CN115933369A - Substrate temperature control method of evaporation coating equipment based on optimized PID algorithm - Google Patents
Substrate temperature control method of evaporation coating equipment based on optimized PID algorithm Download PDFInfo
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Abstract
The invention discloses an evaporation coating equipment substrate temperature control method based on an optimized PID algorithm, which combines a group optimization algorithm with a traditional PID control algorithm, finally obtains an optimal solution through cooperation and information sharing among individuals in a group, realizes the optimization of the PID algorithm, and optimizes k in the traditional PID algorithm p 、k i 、k d The three parameters are used as the solution of the particles, iterative optimization is carried out through a particle swarm algorithm, a linear decreasing weight strategy is introduced, so that the convergence speed is improved, the self-tuning of the parameters is realized, the parameters are given to a PID control system, the overshoot of the system is reduced, the steady-state error is reduced, and the temperature control is faster and more accurate.
Description
Technical Field
The invention relates to the technical field of control, in particular to a substrate temperature control method of evaporation coating equipment based on an optimized PID algorithm.
Background
The evaporation coating technology is to evaporate solid materials such as metal in a vacuum environment to enable the solid materials to be gasified and attached to the surface of a product object and generate a uniform film, so as to achieve the purposes of enhancing the corrosion resistance and the aesthetic property of the product, improving the optical performance of the product and the like. The evaporation coating technology starts in the 20 th century, and is widely applied to the fields of decoration, communication, optics, semiconductors and the like at present through the development of decades. The substrate temperature is a key factor influencing the quality of the film layer during film coating, the film layer falls off due to incomplete film layer deposition reaction when the substrate temperature is too low, and the film layer cracks due to thermal stress difference between the film layer and the substrate when the substrate temperature is too high. Therefore, realizing accurate control of the substrate temperature has become one of the key points of the design and development of the evaporation coating equipment.
The traditional substrate temperature control system of evaporation coating equipment adopts a classical PID control strategy, PID is proportional-integral-derivative control, and the working principle of the system is that real-time data of a controlled object is collected and compared with a given value of the system to obtain an error between the real-time data and the given value of the system, and the proportion, the integral and the derivative of the error are controlled to control the system. The PID control system has the advantages of simple principle, strong robustness, wide application range and the like. However, the temperature in the evaporation coating equipment has the characteristics of strong coupling, large lag and the like, so that the problems of large overshoot, large steady-state error, long system oscillation and the like often exist when the PID control strategy is used for controlling the substrate temperature, and the problems are caused by unreasonable setting of three parameters of kp, ki and kd of the PID control system; the three parameters are usually set by engineering personnel in actual engineering operation by virtue of engineering experience, and an experience setting method has higher requirements on the engineering personnel, wastes a large amount of resources and time and reduces the engineering efficiency; in addition, the existing PID control strategies are directed at temperature control algorithms in a broad range, and even if parameter setting is performed to a certain extent, the requirements of the internal complex temperature field of the evaporation coating equipment on the control system cannot be met, so that a substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm is urgently needed to solve the problems.
Disclosure of Invention
The invention provides a substrate temperature control method of evaporation coating equipment based on an optimized PID algorithm, which can reduce overshoot and steady-state errors of a system and enable temperature control to be faster and more accurate.
In order to achieve the purpose, the invention provides the following technical scheme: an evaporation coating equipment substrate temperature control method based on an optimized PID algorithm comprises the following steps:
s1, calculating a transfer function of a temperature system according to a substrate temperature rise curve of evaporation coating equipment;
s2, using k in the traditional PID control algorithm p 、k i 、k d The three parameters are used as solutions of particles in the particle swarm optimization, iteration optimization is carried out, a linear decreasing weight strategy is introduced, the inertia factor is set to be in a state variable along with iteration, parameter self-tuning is realized, and finally the tuned parameters are given to a PID control algorithm to realize optimization of the PID control algorithm;
and S3, controlling the temperature of the substrate based on the optimized PID control algorithm output.
Preferably, in S1, the substrate temperature system of the evaporation coating apparatus is expressed by an approximate second-order inertia hysteresis function:
wherein, input u is heater voltage, output y is substrate surface temperature, K is open-loop gain of system, tau is system delay, T 1 、T 2 Is the heating system time constant; if the input signal of the second-order system is the unit step function u (t) =1 (t), the Laplace transform of the input signal is
Neglecting the delay link of the transfer function, obtaining a Laplace transformation formula of the unit step response function as follows:
inverse laplace transform is performed on the obtained data to obtain a function as follows:
and then calculating a mathematical model of the transfer function by using a least square method according to the substrate temperature rise curve of the evaporation coating equipment.
Preferably, the mathematical model of the transfer function calculated by using the least square method in combination with the measured temperature rise curve is as follows:
preferably, in S2, before iteration, a particle population size dimension is determined, specifically: in a D-dimensional search space, the size of the population of particles is N, where the ith particle is represented as a D-dimensional vector:
x i ＝(x i1 ，x i2 ，……，x iD )，i＝1，2，……，N；
the velocity of the ith particle is expressed as:
v i ＝(v i1 ，v i2 ，……，v iD )，i＝1，2，……，N。
preferably, the parameter k is p 、k i 、k d As a component of the particle, the vector component of the ith particle is:
x i ＝(x ikp ，x iki ，，x ikd )。
preferably, in S2, an optimal solution of the objective function is found through iteration, where the objective function uses a time integral index, which is:
preferably, during the iteration process, an iteration stop condition F is set min Will output F and F min Making a comparison, if F is less than or equal to F min And outputting a result, otherwise, carrying out iterative updating on the speed and the position of the population particles.
Preferably, in the iterative process, the iterative formula of the particle velocity and the position is as follows:
v i+1 ＝ω×v i +C 1 ×rand()×(Pbest-x i )+C 2 ×rand()×(Gbest-x i )；
x i+1 ＝x i +v i ；
wherein v is i+1 、v i 、x i+1 、x i Respectively representing the velocity and position of the i +1 th generation particle and the i-th generation particle, omega is an inertia factor, C 1 、C 2 The learning factors are respectively Pbest as individual extremum and Gbest as group extremum.
Preferably, the inertia factor ω adopts a linear decreasing weight strategy, and the expression is as follows:
ω＝(ω ini -ω end )(imax-i)/imax+ω end ；
wherein, ω is ini As an initial inertia weight, ω end The iteration is to the maximum number of inertia weights.
Preferably, in S3, simulation is performed using a Simlink module in MATLAB as a tool.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, a group optimization algorithm is combined with a traditional PID control algorithm, an optimal solution is finally obtained through cooperation and information sharing among individuals in a group, three parameters of kp, ki and kd in the traditional PID algorithm are used as solutions of particles, iterative optimization is carried out through a particle swarm algorithm, a linear decreasing weight strategy is introduced, so that the convergence speed is improved, the self-setting of the parameters is realized, and finally the parameters are given to a PID control system, so that the overshoot of the system is reduced, the steady-state error is reduced, and the temperature control is faster and more accurate.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a classical PID control block diagram;
FIG. 2 is a schematic diagram of system temperature control;
FIG. 3 is a control block diagram of the particle swarm optimization PID algorithm of the invention;
FIG. 4 is a control flow chart of the particle swarm optimization PID algorithm of the invention;
FIG. 5 is a system block diagram of the method for controlling the substrate temperature of the coating equipment by particle swarm optimization PID algorithm;
FIG. 6 is a temperature control response curve diagram of the substrate of the coating equipment with the particle swarm optimization PID algorithm.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, a classical PID control block diagram is shown, where r (t) is a given value, y (t) is an actual output, u (t) is an output value of PID control, and e (t) represents a deviation value; obtaining a deviation e (t) by subtracting the output value y (t) from a set value r (t), processing the deviation e (t) by using a proportional integral and differential method, and outputting a PID output value u (t) to a heating system, thereby achieving the purpose of continuously reducing the deviation between the output value of the system and the input set value, wherein K p 、K i 、K d Respectively representing proportional, integral and differential coefficients;
referring to fig. 2, a schematic diagram of system temperature control is shown, in which a heating wire is opened through a solid-state relay to heat, a substrate is heated by a heater through radiation heat transfer, the substrate temperature is monitored in real time by a thermocouple and a current signal is transmitted to a temperature control module, a temperature controller adjusts according to an error between a set value and an actual detection value, transmits the signal to a power controller, adjusts a pulse width, and finally achieves temperature control.
Example (b): an evaporation coating equipment substrate temperature control method based on an optimized PID algorithm comprises the following steps:
s1, calculating a transfer function of a temperature system according to a substrate temperature rise curve of evaporation coating equipment, wherein:
according to the heat transfer principle and empirical judgment, the substrate temperature system of the evaporation coating equipment can be expressed by an approximate second-order inertia hysteresis function:
input u is a heaterVoltage, output y is the substrate surface temperature, K is the open loop gain of the system, T is the system delay, T 1 、T 2 Is the heating system time constant; assuming that the input signal of the second-order system is a unit step function u (t) =1 (t), the Laplace transformation formula of the input signal is
Neglecting the delay link of the transfer function, the Laplace transform formula of the unit step response function can be obtained as follows:
and then, carrying out inverse Laplace transform on the obtained product to obtain a function as follows:
and (3) calculating by combining the temperature rise curve obtained by measurement and utilizing a least square method to obtain a mathematical model of a transfer function of the substrate temperature control system of the evaporation coating equipment:
s2, optimizing a PID algorithm through a particle swarm, and referring to the graph shown in FIG. 3: three parameters of kp, ki and kd in the traditional PID algorithm are used as the solution of the particles, iterative optimization is carried out through a particle swarm algorithm, parameter self-tuning is realized, and finally the parameters are given to a PID control system, so that the overshoot of the system is reduced, and the steady-state error is reduced;
wherein: the principle of the particle swarm algorithm is to simulate the behavior of predation of a bird swarm, obtain an optimal solution finally through cooperation and information sharing among individuals in the swarm, simulate birds in the bird swarm through setting a swarm of particles without mass, endow two attributes of the particle speed and the particle position, search the optimal solution independently in a search space for each particle, record the optimal solution as an individual extreme value, share the individual extreme value in a particle swarm to find the optimal extreme value, adjust the current speed and position of each particle in the swarm according to the swarm extreme value and the individual extreme value, and finally complete the optimal solution optimizing process;
the optimization process is shown in fig. 4, and specifically includes the following steps:
firstly, determining a particle population size dimension to prepare for iteration, setting the particle population size in a D-dimensional search space, wherein the ith particle is represented as a D-dimensional vector:
x i ＝(x i1 ，x i2 ，......，x iD )，i＝1，2，......，N；
the velocity of the ith particle is expressed as:
v i ＝(v i1 ，v i2 ，......，v iD )，i＝1，2，......，N；
then, a group of random particles is initialized, and the particle attributes are given to k in the PID controller p 、k i 、k d And operating a substrate temperature control system model of the evaporation coating equipment in the three parameters, and outputting a target function of a control system, wherein:
the general form of a classical PID control algorithm is:
u (t) represents the output of the controller, e (t) represents the systematic error, k p 、k i 、k d Three parameters of proportion, integral and differential are respectively adopted; setting particles as three-dimensional vectors, parameter k p 、k i 、k d As a component of the particle, the vector composition of the ith particle is:
x i ＝(x ikp ，x iki ，，x ikd )；
an optimal solution is found through iteration for an objective function, where the objective function is chosen as an indicator of time Integration (ITAE), which is defined as:
the optimal solution is k p 、k i 、k d The optimal parameter combination minimizes F.
Then, a stop iteration condition F is set min Outputs F and F min Making a comparison, if F is less than or equal to F min If not, the speed and position of the population particles are iteratively updated;
the speed and position iterative update formula is expressed as follows:
v i+1 ＝ω×v i +C 1 ×rand()×(Pbest-x i )+C 2 ×rand()×(Gbest-x i )；
x i+1 ＝x i +v i ；
v i+1 、v i 、x i+1 、x i respectively representing the velocity and position of the i +1 th generation particle and the i-th generation particle, omega is an inertia factor, C 1 、C 2 Respectively are learning factors, pbest is an individual extremum, gbest is a group extremum;
wherein, the inertia factor ω adopts a linear decrement Weight strategy (LDW), and the expression thereof is as follows:
ω＝(ω ini -ω end )(imax-i)/imax+ω end ；
ω ini is an initial inertia weight, omega end Iteration to maximum number of inertias.
In a specific embodiment, the particle dimension is determined to be 100, the maximum number of iterations imax =1000 c is set 1 ＝C 2 =2; initial inertia weight omega ini Set to 0.9, iterate to the maximum number of times inertia weight omega end Set to 0.4; stop iteration condition F min =1; carrying out simulation experiment on the system by means of a Simulink module in Matlab; setting the target temperature to be 150 ℃, assuming that the initial temperature of the system is 0 ℃, and setting the simulation time to be 2000s; a system block diagram of the substrate temperature control method of evaporation coating equipment with PID and particle swarm optimization PID algorithm respectively established is shown in FIG. 5Shown in the figure.
The response curve of the simulation result is shown in FIG. 6; the rise time of the traditional PID control is 683s, the adjusting time is 1603s, the rise time of the particle swarm optimization PID system is 754s, the adjusting time is 1088s, compared with the traditional PID, the rise speed of the particle swarm optimization PID system is slower, but the adjusting time is fast, the traditional PID adjusting oscillation is obvious, in addition, the overshoot of the traditional PID is 13%, and the overshoot of the particle swarm optimization PID is 7%. Therefore, the effect of the particle swarm optimization PID control system is better.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An evaporation coating equipment substrate temperature control method based on an optimized PID algorithm is characterized by comprising the following steps:
s1, calculating a transfer function of a temperature system according to a substrate temperature rise curve of evaporation coating equipment;
s2, converting k in the traditional PID control algorithm p 、k i 、k d The three parameters are used as solutions of particles in the particle swarm optimization, iteration optimization is carried out, a linear decreasing weight strategy is introduced, the inertia factor is set to be in a state variable along with iteration, parameter self-tuning is realized, and finally the tuned parameters are given to a PID control algorithm to realize optimization of the PID control algorithm;
and S3, controlling the temperature of the substrate based on the optimized PID control algorithm output.
2. The substrate temperature control method of evaporation coating equipment based on optimized PID algorithm as claimed in claim 1, wherein: in S1, the substrate temperature system of the evaporation coating equipment is expressed by an approximate second-order inertia hysteresis function:
wherein, input u is heater voltage, output y is substrate surface temperature, K is system open loop gain, tau is system delay, T 1 、T 2 Is the heating system time constant; if the input signal of the second-order system is the unit step function u (t) =1 (t), the Laplace transform of the input signal is
Neglecting the delay link of the transfer function, obtaining the Laplace transform formula of the unit step response function as:
the inverse laplace transform is carried out to obtain the following function:
and then calculating a mathematical model of the transfer function by using a least square method according to the substrate temperature rise curve of the evaporation coating equipment.
3. The substrate temperature control method of evaporation coating equipment based on optimized PID algorithm as claimed in claim 2, wherein: the mathematical model of the transfer function calculated by the least square method by combining the temperature rise curve obtained by measurement is as follows:
4. the substrate temperature control method of evaporation coating equipment based on optimized PID algorithm as claimed in claim 1, wherein: in S2, before iteration, determining a particle population size dimension, specifically: in a D-dimensional search space, the size of the population of particles is N, where the ith particle is represented as a D-dimensional vector:
x i ＝(x i1 ，x i2 ，......，x iD )，i＝1，2，......，N；
the velocity of the ith particle is expressed as:
v i ＝(v i1 ，v i2 ，......，v iD )，i＝1，2，......，N。
5. the method of claim 4, wherein the substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm is characterized in that: will be the parameter k p 、k i 、k d As a component of the particle, the vector component of the ith particle is:
x i ＝(x ikp ，x iki ，，x ikd )。
6. the method of claim 5, wherein the substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm is characterized in that: in S2, an optimal solution of the objective function is found through iteration, where the objective function uses a time integral index, which is:
7. the method of claim 6, wherein the substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm is characterized in that: in the iteration process, an iteration stopping condition F is set min Outputs F and F min Making a comparison, if F is less than or equal to F min And outputting a result, otherwise, carrying out iterative updating on the speed and the position of the population particles.
8. The method of claim 7, wherein the substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm is characterized in that: in the iterative process, the iterative formula of particle velocity and position is:
v i+1 ＝ω×v i +C 1 ×rand()×(Pbest-x i )+C 2 ×rand()×(Gbest-x i )；
x i+1 ＝x i +v i ；
wherein v is i+1 、v i 、x i+1 、x i Respectively representing the speed and position of the i +1 th generation particle and the i-th generation particle, omega is an inertia factor, C 1 、C 2 The learning factors are respectively Pbest as individual extremum and Gbest as group extremum.
9. The method of claim 8, wherein the substrate temperature control method of the evaporation coating equipment based on the optimized PID algorithm comprises the following steps: the inertia factor omega adopts a linear decrement weight strategy, and the expression is as follows:
ω＝(ω ini -ω end )(imax-i)/imax+ω end ；
wherein, ω is ini Is an initial inertia weight, omega end Iteration is to the maximum number of inertia weights.
10. The method for controlling the substrate temperature of the evaporation coating equipment based on the optimized PID algorithm according to claim 1, wherein the method comprises the following steps: in S3, simulation was performed using the Simlink module in MATLAB as a tool.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116855866A (en) * | 2023-09-04 | 2023-10-10 | 扬州大发螺丝有限公司 | Intelligent equipment control process for hot-dip galvanizing of double-end stud without pickling |
| CN117408052A (en) * | 2023-10-18 | 2024-01-16 | 南栖仙策(南京)高新技术有限公司 | Coating control optimization method, device and equipment for evaporator and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116855866A (en) * | 2023-09-04 | 2023-10-10 | 扬州大发螺丝有限公司 | Intelligent equipment control process for hot-dip galvanizing of double-end stud without pickling |
| CN116855866B (en) * | 2023-09-04 | 2024-04-30 | 扬州大发螺丝有限公司 | Intelligent equipment control process for hot-dip galvanizing of double-end stud without pickling |
| CN117408052A (en) * | 2023-10-18 | 2024-01-16 | 南栖仙策(南京)高新技术有限公司 | Coating control optimization method, device and equipment for evaporator and storage medium |
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