CN106200383A  A kind of three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net  Google Patents
A kind of three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net Download PDFInfo
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 CN106200383A CN106200383A CN201610645413.4A CN201610645413A CN106200383A CN 106200383 A CN106200383 A CN 106200383A CN 201610645413 A CN201610645413 A CN 201610645413A CN 106200383 A CN106200383 A CN 106200383A
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 G—PHYSICS
 G05—CONTROLLING; REGULATING
 G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
 G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
 G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
 G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
 G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
A kind of three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net, relate to the Disturbance Rejection design of control parameter Online Estimation based on adaptive neural network and expansion state state observer, first, according to three axle inertially stabilized platform kinetic models, for the three uncertain feedback control parameters timevarying characteristics caused of axle inertially stabilized platform model parameter, build adaptive neural network and feedback control matrix parameter is carried out Online Estimation, make three axle inertially stabilized platform control accuracies approach expectational model control accuracy；Secondly, the interference compensation controlled quentity controlled variable that the upper bound disturbed for adaptive neural network estimation difference and three axle inertially stabilized platforms the builds impact on control accuracy, build extended state observer interference is estimated and suppresses, it is achieved three axle inertially stabilized platform high accuracy under complex environment control.The present invention has that realtime is good, dynamic parameter response is fast, to advantages such as multisource interference strong adaptabilities, can be used for the high accuracy control etc. under complicated multisource interference environment of the three axle inertially stabilized platforms.
Description
Technical field
The present invention relates to a kind of three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net, suitable
For aerial mapping stabilized platform high accuracy control field.
Background technology
Three axle gondola platforms are fixed on flight carrier by pedestal, support and stablize remotely sensed image load, and isolation multisource is done
Disturb the impact on the remotely sensed image load optical axis, improve remote sensing load pointing accuracy over the ground, be with a wide range of applications.
Three axle inertially stabilized platform interference types in the course of the work are various, do not only exist uncertain RANDOM WIND disturb and
By aircraft body vibration caused angular movement interference, there is also owing to load system barycenter causes not with system origin is misaligned
Trimming moment, the internal interference that platform sensor part measurement error causes, therefore, complexity disturbs the high accuracy controlling party under environment more
Method has become as one of key technology of three axle inertially stabilized platform researchs.
For improving performance, PID control method, robust control, intelligent control method are used for three axle inertially stabilized platforms
High accuracy controls.PID controller simple in construction, but poor anti jamming capability, it is difficult to ensure that three axle inertia under multisource interference effect
Platform stable precision.Robust control can disturb the impact on control accuracy with suppression system model parameter uncertainty and multisource,
But control accuracy has bigger conservative.By substantial amounts of sample training, neutral net infinite can approach nonlinear system,
Thus solve the nonlinear of three axle inertially stabilized platform models, it is achieved highprecision gesture stability, but traditional neutral net needs
Substantial amounts of sample data is wanted to be trained, the shortcoming with poor real.
Summary of the invention
Present invention solves the technical problem that and be: three axle inertially stabilized platform control accuracies are nonlinear and multisource by system model
The impact of interference, is estimated that by adaptive neural network feedback control parameters solves the nonlinear of model in real time, and builds expansion
Observer improves the capacity of resisting disturbance of system, it is achieved three axle inertially stabilized platform high accuracy under complex environment control.
The technical solution of invention is: set up kinetic model, according to expectation first against three axle inertially stabilized platforms
System performance index, builds three axle inertially stabilized platform reference driving force models, by adaptive network in line tracking feedback control
Parameter processed, makes three axle inertially stabilized platform kinetic models approach and reference model；Secondly, build expansion observer, reduce certainly
The sign function gain that the upper bound of adaptation neutral net estimation difference and external disturbance the builds impact on system control accuracy.Its
Realize step as follows:
(1) according to three axle inertially stabilized platform kinetic models, design a model reference adaptive neural network control method,
For the three uncertain feedback control parameters timevarying characteristics caused of axle inertially stabilized platform model parameter, build adaptive neural network net
Network carries out Online Estimation to feedback control matrix parameter, makes three axle inertially stabilized platform control accuracies approach expectational model and controls essence
Degree；
(2) upper bound disturbed for adaptive neural network estimation difference and three axle inertially stabilized platforms build interference
Compensate the controlled quentity controlled variable impact on system control accuracy, build extended state observer and interference is estimated and suppresses, it is achieved be multiple
Three axle inertially stabilized platform high accuracy under heterocycle border control；
The three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net of the present invention, wherein said
Step (1) control based on model reference adaptive ANN Control input u_{j}, adaptive neural network more new lawWith dry
Disturb compensation controlled quentity controlled variable r_{j}Expression formula be respectively
r_{j}=δ_{j}sgn(e^{T}PB_{*j})
Wherein, represent roll frame, j=2 pitching frame during j=1, during j=3, represent orientation frame,It it is adaptive neural network
The weight matrix of network,It is adaptive neural network ideal weight matrix W^{*}The realtime estimated value of jth row,It is
The basic function of adaptive neural network, n is the dimension of system model, and m is the dimension of system input, and l is adaptive neural network
The nodes of hidden layer,For the state variable of system, b_{j}Preferable control for the coefficient matrix B corresponding j row of framework input
Coefficient processed, g_{j}It is the estimated value of system j framework inertia inertia, u_{j} ^{mm}It is the expectation input of system reference model j framework, with reference to mould
Type is
Wherein,For the state equation matrix of reference model,
WithIt is the matrix artificially designed according to three axle inertially stabilized platform dynamic performance requirements, k=1,2,3
Representing corresponding roll frame respectively, pitching frame and orientation frame, e is state variable x of current state amount x and reference model^{mm}Difference
Value, matrix P is the positive definite symmetric solution of state equation,
PA^{mm}+A^{mmT}P=Q
WhereinIt is positive definite symmetrical matrix,Being adaptive law gain matrix, l is adaptive neural network
The nodes of hidden layer, B_{*j}Being the jth row of the coefficient matrix B of framework input, adaptive neural network estimation difference and the external world disturb
The dynamic upper bound is
δ_{j}>ε_{j}x+d_{j}/b_{j}
Wherein, ε_{j}It is the approximate error of adaptive neural network, d_{j}Interference for system j framework；
The three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net of the present invention, wherein said
Extended state observer that step (2) builds and revised interference compensation controlled quentity controlled variable r_{j}Expression formula be respectively
Wherein, z_{1}=e, z_{2}=Δ=B ε x+Dd,WithIt is respectively state variable z_{1}And z_{2}Estimated value, w_{0}> 0 be design
Variable, can makeWithTight tracking e and Δ within the limited time, revised interference compensation controlled quentity controlled variable r_{j}Expression formula
For
Wherein,Residual error B ε x and system interference Dd and disturbance estimated value z is estimated for system selfadaption neutral net_{2}
Residual error
Corresponding system input is
Wherein,It is+ 3 components of jth.
Present invention advantage compared with prior art is:
(1) the present invention infinite extensive approximation capability by adaptive neural network, the feedback control of realtime estimating system
Parameter, solves the feedback control parameters timevariant nonlinear problem caused owing to model parameter is nonlinear, uncertain, makes actual mould
Type approaches reference model, control method simple in construction, and capacity of resisting disturbance is strong；
(2) present invention is in the case of adaptive neural network ensures system stability, estimates further with expansion observer
Counting and suppress in three axle inertially stabilized platform work process suffered disturbance, control accuracy is high, it is possible to meet three axle stable inertias
The high accuracy demand for control of platform；
(3) present invention only requires according to the status information in three axle inertially stabilized platform work process, utilize Li Yapunuo
Husband's function design adaptive neural network right value update matrix, can be with the weights of online updating adaptive neural network, it is not necessary to
Any sample training, has data acquisition convenience, calculates simple advantage.
Accompanying drawing explanation
Fig. 1 is three axle inertially stabilized platform control flows；
Fig. 2 is that in flight experiment, three axle inertially stabilized platform pitch channels control effect；
Fig. 3 is that in flight experiment, three axle inertially stabilized platform roll passages control effect；
Fig. 4 is that in flight experiment, three axle inertially stabilized platform azimuthal channel control effect.
Detailed description of the invention
As it is shown in figure 1, the present invention's is implemented as follows
(1) build based on model reference adaptive neutral net
Based on NewtonEuler equation, the kinetics equation of three axle inertially stabilized platforms is expressed as
Wherein, x=[θ_{j} ω_{j}]^{T},H=0_{3×3}, F=(f_{jk}), j, k=1,2,3,
U=[u_{1} u_{2} u_{3}]^{T},G=[g_{1} g_{2}
g_{3}]^{T}, d=[d_{1} d_{2} d_{3}]^{T},
Wherein, represent roll frame, j=2 pitching frame during j=1, during j=3, represent orientation frame,State for system
Variable, n=6 is the dimension of state variable, θ_{j}For corresponding j framework angle, ω_{j}For corresponding j frame corners speed,For
The coefficient matrix of state variable,For the coefficient matrix of framework input, m=3 is the dimension of system input, u_{j}For accordingly
J frame voltage input, g is the estimated value of the gimbal moment of intertia, d be system framework interference, system interference, frame member be used to
The perturbation value △ g of amount_{j}With the control input perturbation Δ b caused by measurement noise_{j}Constitute, b_{j}Coefficient matrix B phase for framework input
Answering the preferable control coefrficient that j arranges, F is corresponding perfect condition variation coefficient matrix, wherein
Wherein, N represents motordriven ratio, K_{e}Represent back electromotive force constant, K_{t}Represent motor torque coefficient, R_{m}Represent motor
Resistance, J_{m}Represent the rotary inertia of motor, J_{a}=diag (J_{ax},J_{ay},J_{az}) it is that the rotary inertia of orientation frame is in orientation frame coordinate system
Projection on x, y, z direction, J_{p}=diag (J_{px},J_{py},J_{pz}) it is that the rotary inertia of pitching frame is in pitching frame coordinate system x, y, z side
Projection upwards, J_{r}=diag (J_{rx},J_{ry},J_{rz}) be the rotary inertia of roll frame at roll frame in roll coordinate system x, y, z direction
On projection,K=b, r, p, a are that the angular velocity in k system relative inertness space is in k system x, y, z side
Downward mapping；It is respectively roll frame opposite base to exist relative to roll frame at roll coordinate system, pitching frame
Pitching coordinate system, orientation frame relative to pitching frame at the angular velocity of azimuthal coordinates system, θ_{r}Represent roll frame opposite base corner, θ_{p}Table
Show the pitching frame corner relative to roll frame, θ_{a}Represent the orientation frame corner relative to pitching frame, by the codedisc being arranged on gimbal axis
Measurement obtains, T_{dm}For acting on the disturbance torque on motor, T_{dj}For acting on the disturbance torque on j framework, rotary inertia is
J_{1}=J_{az}+N^{2}J_{m},
J_{2}=J_{px}+J_{ax}+N^{2}J_{m},
J_{3}=J_{ry}+(J_{ay}+J_{py})cos^{2}θ_{p}+(J_{az}+J_{pz})sin^{2}θ_{p}+N^{2}J_{m}
Design reference model
WhereinFor the state equation matrix of reference model,WithIt is the matrix artificially designed based on POLE PLACEMENT USING principle according to dynamic performance requirement,It is the control input of desired reference model,
Design of feedback controller
Wherein r=[r_{1} r_{2} r_{3}]^{T}It is the controlled quentity controlled variable designed for compensating interference d, meets Feedback oscillator, then system
If A+BK=A^{mm}, then real system is infinite approaches desired reference model；
Therefore, feedback oscillator
Due to h_{jk},f_{jk}, k, j=1,2,3 is timevariant nonlinear function,Also it is timevariant nonlinear function, therefore, utilizes
The infinite extensive approximation capability of adaptive neural network carrys out Online Estimation timevariant nonlinear function
Wherein,It is the weight matrix of adaptive neural network,It it is adaptive neural network ideal weights square
Battle arrayThe realtime estimated value of jth row,Being the basic function of adaptive neural network, l is that adaptive neural network is hidden
Nodes containing layer, ε_{j}It it is the approximate error of adaptive neural network；
Therefore, control based on model reference adaptive ANN Control input u_{j}, adaptive neural network more new lawWith interference compensation controlled quentity controlled variable r_{j}Expression formula be respectively
r_{j}=δ_{j}sgn(e^{T}PB_{*j})
Wherein, e is state variable x of current state amount x and expectational model^{mm}Difference, matrix P is the positive definite of state equation
Symmetric solution,
PA^{mm}+A^{mmT}P=Q
WhereinIt is positive definite symmetrical matrix,It is adaptive law gain matrix, B_{*j}It is the jth row of B matrix,
The upper bound of adaptive neural network estimation difference and external disturbance is
δ_{j}>ε_{j}x+d_{j}/b_{j}
Wherein, ε_{j}It is the approximate error of adaptive neural network, d_{j}Interference for system j framework；
(2) adaptive neural network is built
Interference compensation controlled quentity controlled variable r built for the upper bound of adaptive neural network estimation difference and external disturbance_{j}To being
The impact of system control accuracy, builds extended state observer and estimates interference and suppress, it is achieved three axles under complex environment
Inertially stabilized platform high accuracy controls,
Definition status variable z_{1}=e, z_{2}=Δ=B ε x+Dd,
Then three axle inertially stabilized platform error state equation are
By z_{2}Introduce error state equation, and
Wherein, s is the rate of change of Δ, and the extended state observer of structure is
Wherein,WithIt is respectively state variable z_{1}And z_{2}Estimated value, w_{0}> 0 it is design variable；
By suitable Selecting All Parameters w_{0}Can makeWithTight tracking e and Δ within the limited time,
Revised interference compensation controlled quentity controlled variable r_{j}Expression formula be
Wherein,Residual error B ε x and system interference Dd and disturbance estimated value z is estimated for system selfadaption neutral net_{2}
Residual error
Corresponding system input is
Wherein,It is+ 3 components of jth.
(3) flight example
In flight course, according to the angle information of highprecision attitude measuring unit, three axle inertially stabilized platform framework systems
System is adjusted correspondingly, it is ensured that the remote sensing load optical axis is vertical over the ground, flight result such as Fig. 2, Fig. 3 and Fig. 4 institute of certain experiment
Show.
Three axle inertially stabilized platforms achieve highprecision control, and the standard deviation of pitch channel is 0.0183 degree, and roll is led to
The standard deviation in road is 0.0157, and the standard deviation of azimuthal channel is 0.0214.
The present invention three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net overcome existing
The deficiency of control method, it is possible to achieve three axle inertially stabilized platforms high accuracy under complexity disturbs environment more controls.
The content not being described in detail in description of the invention belongs to prior art known to professional and technical personnel in the field.
Claims (3)
1. three axle Inertiallystabilizeplatform platform control method based on model reference adaptive neutral net, it is characterised in that realize
Following steps:
(1) according to three axle inertially stabilized platform kinetic models, design a model reference adaptive neural network control method, for
The three uncertain feedback control parameters timevarying characteristics caused of axle inertially stabilized platform model parameter, build adaptive neural network pair
Feedback control matrix parameter carries out Online Estimation, makes three axle inertially stabilized platform control accuracies approach expectational model control accuracy；
(2) the interference compensation control that the upper bound disturbed for adaptive neural network estimation difference and three axle inertially stabilized platforms builds
The amount processed impact on system control accuracy, builds extended state observer and estimates interference and suppress, it is achieved complex environment
Under three axle inertially stabilized platforms high accuracy control.
Three axle inertially stabilized platform controlling parties based on model reference adaptive neutral net the most according to claim 1
Method, it is characterised in that: described step (1) control based on model reference adaptive ANN Control input u_{j}, self adaptation god
Through network more new lawWith interference compensation controlled quentity controlled variable r_{j}Expression formula be respectively
r_{j}=δ_{j}sgn(e^{T}PB_{*j})
Wherein, represent roll frame, j=2 pitching frame during j=1, during j=3, represent orientation frame,It it is adaptive neural network
Weight matrix,It is adaptive neural network ideal weight matrix W^{*}The realtime estimated value of jth row,It is adaptive
Answering the basic function of neutral net, m is the dimension of system input, and l is the nodes of adaptive neural network hidden layer, and n is system
The dimension of model,For the state variable of system, b_{j}The preferable of coefficient matrix B corresponding j row for framework input controls system
Number, g_{j}It is the estimated value of system j framework inertia inertia, u_{j} ^{mm}Being the expectation input of system reference model j framework, reference model is
Wherein,For the state equation matrix of reference model,WithIt is the matrix artificially designed according to three axle inertially stabilized platform dynamic performance requirements, k=1,2,3 points
Not representing corresponding roll frame, pitching frame and orientation frame, e is state variable x of current state amount x and reference model^{mm}Difference,
Matrix P is the positive definite symmetric solution of state equation,
PA^{mm}+A^{mmT}P=Q
WhereinIt is positive definite symmetrical matrix,Being adaptive law gain matrix, l is that adaptive neural network implies
The nodes of layer, B_{*j}It is the jth row of the coefficient matrix B of framework input, adaptive neural network estimation difference and external disturbance
The upper bound is
δ_{j}>ε_{j}x+d_{j}/b_{j}
Wherein, ε_{j}It is the approximate error of adaptive neural network, d_{j}Interference for system j framework.
Three axle inertially stabilized platform controlling parties based on model reference adaptive neutral net the most according to claim 1
Method, it is characterised in that: extended state observer that described step (2) builds and revised interference compensation controlled quentity controlled variable r_{j}Expression
Formula is respectively
Wherein, z_{1}=e, z_{2}=Δ=B ε x+Dd,WithIt is respectively state variable z_{1}And z_{2}Estimated value, w_{0}> 0 it is design variable,
Can makeWithTight tracking e and Δ within the limited time, revised interference compensation controlled quentity controlled variable r_{j}Expression formula be
Wherein,Residual error B ε x and system interference Dd and disturbance estimated value z is estimated for system selfadaption neutral net_{2}Residual
Difference
Corresponding system input is
Wherein,It is+ 3 components of jth.
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Publication number  Priority date  Publication date  Assignee  Title 

CN107894713A (en) *  20171020  20180410  东南大学  A kind of highaccuracy control method without coding two axle inertially stabilized platforms of sensing 
CN108469269A (en) *  20180119  20180831  天津大学  A kind of resonance point test system of wideband inertial reference stabilized platform 
Citations (6)
Publication number  Priority date  Publication date  Assignee  Title 

CN1790197A (en) *  20051222  20060621  安徽工业大学  Simple method for neural network decoupling of multivariable system based on model reference adaptive control 
CN102053628A (en) *  20091027  20110511  北京航空航天大学  Neural networkbased servo control system and method 
CN102298315A (en) *  20110621  20111228  河海大学常州校区  Adaptive control system based on radial basis function (RBF) neural network sliding mode control for microelectromechanical system (MEMS) gyroscope 
CN102437816A (en) *  20111025  20120502  武汉鑫通科创科技发展有限公司  Adaptive motor motion control apparatus based on neural network 
CN102508503A (en) *  20111101  20120620  北京航空航天大学  Compensation method based on generalized inner module for eccentric torque of threeshaft inertially stabilized platform 
CN105785762A (en) *  20160317  20160720  北京航空航天大学  Biaxis inertiallystabilized platform highprecision control method based on selfadaptive backstepping sliding mode 

2016
 20160808 CN CN201610645413.4A patent/CN106200383B/en active Active
Patent Citations (6)
Publication number  Priority date  Publication date  Assignee  Title 

CN1790197A (en) *  20051222  20060621  安徽工业大学  Simple method for neural network decoupling of multivariable system based on model reference adaptive control 
CN102053628A (en) *  20091027  20110511  北京航空航天大学  Neural networkbased servo control system and method 
CN102298315A (en) *  20110621  20111228  河海大学常州校区  Adaptive control system based on radial basis function (RBF) neural network sliding mode control for microelectromechanical system (MEMS) gyroscope 
CN102437816A (en) *  20111025  20120502  武汉鑫通科创科技发展有限公司  Adaptive motor motion control apparatus based on neural network 
CN102508503A (en) *  20111101  20120620  北京航空航天大学  Compensation method based on generalized inner module for eccentric torque of threeshaft inertially stabilized platform 
CN105785762A (en) *  20160317  20160720  北京航空航天大学  Biaxis inertiallystabilized platform highprecision control method based on selfadaptive backstepping sliding mode 
NonPatent Citations (3)
Title 

YINGZOU，等: "A compound control method based on the adaptive neural network and sliding mode control for inertial stable platform", 《NEUROCOMPUTING》 * 
夏青元,等: "三轴式无人旋翼飞行器及自适应飞行控制系统设计", 《航空学报》 * 
钟麦英,等: "基于PMI的三轴惯性稳定平台干扰力矩补偿方法研究", 《仪器仪表学报》 * 
Cited By (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN107894713A (en) *  20171020  20180410  东南大学  A kind of highaccuracy control method without coding two axle inertially stabilized platforms of sensing 
CN107894713B (en) *  20171020  20201106  东南大学  Highprecision control method for twoaxis inertial stabilization platform without coding sensing 
CN108469269A (en) *  20180119  20180831  天津大学  A kind of resonance point test system of wideband inertial reference stabilized platform 
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