CN106597847A - Recurrent-neural-network-based maneuver load controller and controlling method thereof - Google Patents
Recurrent-neural-network-based maneuver load controller and controlling method thereof Download PDFInfo
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Abstract
The invention discloses a recurrent-neural-network-based maneuver load controller and a controlling method thereof. A neural network of the maneuver load controller is formed by series connection of a recurrent identification neural network and a recurrent control neural network; and each of the recurrent identification neural network and the recurrent control neural network uses six neurons for formation. In addition, the invention also discloses a controlling method of a recurrent-neural-network-based maneuver load controller. With the recurrent neural network method, the controller has advantages of simple structure, high working performance stability and robustness, and high adaptability. Effective self-adaptive maneuver load retarding of an airplane with variable-mach-number flight can be realized. A design of a recurrent-neural-network-based maneuver load controller of an airplane within a wide mach number range can be realized; and adaptive control of the maneuver load of the airplane on the complicated flight conditions can be realized.
Description
Technical field
The invention belongs to flying vehicles control technical field, and in particular to a kind of maneuver load control based on recurrent neural network
Device processed and its control method.
Background technology
Aircraft can produce larger moment of flexure in maneuvering at wing root, so as to affect the life-span of aircraft, bring peace
Full hidden danger.Redistribute can the aerodynamic force of aircraft wing by using maneuver load controller, reach reduction wing root bending moment
Purpose.
Traditional maneuver load controller, such as PID cannot realize becoming the Self Adaptive Control of Mach number, work as mission requirements change
When, controller parameter needs to choose again, it is impossible to accomplish the control of real-time online, cannot play during aircraft practical flight
Effectively effect.Therefore a kind of maneuver load controller is designed, and air maneuver Load alleviation is realized using the controller
Method is always those skilled in the art's technical barrier to be solved.
The content of the invention
The present invention is directed to problems of the prior art, discloses a kind of maneuver load based on recurrent neural network
Controller and its control method, carry out system identification and maneuver load slow down two stages by recurrent neural network, can
Wing root bending moment slows down when realizing that aircraft becomes Mach number, while ensure the maneuver of aircraft can normally complete.
The present invention is achieved in that a kind of maneuver load controller based on recurrent neural network disclosed by the invention,
The neutral net of described maneuver load controller recognizes neutral net and a recursion control neutral net string by a recurrence
Connection is formed, and respectively constitutes network using 6 neurons.
Further, the basic mathematic model of described neutral net is:
In formula, vkFor internal net activation, ujRepresent input vector, wkjThe weight matrix of synapse is represented,For activation primitive,
ykOutput vector is represented, p is to include being biased in interior neutral net input sum.
The invention also discloses a kind of control method of the maneuver load controller based on recurrent neural network, concrete steps
It is as follows:
Step 1, using the normal g-load signal of aircraft, the angle of attack and wing root bending moment signal as input, horizontal tail and aileron
Deflection is used as output;Normal g-load aircraft normal g-load and the angle of attack be able to can be passed through by corresponding sensor acquisition, wing root bending moment
The mode that foil gauge is pasted at wing root gathers strain signal, and wing root bending moment is calculated in real time.Horizontal tail is deflected through work with aileron
Dynamic device is realizing.
Step 2, the parameter and initial weight matrix of neutral net activation primitive are recognized to system identification by adjusting recurrence;
Recurrence identification network is used for the sensor signal of identification system.At the n moment, when recurrence identification network predicts n+1
The signal at quarterAs the input of controlling network;Recurrence identification neutral net is learned online by realtime recurrent algorithm
Practise, recognize normal g-load, the angle of attack and the wing root bending moment signal of sensor acquisition.
Step 3, by adjust recursion control neutral net activation primitive parameter and initial weight matrix to normal g-load,
The tracking of the angle of attack and the suppression to wing root bending moment.Recursion control neutral net is equally learned online using realtime recurrent algorithm
Practise, normal g-load, the angle of attack and the wing root bending moment of neural network identification output are recognized as input signal with recurrence, realize to normal direction
Overload, the real-time tracing of the angle of attack and the real-time control to wing root bending moment.
Further, the activation primitive that described recurrence identification neutral net and recursion control neutral net are adopted for:
The quiet activation v of neutral netjWith input uiBetween meet:
In formula:wijFor weight matrix;vjFor the quiet activation of neutral net, ujFor neutral net input vector.
Further, described step 2 is specially:
2.1, recurrence identification network carries out on-line study, calculation error gradient, recurrence identification god by realtime recurrent algorithm
The error function of Jing networks is:
In formula, EidFor object function, nm is sensor sum,It is the error of real sensor signal and identification signal.
2.2, recognize neutral net is recognized n-th in recurrence according to the error of real sensor signal and identification signal
Individual time step, expression formula is:
Recurrence identification neutral net neuron is output as:
In formula,For the output of neuron, nid is neutral net input sum.
2.3, the design parameter of recurrence identification network, i.e. E are made by identification processidMinimum is reached, its detailed process is:
In formula, ηidFor the learning rate of neutral net.
Further, described step 3 is specially:
3.1, recursion control neutral net is similar with recurrence identification neural network structure, recursion control neutral net neuron
Output expression formula be:
Object function is:
Error expression between system reality output and desired output is:
The minimum of object function, need to be iterated to weight matrix:
Target function gradient is represented by:
In formula, nm is the output sum of recurrence identification network;Ni is the output sum of recursion control network, and γ is controlled to adjust
The parameter that device processed is activated only;ecoFor the error between system reality output and desired output;ηcoFor the study of recursion control network
Rate.Recursion control neutral net still carries out on-line study using realtime recurrent algorithm.
The present invention is relative to the beneficial effect of prior art:Maneuver load control of the present invention based on recurrent neural network
Utensil processed has adaptivity, and the maneuver load that can realize the aircraft for carrying out change Mach number slows down;By using recurrence god
The method of Jing networks, with simple structure, stable work in work is good, robustness is high, self adaptation is good the characteristics of, can to become horse
The aircraft of conspicuous number flight carries out efficient adaptive maneuver load to be slowed down;Present invention achieves aircraft is based in wide range of Mach numbers
The design of the maneuver load controller of recurrent neural network, realize aircraft under complicated flying condition maneuver load it is adaptive
Should control.
Description of the drawings
Fig. 1 is maneuver load controller architecture schematic diagram of the present invention based on recurrent neural network;
Fig. 2 is the schematic diagram that the present invention realizes air maneuver Load alleviation;
Fig. 3 is the system identification stage control deflecting facet schematic diagram of the present invention;
Fig. 4 is the maneuver load deceleration phase horizontal tail deflection schematic diagram of the present invention;
Fig. 5 is the system identification result schematic diagram of the present invention;
Fig. 6 is the Load alleviation result schematic diagram of the present invention;
Fig. 7 is the change Mach number Load alleviation schematic diagram of the present invention;
Fig. 8 is the closed loop control deflecting facet schematic diagram of the present invention.
Specific embodiment
The present invention provides a kind of maneuver load controller and its control method based on recurrent neural network, to make the present invention
Purpose, technical scheme and effect it is clearer, clearly, and referring to the drawings and give an actual example to the present invention further specifically
It is bright.It should be understood that described herein be embodied as, only to explain the present invention, being not intended to limit the present invention.
Fig. 1 is maneuver load controller architecture schematic diagram of the present invention based on recurrent neural network.One recurrence of controller
Identification neutral net and a recursion control neutral net are formed by connecting, and respectively constitute network using 6 neurons, neutral net
Basic mathematic model is:
Recurrence identification network is used for the sensor signal of identification system.At the n moment, when recurrence identification network predicts n+1
The signal at quarterAs the input of controlling network.
Recurrence identification network carries out on-line study by realtime recurrent algorithm.Realtime recurrent algorithm is that a kind of gradient declines
Method, its main algorithm is the error gradient for calculating certain particular expression formula in each time step.Recurrence identification neutral net
Error function is:
Real sensor signal recognizes n-th time step that neutral net is recognized with the error of identification signal in recurrence
Expression formula be:
Recurrence identification neutral net neuron is output as:
The target of identification process is by each time successive step weight matrix, so that the design ginseng of recurrence identification network
Number, i.e. EidMinimum is reached, its detailed process is:
Recursion control neutral net and recurrence identification neural network structure is similar, recursion control neutral net neuron it is defeated
Going out expression formula is:
Object function is:
Error expression between system reality output and desired output is:
The minimum of object function need to be iterated to weight matrix:
Target function gradient is represented by:
Recursion control neutral net still carries out on-line study using realtime recurrent algorithm, and specific derivation process is as follows:
In formula, nco is that recursion control network inputs are total, δtrIt is kronecker delta, nhi is the number of implicit input,
That is the virtual output number of recursion control network-feedback.
Fig. 2 is to realize air maneuver Load alleviation based on the maneuver load controller of recurrent neural network using the present invention
Schematic diagram, the input of controller includes aircraft normal g-load, the flying angle of aircraft, wing root bending moment, and output includes that horizontal tail is deflected
Angle, aileron movement angle.Air maneuver load is realized based on the maneuver load controller of recurrent neural network using the present invention
Slow down and be divided into two stages:1st, system identification;2nd, maneuver load slows down.Specific embodiment is as follows:
Controller parameter is designed under Mach 2 ship 0.6.
The system identification stage lasts 10s, and horizontal tail is with aileron initial input as shown in figure 3, recurrence identification neural network parameter
It is as follows:
Neural network learning rate:ηid=0.5
Activation primitive is:
Recurrence identification neutral net optimum initial weight matrix is obtained by numerical simulation.System identification result such as Fig. 5 institutes
Show, it can be seen that recurrence identification network output can quickly and accurately tracing system open loop output.
Maneuver load deceleration phase lasts 10s, and an open loop input horizontal tail is deflected, as shown in Figure 4.Recursion control nerve net
Network parameter is as follows:
Neural network learning rate:ηco=0.02
Activation primitive is:
Recursion control neutral net optimum initial weight matrix is obtained by numerical simulation.System control result such as Fig. 6 institutes
Show.It can be seen that in Mach 2 ship 0.6, this can make the wing based on the maneuver load controller of recurrent neural network
Root bending moment ratio does not adopt controller to reduce by 26%, and normal g-load and the angle of attack be consistent when not adopting controller.
After the completion of maneuver load controller parameter design based on recurrent neural network, parameter is applied to into other Mach numbers
Under, and the parameter lambda related to Mach number is introduced, as recurrence identification neutral net initial weight matrix and recursion control nerve net
The multiplier of network initial weight matrix, λ is as follows with the relation of Mach number Ma:
λ=0.29352-1.969251Ma+10.0211Ma2-8.10263Ma3
For aircraft, the maneuvering in the range of Mach number 0.3 to Mach number 0.69 is suitable for this parameter, is shown in Fig. 7
Maneuver load slows down situation under each Mach number, it can be seen that wing root bending moment highest can slow down 49%.Under different Mach number horizontal tail with
Aileron movement is as shown in Figure 8.
The above is preferred embodiment of the invention, and here description of the invention and application are illustrative;It should be pointed out that
For those skilled in the art, some changing can also be made under the premise without departing from the principles of the invention
Enter, these improvement also should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of maneuver load controller based on recurrent neural network, it is characterised in that described maneuver load controller
Neutral net recognizes neutral net by a recurrence and a recursion control neutral net is in series.
2. the maneuver load controller based on recurrent neural network according to claim 1, it is characterised in that described passs
Return identification neutral net be with the basic mathematic model of recursion control neutral net:
In formula, vkFor internal net activation, ujRepresent input vector, wkjThe weight matrix of synapse is represented,For activation primitive, ykRepresent
Output vector, p is to include being biased in interior neutral net input sum.
3. a kind of control method of the maneuver load controller based on recurrent neural network, it is characterised in that comprise the following steps that:
Step 1, using the normal g-load signal of aircraft, the angle of attack and wing root bending moment signal as input, the deflection of horizontal tail and aileron
As output;
Step 2, the parameter and initial weight matrix of neutral net activation primitive are recognized to system identification by adjusting recurrence;
Step 3, by adjusting the parameter and initial weight matrix of recursion control neutral net activation primitive to normal g-load, the angle of attack
Tracking and the suppression to wing root bending moment.
4. the control method of a kind of maneuver load controller based on recurrent neural network according to claim 3, it is special
Levy and be, the activation primitive that described recurrence identification neutral net and recursion control neutral net are adopted for:
The quiet activation v of neutral netjWith input uiBetween meet:
In formula:wijFor weight matrix, vjFor the quiet activation of neutral net, ujFor neutral net input vector.
5. the control method of a kind of maneuver load controller based on recurrent neural network according to claim 3 or 4, its
It is characterised by, described step 2 is specially:
2.1, recurrence identification network carries out on-line study, calculation error gradient, recurrence identification nerve net by realtime recurrent algorithm
The error function of network is:
In formula, EidFor object function, nm is sensor sum,It is the error of real sensor signal and identification signal;
2.2, according to the error of real sensor signal and identification signal when recurrence recognizes n-th that neutral net recognized
Spacer step, expression formula is:
Recurrence identification neutral net neuron is output as:
In formula,For the output of neuron, nid is neutral net input sum;
2.3, the design parameter of recurrence identification network, i.e. E are made by identification processidMinimum is reached, its detailed process is:
In formula, ηidFor the learning rate of neutral net.
6. the control method of a kind of maneuver load controller based on recurrent neural network according to claim 5, it is special
Levy and be, described step 3 is specially:
Recursion control neutral net carries out on-line study, the output of recursion control neutral net neuron using realtime recurrent algorithm
Expression formula is:
Object function is:
Error expression between system reality output and desired output is:
Weight matrix is iterated:
Target function gradient is represented by:
In formula, nm is the output sum of recurrence identification network;Ni is the output sum of recursion control network, and γ is regulation controller
The parameter of net activation;ecoFor the error between system reality output and desired output;ηcoFor the learning rate of recursion control network.
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