CN102998976A - Online real-time control method for intelligent seismic reduction structure - Google Patents
Online real-time control method for intelligent seismic reduction structure Download PDFInfo
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Abstract
The invention discloses an online real-time control method for an intelligent seismic reduction structure. The online real-time control method includes steps of predicting a next-moment displacement response and a next-moment speed response of the intelligent seismic reduction structure via a neural network in real time and inputting the displacement response into a fuzzy controller; enabling the fuzzy controller to select control current of a magneto-rheological damper according to measured seismic acceleration excitation and the predicted displacement response in real time; computing according to the speed response and the control current of the magneto-rheological damper to obtain control force of the magneto-rheological damper; and measuring actual seismic responses and actual seismic acceleration excitation of the intelligent seismic reduction structure. The seismic responses include a displacement response and a speed response. The online real-time control method has the advantages that problems of inherent time lag and the requirement on instantaneous parameter determination in a procedure for selecting control current of a magneto-rheological damper by the traditional control strategy are solved, the control current is accurately and timely selected, and accordingly the magneto-rheological damper plays an effective role in the aspect of controlling vibration of the structure.
Description
Technical field
The invention belongs to intelligence structure vibration control field, be specifically related to a kind of on line real time control method of intelligent damping structure, determine the control electric current of MR damper.
Background technology
MR damper is a kind of outstanding semi-automatic control device that reduces the structural earthquake response, itself has and exerts oneself greatly the characteristics of good stability.When structure deforms owing to vibration, be installed in the central MR damper of structure and can adjust characteristic parameter by the corresponding control strategy of basis, thus the energy of absorption vibration, the purpose of arrival damping.This damper has exerts oneself greatly, inputs the advantages such as energy is little, overcome the energy input problem of ACTIVE CONTROL damper, be a kind of damper that combines ACTIVE CONTROL damper and Passive Control damper advantage, have boundless application prospect at field of vibration control.
For the intelligence structure that MR damper has been installed, how real-time basic problem be exactly the control electric current according to seismic stimulation and architectural characteristic selection MR damper accurately and timely, this has directly determined the work of MR damper can, reduces better the vibratory response of structure.For the damper of half ACTIVE CONTROL, the typical control strategy of tradition comprises by the time-history analysis cycle calculations determines control electric current, dual control strategy and LQR, the control algolithms such as LQG.Determine the control electric current by the time-history analysis cycle calculations, that the structure that has added semi-automatic control device is carried out time-history analysis, determine the parameter of semi-automatic control device by cycle calculations, can reach very high precision on this theoretical method, but cycle calculations needs long time, and this is difficult to realize in extremely short earthquake time step.Dual control is namely: in the time of the structure away from equilibrium location, just export the control electric current of damper, export maximum control; When structure is got back to the equilibrium position, with regard to the output of close current, export minimum control.This dual control strategy ratio is easier to realize, but has output delay and the poor problem of control accuracy.Can not effectively bring into play the effect of damper, be not ideal to the control effect of the vibratory response of structure.And for LQR, the Algorithm of Active Controls such as LQG, a very important supposition is to think that structure its kinematic behavior in the middle of the process that earthquake occurs remains unchanged, in fact along with the propelling of Occurence Time of Earthquakes, structure can enter mecystasis gradually, and the state of the kinematic behavior of structure when initial can change a lot.
Summary of the invention
Goal of the invention: for the problem and shortage of above-mentioned prior art existence, the on line real time control method that the purpose of this invention is to provide a kind of intelligent damping structure, solve intrinsic time lag and the instantaneous definite problem of parameter that traditional control strategy exists in the process of selecting MR damper control electric current, selection control electric current accurately and timely, thereby so that MR damper more effectively effect of performance in the control structure vibration.
Technical scheme: for achieving the above object, the technical solution used in the present invention is a kind of on line real time control method of intelligent damping structure, comprises the steps:
(1) passes through neural network real-time estimate intelligent damping structure at next displacement response and speed responsive constantly, and the displacement response is input to fuzzy controller;
(2) fuzzy controller is according to the control electric current of the displacement response real-time selection MR damper of the seismic acceleration excitation of surveying and prediction;
(3) according to the control electric current of speed responsive and MR damper, calculate the control of MR damper;
(4) actual seismic response and the seismic acceleration of measuring the intelligent damping structure encourage, and described seismic response comprises displacement response and speed responsive;
(5) control, actual seismic response and the seismic acceleration excitation that calculates is input in the neural network as the input data, neural network will be inputted next seismic response constantly of data prediction intelligent damping structure according to these.
Further, described neural network is feedforward neural network; With the intelligent damping structure of the response of intelligent damping structure shift and the speed responsive of actual measurement and prediction at the displacement response in the identical moment and speed responsive as training data to real-time neural network training: based on the connection weight of the first algorithm correction neural network.Further, described the first algorithm is the Levenberg-Marquardt algorithm.The input layer of trained neural network reads in control and the seismic acceleration excitation of displacement response that described intelligent damping structure occured and speed responsive, MR damper, and output intelligent damping structure is at next displacement response and speed responsive constantly.Described neural network is abandoned old training data pair in real time in seismic process, adopt the new data training that gathers right, the kinematic behavior of real-time follow-up intelligent damping structure in seismic process.
Further, the pattern that the membership function of described fuzzy controller adopts Triangleshape grade of membership function and trapezoidal membership function to combine: when the excitation of earthquake acceleration and displacement respond very large or very little the time, select trapezoidal membership function; Otherwise select Triangleshape grade of membership function.
Specifically, at first by next displacement response and speed responsive constantly of neural network prediction intelligent damping structure (being called for short " structure ").With the structural earthquake response (comprising displacement response and speed responsive) of actual measurement and the structure predicted at identical moment seismic response as training data to real-time neural network training, constantly revise simultaneously the connection weight of neural network based on the Levenberg-Marquardt algorithm, can guarantee like this variation of kinematic behavior of the structural earthquake response energy tracking structure reality of neural network prediction, make displacement structure response and the speed responsive of prediction can satisfy set accuracy requirement.Then the displacement response with neural network prediction is input in the fuzzy controller, and fuzzy controller will be controlled electric current according to the displacement structure response of neural network prediction and the instantaneous selection of acceleration earthquake excitation of actual measurement.The pattern that the membership function of fuzzy controller has adopted Triangleshape grade of membership function and trapezoidal membership function to combine: when earthquake acceleration excitation and displacement respond very large or very little the time, select trapezoidal membership function to guarantee its stability, avoid Triangleshape grade of membership function too sensitive, and caused the shake of structural response (mainly being the acceleration responsive of structure); Remaining membership function is then selected Triangleshape grade of membership function, can make like this control electric current of MR damper that acceleration excitation and displacement response are kept susceptibility.In the process of selecting the control electric current, adopt the control of five ~ ten segmentations, and rule of thumb formulated the fuzzy rule of selecting the control electric current, the size of control electric current changes along with the size of acceleration excitation and displacement response, when acceleration excitation and displacement response ratio are larger, just increase electric current, otherwise just reduce electric current.
Beneficial effect: (1) the present invention has brought into play fuzzy control and nerual network technique advantage separately, with both combinations, has solved intrinsic time lag and the instantaneous definite problem of electric current in the MR damper Based Intelligent Control; (2) neural network is trained in real time, can catch in real time the variation of architectural characteristic in the seismic process, thereby the control electric current of more accurately determining MR damper reaches the energy-dissipating and shock-absorbing effect of effectively bringing into play MR damper, has preferably robustness and stability; (3) adopt the instantaneous selection control electric current of fuzzy controller, realized effective control to seismic response by selecting different membership functions.
Description of drawings
Fig. 1 is the process flow diagram of the on line real time control method of intelligent damping structure of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, further illustrate the present invention, should understand these embodiment only is used for explanation the present invention and is not used in and limits the scope of the invention, after having read the present invention, those skilled in the art all fall within the application's claims limited range to the modification of the various equivalent form of values of the present invention.
The present invention combines nerual network technique and fuzzy controller effectively: the data that at first sensor collected are input in the neural network, utilize the neural network can autonomous learning and next dynamic response constantly of ability predict of Data induction, solve the intrinsic Time Delay of semi-active damper device; When selecting the control electric current, adopt fuzzy controller to realize the instantaneous selection of electric current, the displacement response of its input the data neural network prediction and actual measureed value of acceleration excitation can realize selection control electric current accurately and timely.
The present invention will be described with reference to Fig. 1 for the below, and the specific embodiment of the invention is divided into four parts.
First is training feed forward type neural network.Neural network must through training can predict seismic response, structural earthquake response (comprising displacement response and speed responsive) with actual measurement during training responds contrast with the structural earthquake that passes through neural network prediction, both differences will be fed back in the neural network, neural network will be come the real-time update connection weight according to this difference, the exactly variation of the kinematic behavior of tracking structure in earthquake generating process, the seismic response of neural network prediction can approach actual seismic response more like this.
In order effectively to revise connection weight, introduced the Levenberg-Marquardt algorithm, concrete correction formula is:
Wherein i is cycle index, power function
L is the neuronic number of output layer,
Be output layer l neuronic output quantity (the present invention is displacement response and the speed responsive of prediction) that y (l) is the expectation value (the present invention is displacement response and the speed responsive in the identical moment of actual measurement) of output quantity, e
lError (being e among Fig. 1) for output layer l neuronic output quantity and its expectation value.
For power function E to connection weight matrix w
iGradient, μ 〉=0th, study the factor, I is unit matrix.
Trained neural network is read in the displacement response (x that structure has occured by input layer
K-2, x
K-1, x
k), speed responsive
Control (the f of MR damper output
Dk-1, f
Dk) and the seismic acceleration excitation
Then next step displacement response of export structure
And speed responsive
The initial input of neural network should be consistent with real initial environment, and namely all value is 0.
In feedforward neural network, one group of output data communication device vector commonly used of inputting data and prediction represents the k that for example inputs seismic response x constantly
k,
With the seismic response of the structure of predicting in the identical moment
Consist of a vector, be called one group of training data.Analyze by the training data to different capabilities, find that 200 groups training data satisfies the training requirement of neural network fully.When sample size was less than 200 groups of data, training data will increase in seismic process in time step by step, and when sample size surpassed 200 groups, training data early will be abandoned.
Second portion is fuzzy controller, and fuzzy controller at first needs to determine the interval range of each input parameter, and the interval range that each input parameter is determined is less, and the control accuracy of fuzzy controller is just higher.Generally speaking, the interval range of seismic acceleration excitation can be determined according to the amplitude of acceleration excitation, for prior ignorant seismic acceleration, usually be defined as 0～10m/s
2If structure is in low intensity area, can dwindle the interval of seismic acceleration, can get 0～4m/s such as 7 degree districts
2, 0～6m/s can get in 8 degree districts
2Find to get different acceleration interval ranges to result's impact and little by calculating; Can determine that according to design experiences and existing Chinese earthquake resistant code the structure shift responding range probably is 0 ~ h/200 (h is the floor height of structure); The interval range of control electric current can be defined as 0 ~ 2A according to the working current of MR damper.
The fuzzy rule of fuzzy controller of the present invention is that seismic acceleration is encouraged
(among Fig. 1 be
), the displacement structure response of prediction
(among Fig. 1 be
) and the control electric current I
c(be I among Fig. 1
K+1) interval range be divided into 5 grade: VS (very little), S (little), M (medium), B (greatly) and VB (very large), select corresponding control electric current according to the input acceleration excitation grade different with the displacement response, when the displacement response ratio of acceleration excitation and prediction is larger, just increase electric current, otherwise just reduce electric current.The pattern that the membership function of fuzzy controller has adopted Triangleshape grade of membership function and trapezoidal membership function to combine: when earthquake acceleration excitation and displacement respond very large or very little the time, select trapezoidal membership function to guarantee its stability, avoid Triangleshape grade of membership function too sensitive, and caused the shake of structural response (mainly being the acceleration responsive of structure); Remaining membership function is then selected Triangleshape grade of membership function, can make like this control electric current of MR damper that acceleration excitation and displacement response are kept susceptibility.The detailed fuzzy rule of fuzzy controller is as shown in table 1, and in the table 1, second walks to the size that the 6th row, secondary series to the six row are depicted as the control electric current.
Table 1 fuzzy controller fuzzy rule
Third part is to calculate the control of MR damper.Control electric current I according to fuzzy controller output
c(the k+1 control electric current I constantly for exporting among Fig. 1
K+1), by formula:
τ
y=A
1e^(-I
c)+A
2ln(I
c+e)+A
3I
c
Calculate magnetic flow liquid surrender shear resistance, wherein A
1, A
2, A
3The parameter relevant with magnetic flow liquid.Then calculating the control of damper, is the shearing valve type magneto-rheological damper if adopt at present the most frequently used, can be according to formula:
Calculate the control f of corresponding damper
d(be k+1 control f constantly among Fig. 1
Dk+1), wherein
Be the structure speed responsive of neural network prediction, η is the coefficient of viscosity, L, A
p, D
h, D is the parameter relevant with MR damper.
The control that calculates will be input in the middle of the neural network as the input data.
The 4th part is actual seismic response and the seismic acceleration excitation of measuring structure.Earthquake displacement and the speed responsive of actual measurement will be fed back to neural network in real time, and compare with the value of neural network prediction, and the difference between them will be used for revising link weight coefficients.
In the middle of the process of whole vibration, connection weight will be according to the real-time renewal correction of training data, until satisfy set accuracy requirement.
Claims (6)
1. the on line real time control method of an intelligent damping structure comprises the steps:
(1) passes through neural network real-time estimate intelligent damping structure at next displacement response and speed responsive constantly, and the displacement response is input to fuzzy controller;
(2) fuzzy controller is according to the control electric current of the displacement response real-time selection MR damper of the seismic acceleration excitation of surveying and prediction;
(3) according to the control electric current of speed responsive and MR damper, calculate the control of MR damper;
(4) actual seismic response and the seismic acceleration of measuring the intelligent damping structure encourage, and described seismic response comprises displacement response and speed responsive;
(5) control, actual seismic response and the seismic acceleration excitation that calculates is input in the neural network as the input data, neural network will be inputted next seismic response constantly of data prediction intelligent damping structure according to these.
2. the on line real time control method of described a kind of intelligent damping structure according to claim 1, it is characterized in that: described neural network is feedforward neural network; With the intelligent damping structure of the response of intelligent damping structure shift and the speed responsive of actual measurement and prediction at the displacement response in the identical moment and speed responsive as training data to real-time neural network training: based on the connection weight of the first algorithm correction neural network.
3. the on line real time control method of described a kind of intelligent damping structure according to claim 2, it is characterized in that: described the first algorithm is the Levenberg-Marquardt algorithm.
4. the on line real time control method of described a kind of intelligent damping structure according to claim 2, it is characterized in that: the input layer of trained neural network reads in control and the seismic acceleration excitation of displacement response that described intelligent damping structure occured and speed responsive, MR damper, and output intelligent damping structure is at next displacement response and speed responsive constantly.
5. the on line real time control method of described a kind of intelligent damping structure according to claim 2, it is characterized in that: described neural network is in seismic process, abandon in real time old training data pair, adopt the new data training that gathers right, the kinematic behavior of real-time follow-up intelligent damping structure in seismic process.
6. the on line real time control method of described a kind of intelligent damping structure according to claim 1, it is characterized in that: the pattern that the membership function of described fuzzy controller adopts Triangleshape grade of membership function and trapezoidal membership function to combine: when the excitation of earthquake acceleration and displacement respond very large or very little the time, select trapezoidal membership function; Otherwise select Triangleshape grade of membership function.
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| CN112555341A (en) * | 2020-12-30 | 2021-03-26 | 江苏科能电力工程咨询有限公司 | Magnetorheological damper vibration reduction system for power transformer |
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Application publication date: 20130327 |