CN103412488B - A kind of miniature self-service gyroplane high-accuracy control method based on adaptive neural network - Google Patents
A kind of miniature self-service gyroplane high-accuracy control method based on adaptive neural network Download PDFInfo
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Abstract
A kind of miniature self-service gyroplane high-accuracy control method based on adaptive neural network is related to miniature self-service gyroplane feedback control, the Composite Controller Design that the structure for the adaptive neural network that no specimen is trained is combined with optimization.First, for miniature self-service gyroplane kinetic model, feedback control coefficient matrix is built by pole-assignment to ensure the primary stability of system；Secondly, designing has the adaptive neural network of autonomous update weights characteristic, builds adaptive network right value update matrix come the weight matrix of online updating neural network based on control information, realizes estimation and inhibition to disturbance；And design adaptive threshold optimisation strategy, error mean square based on physical location and desired locations in time window is poor, online updating is carried out to the control residual error upper limit threshold of adaptive neural network, reduce the influence of control residual error upper bound lax pair neural network disturbance controlled quentity controlled variable, and then optimize adaptive neural network and disturb controlled quentity controlled variable, realize the miniature self-service gyroplane high-precision attitude control under complex environment.The present invention has many advantages, such as that real-time is good, dynamic parameter response is fast, interferes adaptable, the high-precision control that can be used under miniature self-service gyroplane complexity multi-source interference environment to multi-source.
Description
Technical Field
The invention relates to a high-precision control method of a small unmanned gyroplane based on a self-adaptive neural network, which is suitable for the field of autonomous control of unmanned robots working in the air.
Background
The small unmanned rotor wing machine has the characteristics of vertical take-off and landing, hovering and the like, can perform tasks such as observation, information collection and the like in narrow spaces such as dangerous areas or urban streets through various sensors carried by the small unmanned rotor wing machine, and has wide application prospect. Along with the expansion of the application field, the working environment of the small unmanned gyroplane is also complex and changeable, and the small unmanned gyroplane with strong immunity and high stability is controlled to be a research hotspot at high precision.
As a complex multi-input multi-output control system, the small unmanned gyroplane has the characteristics of nonlinearity, strong coupling, high control difficulty and the like. In addition, various interferences, such as wind interference, atmospheric turbulence, ground interference, system electromagnetic interference and the like, exist in the flying process of the small-sized unmanned gyroplane, so that high-precision control of the small-sized unmanned gyroplane under the disturbance is one of key technologies of a flight control system.
To improve performance, various control methods such as an intelligent PID control method, robust control, and intelligent control method are used for flight control of the small-sized unmanned gyroplane. The intelligent PID controller is simple in structure, but poor in anti-interference capability, and the control performance of the small unmanned gyroplane is easily affected by external interference and reduced. The robust control can well eliminate the problems of model parameter inaccuracy and external interference of the small unmanned gyroplane in the flying process, but has the characteristics of poor instantaneity and slow dynamic parameter response. Through a large amount of sample training, the neural network can realize nonlinear adaptive control, overcome the model uncertainty that small-size unmanned gyroplane has, and there are multisource interference scheduling problem, realize the attitude control of high accuracy, but traditional neural network needs a large amount of sample data to train, has the poor shortcoming of real-time.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problem that the control performance of a small unmanned gyroplane is easily affected by external interference when the small unmanned gyroplane executes tasks, a composite control method based on the combination of an adaptive neural network and a pole allocation method is provided, multi-source interference borne by the small unmanned gyroplane in flight is estimated and restrained, and high-precision control of a large envelope range is achieved.
The technical solution of the invention is as follows: aiming at a small unmanned gyroplane dynamics model, a feedback control coefficient matrix is constructed by a pole allocation method to ensure the initial stability of the system; designing a self-adaptive neural network with the characteristic of autonomously updating the weight, and constructing a self-adaptive network weight updating matrix based on error information to update the weight matrix of the neural network on line so as to realize the estimation and the suppression of disturbance; and designing a self-adaptive threshold optimization strategy, and updating the control residual upper limit threshold of the self-adaptive neural network on line based on the mean square error between the actual position and the expected position in the time window, so as to realize the high-precision attitude control of the small unmanned gyroplane in the complex environment. The method comprises the following implementation steps:
(1) aiming at a small unmanned gyroplane dynamics model, a feedback control coefficient matrix is constructed by a pole allocation method to ensure the initial stability of the system;
(2) for multi-source interference existing in flight, a self-adaptive neural network with an autonomous weight updating characteristic is designed, a weight matrix of the neural network is updated on line by constructing a self-adaptive network weight updating matrix based on error information, online estimation of the multi-source interference borne by the small unmanned gyroplane in flight is realized, and the self-adaptive neural network weight updating matrix and a disturbance estimator expression are as follows:
wherein,is a weight matrix of the adaptive neural network,a disturbance estimator for an adaptive neural network; gamma-shapediP is a symmetric positive definite matrix, and the input e of the adaptive neural network is x-xdIs a desired state variable xdAnd the actual state variable x, B is a small unmanned rotorcraft control state transition matrix, αwFor the control residual upper limit threshold of the adaptive neural network, i is the ith row vector of the corresponding matrix, i is the ith column vector of the corresponding matrix, s (e) is the node function of the hidden layer of the adaptive neural network, defined as a Gaussian function, and the node function of the corresponding jth hidden layer is expressed as follows:
wherein, muj,Respectively is the central value and the width of the Gaussian function of the hidden layer of the adaptive neural network, and l is the number of hidden nodes of the hidden layer of the adaptive neural network;
(3) and designing a self-adaptive threshold optimization strategy, and updating the control residual upper limit threshold of the self-adaptive neural network on line based on the mean square error between the actual position and the expected position in the time window to realize the high-precision attitude control of the small unmanned gyroplane in the complex environment.
The invention discloses a small-sized unmanned gyroplane high-precision control method based on an adaptive neural network, wherein the step (3) of constructing an adaptive threshold optimization strategy is defined as follows
Formula (III) αwα is the control residual error upper threshold value of the adaptive neural network in the sampling period of the current time windoww-1α control residual error upper threshold value of adaptive neural network in last time window sampling periodw-2The control residual error upper limit threshold value of the self-adaptive neural network in the sampling period of the last two time windows is set; chi shapekFor actual position xx of small unmanned rotorcraft in last time window sampling periodmAnd desired position xxdThe mean square error of (d); chi shapek-tFor actual position xx of small unmanned gyroplane in last two time window sampling periodsmAnd desired position xxdThe mean square error of (d); eeiSampling the actual position xx of the small-sized unmanned gyroplane in the period for a time windowmAnd desired position xxdA difference of (d); t is the number of sampling points in the sampling period of the time window; k is a radical of1To control the parameters, η1,η2Respectively, the actual position xx of the small-sized unmanned gyroplane in the sampling period of the time windowmAnd desired position xxdThe maximum absolute error value and the average error value of (c).
Compared with the prior art, the invention has the advantages that:
(1) on the basis of ensuring the initial stability of the system by constructing a feedback control coefficient matrix through a pole allocation method, the disturbance borne by the small unmanned gyroplane in the flight process is estimated and suppressed through a sample-free training adaptive neural network, and the small unmanned gyroplane has the advantages of strong anti-interference performance and convenience in design;
(2) under the condition that the traditional feedback control ensures the stability of the system, the adaptive neural network is further utilized to estimate and inhibit the disturbance borne by the small unmanned gyroplane in the flight process, the rudder amount can be adjusted in real time according to the state information of the aircraft, and the method has the characteristics of simple structure and convenience in control, has good real-time performance and quick dynamic parameter response, and can meet the high-precision control requirement under the load environment of the small unmanned gyroplane;
(3) according to the method, the weight of the adaptive neural network can be updated on line only according to the state information acquired in the actual flight process of the small unmanned gyroplane and based on the position error obtained by calculation, any sample training is not needed, and the method has the advantages of convenience in data acquisition and simplicity in calculation.
Drawings
FIG. 1 is a flow diagram of an autonomous control process for a small unmanned rotorcraft;
FIG. 2 illustrates the effect of a small unmanned rotorcraft of the present invention operating a four-waypoint cruise flight in a 3.2m/s wind-disturbed environment.
Detailed Description
As shown in fig. 1, the specific implementation method of the present invention is as follows:
(1) feedback control based on pole allocation
Based on a linearization method, the small-sized unmanned rotorcraft kinetic equation is expressed as
Wherein the state variable x ∈ RnRepresenting corresponding speed, angle and angular velocity information for the small-scale unmanned rotorcraft system. Control variable u e RmRespectively representing lateral periodic variable pitch, longitudinal periodic variable pitch, total pitch control signals and course control signals of the small unmanned gyroplane; a is an element of Rn×nAnd B ∈ Rn×mA state transition matrix and a control transition matrix which are respectively a state variable and a control variable; d is equal to RmRepresenting bounded composite interference due to wind disturbances, atmospheric turbulence, ground disturbances, system electromagnetic interference, sensor measurement errors, and due to small unmanned rotorcraft system parameter uncertainty and modal-free dynamics.
The controller input of the small unmanned gyroplane consists of two parts, one part is the state feedback input Kx (t), and the other part is the disturbance estimator of the adaptive neural networkIs that
The feedback coefficient K is obtained according to a pole allocation theory and is used for ensuring the initial stability of the system;
(2) constructing an adaptive neural network
For multisource interference existing in flight, a self-adaptive neural network with an autonomously updated weight matrix is designed to improve the control precision of the system, and online estimation and inhibition of multisource interference borne by the small unmanned gyroplane in flight are realized.
The adaptive neural network is composed of an input layer, a hidden layer and an output layerForming; the input of the input layer of the adaptive neural network is the expected state variable xdAnd the actual state variable x, i.e. e ═ x-xd；
The hidden layer is composed of a plurality of Gaussian functions and defined as s (e), and the node function expression of the corresponding j-th hidden layer is as follows:
wherein, muj,Respectively the central value and the width of the Gaussian function of the hidden layer of the adaptive neural network, l is the number of hidden nodes of the hidden layer of the adaptive neural network, and the central value mu of the node of the adaptive radial basis functionjWidth, widthAt the discretion of the user.
Estimation of multi-source interference by adaptive output layer
Wherein,is a weight matrix of the adaptive neural network; gamma-shapediP is a symmetric positive definite matrix, αwFor the control residual error upper limit threshold value of the adaptive neural network, i is the ith row vector of the corresponding matrix, and i is the ith column vector of the corresponding matrix, wherein the weight matrix of the adaptive neural network is automatically updated according to the following rules
Wherein P is a symmetric positive solution of the equation
(A+BK)TP+P(A+BK)＝-Q
Wherein the symmetric positive definite matrix Q ═ I.
(3) Designing adaptive threshold optimization strategies
And on the basis of the mean square error between the actual position and the expected position in the time window, the control residual error upper limit threshold of the self-adaptive neural network is updated on line, and the high-precision attitude control of the small unmanned gyroplane in the complex environment is realized.
The adaptive threshold optimization strategy is defined as follows:
formula (III) αwα is the control residual error upper threshold value of the adaptive neural network in the sampling period of the current time windoww-1α control residual error upper threshold value of adaptive neural network in last time window sampling periodw-2The control residual error upper limit threshold value of the self-adaptive neural network in the sampling period of the last two time windows is set; chi shapekFor actual position xx of small unmanned rotorcraft in last time window sampling periodmAnd desired position xxdThe mean square error of (d); chi shapek-tFor actual position xx of small unmanned gyroplane in last two time window sampling periodsmAnd desired position xxdThe mean square error of (d); eeiSampling the actual position xx of the small-sized unmanned gyroplane in the period for a time windowmAnd desired position xxdA difference of (d); t is the number of sampling points in the sampling period of the time window; k is a radical of1To control the parameters, η1,η2Respectively, the actual position xx of the small-sized unmanned gyroplane in the sampling period of the time windowmAnd desired position xxdThe maximum absolute error value and the average error value of (c).
(5) Flight example
Four waypoint flight experimental verifications are carried out based on small-size unmanned gyroplane. The four-waypoint patrol flies with the height of 20 meters, takes waypoints (10, -20, 20) as a starting point, passes through waypoints (40, -20, 20), (40, 0, 20) and (10, 0, 20) respectively, and finally hovers at the starting point. The comparison result of the feedback control method based on the same feedback control matrix parameter and the adaptive neural network control method is shown in fig. 2, under the maximum wind disturbance environment of 3.2m/s, the line pressing precision of the small unmanned gyroplane based on the adaptive neural network is 1.56m when the four-waypoint line patrol task is executed, and the line pressing precision in the hovering stage is 0.83 m, which are superior to the traditional feedback control method.
The high-precision control method of the small-sized unmanned gyroplane based on the adaptive neural network overcomes the defects of the existing control method, and can realize high-precision flight control and the like of the small-sized unmanned gyroplane in a complex and multi-disturbance environment.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
Claims (2)
1. A high-precision control method of a small unmanned gyroplane based on an adaptive neural network is characterized by comprising the following steps:
(1) aiming at a small unmanned gyroplane dynamics model, a feedback control coefficient matrix is constructed by a pole allocation method to ensure the initial stability of the system;
(2) for multi-source interference existing in flight, a self-adaptive neural network with an autonomous weight updating characteristic is designed, a weight matrix of the neural network is updated on line by constructing a self-adaptive network weight updating matrix based on error information, online estimation of the multi-source interference borne by the small unmanned gyroplane in flight is realized, and the self-adaptive neural network weight updating matrix and a disturbance estimator expression are as follows:
wherein,is a weight matrix of the adaptive neural network,a disturbance estimator for an adaptive neural network; gamma-shapediP is a symmetric positive definite matrix, and the input e of the adaptive neural network is x-xdIs a desired state variable xdAnd the actual state variable x, B is a small unmanned rotorcraft control state transition matrix, αwFor the control residual upper limit threshold of the adaptive neural network, i is the ith row vector of the corresponding matrix, i is the ith column vector of the corresponding matrix, s (e) is the node function of the hidden layer of the adaptive neural network, defined as a Gaussian function, and the node function of the corresponding jth hidden layer is expressed as follows:
wherein, muj,Respectively is the central value and the width of the Gaussian function of the hidden layer of the adaptive neural network, and l is the number of hidden nodes of the hidden layer of the adaptive neural network;
(3) and designing a self-adaptive threshold optimization strategy, and updating the control residual upper limit threshold of the self-adaptive neural network on line based on the mean square error between the actual position and the expected position in the time window to realize the high-precision attitude control of the small unmanned gyroplane in the complex environment.
2. The adaptive neural network-based small-sized unmanned rotorcraft high-precision control method according to claim 1, wherein: the step (3) of constructing an adaptive threshold optimization strategy is defined as follows:
formula (III) αwα is the control residual error upper threshold value of the adaptive neural network in the sampling period of the current time windoww-1α control residual error upper threshold value of adaptive neural network in last time window sampling periodw-2The control residual error upper limit threshold value of the self-adaptive neural network in the sampling period of the last two time windows is set; chi shapekFor actual position xx of small unmanned rotorcraft in last time window sampling periodmAnd desired position xxdThe mean square error of (d); chi shapek-tFor actual position xx of small unmanned gyroplane in last two time window sampling periodsmAnd desired position xxdThe mean square error of (d); eeiSampling the actual position xx of the small-sized unmanned gyroplane in the period for a time windowmAnd desired position xxdA difference of (d); t is the number of sampling points in the sampling period of the time window; k is a radical of1To control the parameters, η1,η2Respectively, the actual position xx of the small-sized unmanned gyroplane in the sampling period of the time windowmAnd desired position xxdThe maximum absolute error value and the average error value of (c).
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