CN108549229A - A kind of overhead crane neural network adaptive controller and its design method - Google Patents
A kind of overhead crane neural network adaptive controller and its design method Download PDFInfo
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Abstract
The invention discloses a kind of overhead crane neural network adaptive controller and its design methods, establish the kinetic model of overhead crane, linearization process is carried out to the kinetic model of overhead crane, and introduce external interference factor compensation term d, obtain the linear model of overhead crane, it designs to obtain neural network adaptive controller, including trolley controller and load controller based on RBF neural.The present invention, which is positioned and loaded to the trolley of crane respectively using neural network adaptive approach, anti-sway devises bi-feedback adaptive controller, by self-learning method in crane modeling process model error and the non-linear partials such as external interference arbitrarily approached, to realize stability control.
Description
Technical field
The present invention relates to a kind of overhead crane neural network adaptive controller and its design methods.
Background technology
Along with the fast development of China's transport service and intelligence manufacture industry, overhead crane (also known as bridge crane) is
As key equipment indispensable in modern industry automated production, the floor space that has is small, convenient and efficient, cargo is removed
Transport the features such as efficiency is higher and simple in structure so that will be used wider and wider for overhead crane is general.Currently, to meet global trade
Easy integrated demand, overhead crane just towards the speed of service be getting faster with higher and higher two aspect development of hoisting depth,
It is widely used in each department and the field of the national economic development such as harbour and construction site.
The controlling difficulties of overhead crane essentially consist in the operation location control of trolley and the swing of load inhibits control, existing
Most of technology is positioned at the anti-swing control research of load, and there are many deficiencies for used method, such as input setting method
The pivot angle of pendulum of disappearing changes greatly；Closed loop PID control method is poor to extraneous anti-interference ability；There are quiet for fuzzy self-adaption method
Poor problem and operand is big, reaction speed is slow；There are many restrict conditions for other general approach mostly band, and practicability is not
Foot；Portion of techniques uses neural network method, but is only limited to the load anti-swing control to crane.
Therefore, how to design a kind of can run location control and hunting of load to the trolley of overhead crane and inhibit control
Controller is still technical problem to be solved.
Invention content
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of overhead crane Neural Network Adaptive Controls
Device and its design method, the trolley of crane is positioned and is loaded respectively using neural network adaptive approach it is anti-sway devise it is double anti-
Adaptive controller is presented, by self-learning method to the model error and the non-linear partials such as external interference in crane modeling process
It is arbitrarily approached, to realize stability control.
The technical solution adopted in the present invention is：
A kind of overhead crane neural network adaptive controller, the neural network adaptive controller include trolley control
Device and load controller, the trolley controller and load controller are respectively：
Wherein, u1And u2It inputs in order to control；M is trolley quality；M is load quality；G is acceleration of gravity；L is that lifting rope is long
Degree；x3For state variable；x3=θ, θ are load pivot angle；E is error matrix；K gain matrixs in order to control；WithFor external disturbance
To the influence factor d of trolley movement positioning1With the influence factor d of load pivot angle transformation2Estimation.
Further, describedWithExpression formula be respectively：
Wherein, h1(x) and h2(x) it is respectively radial base vector；WithRespectively ideal weights W1And W2Estimated value.
Further, the expression formula of described K, E are respectively：
K=[kp, kd]T
Wherein, kpFor ratio controlling elements；kdFor derivative control factors, e is error.
A kind of design method of overhead crane neural network adaptive controller, this method include：
(1) kinetic model of overhead crane is established；
(2) linearization process is carried out to the kinetic model of overhead crane, and introduces external interference factor compensation term d, obtained
To the linear model of overhead crane；
(3) it designs to obtain neural network adaptive controller based on RBF neural, including trolley controller and load are controlled
Device processed.
Further, the construction method of the kinetic model of the overhead crane is：
Assuming that during crane works, state vectorIt utilizes
Lagrange equations obtain the kinetic model of overhead crane, and the kinetic model of overhead crane is：
Wherein, M and m indicates that the quality of trolley and load, l represent lifting rope length respectively, and u is inputted in order to control, and g adds for gravity
Velocity constant, ω1(x, t) and ω2(x, t) indicates that external disturbance positions crane and the influence factor of hunting of load, x are respectively
State variable,R indicates that trolley displacement, θ are load pivot angle；Indicate ginseng
Number x1Second dervative,WithExpression parameter x3First derivative and second dervative,The second dervative of expression parameter l.
Further, the linear model of the overhead crane is：
Wherein,
In formula, d1And d2It is expressed as external interference factor, ω1And ω2Respectively indicate external disturbance to crane positioning and
The influence factor of hunting of load；M is trolley quality；M is load quality；G is acceleration of gravity；L is lifting rope length.
Further, described the step of designing to obtain neural network adaptive controller based on RBF neural, includes：
To d in external interference factor compensation term d1And d2On-line Estimation is carried out respectively, obtains d1Estimated valued2Estimation
Value
By d1Estimated valued2Estimated valueIt is input in RBF neural and carries out dynamic learning；
By the output of RBF neuralWithAnd it is respectively sent to trolley controller and load controller, obtain nerve
Network self-adapting controller.
Further, d in the d to external interference factor compensation term1And d2The process of progress On-line Estimation is respectively：
Base Function and RBF neural ideal output function are respectively：
D (x)=WT/h(x)+ε.
Wherein, hjFor Base Function, d (x) indicates that neural network ideal output function, x and i are respectively RBF god
Input value through network and input number, j are hidden layer node, and h is radial base vector, h=[h1,h2,…hn]T, cijFor nerve
Network Basis Function Center point；bjFor Base Function variance；W is RBF neural ideal weights, and ε is approximate error and ε
≤εn；
The input value of RBF neural is taken to beThe output d (x) of RBF neural is estimated, is obtained
To estimated valueFor：
For the estimated value of ideal weights W.
Further, weights are selectedMore new law be：
Wherein, γ is normal number, and h is radial base vector, h=[h1,h2,…hn]T, matrix P is positive definite symmetric matrices and expires
Sufficient Lyapunov equations：ΛTP+P Λ=- Q, whereinQ >=0,B is constant, kpFor than
Example controlling elements；kdFor derivative control factors.
Further, the expression for overhead crane neural network adaptive controller being obtained in the step (3) is：
Wherein, u1And u2It inputs in order to control；M is trolley quality；M is load quality；G is acceleration of gravity；L is that lifting rope is long
Degree；x3For state variable；x3=θ, θ are load pivot angle；E is error matrix；K gain matrixs in order to control；WithIt is disturbed for outside
The dynamic influence d that trolley movement is positioned1With the influence d of load pivot angle transformation2Estimation.
Compared with prior art, the beneficial effects of the invention are as follows：
(1) present invention can carry out nonlinear compensation to the uncertain of overhead crane model, realize to overhead crane platform
The swing inhibition control for being accurately positioned control and load of vehicle, the static difference phenomenon after no control, controller data operand is small, instead
Answer speed fast, it can be to realizing stability contorting with the crane system under extraneous uncertain noises factor；
(2) present invention introduces RBF neural method to overhead crane in terms of locating and tracking and pivot angle inhibit two
It realizes the nonlinear compensation based on the factors such as modeling error and external disturbance, devises overhead crane Neural Network Adaptive Control
Device, and stability analysis has been carried out based on Lyapunov theories, it was demonstrated that the stability of closed-loop system, emulation experiment then demonstrate
The feasibility and validity of this method.
Description of the drawings
The accompanying drawings which form a part of this application are used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation do not constitute the improper restriction to the application for explaining the application.
Fig. 1 is bridge type crane system model schematic；
Fig. 2 is neural network adaptive controller system block diagram；
Fig. 3 is the adaptive control laws tracking simulation result diagram of system position and speed；
Fig. 4 is that the control input of system approaches simulation result diagram with external disturbance；
Fig. 5 is that system load swing angle inhibits and pivot angle change rate simulation result diagram；
Fig. 6 is the control input simulation result diagram that system pivot angle inhibits.
Specific implementation mode
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific implementation mode, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or combination thereof.
As background technology is introduced, exist in the prior art that operand is big, and reaction speed is slow, there are many constraint limits for band
Condition processed lacks practicability, can only be to the deficiency of the load anti-swing control of crane, in order to solve technical problem as above, this Shen
A kind of overhead crane neural network adaptive controller and its design method please be propose, neural network adaptive controller is answered
For the control of overhead crane, by means of the None-linear approximation characteristic of neural network to the bridge type crane system with external disturbance
Nonlinear characteristic compensate, devise neural network adaptive controller, and be according to establishing with practical hoisting device
System model, Simulation results show that designed controller has applicable generality and practicability.
Due to the presence of various uncertain factors, the modeling of system is caused the actual operation of system can not possibly to be fully described
State, be easy to causeing the theoretical simulation result of designed controller, there are larger differences with practical control result, give crane control
System brings adverse effect, therefore is arbitrarily approached the non-linear partial of overhead crane by neural network method, and combines
Self adaptive control puts control to realize the location control to crane and disappear, final to realize designed controller application in one
In the model emulation of practical bridge type crane system 3DCrane, designed controller is verified by the simulation experiment result
Validity.
1, problem describes
The composition of overhead crane includes trolley and load lift heavy two parts, and schematic diagram is as shown in Figure 1, wherein M and m difference
The quality of trolley and load is represented, l (t) is lifting rope length, and r (t) is displacement of the trolley on the directions external traction force F, θ (t)
To load pivot angle, d is external disturbance, and u is inputted in order to control.
It is in below trolley always assuming that crane loads during the work time and meets following two conditions：
(1) lifting rope is considered as rigid connection, and ignores its quality；
(2) lifting rope pulley radius is zero, and two-dimentional crane load pivot angle is
Enable parameter transformation：
Then state vectorR indicates that trolley displacement, θ are load pivot angle；
The kinetic model that overhead crane is obtained using Lagrange equations is：
Wherein, M and m indicates that the quality of trolley and load, l represent lifting rope length respectively, and u is inputted in order to control, and g adds for gravity
Velocity constant, ω1(x, t) and ω2(x, t) indicates that the external disturbances such as air drag, site wind and frictional force are fixed to crane respectively
The influence factor of position and hunting of load, x is state variable；Expression parameter x1Second dervative,WithExpression parameter x3One
Order derivative and second dervative,The second dervative of expression parameter l.
For ease of the design of nerve network controller, need the trolly cranes model progress linearization process of above-mentioned (1) obtaining letter
Change model.Since peak acceleration is much smaller than acceleration of gravity to overhead crane in the process of running and lifting rope length is kept not substantially
Become, therefore hasI=0；Have in the case where hunting of load is little | θ ＜ ＜ 1 |, therefore have
Due to the approximation of trigonometric function, the high-order term in nonlinear system is ignored, and then when hunting of load is smaller, original is non-thread
Sexual system can be reduced to following Linear system model：
Wherein,
In formula, d ∈ R4×1Can be air drag, site wind, frictional force and due to model line for external interference factor
The influence that the various irresistible power such as the error that propertyization generates generate crane system；d1And d2Indicate external disturbance to platform respectively
The influence factor of vehicle motion positions and load pivot angle transformation, ω1And ω2It indicates that external disturbance positions crane and loads respectively to put
Dynamic influence factor；M is trolley quality；M is load quality；G is acceleration of gravity；L is lifting rope length.
Designed controller will utilize the approximation properties of RBF neural respectively to external disturbance to trolley movement below
The influence d of positioning1With the influence d of load pivot angle transformation2It is approached and is estimated.
2, controller design
It is as follows using the design process of Gauss RBF neural controller to system (2)：
It can be obtained by system (2)
Select control law for：
Wherein, u1And u2It inputs in order to control；M is trolley quality；M is load quality；G is acceleration of gravity；L is that lifting rope is long
Degree；x3For state variable；x3=θ, θ are load pivot angle；E is error matrix,E is error；K gain squares in order to control
Battle array, K=[kp, kd]T, kpFor ratio controlling elements；kdFor derivative control factors；WithIt is fixed to trolley movement for external disturbance
The influence factor d of position1With the influence factor d of load pivot angle transformation2Estimation, approach the estimation of unknown function for RBF neural
Value.
Wherein,WithExpression formula be respectively：
Wherein, h1(x) and h2(x) it is respectively radial base vector；WithRespectively ideal weights W1And W2Estimated value.
To d in compensation term d1And d2On-line Estimation is carried out respectively, is obtainedWithSpecific algorithm process be：
Base Function hjExpression formula with neural network ideal output function d (x) is：
D (x)=WTh(x)+ε.
Wherein, x and i is respectively the input value and input number of RBF neural, and j is hidden layer node, and h is radial base
Vector, h=[h1,h2,…hn]T, cijFor Base Function central point；bjFor Base Function variance；W is RBF god
Through network ideal weights, ε is approximate error and ε≤εn；
The input value of RBF neural is taken to beThe output d (x) of RBF neural is estimated, is obtained
To estimated valueFor：
RBF neural controller architecture figure for the estimated value of ideal weights W, the design is as shown in Figure 2.
It is h to take h (x)1(x),ForIt can obtainSimilarly, it is h to take h (x)2(x),ForIt can obtain
By control law (2), select right value update rule for：
Wherein, γ is normal number, and h is radial base vector, h=[h1,h2,…hn]T, matrix P is positive definite symmetric matrices and expires
Sufficient Lyapunov equations：ΛTP+P Λ=- Q, whereinQ >=0,B is constant, kpFor than
Example controlling elements；kdFor derivative control factors.
3, stability analysis
Consider following Nonlinear Second Order System
Wherein,For known nonlinear function,For unknown nonlinear function, g ∈ Rn,u∈RnRespectively it is
System output and control input.
Without loss of generality, consider that control law (3), which is substituted into Nonlinear Second Order System (6), to be obtained：
It might as well set
Then formula (7) can be written as
Positioning optimal ideal weights expression formula is：
Defining approximate error is
Then expression formula (8) can be rewritten into
It can be obtained by formula (4)And it substitutes into (9) equation of closed-loop system can be obtained and be
Choosing Lyapunov candidate functions is
γ in formula is normal number, and P is that positive definite is poised for battle matrix and meets Lyapunov equations：
ΛTP+P Λ=- Q
Wherein,Q≥0.
It chooses respectivelyAnd it enables
Then system closed loop equation (9) can be written as
Respectively to V1, V2Derivation then has
BecauseThe expression formula of M, which is brought into, to be obtained
And because
So having
Adaptive law (5) is substituted into formula (15) to obtain
BecauseSo as long as selected suitable RBF neural so that approximate error ω is sufficiently small
It can ensure V≤0.
Assuming that crane loads during the work time is in below trolley and under meeting following two Conditions Conditions always, deposit
It can ensure that incomplete under-actuated systems (1) realize load while tracing into desired trajectory in RBF adaptive controllers (3)
Disappear pendulum control.
4, it emulates
In order to verify the validity of designed neural network adaptive controller, according to practical three-dimensional overhead crane platform
Parameter, emulated using theoretical and numerical method, the feasibility of proposed control program can be verified from kinematics,
Emulation environment used is MATLAB/Simulink2015B, and the parameter of 3DCrane crane platforms is shown in Table 1.
1 3DCrane overhead crane experiment porch parameter lists of table
The parameters such as trolley and lift heavy quality, lifting rope length and acceleration of gravity are respectively set as follows：
M=6.157kg, m=1kg, l=0.37m, g=9.8m/s2. (17)
Select document [A motion planning-based adaptive controlmethod for an
Underactuated crane system] in the desired reference track that is positioned as trolley of S types track, expression formula is such as
Under：
Wherein,The target location of ε=3.5, trolley is set as pdX=pr=
0.6m, set external interference signals are d1=sin (t).
The number of nodes of nerve network controller is N=2 × 5 × 1=10, is evenly distributed on [- 2 2] × [- 2 2] × [- 2
2] in the range of, width bi=0.2, initial weightSystem initial state X (0)=[0:052；0].Selected
Control inputs
Selected adaptive law is
Wherein, γ=1200, kd=50, kp=30,
The simulation result of trolley position is as shown in Figures 3 and 4, and by scheming, 3 it can be seen that designed based on RBF neural
Adaptive controller can complete the tracking to set objective, fast response time, to the tracking Approximation effect of S curve well
Preferably.Control input as seen from Figure 4 can inhibit external factor d well1The influence that=sin (t) is brought, and RBF
To the estimated value of external disturbancePreferable fitting is also achieved with external interference signals, illustrate the compensation of neural network and is approached
Effect is all satisfactory.
The anti-sway simulation result of lift heavy in crane operational process is as it can be seen in figures 5 and 6, set external disturbance is d2=
Sin (t), as seen from Figure 5, the change rate for loading pivot angle are controlled always in relatively low range, and are finally tended towards stability,
Angular speed variation is more steady, and concussion is smaller, and Fig. 6 then shows that controlling input energy well approaches external interference, institute
The adaptive control laws of use preferably inhibit the swing of load.
The present invention has studied the Stabilization of a kind of drive lacking overhead crane with exterior nonlinear disturbance, including positioning
Tracking and pivot angle inhibit two aspects, introduce RBF neural method and are based on modeling error and outside to overhead crane realization
The nonlinear compensation of the factors such as interference, devises neural network adaptive controller, and carried out surely based on Lyapunov theories
Qualitative analysis, it was demonstrated that the stability of closed-loop system, emulation experiment then demonstrate the feasibility and validity of this method.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (10)
1. a kind of overhead crane neural network adaptive controller, characterized in that the neural network adaptive controller includes
Trolley controller and load controller, the trolley controller and load controller are respectively：
Wherein, u1And u2It inputs in order to control；M is trolley quality；M is load quality；G is acceleration of gravity；L is lifting rope length；x3
For state variable；x3=θ, θ are load pivot angle；E is error matrix；K gain matrixs in order to control；WithIt is external disturbance to platform
The influence factor d of vehicle motion positions1With the influence factor d of load pivot angle transformation2Estimation.
2. overhead crane neural network adaptive controller according to claim 1, characterized in that describedWithTable
It is respectively up to formula：
Wherein, h1(x) and h2(x) it is respectively radial base vector；WithRespectively ideal weights W1And W2Estimated value.
3. overhead crane neural network adaptive controller according to claim 1, characterized in that the expression of described K, E
Formula is respectively：
K=[kp, kd]T
Wherein, kpFor ratio controlling elements；kdFor derivative control factors, e is error.
4. a kind of design method of overhead crane neural network adaptive controller, characterized in that including：
(1) kinetic model of overhead crane is established；
(2) linearization process is carried out to the kinetic model of overhead crane, and introduces external interference factor compensation term d, obtain bridge
The linear model of formula crane；
(3) it designs to obtain neural network adaptive controller based on RBF neural, including trolley controller and load control
Device.
5. the design method of overhead crane neural network adaptive controller according to claim 4, characterized in that described
The construction method of the kinetic model of overhead crane is：
Assuming that during crane works, state vectorIt utilizes
Lagrange equations obtain the kinetic model of overhead crane, and the kinetic model of overhead crane is：
Wherein, M and m indicates that the quality of trolley and load, l represent lifting rope length respectively, and u is inputted in order to control, and g is acceleration of gravity
Constant, ω1(x, t) and ω2(x, t) indicates external disturbance to the influence factor of crane positioning and hunting of load respectively, and x is state
Variable,R indicates that trolley displacement, θ are load pivot angle；Expression parameter x1
Second dervative,WithExpression parameter x3First derivative and second dervative,The second dervative of expression parameter l.
6. the design method of overhead crane neural network adaptive controller according to claim 4, characterized in that described
The linear model of overhead crane is：
Wherein,
In formula, d1And d2It is expressed as external interference factor, ω1And ω2Indicate that external disturbance is positioned and loaded to crane respectively
The influence factor of swing；M is trolley quality；M is load quality；G is acceleration of gravity；L is lifting rope length.
7. the design method of overhead crane neural network adaptive controller according to claim 1, characterized in that described
The step of designing to obtain neural network adaptive controller based on RBF neural include：
To d in external interference factor compensation term d1And d2On-line Estimation is carried out respectively, obtains d1Estimated valued2Estimated value
By d1Estimated valued2Estimated valueIt is input in RBF neural and carries out dynamic learning；
By the output of RBF neuralWithAnd it is respectively sent to trolley controller and load controller, obtain neural network
Adaptive controller.
8. the design method of overhead crane neural network adaptive controller according to claim 1, characterized in that described
To in external interference factor compensation term dWithThe process of progress On-line Estimation is respectively：
Base Function and RBF neural ideal output function are respectively：
D (x)=WTh(x)+ε.
Wherein, hjFor Base Function, d (x) indicates neural network ideal output function, and x and i are respectively RBF neural
Input value and input number, j is hidden layer node, and h is radial base vector, h=[h1,h2,…hn]T, cijFor neural network base
Function central point；bjFor Base Function variance；W is RBF neural ideal weights, and ε is approximate error and ε≤εn；
The input value of RBF neural is taken to beThe output d (x) of RBF neural is estimated, is estimated
EvaluationFor：
For the estimated value of ideal weights W.
9. the design method of overhead crane neural network adaptive controller according to claim 8, characterized in that selection
WeightsMore new law be：
Wherein, γ is normal number, and h is radial base vector, h=[h1,h2,…hn]T, matrix P is positive definite symmetric matrices and satisfaction
Lyapunov equations：ΛTP+P Λ=- Q, whereinQ >=0,B is constant, kpFor ratio
Controlling elements；kdFor derivative control factors.
10. the design method of overhead crane neural network adaptive controller according to claim 1, characterized in that institute
It states and obtains the expression of overhead crane neural network adaptive controller in step (3) and be：
Wherein, u1And u2It inputs in order to control；M is trolley quality；M is load quality；G is acceleration of gravity；L is lifting rope length；x3
For state variable；x3=θ, θ are load pivot angle；E is error matrix；K gain matrixs in order to control；WithIt is external disturbance to platform
The influence d of vehicle motion positions1With the influence d of load pivot angle transformation2Estimation.
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| CN114488801A (en) * | 2022-01-18 | 2022-05-13 | 无锡安起科技有限公司 | Bridge crane model prediction control method based on data driving |
| CN114488801B (en) * | 2022-01-18 | 2023-12-22 | 无锡安起科技有限公司 | Bridge crane model prediction control method based on data driving |
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