CN109901603B - Multi-spacecraft attitude cooperative control method under input time delay - Google Patents
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Abstract
The invention relates to a multi-space vehicle attitude cooperative control method under input time delay, in particular to a multi-space vehicle distributed attitude cooperative control method based on a fuzzy theory, and the input time delay existing in a controller is considered; constructing a multi-aircraft attitude dynamics system into a fuzzy system consisting of a series of fuzzy logics by utilizing a T-S fuzzy theory; designing a distributed controller aiming at the constructed fuzzy system to obtain a closed-loop system, and carrying out equivalent transformation on the closed-loop system; aiming at the converted equivalent system, a sufficient condition for ensuring the stability of the system is given by utilizing a time delay dependence Lyapunov stability theory, and the parameters of the controller are designed by utilizing a linear matrix inequality method. Compared with the traditional nonlinear compensation control method, the control method based on the fuzzy theory has the advantages of simpler structure, stronger robustness aiming at input time delay and lower conservatism.
Description
Technical Field
The invention belongs to a multi-space vehicle attitude cooperative control method, and relates to a multi-space vehicle distributed attitude cooperative control method based on a fuzzy theory under input time delay.
Background
With the continuous progress of science and technology, the ability of human beings to develop and utilize space and the strategic demand are increasing. The spacecraft is an intelligent robot for human to develop and utilize space, and has particularly important research value and strategic significance. The formation system of the spacecraft is composed of a group of aircrafts which are distributed in space and fly around each other, and all member aircrafts in the formation cooperatively work on the basis of inter-satellite information interaction to form a virtual aircraft so as to complete space tasks which cannot be completed by a traditional single aircraft. Compared with the traditional single space aircraft, the multi-aircraft formation system has incomparable advantages in the aspects of system robustness, redundancy, reconfigurability and the like. Attitude coordination becomes one of the most important behaviors in a spacecraft formation system, for example, in the tasks of 'distributed space interferometer' and 'synthetic aperture radar', the relative attitude between aircrafts is required to form a specific angle. In addition, in practical terms, the nature of the various electronic components and circuitry itself causes inevitable delays in the feedback loop, actuators and sensors of the control system. For a spacecraft formation system, in the process of realizing relative information interaction by utilizing wireless communication among aircrafts, the time delay phenomenon is particularly obvious due to the fact that the communication distance is long and a large amount of electromagnetic interference exists in the space environment. Mishandling of the latency problem can not only affect the performance of the system, but can even lead to divergence of the overall control system.
Most of the existing researches adopt the traditional nonlinear compensation method to solve the attitude cooperative control problem of a multi-space aircraft formation system, and nonlinear items in the system are used as control compensation items to be fed back to the original system. The nonlinear compensation control method has the advantages that the controller is easy to design, but the defects are obvious, and mainly comprise: 1) the controller has a complex structure, and the burden of a control system is increased; 2) the robustness for input delay is weak, and the stability of the system even cannot be ensured under the condition of larger delay; 3) the limitation on the topological structure between the aircrafts is strong, and the conservation of the result is further increased.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides the multi-space vehicle attitude cooperative control method under the input delay, and compared with the traditional nonlinear compensation method, the improved method provided by the invention can reduce the burden of a control system, and has stronger robustness and lower conservative property aiming at the input delay.
Technical scheme
A multi-space aircraft attitude cooperative control method under input time delay is characterized by comprising the following steps:
the multi-aircraft attitude dynamics model:
in the formula, JiRepresenting an inertia matrix; q. q.si(t) and qi0(t) vector and scalar sections representing attitude quaternions, respectively; omegai(t) represents an attitude angular velocity; u. ofi(t) represents control inputs acting on the aircraft; τ (t) represents an input delay variable and has a sum of 0 ≦ τ (t) ≦ τ
Where τ and ρ are positive constants;
equation (1) is formulated as a state space equation as follows:
the nonlinear system shown in equation (2) is systemized as the following fuzzy system using the T-S fuzzy system criterion:
system fuzzy rule mi: if xi1(t) is
And … and xi6(t) is
Then
Wherein,
representing a fuzzy set of the system, and r representing the total number of fuzzy rules;
using the weighted average of each linear subsystem, the following system is obtained:
b isiThe expression of (a) is as follows:
said xi(t)、ωi(t)、qi(t) is defined as follows:
xi(t)＝[xi1(t)xi2(t)xi3(t)xi4(t)xi5(t)xi6(t)]T,
ωi(t)＝[ωi1(t)ωi2(t)ωi3(t)]T, (6)
qi(t)＝[qi1(t)qi2(t)qi3(t)]T.
a is describedi(xi(t)) and the membership functions of the fuzzy weight terms are expressed as follows:
the following fuzzy controller was designed using fuzzy criteria:
controller fuzzy rule mi: if xi1(t) is
And … and xi6(t) is
Then
Wherein
For the control gain matrix to be solved, aijRepresenting the weight of the communication state between the aircrafts, biThe acquisition capability of the aircraft on the self state information is represented; using the weighted average of each linear subsystem, the controller is obtained as follows:
substituting equation (9) into equation (4) results in a closed loop system as shown below:
using fuzzy weighting terms
And
the property of (2) is equivalently transformed into the following form:
said Ki(xi(t)) and the membership functions of the fuzzy weight terms are expressed as follows:
the X (t), U (t-t (t)), (ii),
The expression of (a) is as follows:
the above-mentioned
The expression of (a) is as follows:
for the fuzzy system (11) after the equivalent transformation, the following Lyapunov function is chosen:
wherein P and Q are positive definite symmetric matrices; according to the Lyapunov stability theory, a positive definite symmetric matrix R is given, and if the following linear matrix inequality has the positive definite symmetric matrix
As its feasible solution:
the closed loop system (11) is gradually stabilized and the control gain matrix is
According to the expression of the designed control gain matrix
And fuzzy weighted term
Independently, the controller designed in equation (9) is implemented as follows:
the controller given in equation (17) is a linear controller, and the controller does not require that each aircraft be able to obtain its own state information xi(t) as long as at least one aircraft can obtain self-state information, namely at least one node i exists, so that bi≠0；
The above-mentioned
The expression of (a) is as follows:
Advantageous effects
The invention provides a multi-space vehicle attitude cooperative control method under input delay, in particular to a multi-space vehicle distributed attitude cooperative control method based on a fuzzy theory, and the input delay existing in a controller is considered; constructing a multi-aircraft attitude dynamics system into a fuzzy system consisting of a series of fuzzy logics by utilizing a T-S fuzzy theory; designing a distributed controller aiming at the constructed fuzzy system to obtain a closed-loop system, and carrying out equivalent transformation on the closed-loop system; aiming at the converted equivalent system, a sufficient condition for ensuring the stability of the system is given by utilizing a time delay dependence Lyapunov stability theory, and the parameters of the controller are designed by utilizing a linear matrix inequality method. Compared with the traditional nonlinear compensation control method, the control method based on the fuzzy theory has the advantages of simpler structure, stronger robustness aiming at input time delay and lower conservatism.
The invention has the following beneficial effects:
(1) compared with the traditional nonlinear compensation control method, the distributed control method based on the fuzzy theory provided by the invention can enable the structure of the controller to be simpler and reduce the burden of a control system. The computer configuration adopted in the simulation is as follows: 8-core CPU i7-7700, 32GB RAM, the simulation step length is 0.1 second, and Runge-Kutta is adopted by the resolver. When the input time delay is four cases of tau (t) being 0.1sin (0.1t) +0.1, tau (t) being sin (0.1t) +1, tau (t) being 3sin (0.1t) +3 and tau (t) being 5sin (0.1t) +5, respectively, the computer calculation time required by adopting the method provided by the invention is respectively 0.25 times, 0.33 times, 0.37 times and 0.38 times of that of adopting the traditional method. Therefore, compared with the traditional method, the method provided by the invention can greatly improve the resolving speed of the computer and reduce the burden of the computer.
(2) Compared with the traditional nonlinear compensation control method, the distributed control method based on the fuzzy theory provided by the invention has stronger robustness to input time delay. Taking four cases that input time delay is tau (t) 0.1sin (0.1t) +0.1, tau (t) sin (0.1t) +1, tau (t) 3sin (0.1t) +3 and tau (t) 5sin (0.1t) +5 respectively, when adopting a traditional method, the time for realizing attitude synchronous convergence of a multi-aircraft system is 40 seconds, 100 seconds and 110 seconds respectively, and the maximum control input torque is 0.35 N.m, 0.48 N.m, 6.6 N.m and 7 N.m respectively; the method provided by the invention can ensure that the postures of the multi-aircraft system are synchronously converged within 40 seconds, 50 seconds and 90 seconds respectively, and the required maximum control input torques are 0.17 N.m, 0.19 N.m, 0.3 N.m and 0.51 N.m respectively. Therefore, compared with the traditional method, the method provided by the invention can realize faster attitude angle convergence speed with smaller control torque, and the larger the input time delay is, the more obvious the advantages of the method provided by the invention are.
(3) In addition, the method provided by the invention is a distributed control method, namely all aircrafts are not required to be capable of acquiring the absolute state information of the aircrafts, and only communication topology among the aircrafts is required to be ensured to form a spanning tree; however, the conventional non-linear compensation controller includes the non-linear term of each aircraft, so that all aircraft are required to acquire the absolute state information of each aircraft. Therefore, the control method provided by the invention has weaker limitation on the communication topology among multiple aircrafts, and can be suitable for the situation that the traditional control method cannot be applied.
Drawings
FIG. 1: according to the communication topological structure among the 3 spacecraft in the Case I, all 3 aircrafts can obtain self state information;
FIG. 2: in the embodiment of the invention, under the action of a control method based on a fuzzy theory, a relative attitude angle error curve between the 1 st aircraft and the 2 nd aircraft is obtained;
FIG. 3: in the embodiment of the invention, under the action of a nonlinear compensation control method, a relative attitude angle error curve between the 1 st aircraft and the 2 nd aircraft is obtained;
FIG. 4: in the embodiment of the invention, under the action of a control method based on a fuzzy theory, the control input curve of the 1 st aircraft is obtained;
FIG. 5: in the embodiment of the invention, under the action of a nonlinear compensation control method, a control input curve of the 1 st aircraft is obtained;
FIG. 6: in the embodiment of the invention, the communication topological structure among 3 spacecraft in Case II, only the 1 st spacecraft can obtain the self state information;
FIG. 7: in the embodiment of the invention, 3 spacecraft in Case II have attitude angle curves under the action of a control method based on a fuzzy theory;
FIG. 8: in the embodiment of the invention, 3 spacecraft in Case II control input curves under the action of a control method based on a fuzzy theory;
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
the method comprises the following steps: the multi-aircraft attitude dynamics system is constructed into a fuzzy system consisting of a series of fuzzy logics by utilizing the T-S fuzzy theory. Firstly, the following multi-aircraft attitude dynamics models are given:
in the formula, JiRepresenting an inertia matrix; q. q.si(t) and qi0(t) vector and scalar sections representing attitude quaternions, respectively; omegai(t) represents an attitude angular velocity; u. ofi(t) represents control inputs acting on the aircraft; τ (t) represents an input delay variable and has a sum of 0 ≦ τ (t) ≦ τ
Where τ and ρ are positive constants. Furthermore, equation (1) can also be formulated as the following equation of state space:
in the formula,
to facilitate the construction of the fuzzy system, the following variables are defined:
xi(t)＝[xi1(t)xi2(t)xi3(t)xi4(t)xi5(t)xi6(t)]T,
ωi(t)＝[ωi1(t)ωi2(t)ωi3(t)]T,
qi(t)＝[qi1(t)qi2(t)qi3(t)]T.
the nonlinear system shown in equation (2) is systemized as the following fuzzy system using the T-S fuzzy system criterion:
system fuzzy rule mi: if xi1(t) is
And … and xi6(t) is
Then
Wherein,
representing the fuzzy set of the system and r representing the total number of fuzzy rules. With a weighted average of the various linear subsystems, a system can be obtained as follows:
in the formula,
step two: and designing a distributed controller aiming at the constructed fuzzy system (4) to obtain a closed-loop system, and carrying out equivalent transformation on the closed-loop system. Firstly, the following fuzzy controllers are designed by using fuzzy criteria:
controller fuzzy rule mi: if xi1(t) is
And … and xi6(t)Is that
Then
Wherein
For the control gain matrix to be solved, aijRepresenting the weight of the communication state between the aircrafts, biThe aircraft state information acquisition capability is shown. With a weighted average of the various linear subsystems, the controller can be derived as follows:
in the formula,
by substituting equation (9) into equation (4), a closed loop system as shown below can be obtained:
in the formula,
since the structure of the equation (10) is complicated, it is difficult to analyze the stability, and thus an equivalent transformation is required. Using fuzzy weighting terms
And
the equation (10) can be equivalently transformed into the following form:
in the formula,
step three: aiming at the converted equivalent system, a time delay dependence Lyapunov stability theory and a linear matrix inequality method are utilized to provide sufficient conditions for ensuring the stability of the system, and controller parameters are designed. For the fuzzy system (11) after the equivalent transformation, the following Lyapunov function is chosen:
where P and Q are positive definite symmetric matrices. According to the Lyapunov stability theory, a positive definite symmetric matrix R is given, and if the following linear matrix inequality has the positive definite symmetric matrix
As its feasible solution:
in the formula,
In addition, the expression of the control gain matrix is designed
In a clear view of the above, it is known that,
in fact with fuzzy weight terms
Is irrelevant. Therefore, the controller designed in equation (9) can be implemented as follows:
the controller given in equation (17) is actually a linear controller, and the controller does not require that each aircraft be able to obtain its own state information xi(t) as long as at least one aircraft can obtain self-state information, namely at least one node i exists, so that biAnd (4) not being equal to 0.
The following examples were used to demonstrate the beneficial effects of the present invention:
1) case I: assuming that there are 3 aircrafts in the system and all of the 3 aircrafts can acquire their own state information, the communication topology structure is as shown in fig. 1, so the laplacian matrix corresponding to the communication topology and the weight matrix describing their own state information are
The inertia matrixes of 3 aircrafts are respectively taken as
Selecting
As 4 groups of operating points of the fuzzy system, the membership function is as follows:
substituting 4 groups of working points into the original system to obtain 4 groups of coefficient matrixes corresponding to fuzzy rules. Then let R ═ I3Then, the control gain matrix corresponding to the controller given in equation (9) can be calculated as follows:
further, using the principle of the conventional nonlinear compensation control method, a nonlinear compensation controller is given as follows:
for the nonlinear compensation controller in equation (30), k is taken to be 0.05.
Selecting the initial state values of 3 aircrafts as
q1(0)＝[0.50.50.5]T,ω1(0)＝[-0.1-0.1-0.1]T,
q2(0)＝[0.40.40.4]T,ω2(0)＝[-0.08-0.08-0.08]T,
q3(0)＝[0.30.30.3]T,ω3(0)＝[-0.06-0.06-0.06]T.
And then four different input time delays tau (t) are selected, so that an attitude angle error curve and a control input curve of 3 aircrafts under the action of the nonlinear compensation controller (30) and the fuzzy theory-based controller (8) under the influence of different input time delays can be obtained, which are respectively shown in fig. 2, fig. 3, fig. 4 and fig. 5. Wherein, fig. 2 and fig. 3 show relative attitude angle error curves between the 1 st and 2 nd aircraft under the action of the controller (8) and the controller (30), respectively; fig. 4 and 5 show the control input curves of the 1 st aircraft under the action of the controller (8) and the controller (30), respectively. Compared with a nonlinear compensation controller, the controller based on the fuzzy theory designed by the invention can ensure that the aircraft can realize attitude synchronization more quickly, and the required control moment is smaller. In addition, table 1 also gives the computer solution times under the action of two controllers, and the computer configuration used in the simulation is: 8-core CPU i7-7700, 32GB RAM, the simulation step length is 0.1 second, and Runge-Kutta is adopted by the resolver. As can be seen from table 1, when the input delay is τ (t) 0.1sin (0.1t) +0.1, τ (t) sin (0.1t) +1, τ (t) 3sin (0.1t) +3, and τ (t) 5sin (0.1t) +5, respectively, the computer solution time required by the controller designed according to the present invention is 0.25 times, 0.33 times, 0.37 times, and 0.38 times that of the conventional controller. Therefore, compared with the traditional controller, the controller designed by the invention can greatly improve the resolving speed of the computer and reduce the burden of the computer.
TABLE 1
1) Case II: still assume that there are 3 aircrafts in the system, but only the 1 st aircraft can obtain its own state information, and the communication topology structure is as shown in fig. 6, so the laplacian matrix corresponding to the communication topology and the weight matrix describing its own state information are
The inertia matrixes of 3 aircrafts are respectively taken as
And still select
As 4 sets of operating points for the fuzzy system. Still let R ═ I3Then, a control gain matrix corresponding to the distributed linear controller given in equation (17) can be calculated as follows:
taking the input time delay as τ (t) to sin (0.1t) +1, the attitude angle curve and the control input curve of 3 aircrafts under the action of the distributed linear controller (17) can be obtained, as shown in fig. 7 and fig. 8 respectively. According to the simulation curve, the controller designed by the invention is suitable for the situation that only 1 aircraft can obtain the self state information. However, for the nonlinear compensation controller (30), the control input contains nonlinear compensation terms
That is, the self-state information of each aircraft is indispensable information for the controller, so that the conventional nonlinear compensation controller cannot be applied to the case where only 1 aircraft can acquire the self-state information.
The contents (such as graph theory, linear matrix inequality and Lyapunov stability theory) which are not described in detail in the invention belong to the common general knowledge in the field.
Claims (1)
1. A multi-space aircraft attitude cooperative control method under input time delay is characterized by comprising the following steps:
step 1, constructing a multi-aircraft attitude dynamics system into a fuzzy system consisting of a series of fuzzy logics by utilizing a T-S fuzzy theory:
the multi-aircraft attitude dynamics model:
in the formula, JiRepresenting an inertia matrix; q. q.si(t) and qi0(t) vector and scalar sections representing attitude quaternions, respectively; omegai(t) represents an attitude angular velocity; u. ofi(t) represents control inputs acting on the aircraft; τ (t) represents an input delay variable and has a sum of 0 ≦ τ (t) ≦ τ
Where τ and ρ are positive constants;
equation (1) is formulated as a state space equation as follows:
the nonlinear system shown in equation (2) is systemized as the following fuzzy system using the T-S fuzzy system criterion:
system fuzzy rule mi: if xi1(t) is
And … and xi6(t) is
Then
Wherein,
representing a fuzzy set of the system, and r representing the total number of fuzzy rules;
using the weighted average of each linear subsystem, the following system is obtained:
b isiThe expression of (a) is as follows:
said xi(t)、ωi(t)、qi(t) is defined as follows:
a is describedi(xi(t)) and the membership functions of the fuzzy weight terms are expressed as follows:
step 2, designing a distributed controller aiming at the constructed fuzzy system to obtain a closed-loop system, and carrying out equivalent transformation on the closed-loop system:
the following fuzzy controller was designed using fuzzy criteria:
controller fuzzy rulesmi: if xi1(t) is
And … and xi6(t) is
Then
Wherein
For the control gain matrix to be solved, aijRepresenting the weight of the communication state between the aircrafts, biThe acquisition capability of the aircraft on the self state information is represented; using the weighted average of each linear subsystem, the controller is obtained as follows:
substituting equation (9) into equation (4) results in a closed loop system as shown below:
using fuzzy weighting terms
And
the property of (2) is equivalently transformed into the following form:
said Ki(xi(t)) and the membership functions of the fuzzy weight terms are expressed as follows:
the X (t), U (t-t (t)), (ii),
The expression of (a) is as follows:
the above-mentioned
The expression of (a) is as follows:
step 3, aiming at the converted equivalent system, providing sufficient conditions for ensuring the stability of the system by utilizing a time delay dependence Lyapunov stability theory and a linear matrix inequality method, and designing parameters of a controller:
for the fuzzy system (11) after the equivalent transformation, the following Lyapunov function is chosen:
wherein P and Q are positive definite symmetric matrices; according to the Lyapunov stability theory, a positive definite symmetric matrix R is given, and if the following linear matrix inequality has the positive definite symmetric matrix
As its feasible solution:
the fuzzy system (11) is progressively stabilized and the gain matrix is controlled to be
According to the expression of the designed control gain matrix
And fuzzy weighted term
Independently, the controller designed in equation (9) is thus modeled as follows:
the controller given in equation (17) is a linear controller, and the controller does not require that each aircraft be able to obtain its own state information xi(t) as long as at least one aircraft can obtain self-state information, namely at least one node i exists, so that bi≠0；
The above-mentioned
The expression of (a) is as follows:
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102636771A (en) * | 2012-04-25 | 2012-08-15 | 西北工业大学 | AUV (Autonomous Underwater Vehicle) underwater acoustic locating method based on double mobile beacons |
| CN103592850A (en) * | 2013-11-21 | 2014-02-19 | 冶金自动化研究设计院 | Nonlinear multi-time-scale delay system modeling and control method |
| CN104460322A (en) * | 2014-12-23 | 2015-03-25 | 冶金自动化研究设计院 | Feedback control method for fuzzy time delay state of uncertainty time-delay two-time scale systems (UTDNTTSSs) |
| CN106647781A (en) * | 2016-10-26 | 2017-05-10 | 广西师范大学 | Neural-fuzzy PID control method of four-rotor aircraft based on repetitive control compensation |
-
2019
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102636771A (en) * | 2012-04-25 | 2012-08-15 | 西北工业大学 | AUV (Autonomous Underwater Vehicle) underwater acoustic locating method based on double mobile beacons |
| CN103592850A (en) * | 2013-11-21 | 2014-02-19 | 冶金自动化研究设计院 | Nonlinear multi-time-scale delay system modeling and control method |
| CN104460322A (en) * | 2014-12-23 | 2015-03-25 | 冶金自动化研究设计院 | Feedback control method for fuzzy time delay state of uncertainty time-delay two-time scale systems (UTDNTTSSs) |
| CN106647781A (en) * | 2016-10-26 | 2017-05-10 | 广西师范大学 | Neural-fuzzy PID control method of four-rotor aircraft based on repetitive control compensation |
Non-Patent Citations (3)
| Title |
|---|
| "Distributed Attitude Control for Multispacecraft via Takagi–Sugeno Fuzzy Approach";Zhuo Zhang等;《IEEE Transactions on Aerospace and Electronic Systems》;20181231;第54卷(第2期);全文 * |
| "Research and Implementation of UHPLC Pump Dual-axis Cooperative Control";Xingya Wei等;《2018 5th International Conference on Systems and Informatics (ICSAI)》;20181231;全文 * |
| "空间飞行器姿态网络控制系统的模糊变结构控制";徐利杰等;《宇航学报》;20080331;第29卷(第2期);全文 * |
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