CN112286217A - Automatic pilot based on radial basis function neural network and decoupling control method thereof - Google Patents
Automatic pilot based on radial basis function neural network and decoupling control method thereof Download PDFInfo
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Abstract
The invention discloses a self-adaptive full-decoupling autopilot based on Radial Basis Function (RBF) neural network control and a control method, wherein the system comprises a required overload receiving module for receiving required overload information transmitted by a guidance system in real time, an aircraft parameter measuring module for obtaining flight parameters of an aircraft in real time, and a decoupling control module for obtaining an available rudder instruction, wherein the rudder instruction for controlling decoupling is obtained according to the required overload information and the flight parameters of the aircraft, a transitional rudder instruction is obtained by combining the flight parameters of the aircraft, and the available rudder instruction is obtained by combining the flight parameters of the aircraft, so that a steering engine is controlled to steer; when the decoupling control module is used for decoupling calculation, the related state feedback matrix and feedforward compensation matrix are obtained in real time through the radial basis function neural network model and the current condition of the aircraft, and therefore the control performance is further improved.
Description
Technical Field
The invention relates to a control system and a control method of a rotary aircraft, in particular to a self-adaptive full-decoupling autopilot based on radial basis function neural network control and a control method thereof.
Background
The rotary aircraft can bring a plurality of benefits by adopting a spinning mode, such as effectively reducing the influence of the structural design deviation of the aircraft on the trajectory of the aircraft, simplifying the design of a control system, omitting a rolling control mechanism and the like. However, the aircraft has a plurality of advantages and disadvantages. Because the aircraft can generate a larger rolling angular velocity after autorotation, the aircraft generates characteristics such as pneumatic coupling, inertial coupling, control coupling and the like, the pitching channel and the yawing channel are mutually coupled and crosslinked, and the accurate control on the pitching channel and the yawing channel of the aircraft is not facilitated. In addition, the hysteresis characteristic of the steering engine has a great influence on the control of the rotary aircraft, so that the influence of a steering engine link on an aircraft control system is necessary to be considered when the self-driving instrument is designed, the steering engine is often defaulted to be a first-order inertia link in the traditional design method, the control coupling characteristic of the rotary aircraft is ignored in the method, the steering engine model is not accurately established, the risk of the designed self-driving instrument is increased, if the steering engine is considered to be a second-order inertia link, the rotary aircraft dynamic model is an eight-order system, the model is complex, inversion operation can be involved in the resolving process, the inversion of the eight-order system is too difficult, and processing equipment of the aircraft is difficult to resolve and complete in time; these problems present a significant challenge to the precise and stable control of a rotary aircraft control system;
in the prior art, in the process of actually controlling the rotary aircraft, the coupling influence and the second-order inertia link are ignored, so that a certain deviation exists in the actual control process of the aircraft, but the final guidance control effect is still to be improved.
In addition, the control parameters of the existing decoupling autopilot change along with the change of the power coefficient of the rotary aircraft, so that one group or a plurality of groups of control parameters of the autopilot are not enough to meet the requirements of practical application.
In order to solve the above problems, the present inventors have conducted intensive studies on the existing pilot, and have a desire to design an adaptive fully-decoupled autopilot based on radial basis function neural network control and a decoupling control method thereof, which can solve the above problems.
Disclosure of Invention
In order to overcome the problems, the inventor of the invention carries out intensive research and designs a self-adaptive full-decoupling autopilot based on radial basis function neural network control and a control method thereof, wherein the system comprises a required overload receiving module for receiving required overload information transmitted by a guidance system in real time, an aircraft parameter measuring module for obtaining flight parameters of an aircraft in real time, and a decoupling control module for obtaining available rudder instructions, wherein the rudder instructions for controlling decoupling are obtained according to the required overload information and the flight parameters of the aircraft, transitional rudder instructions are obtained by combining the flight parameters of the aircraft, available rudder instructions are obtained by combining the flight parameters of the aircraft, and accordingly the steering engine is controlled to steer, wherein when the decoupling control module is used for decoupling calculation, related state feedback matrixes and feedforward compensation matrixes are obtained in real time through a radial basis function neural network model and the current conditions of the aircraft, thereby further improving the control performance, and thus the present invention has been completed.
In particular, the invention aims to provide an adaptive fully-decoupled autopilot based on radial basis function neural network control, the system is installed on a rotary aircraft and comprises
The overload receiving module 1 is connected with a guidance system on the rotary aircraft and used for receiving the overload information which is transmitted by the guidance system in real time,
an aircraft parameter measuring module 2 for obtaining flight parameters of the aircraft in real time,
a decoupling control module 3 for obtaining available rudder instructions in real time according to the overload information required and the flight parameters of the aircraft, and
and the radial basis function neural network model 4 is used for solving a state feedback matrix and a feedforward compensation matrix required for obtaining available rudder instructions in real time.
The aircraft parameter measuring module 2 comprises a steering engine attitude sensor 21, a steering engine angular rate sensor 22, an accelerometer 23, an inertial gyroscope 24 and an estimator 25;
wherein, the steering engine attitude sensor 21 is used for measuring pitch rudder deflection angle information and yaw rudder deflection angle information of the aircraft in real time,
the steering engine angular rate sensor 22 is used for measuring and obtaining pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft in real time,
the accelerometer 23 is used for measuring acceleration information of the aircraft in real time,
the inertial gyroscope 24 is used for measuring yaw rate information and pitch rate information of the aircraft in real time,
and the estimator 25 is used for estimating in real time according to the acceleration information to obtain attack angle information and sideslip angle information of the aircraft.
The decoupling control module 3 comprises a rudder instruction resolving submodule 31 for controlling decoupling, a transitional rudder instruction resolving submodule 32 and an available rudder instruction resolving submodule 33;
the rudder instruction resolving submodule 31 for controlling decoupling is used for obtaining a rudder instruction for controlling decoupling in real time according to overload information required and flight parameters of an aircraft;
the transitional rudder instruction resolving submodule 32 is used for obtaining a transitional rudder instruction in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling;
the available rudder instruction resolving submodule 33 is used for obtaining available rudder instructions in real time according to flight parameters of the aircraft and transitional rudder instructions.
Wherein, the rudder instruction resolving submodule 31 for controlling decoupling obtains the rudder instruction for controlling decoupling in real time through the following formula (I),
u2＝-K2x2+L2v2(A)
Wherein u is2Rudder command, K, indicating control decoupling2Representing a state feedback matrix, L2Representing a feed forward compensation matrix, x2State variables, v, representing the space expression of the steering engine state2Indicating that overload is required.
Wherein the transitional rudder instruction resolving submodule 32 obtains the transitional rudder instruction in real time through the following formula (II),
y2＝C2∫(A2x2+B2u2) dt (two)
Wherein, y2A rudder instruction representing a transition is provided,
A2、B2、C2are indicative of the steering engine system parameters.
Wherein the available rudder instruction resolving submodule 33 obtains the available rudder instruction in real time by the following formula (three),
u1＝-K1x1+L1v1(III)
Wherein u is1Indicating available rudder commands, K1Representing a state feedback matrix, L1Representing a feed forward compensation matrix, x1State variables, v, representing aircraft state space expressions1A rudder command representing a transition.
The decoupling control module 3 is connected with a steering engine 5 of the aircraft, and the steering engine 5 steers according to available steering instructions.
Before the rotary aircraft is launched, selecting a certain number of characteristic points with different heights and different speeds from a simulated aircraft track as samples, and flushing and training the radial basis function neural network model 4;
preferably, the radial basis function neural network model 4 is trained to be capable of flying according to expected pitch overload, expected yaw overload and rotation in real timeThe flight speed of the aircraft and the flight height information of the rotary aircraft, and an output state feedback matrix K1State feedback matrix K2Feedforward compensation matrix L1And a feedforward compensation matrix L2。
The invention also provides a decoupling control method of the self-adaptive full-decoupling automatic pilot based on radial basis function neural network control, which comprises the following steps,
and 4, repeating the steps 1-3 in real time, so as to obtain the available rudder instruction in real time.
Wherein the step 2 comprises the following sub-steps,
substep 2-1, obtaining pitching rudder deflection angle information and yawing rudder deflection angle information of the aircraft through real-time measurement of the steering engine attitude sensor 21,
substep 2-2, obtaining pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft through real-time measurement of the steering engine angular rate sensor 22,
substep 2-3, obtaining acceleration information of the aircraft through real-time measurement of the accelerometer 23, obtaining yaw rate information and pitch rate information of the aircraft through real-time measurement of the inertial gyroscope 24,
and in substep 2-4, estimating in real time according to the triaxial acceleration information by the estimator 25 to obtain the attack angle and the sideslip angle of the aircraft.
Wherein the step 3 comprises the sub-steps of,
in the substep 3-1, a rudder instruction for controlling decoupling is obtained in real time according to the overload information required and the flight parameters of the aircraft by a rudder instruction resolving submodule 31 for controlling decoupling;
in the substep 3-2, a transitional rudder instruction is obtained in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling through a transitional rudder instruction resolving submodule 32;
and a substep 3-3, obtaining available rudder instructions in real time according to the flight parameters of the aircraft and the transitional rudder instructions through the available rudder instruction resolving submodule 33.
The invention has the advantages that:
(1) the self-adaptive full-decoupling autopilot based on radial basis neural network control and the decoupling control method thereof can provide more reasonable steering engine control instructions for the steering engine by combining the current flight condition of the rotary aircraft and considering the dynamics and coupling condition of a second-order steering engine on the basis of received overload required, thereby enhancing the control effect of the rotary aircraft and improving the control precision of the rotary aircraft;
(2) the self-adaptive full-decoupling autopilot based on radial basis function neural network control and the decoupling control method thereof consider the power of a second-order steering engine, simplify the calculation process, convert an eight-order system into two four-order systems in a model conversion mode for calculation control, save the calculation time and enable the eight-order system to meet the requirement of an aircraft for obtaining information in real time;
(3) in the self-adaptive full-decoupling automatic pilot based on radial basis neural network control and the decoupling control method thereof, the control parameters of the automatic pilot, namely a feedforward compensation matrix and a state feedback matrix, are obtained in real time through the neural network control, so that the decoupling control precision is further improved.
Drawings
FIG. 1 is a logic diagram of an adaptive fully-decoupled autopilot based on radial basis function neural network control according to a preferred embodiment of the invention;
FIG. 2 shows the overload and response curve required in the pitch direction in a simulation experiment;
FIG. 3 shows the yaw direction overload requirement and response curves in the simulation experiment;
FIG. 4 shows a pitch angle rate variation curve in a simulation experiment;
fig. 5 shows a yaw rate variation curve in a simulation experiment.
The reference numbers illustrate:
1-overload receiving module
2-aircraft parameter measuring module
3-decoupling control module
21-steering engine attitude sensor
22-steering engine angular rate sensor
23-accelerometer
24-inertia gyroscope
25-estimator
26-satellite signal receiving module
31-control decoupling rudder instruction resolving submodule
32-transition rudder instruction resolving submodule
33-available Rudder Command resolution submodule
4-radial basis function neural network model
5-steering engine
Detailed Description
The invention is explained in more detail below with reference to the figures and examples. The features and advantages of the present invention will become more apparent from the description.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
According to the self-adaptive fully-decoupled autopilot based on radial basis function neural network control provided by the invention, as shown in fig. 1, the system is installed on a rotary aircraft, and the rotary aircraft is preferably a high-dynamic rotary aircraft, namely a rotary aircraft with the rotating speed of more than 10 r/s; the coupling means that when the pitch direction and the yaw direction of the aircraft are controlled respectively, a control command in one direction influences and interferes with the other direction, and particularly when the pitch direction of the aircraft is controlled, due to rotation, acting force generated by a steering engine has a certain component force in the horizontal direction, and the component force can cause the aircraft to deflect in the yaw direction.
The system comprises a required overload receiving module 1, an aircraft parameter measuring module 2 and a decoupling control module 3;
wherein, the overload receiving module 1 is connected with a guidance system on the rotary aircraft and used for receiving the overload information which is required and transmitted by the guidance system in real time,
the guidance system is also installed on the aircraft, and can give the overload that needs to be used in real time according to aircraft self information and target information that sensing device obtained on the aircraft, and the steering engine is controlled according to this needs to beat the rudder work again under general condition, but in the scheme that this application provided, this overload that needs will not directly transmit to the steering engine, but transmit to need to use overload receiving module 1, pass through after handling the usable rudder instruction that obtains and transmit to the steering engine. Therefore, the steering work of the steering engine is more targeted, and the control effect on the rolling aircraft is better.
The guidance system is a guidance system existing in the field, and an existing guidance law such as a proportional guidance law, a gravity compensation guidance law and the like can be adopted.
The aircraft parameter measuring module 2 is used for obtaining flight parameters of an aircraft in real time, wherein the flight parameters comprise a pitch rudder deflection angle, a yaw rudder deflection angle, a pitch steering engine angular rate, a yaw steering engine angular rate, acceleration, speed, a yaw angular rate, a pitch angular rate, an attack angle and a sideslip angle; the aircraft parameter measuring module 2 can call the power coefficient related to the flight parameter in real time from a memory chip carried by the aircraft parameter measuring module.
And the decoupling control module 3 is used for acquiring available rudder instructions in real time according to the overload information required and flight parameters of the aircraft, transmitting the rudder instructions to the steering engine, and steering the steering engine according to the rudder instructions.
In a preferred embodiment, the overload need information includes an expected pitch overload and an expected yaw overload; the expected pitch overload is the overload which is solved by the guidance system and needs to be provided in the pitch direction; the expected yaw overload is the overload that the guidance system solves for, and needs to provide in the yaw direction.
The control decoupling rudder instruction, the transition rudder instruction and the available rudder instruction are all rudder instructions, and the difference is mainly that the precision and the accuracy are different, and the rudder instructions comprise a pitching rudder instruction and a yawing rudder instruction; the pitching rudder instruction represents an instruction which is finally transmitted to the steering engine and is executed by the steering engine in a pitching direction; the yaw rudder command represents a command to be finally transmitted to the steering engine and executed by the steering engine in the yaw direction.
When a guidance system of an aircraft obtains overload needing to be used, if the overload needing to be used is directly transmitted to a steering engine, the steering engine inevitably solves a corresponding pitching rudder instruction according to expected pitching overload in the overload needing to be used, and solves a corresponding yawing rudder instruction according to expected yawing overload in the overload needing to be used, in the execution process of the steering engine, the hysteresis characteristic of the steering engine has a large influence on the control of the rotating aircraft, and the conventional design method usually defaults the steering engine to be a first-order inertia link, so that the control coupling characteristic of the rotating aircraft is ignored in the method, and the deviation between the final steering result and the expected value is overlarge; after the overload is resolved by the decoupling control module, the influence caused by interference factors such as lag, coupling and the like of the steering engine is considered and calculated in advance, so that the steering work is carried out according to the finally obtained available steering instructions, the steering result is closer to the expected value, and the control effect is better.
In a preferred embodiment, the aircraft parameter measurement module 2 comprises a steering engine attitude sensor 21, a steering engine angular rate sensor 22, an accelerometer 23, an inertial gyro 24 and an estimator 25;
the steering engine attitude sensor 21 is used for measuring pitch rudder deflection angle information and yaw rudder deflection angle information of the aircraft in real time;
the steering engine angular rate sensor 22 is used for measuring in real time to obtain pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft;
the accelerometer 23 is used for measuring acceleration information of the aircraft in real time,
the inertial gyroscope 24 is used for measuring yaw rate information and pitch rate information of the aircraft in real time,
and the estimator 25 is used for estimating in real time to obtain an attack angle and a sideslip angle of the aircraft according to the triaxial acceleration information.
Wherein, said accelerometer 23 is provided with a plurality of, preferably at least 3, at least one of them is located on the center of mass of the aircraft, and it is installed towards the aircraft traveling direction along the aircraft axis, so as to measure the acceleration of the aircraft along the axis direction, i.e. the acceleration of the aircraft itself, and the acceleration can be integrated to obtain the speed information of the aircraft;
in addition, two accelerometers are arranged on the axis of the aircraft and deviate from the center of mass by a certain distance, the installation directions of the two accelerometers are perpendicular to each other, the two accelerometers are connected with the estimator 25, the accelerometers can measure the acceleration value of the position where the accelerometers are located in real time, the velocity of the point can be obtained after integration, the angular velocity of the point can be obtained by multiplying the distance between the point and the center of mass, and the angle can be obtained by integration; preferably, the estimator is further connected with a geomagnetic sensor on the aircraft, and the geomagnetic sensor can acquire the roll angle of the aircraft in real time, so that the sideslip angle and the attack angle of the aircraft can be obtained through the two accelerometers and the roll angle information respectively. The distance between the accelerometer and the centroid is stored in the estimator, and integral calculation can be carried out in the estimator, so that the estimator can give sideslip angle information and attack angle information of the aircraft in real time.
Preferably, the aircraft parameter measuring module 2 further comprises a satellite signal receiving module 26, which is used for receiving the satellite signal in real time and calculating the flying speed of the rotary aircraft and the flying height of the rotary aircraft.
In a preferred embodiment, the decoupling control module 3 includes a rudder instruction resolving submodule 31 for controlling decoupling, a transitional rudder instruction resolving submodule 32 and an available rudder instruction resolving submodule 33;
the rudder instruction resolving submodule 31 for controlling decoupling is used for obtaining a rudder instruction for controlling decoupling in real time according to overload information required and flight parameters of an aircraft;
the transitional rudder instruction resolving submodule 32 is used for obtaining a transitional rudder instruction in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling;
the available rudder instruction resolving submodule 33 is used for obtaining available rudder instructions in real time according to flight parameters of the aircraft and transitional rudder instructions.
Preferably, the control-decoupled rudder instruction resolving submodule 31 obtains the control-decoupled rudder instruction in real time through the following formula (one),
u2＝-K2x2+L2v2(A)
Wherein u is2Rudder command, K, indicating control decoupling2Representing a state feedback matrix, L2Representing a feed forward compensation matrix, x2State variables, v, representing the space expression of the steering engine state2Indicating a demand for overload;
preferably, the first and second electrodes are formed of a metal,
v2＝[vy vz]T
δyrepresenting pitch rudder angle, deltazThe yaw-rudder deflection angle is indicated,
representing pitch steering engine angular rate, representing
Yaw steering engine angular rate; v. ofyIndicating an expected pitch overload, vzIndicating a desired yaw overload;
preferably, the transitional rudder instruction resolving submodule 32 obtains the transitional rudder instruction in real time by the following equation (two),
y2＝C2∫(A2x2+B2u2) dt (two)
Wherein, y2A rudder instruction representing a transition is provided,
A2、B2、C2are indicative of the steering engine system parameters,
d3、d11、d12、d21、d22all represent the power coefficient of the steering engine model, and can be obtained in real time based on the self parameters of the aircraft and the self rotating speed of the aircraft.
Preferably, the available rudder instruction resolving submodule 33 obtains the available rudder instruction in real time by the following formula (three),
u1＝-K1x1+L1v1(III)
Wherein u is1Indicating available rudder commands, K1Representing a state feedback matrix, L1Representing a feed forward compensation matrix, x1State variables, v, representing aircraft state space expressions1And y2As such, all represent transitional rudder commands;
alpha denotes an angle of attack, beta denotes a sideslip angle,
representing pitch angleThe rate of the speed of the motor is,
representing the yaw rate.
In a preferred embodiment, the power coefficient of the steering engine model is obtained by the following formula:
wherein the content of the first and second substances,
indicating the rotational speed, which parameter is detected in real time, musIndicating steering engine damping ratio, TsIndicating steering engine command delay, ksIndicates steering engine gain, μs、TsAnd ksAre parameters that are pre-installed in the aircraft at the time of shipment.
In a preferred embodiment, the decoupling control module 3 is connected with a steering engine 5 of the aircraft, and the steering engine 5 performs steering operation according to available steering instructions;
the actual overload obtained by the steering engine 5 according to the available steering instruction steering work can be known by the following formula (four):
y1＝C1∫(A1x1+B1u1) dt (four)
Wherein, y1＝[ay az]TIndicating an actual response overload, ayIndicating pitch response to overload, azIndicating yaw-direction response to overload, A1、B1、C1Are indicative of an aircraft system parameter or parameters,
a25、a24、a27、a22、a28and a34The dynamic coefficient of the rotary aircraft is known data preinstalled in the aircraft, and is generally obtained by calculation in wind tunnel experiments and other ways before the aircraft leaves a factory, and the data can be called at any time in the flight process of the aircraft.
In a preferred embodiment, the radial basis function neural network model 4 is pre-installed on an aircraft, before the rotating aircraft is launched, a certain number of feature points with different heights and different speeds are selected from a simulated aircraft track as sampling points, and the radial basis function neural network model 4 is obtained through sample flushing training;
the heights of the characteristic points in the sampling points are different, and the flight speeds of aircrafts on the characteristic points are also different;
preferably, 12 groups of feature points are selected, 144 groups of feature points can be obtained by arranging and combining the 12 groups of feature points, and each group of feature points is substituted into the solution formulas (one), (two) and (three) of the decoupling control module 3 to carry out the state feedback matrix K1、K2And a feedforward compensation matrix L1、L2And (4) taking a group of calculation results and corresponding feature points as a sample, and performing flushing training through the 144 groups of samples to obtain the radial basis function neural network model.
Preferably, after the radial basis function neural network model 4 is trained, the state feedback matrix K can be output according to the information of expected pitch overload, expected yaw overload, the flying speed of the rotary aircraft and the flying height of the rotary aircraft in real time1State feedback matrix K2Feedforward compensation matrix L1And a feedforward compensation matrix L2。
The specific information of the 12 sets of feature points with different speeds at different heights is as follows:
the height is 200m, 500m, 1000m, 2000m, 3000m, 4000m, 5000m, 6000m, 7000m, 8000m, 9000m and 10000 m; the corresponding speeds are 685m/s, 880m/s, 1080m/s, 950m/s, 700m/s, 585m/s, 294m/s, 395m/s, 486m/s, 535m/s, 640m/s, 720 m/s.
Due to the axial symmetry of the rotary aircraft, the designed control parameters also have the symmetrical characteristic, and the feedforward compensation matrix L2Is only related to the desired natural frequency, the control parameters to be trained can be reduced to a state feedback matrix K1First row of control parameters K11、K12、K13、K14The state feedback matrix K2First row of control parameters K21、K22、K23、K24And a feedforward compensation matrix L1First row of control parameters L11、L12Then 144 sets of the above control parameters need to be calculated before training the radial basis function neural network model.
The following tables 1, 2 and 3 respectively show the state feedback matrix K1、K2And a feedforward compensation matrix L1The following tables 4, 5 and 6 show the control parameters corresponding to different speeds when the height H is 8000m, respectively, and show the state feedback matrix K1、K2And a feedforward compensation matrix L1And controlling parameters corresponding to different heights when the speed V is 535 m/s.
TABLE 1
TABLE 2
TABLE 3
TABLE 4
TABLE 5
TABLE 6
The invention also provides a decoupling control method of the self-adaptive full-decoupling automatic pilot based on the radial basis function neural network control, which is realized by the self-adaptive full-decoupling automatic pilot based on the radial basis function neural network control,
the method comprises the following steps of,
and 4, repeating the steps 1-3 in real time, so as to obtain the available rudder instruction in real time.
Preferably, said step 2 comprises the sub-steps of,
substep 2-1, obtaining pitching rudder deflection angle information and yawing rudder deflection angle information of the aircraft through real-time measurement of the steering engine attitude sensor 21,
substep 2-2, obtaining pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft through real-time measurement of the steering engine angular rate sensor 22,
substep 2-3, obtaining acceleration information of the aircraft through real-time measurement of the accelerometer 23, obtaining yaw rate information and pitch rate information of the aircraft through real-time measurement of the inertial gyroscope 24,
and in substep 2-4, estimating in real time according to the triaxial acceleration information by the estimator 25 to obtain the attack angle and the sideslip angle of the aircraft.
Preferably, said step 3 comprises the sub-steps of,
in the substep 3-1, the convergence error of the attack angle and the convergence error of the sideslip angle are obtained in real time according to the overload information required and the flight parameters of the aircraft by controlling the decoupled rudder instruction resolving submodule 31;
in the substep 3-2, the rudder instruction for controlling decoupling is obtained in real time according to the overload information required and the flight parameters of the aircraft by the rudder instruction resolving submodule 31 for controlling decoupling;
in the substep 3-2, a transitional rudder instruction is obtained in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling through a transitional rudder instruction resolving submodule 32;
and a substep 3-3, obtaining available rudder instructions in real time according to the flight parameters of the aircraft and the transitional rudder instructions through the available rudder instruction resolving submodule 33.
Before step 1 is executed, filling the radial basis function neural network model into the aircraft, and solving a state feedback matrix K in real time according to the speed and altitude information of the aircraft through the radial basis function neural network model1、K2And a feedforward compensation matrix L1、L2。
Experimental example:
the method comprises the steps that a guidance system and a steering engine system of the rotary aircraft are directly simulated through a computer, the guidance system can give guidance instructions in real time, namely overload needing to be used, specifically comprises expected pitch overload and expected yaw overload, the expected pitch overload is in a sine alternating state, the amplitude is 10m/s2, the frequency is 1rad/s, the yaw overload instruction is zero, and the change track of the overload needing to be used along with time is shown as a solid line in fig. 2 and fig. 3; the steering engine system controls the steering engine to steer according to a guidance instruction or overload, and directly gives an overload condition which can be actually provided for the rotary aircraft after the steering engine is controlled to work according to the overload;
in an experimental example, required overload given by a guidance system in a computer is intercepted, the required overload is not directly transmitted to a steering engine system, and the required overload is respectively transmitted to a self-adaptive full decoupling automatic pilot (RBF) based on radial basis function neural network control and a decoupling automatic pilot (Gain-Schedule) adopting a Gain scheduling method;
after overload is transmitted to the RBF, an available rudder instruction is obtained through the decoupling control method of the self-adaptive full-decoupling autopilot based on radial basis function neural network control, and then the available rudder instruction is transmitted to a steering engine system, so that the steering engine is controlled to work, and the overload condition which can be actually provided for a rotary aircraft after the steering engine works is obtained;
the system comprises a required overload receiving module, a computer, a controller and a controller, wherein the required overload receiving module receives required overload information, and simulated flight parameters of an aircraft are given in real time through the computer, wherein the simulated flight parameters comprise a pitch rudder deflection angle, a yaw rudder deflection angle, a pitch steering engine angular rate, a yaw steering engine angular rate attack angle, a sideslip angle, a speed, a pitch angle and a yaw angle; and the power coefficient of the rotary aircraft is given as follows:
the steering engine parameter table is as follows:
resolving through the following formulas (I), (II) and (III) to obtain a pitching direction steering engine response instruction and a yawing direction steering engine response instruction;
u2＝-K2x2+L2v2(A)
y2＝C2∫(A2x2+B2u2) dt (two)
u1＝-K1x1+L1v1(III)
v2＝[vy vz]T，vyIndicating an expected pitch overload, vzIndicating that an overload in yaw is desired,
alpha denotes an angle of attack, beta denotes a sideslip angle,
the pitch angle rate is expressed in terms of,
representing yaw rate, deltayRepresenting pitch rudder angle, deltazThe yaw-rudder deflection angle is indicated,
representing the angular rate of the pitch steering engine,
representing yaw steering engine angular rate; u. of1Representing available rudder commands, including pitch steering engine response commands and yaw steering engine response commands;
L1、L2、K1、K2the method is obtained by solving a basic neural network model in real time;
after the steering engine response command is transmitted to the steering engine system in the computer, the overload condition actually provided for the rotating aircraft after the steering engine works is obtained through simulation and is shown as a dotted line "RBF" in fig. 2, fig. 3, fig. 4 and fig. 5.
In the comparative example, after the overload is transmitted to the Gain-Schedule, the Gain-Schedule system responds to the overload to obtain the steering engine control command, and controls the steering engine accordingly to finally obtain the overload condition provided by the steering engine, as shown by the dotted line 'Gain-Schedule' in fig. 2, fig. 3, fig. 4 and fig. 5.
Fig. 2 is a pitch overload response variation curve, fig. 3 is a yaw overload response variation curve, fig. 4 is a pitch angle rate variation curve, and fig. 5 is a yaw angle rate variation curve. As can be seen from fig. 2 and 3, the pitch overload response of the conventional gain scheduling method has a significant steady-state error, and the yaw channel also has an overload response fluctuating in a sine form, so that the system is not completely decoupled and still has a coupling effect; the pitch overload response of the self-adaptive full-decoupling autopilot based on radial basis neural network control is basically consistent with the overload instruction, the yaw channel almost has no coupling response, the overload required can be accurately tracked, and the complete decoupling of inertial coupling, pneumatic coupling and control coupling of the rotary aircraft is realized. As can be seen from fig. 4 and 5, the pitch angle rate of the conventional gain scheduling method is small, and the yaw angle rate has large oscillation, so that a steady-state error exists in pitch overload, and a coupling effect exists in a yaw channel. In contrast, the self-adaptive fully-decoupled autopilot based on radial basis function neural network control has better control performance and decoupling performance than a gain scheduling method under the condition of sinusoidal signal input.
The present invention has been described above in connection with preferred embodiments, but these embodiments are merely exemplary and merely illustrative. On the basis of the above, the invention can be subjected to various substitutions and modifications, and the substitutions and the modifications are all within the protection scope of the invention.
Claims (10)
1. An adaptive fully decoupled autopilot based on radial basis neural network control, the autopilot being mounted on a rotating aircraft, the autopilot comprising:
the overload receiving module (1) is connected with a guidance system on the rotary aircraft and used for receiving the overload information which is transmitted by the guidance system in real time,
an aircraft parameter measurement module (2) for obtaining flight parameters of the aircraft in real time,
the decoupling control module (3) is used for obtaining available rudder instructions in real time according to the overload information required and flight parameters of the aircraft; and
and the radial basis function neural network model (4) is used for solving a state feedback matrix and a feedforward compensation matrix required for obtaining available rudder instructions in real time.
2. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 1,
the aircraft parameter measuring module (2) comprises a steering engine attitude sensor (21), a steering engine angular rate sensor (22), an accelerometer (23), an inertial gyro (24) and an estimator (25);
wherein the steering engine attitude sensor (21) is used for measuring pitch rudder deflection angle information and yaw rudder deflection angle information of the aircraft in real time,
the steering engine angular rate sensor (22) is used for measuring and obtaining pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft in real time,
the accelerometer (23) is used for measuring acceleration information of the aircraft in real time,
the inertial gyroscope (24) is used for measuring yaw rate information and pitch rate information of the aircraft in real time,
and the estimator (25) is used for estimating and obtaining the attack angle information and the sideslip angle information of the aircraft in real time according to the acceleration information.
3. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 1,
the decoupling control module (3) comprises a rudder instruction resolving submodule (31) for controlling decoupling, a transitional rudder instruction resolving submodule (32) and an available rudder instruction resolving submodule (33);
the rudder instruction resolving submodule (31) for controlling decoupling is used for obtaining a rudder instruction for controlling decoupling in real time according to overload information required and flight parameters of an aircraft;
the transitional rudder instruction resolving submodule (32) is used for obtaining a transitional rudder instruction in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling;
and the available rudder instruction resolving submodule (33) is used for obtaining available rudder instructions in real time according to flight parameters of the aircraft and transitional rudder instructions.
4. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 3,
the rudder instruction resolving submodule (31) for controlling decoupling obtains a rudder instruction for controlling decoupling in real time through the following formula (I),
u2＝-K2x2+L2v2(A)
Wherein u is2Rudder command, K, indicating control decoupling2Representing a state feedback matrix, L2Representing a feed forward compensation matrix, x2State variables, v, representing the space expression of the steering engine state2Indicating that overload is required.
5. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 3,
the transitional rudder instruction resolving submodule (32) obtains a transitional rudder instruction in real time through the following formula (II),
y2＝C2∫(A2x2+B2u2) dt (two)
Wherein, y2A rudder instruction representing a transition is provided,
A2、B2、C2are indicative of the steering engine system parameters.
6. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 3,
the available rudder instruction resolving submodule (33) obtains the available rudder instruction in real time through the following formula (III),
u1＝-K1x1+L1v1(III)
Wherein, preferably, u1Indicating available rudder commands, K1Representing a state feedback matrix, L1Representing a feed forward compensation matrix, x1State variables, v, representing aircraft state space expressions1A rudder command representing a transition;
preferably, the decoupling control module (3) is connected with a steering engine (4) of the aircraft, and the steering engine (4) performs steering operation according to available steering instructions.
7. The adaptive fully-decoupled autopilot based on radial basis neural network control of claim 1,
before the rotary aircraft is launched, selecting a certain number of characteristic points with different heights and different speeds from a simulated aircraft track as sampling points, and obtaining the radial basis function neural network model (4) through sample washout training;
preferably, after the radial basis function neural network model (4) is trained, a state feedback matrix K can be output according to information of expected pitch overload, expected yaw overload, rotary aircraft flying speed and rotary aircraft flying height in real time1State feedback matrix K2Feedforward compensation matrix L1And a feedforward compensation matrix L2。
8. A decoupling control method of a self-adaptive full-decoupling automatic pilot based on radial basis function neural network control is characterized in that,
the method comprises the following steps of,
step 1, receiving overload information required to be used transmitted by a guidance system through an overload receiving module (1);
step 2, obtaining flight parameters of the aircraft through the aircraft parameter measuring module (2);
step 3, obtaining available rudder instructions through the decoupling control module (3) according to the overload information required and flight parameters of the aircraft;
and 4, repeating the steps 1-3 in real time, so as to obtain the available rudder instruction in real time.
9. The decoupling control method of the adaptive fully decoupled autopilot based on radial basis function neural network control of claim 8,
said step 2 comprises the sub-steps of,
substep 2-1, obtaining pitching rudder deflection angle information and yawing rudder deflection angle information of the aircraft through real-time measurement of a steering engine attitude sensor (21),
substep 2-2, obtaining pitching steering engine angular rate information and yawing steering engine angular rate information of the aircraft through real-time measurement of a steering engine angular rate sensor (22),
substep 2-3, obtaining acceleration information of the aircraft through real-time measurement of an accelerometer (23), obtaining yaw rate information and pitch rate information of the aircraft through real-time measurement of an inertial gyroscope (24),
and in the substep 2-4, the attack angle and the sideslip angle of the aircraft are obtained by estimating in real time through an estimator (25) according to the triaxial acceleration information.
10. The decoupling control method of the adaptive fully decoupled autopilot based on radial basis function neural network control of claim 8,
said step 3 comprises the sub-steps of,
substep 3-1, obtaining a rudder instruction for controlling decoupling according to overload information required and flight parameters of the aircraft in real time through a rudder instruction resolving submodule (31) for controlling decoupling;
substep 3-2, obtaining a transitional rudder instruction in real time according to the flight parameters of the aircraft and the rudder instruction for controlling decoupling through a transitional rudder instruction resolving submodule (32);
and a substep 3-3, obtaining available rudder instructions in real time according to flight parameters of the aircraft and the transitional rudder instructions through an available rudder instruction resolving submodule (33).
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2022105408A1 (en) * | 2020-11-18 | 2022-05-27 | 北京理工大学 | Autopilot employing radial basis function neural network and decoupling control method thereof |
| CN114779826A (en) * | 2022-06-20 | 2022-07-22 | 中国人民解放军国防科技大学 | Axial symmetry aircraft lateral control method suitable for non-zero roll angle |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116126033B (en) * | 2023-04-19 | 2023-07-21 | 北京理工大学 | Image composite aircraft guidance control method |
| CN116661495B (en) * | 2023-05-30 | 2024-02-20 | 北京理工大学 | Near-range deceleration control method for aircraft |
| CN116700306B (en) * | 2023-06-02 | 2024-02-27 | 北京理工大学 | Integrated guidance control method for strapdown guided aircraft |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104049640A (en) * | 2014-06-27 | 2014-09-17 | 金陵科技学院 | Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer |
| CN108363305A (en) * | 2018-03-14 | 2018-08-03 | 中国人民解放军国防科技大学 | Tactical missile robust overload autopilot design method based on active interference compensation |
| US20180239365A1 (en) * | 2015-08-13 | 2018-08-23 | The Board Of Regents For Oklahoma State University | Modular autopilot design and development featuring bayesian non-parametric adaptive control |
| EP3486740A1 (en) * | 2017-11-21 | 2019-05-22 | Honeywell International Inc. | Systems and methods for providing predicted mode change data for decoupled vertical navigation (vnav) and lateral navigation (lnav) autopilot operations |
| CN110081883A (en) * | 2019-04-29 | 2019-08-02 | 北京理工大学 | Low cost integrated navigation system and method suitable for high speed rolling flight device |
| CN111324142A (en) * | 2020-01-07 | 2020-06-23 | 湖北航天技术研究院总体设计所 | Missile navigator disturbance compensation control method |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106054612A (en) * | 2016-06-29 | 2016-10-26 | 河南科技大学 | BTT missile flight trajectory automatic control method |
| FR3062730B1 (en) * | 2017-02-08 | 2019-03-15 | Airbus Helicopters | SYSTEM AND METHOD FOR AUTOMATICALLY CONTROLLED AIRCRAFT AND AIRCRAFT |
| CN107491081B (en) * | 2017-07-12 | 2020-10-27 | 西北工业大学 | Anti-interference four-rotor unmanned aerial vehicle attitude control method |
| CN110895418B (en) * | 2019-12-30 | 2020-12-01 | 北京理工大学 | Low-speed rotating aircraft control method and system for compensating dynamic lag of steering engine |
| CN112286217A (en) * | 2020-11-18 | 2021-01-29 | 北京理工大学 | Automatic pilot based on radial basis function neural network and decoupling control method thereof |
-
2020
- 2020-11-18 CN CN202011295688.2A patent/CN112286217A/en active Pending
-
2021
- 2021-09-22 JP JP2023529987A patent/JP2023550744A/en active Pending
- 2021-09-22 WO PCT/CN2021/119503 patent/WO2022105408A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104049640A (en) * | 2014-06-27 | 2014-09-17 | 金陵科技学院 | Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer |
| US20180239365A1 (en) * | 2015-08-13 | 2018-08-23 | The Board Of Regents For Oklahoma State University | Modular autopilot design and development featuring bayesian non-parametric adaptive control |
| EP3486740A1 (en) * | 2017-11-21 | 2019-05-22 | Honeywell International Inc. | Systems and methods for providing predicted mode change data for decoupled vertical navigation (vnav) and lateral navigation (lnav) autopilot operations |
| CN108363305A (en) * | 2018-03-14 | 2018-08-03 | 中国人民解放军国防科技大学 | Tactical missile robust overload autopilot design method based on active interference compensation |
| CN110081883A (en) * | 2019-04-29 | 2019-08-02 | 北京理工大学 | Low cost integrated navigation system and method suitable for high speed rolling flight device |
| CN111324142A (en) * | 2020-01-07 | 2020-06-23 | 湖北航天技术研究院总体设计所 | Missile navigator disturbance compensation control method |
Non-Patent Citations (1)
| Title |
|---|
| "滚转导弹解耦过载驾驶仪及其 BP 自适应调度法", 固体火箭技术, vol. 40, no. 06, pages 786 - 788 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022105408A1 (en) * | 2020-11-18 | 2022-05-27 | 北京理工大学 | Autopilot employing radial basis function neural network and decoupling control method thereof |
| CN113064443A (en) * | 2021-03-08 | 2021-07-02 | 北京理工大学 | Gain online adjustment method and damping loop control method using same |
| CN114779826A (en) * | 2022-06-20 | 2022-07-22 | 中国人民解放军国防科技大学 | Axial symmetry aircraft lateral control method suitable for non-zero roll angle |
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| Publication number | Publication date |
|---|---|
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