
| CN105929844A (en) * | 2016-04-26 | 2016-09-07 | 哈尔滨工业大学 | Obstacle avoidance method for soft landing of object outside earth under multi-obstacle constraint environment |
| CN105867402A (en) * | 2016-05-10 | 2016-08-17 | 北京航空航天大学 | Anti-interference composite online guiding method of atmosphere entering period of Mars lander |
| CN106778012A (en) * | 2016-12-29 | 2017-05-31 | 北京理工大学 | A kind of small feature loss attachment detection descending trajectory optimization method |
| CN107300386A (en) * | 2017-06-05 | 2017-10-27 | 西北工业大学 | A kind of convex optimization Optimal Rendezvous method of guidance of closed loop navigated based on only angle measurement |
| CN107202584A (en) * | 2017-07-06 | 2017-09-26 | 北京理工大学 | A kind of planet precision landing anti-interference method of guidance |
Non-Patent Citations (1)
| Title |
|---|
| 林晓辉 等: "基于凸优化理论的含约束月球定点着陆轨道优化", 《宇航学报》 * |
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| CN110562493A (en) * | 2019-09-06 | 2019-12-13 | 北京理工大学 | Mars power descending trajectory planning method based on vector trajectory |
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| CN113467498A (en) * | 2021-07-14 | 2021-10-01 | 西北工业大学 | Carrier rocket ascending section trajectory planning method based on Bezier-convex optimization |
| CN115291526A (en) * | 2022-09-30 | 2022-11-04 | 北京航天自动控制研究所 | Optimal tracking guidance method based on rolling time domain |
| CN118349020A (en) * | 2024-06-17 | 2024-07-16 | 西北工业大学宁波研究院 | Single-rolling motion control method of wheel control detector based on preset performance |
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