CN111459014A - Neural network PID-based crown block swing angle model control method - Google Patents
Neural network PID-based crown block swing angle model control method Download PDFInfo
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Abstract
The invention relates to a crown block swing angle model control method based on a neural network PID, which comprises the following steps: designing a network structure and a network layer number of a neural network; designing nodes of a network input layer and a network output layer of the neural network; determining a transfer function of a neural network, if a Sigmoid function is selected, setting a learning rate to be 0.02, selecting a difference value between a swing angle set value of an unmanned overhead travelling crane and a field actual swing angle value as a performance index, and selecting smaller random numbers from initial weights of an input layer and an output layer; and (3) carrying out self-adaptive control adjustment on the neural network according to the swing angle set value of the overhead travelling crane and the field actual swing angle value, storing the optimized P, I, D parameter in an oracle database of the unmanned overhead travelling crane, and transmitting the parameter to an PID (proportion integration differentiation) controller of the unmanned overhead travelling crane to carry out speed adjustment on the overhead travelling crane. The method controls the swing angle of the unmanned overhead travelling crane during operation within the error allowable range, and effectively ensures the accurate stacking of the steel coils in the storehouse and the safety of the steel coil transportation process.
Description
Technical Field
The application belongs to the technical field of metallurgy automatic control, and particularly relates to a crown block swing angle model control method based on a neural network PID.
Background
The unmanned overhead traveling crane is a brand new overhead traveling crane operation mode which replaces manual operation of the overhead traveling crane by the operation of a full-automatic overhead traveling crane. The unmanned overhead traveling crane control system is a product formed along with the development and informatization of enterprise logistics, and the improvement of the unmanned overhead traveling crane automation technology can obviously improve the production and transportation efficiency. The unmanned overhead traveling crane technology is an advanced automatic overhead traveling crane control means, has high requirements on related technical equipment of overhead traveling cranes and working states of the overhead traveling cranes, and has more and more significance as the swing angle control of overhead traveling crane clamps which are important factors for the stable operation of the unmanned overhead traveling cranes and the influence on the working efficiency of the overhead traveling cranes. One of the most remarkable characteristics of the unmanned crown block during the operation of the technology is that the clamp swing amplitude is very small and the swing angle is controllable in the moving and stopping processes of the crown block, so that a solid foundation is provided for the precise positioning and safe coil taking and releasing of the crown block, and the precise stacking of steel coils in a storehouse and the safety of the steel coil transportation process are effectively guaranteed.
The control of the swing angle of the clamp in the operation process of the unmanned overhead travelling crane is very important, so an effective control method needs to be developed to accurately control the swing angle of the clamp.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a neural network PID-based crown block swing angle model control method, which can perform self-adaptive operation to adjust the parameters of a crown block PID controller when the swing angle of a crown block exceeds an error allowable range, so as to adjust the running speed of the crown block and further control the swing angle within the error allowable range.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a crown block swing angle model control method based on neural network PID comprises the following steps:
step a, determining a network structure and a network layer number of a neural network;
b, determining a network input layer node and a network output layer node of the neural network;
c, determining a transfer function, a performance index, a learning rate and initial weights of an input layer and an output layer of the neural network;
d, when the unmanned overhead traveling crane runs, the unmanned overhead traveling crane system database automatically receives the actual value of the on-site running swing angle of the overhead traveling crane and determines a neural network input layer node;
and e, adjusting the swing angle, wherein when the actual operation swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, the neural network controls and adjusts, stores the adjusted P, I, D parameter in the unmanned overhead traveling crane database and then transmits the stored parameter to the unmanned overhead traveling crane PID controller to adjust the speed of the overhead traveling crane, so that the adaptive control of the swing angle of the unmanned overhead traveling crane is realized.
The technical scheme of the invention is further improved as follows: in the step a, the neural network adopts a three-layer neural network structure.
The technical scheme of the invention is further improved as follows: in the step b, according to the field condition of the unmanned overhead traveling crane, selecting a swing angle set value of the unmanned overhead traveling crane and a field actual swing angle value of the unmanned overhead traveling crane as network input layer nodes of the neural network, and selecting P, I, D three control parameters of the PID controller of the unmanned overhead traveling crane as network output layer nodes of the neural network.
The technical scheme of the invention is further improved as follows: in step c, the hidden layer transfer function of the neural network selects a Sigmod function with positive and negative symmetry:
the transfer function of the output layer of the neural network chooses a non-negative Sigmod function:
the learning rate of the neural network is set to be 0.02, and the difference value between the swing angle set value of the unmanned overhead travelling crane and the on-site actual swing angle value of the unmanned overhead travelling crane is selected as a performance index:
the initial weights of the input layer and the output layer of the neural network select smaller random numbers, and the smaller random numbers are randomly selected smaller values, such as 0.3243, 0.1482 and the like.
The technical scheme of the invention is further improved as follows: and d, receiving the actual swing angle value of the unmanned overhead traveling crane operated on site by adopting an unmanned overhead traveling crane process automatic oracle database system, and taking the actual swing angle value of the unmanned overhead traveling crane and the set swing angle value of the unmanned overhead traveling crane as neural network input layer nodes.
The technical scheme of the invention is further improved as follows: in the step e, a neural network online adjustment mode is adopted, when the unmanned overhead traveling crane runs, if the actual running swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, the neural network performs adaptive control to adjust the output value of the neural network P, I, D parameter, the adjusted and optimized P, I, D parameter is stored in an unmanned overhead traveling crane process automation system oracle database and is transmitted to an unmanned overhead traveling crane PID controller to perform overhead traveling crane speed adjustment, and the adaptive control of the swing angle of the unmanned overhead traveling crane is realized.
Due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the neural network is combined with the PID controller of the unmanned overhead traveling crane, and the self-adaptive control of the swing angle of the unmanned overhead traveling crane is realized.
2. And the neural network is adjusted in real time according to the actual on-site swing angle value of the crown block in an online adjustment mode, so that the real-time control of the swing angle of the crown block is realized.
3. The portability is strong, and other overhead traveling crane production lines can be generally transplanted.
The invention adopts a mode of combining a neural network and a PID controller of an unmanned overhead traveling crane, takes a swing angle set value of the unmanned overhead traveling crane and a field actual swing angle value of the unmanned overhead traveling crane as input layer nodes of the neural network, takes P, I, D three control parameters as output layer nodes of the neural network, selects a difference value between the swing angle set value of the unmanned overhead traveling crane and the field actual swing angle value of the unmanned overhead traveling crane as a performance index, adopts an online adjustment mode to carry out self-adaptive control on the swing angle of the unmanned overhead traveling crane, controls the swing angle of the overhead traveling crane when the unmanned overhead traveling crane runs within an error allowable range, and effectively ensures the safety of the steel coil in the storehouse in the processes of accurate stacking and steel coil transportation.
Drawings
FIG. 1 is a diagram of a neural network model architecture according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a neural network PID control of an embodiment of the invention;
FIG. 3 is a control flow diagram of an embodiment of the present invention;
fig. 4 is a schematic diagram of network error control according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
The invention discloses a control method of a crown block swing angle model based on a neural network PID, which comprises the following steps with reference to FIGS. 1-4:
step a, determining a network structure and network layer number of a neural network, wherein the invention adopts a three-layer neural network structure, and the figure 1 is shown.
And b, determining a network input layer node and a network output layer node of the neural network, wherein the input layer node of the neural network selects a swing angle set value of the unmanned overhead traveling crane and a field actual swing angle value of the unmanned overhead traveling crane, and the output layer node of the neural network selects P, I, D three control parameters.
And c, determining a transfer function, a performance index, a learning rate and initial weights of an input layer and an output layer of the neural network, selecting a Sigmoid function for the transfer function, setting the learning rate to be 0.02, selecting a difference value between a swing angle set value of the unmanned overhead travelling crane and a field actual swing angle value of the unmanned overhead travelling crane as the performance index, and selecting a smaller random number for the initial weights of the input layer and the output layer.
And d, when the unmanned crown block runs, automatically receiving the actual swing angle value of the crown block running on site by the unmanned crown block system oracle database, and taking the actual swing angle value of the unmanned crown block and the set swing angle value of the unmanned crown block as neural network input layer nodes.
And e, adopting an online adjustment mode, when the actual operation swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, carrying out adaptive control adjustment on the neural network, storing the optimized P, I, D parameter into an unmanned overhead traveling crane oracle database, and transmitting the parameter to an unmanned overhead traveling crane PID controller for carrying out overhead traveling crane speed adjustment, thereby realizing the adaptive control on the swing angle of the unmanned overhead traveling crane.
In the embodiment, a PID parameter control model is established by using a neural network, referring to fig. 1 to 4, and a C # program is applied to an automated system of an unmanned overhead traveling crane process for development and implementation, including the following steps:
step a, establishing a network structure and a network layer number of a neural network, wherein the invention adopts a three-layer neural network structure which respectively corresponds to a layer j, a layer i and a layer l in the figure 1; the main codes are as follows:
Int kue＝0.20；
Ce(x)＝3；
IN＝2；H＝3；Out＝3；
Int wo＝0；
w1＝wo；w2＝wo；w3＝wo；w4＝wo；w5＝wo；w6＝w0；
U＝[0,0]；
u1＝0；u2＝0；u3＝0；
Y＝[0,0,0]；
y1＝0；y2＝0；y3＝0；
end；
b, determining a network input layer node and a network output layer node of the neural network according to the field condition of the unmanned overhead traveling crane, wherein the input layer node of the neural network selects a swing angle set value of the unmanned overhead traveling crane and a field actual swing angle value of the unmanned overhead traveling crane, and the output layer node of the neural network selects P, I, D three control parameters; c # uses the main codes as follows:
Tk＝hyte(H,1)；
t＝ws；
for ut＝1:1:5000
xite(X)＝x*Wi；
if H＝＝1
yout(x)＝1.0；
else
if H＝＝2
u1(t)＝Sin(x1*x2*x3)；
end
Rout(x)＝h(x)*y1/(1+y1^2)+u1；
error(x)＝(rin(x)-yout(x))*(rin(x)-yout(x))/2；
xi＝[x(Target_pendulum),x(Actual_pendulum),]；
yi＝[y1,y2,y3]；
end；
step c, determining a transfer function of the neural network, selecting a Sigmoid function as the transfer function, and selecting a Sigmod function with positive and negative symmetry as a hidden layer transfer function of the neural network:
the transfer function of the output layer of the neural network chooses a non-negative Sigmod function:
setting the learning rate of a neural network to be 0.02, selecting the difference value between the swing angle set value of the unmanned overhead travelling crane and the on-site actual swing angle value of the unmanned overhead travelling crane as a performance index, and selecting smaller random numbers for the initial weights of an input layer and an output layer of the neural network; the main codes are as follows:
d, when the unmanned crown block runs, automatically receiving the actual swing angle value of the crown block running on site by an unmanned crown block system oracle database, and taking the actual swing angle value of the unmanned crown block and the set swing angle value of the unmanned crown block as neural network input layer nodes; the main codes are as follows:
string X(in)＝"select*from Angle_pendulum"；
DataTable dtTl＝new DataTable()；
DB.FillDataTable(X(in),dt1)；
Int[x(Target_pendulum),x(Actual_pendulum)]；
X(Target_pendulum)＝dt(1)；
X(Actual_pendulum)＝dt(2)；
step e, adopting a neural network online adjustment mode, when the actual operation swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, carrying out adaptive control adjustment on the neural network, storing the optimized P, I, D parameter into an unmanned overhead traveling crane oracle database, and transmitting the parameter to an unmanned overhead traveling crane PID controller for carrying out unmanned overhead traveling crane speed adjustment, thereby realizing the adaptive control on the swing angle of the unmanned overhead traveling crane; the main codes are as follows:
Hi(x)＝1:1:In；
error2＝error1；
error1＝error(x)；
U(t)＝2/(exp(y(x))+exp(-y(x)))^2；
for K＝1:1:In
kuyt(x)＝error(t)*etw(t)*refs(t)*H(t)；
for X＝1:1:Out；
for k＝1:1:In；
dwo＝refs*dfhy3(x)*F(x)+sdws*(wo1*w02*wo3)；
wo＝wo1+dwo+refs*(wo1*wo2*wo3)；
for t＝1:1:out；
dK(t)＝4/(exp(H(t))+exp(-H(t)))^2；
refs＝refs2*wo；
for t＝1:1:out；
f(t)＝d(t)*refs(t)；
dwi＝xite*refs*xi；
wi＝wi+d_wi+sdfs*(wi1*wi2*wi3)；
y(t)＝Yout(x)；
y3＝y2；y2＝y1；y1＝y(t)；
In(t)＝[x(Target_pendulum),x(Actual_pendulum)]
u2＝u1；u1＝In(t)；
wi4＝wi3；wi3＝wo2；wi2＝wo1；wi1＝wi；
wo6＝wo5；wo5＝wo4；wo4＝wo3；wo3＝wo2；wo2＝wo1；wo1＝wo；
yourt＝[y1 y2 y3]；
P＝y1；I＝y2；D＝y3；
end；
in fig. 2, PID is a PID controller, y is a field actual swing angle value of the unmanned overhead traveling crane, r is a swing angle set value of the unmanned overhead traveling crane, e is a difference between the actual swing angle value of the overhead traveling crane and the swing angle set value of the overhead traveling crane, and k is a valuep、Ki、KdP, I, D are three parameters.
When the unmanned overhead traveling crane runs, a neural network (BPNN) adjusts P, I, D three parameters of a PID controller according to a swing angle set value r of the unmanned overhead traveling crane and a field actual swing angle value y of the unmanned overhead traveling crane, so as to control an output value of the PID controller, further control the running speed of the unmanned overhead traveling crane (Plant), realize the control of the swing angle of the unmanned overhead traveling crane through the adjustment of the running speed of the unmanned overhead traveling crane, feed back the field actual swing angle value y of the unmanned overhead traveling crane to the neural network, finally realize the self-adaptive control of the running swing angle of the unmanned overhead traveling crane, and control the swing angle size of the unmanned overhead traveling crane during running within an error allowable range.
The invention comprises a network structure composition of a neural network PID control model, network input layer node selection of the neural network PID control model, output layer node selection of the neural network PID control model, transfer function setting of the neural network PID control model, learning rate setting of the neural network PID control model, performance index selection of the neural network PID control model, input layer data transmission of the neural network PID control model, output layer data transmission of the neural network PID control model and a self-adaptive learning method of the neural network PID control model. When the unmanned overhead traveling crane operates, the operation swing angle of the unmanned overhead traveling crane is adaptively controlled according to the swing angle set value of the unmanned overhead traveling crane and the field actual swing angle value of the unmanned overhead traveling crane, the swing angle of the unmanned overhead traveling crane during operation is controlled within an error allowable range, and the safety of steel coil stacking and steel coil transportation in a warehouse is effectively guaranteed. As can also be seen from fig. 4, the network error gradually decreases to a gentle level with the increase of the training times, and the error rate is below 0.1, which has high control accuracy.
Claims (6)
1. A crown block swing angle model control method based on a neural network PID is characterized by comprising the following steps:
step a, determining a network structure and a network layer number of a neural network;
b, determining a network input layer node and a network output layer node of the neural network;
c, determining a transfer function, a performance index, a learning rate and initial weights of an input layer and an output layer of the neural network;
d, when the unmanned overhead traveling crane runs, the unmanned overhead traveling crane system database automatically receives the actual value of the on-site running swing angle of the overhead traveling crane and determines a neural network input layer node;
and e, adjusting the swing angle, wherein when the actual operation swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, the neural network controls and adjusts, stores the adjusted P, I, D parameter in the unmanned overhead traveling crane database and then transmits the stored parameter to the unmanned overhead traveling crane PID controller to adjust the speed of the overhead traveling crane, so that the adaptive control of the swing angle of the unmanned overhead traveling crane is realized.
2. The crown block swing angle model control method based on the neural network PID as claimed in claim 1, characterized in that: in the step a, the neural network adopts a three-layer neural network structure.
3. The crown block swing angle model control method based on the neural network PID as claimed in claim 2, characterized in that: in the step b, according to the field condition of the unmanned overhead traveling crane, selecting a swing angle set value of the unmanned overhead traveling crane and a field actual swing angle value of the unmanned overhead traveling crane as network input layer nodes of the neural network, and selecting P, I, D three control parameters of the PID controller of the unmanned overhead traveling crane as network output layer nodes of the neural network.
4. The crown block swing angle model control method based on the neural network PID according to the claim 3, characterized in that: in step c, the hidden layer transfer function of the neural network selects a Sigmod function with positive and negative symmetry:
the transfer function of the output layer of the neural network chooses a non-negative Sigmod function:
5. The crown block swing angle model control method based on the neural network PID as claimed in claim 4, characterized in that: and d, receiving the actual swing angle value of the unmanned overhead traveling crane operated on site by adopting an unmanned overhead traveling crane process automatic oracle database system, and taking the actual swing angle value of the unmanned overhead traveling crane and the set swing angle value of the unmanned overhead traveling crane as neural network input layer nodes.
6. The crown block swing angle model control method based on the neural network PID as claimed in claim 5, characterized in that: in the step e, a neural network online adjustment mode is adopted, when the unmanned overhead traveling crane runs, if the actual running swing angle deviation of the unmanned overhead traveling crane system exceeds a set value, the neural network performs adaptive control to adjust the output value of the neural network P, I, D parameter, the adjusted and optimized P, I, D parameter is stored in an unmanned overhead traveling crane process automation system oracle database and is transmitted to an unmanned overhead traveling crane PID controller to perform overhead traveling crane speed adjustment, and the adaptive control of the swing angle of the unmanned overhead traveling crane is realized.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113093728A (en) * | 2021-03-08 | 2021-07-09 | 唐山钢铁集团有限责任公司 | Unmanned overhead crane swing angle control method based on particle swarm algorithm |
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Application publication date: 20200728 |