Coating equipment servo motion control method and system based on optimized fuzzy PID (Proportion Integration Differentiation)

Abstract

The invention discloses a coating equipment servo motion control method and system based on optimized fuzzy PID (Proportion Integration Differentiation). The method comprises the steps that a virtual simulation platform is built, a motion model is built on the simulation platform, external environment data of a PI coating equipment system is input into the motion model, and the target rotating speed of a glue uniformizing roller of PI coating equipment is output; measuring the actual rotating speed of a glue uniformizing roller on the PI coating equipment; subtracting the target rotating speed from the actual rotating speed to obtain a rotating speed deviation, and deriving time by using the rotating speed deviation to obtain a deviation change rate; and the rotating speed deviation and the deviation change rate are input into a fuzzy PID controller, a fuzzy PID algorithm combined with a self-adaptive particle swarm optimization algorithm is used for calculation, PID control parameters are obtained, the PID control parameters are input into the PID controller for adjustment, and the actual rotating speed of the glue uniformizing roller reaches the target rotating speed. The control precision of the PID control algorithm can be effectively improved, and the stability of the PID control algorithm is improved.

Description

Technical Field

[0001] The present invention belongs to the field of PI coating equipment, and more specifically, relates to a coating equipment servo motion control method and system based on optimized fuzzy PID. Background Art

[0002] Polyimide (PI) coating equipment mainly coats an alignment film on TFT&CF substrates to lay the foundation for the liquid crystal molecules to form an alignment on the alignment film. Therefore, the control accuracy requirements for PI coating equipment are very high, and the current motion control method for PI coating equipment mainly uses the "conventional PID controller" method.

[0003] Conventional PID controllers use traditional PID closed-loop control. They have a simple structure, a long history of development, are easy to implement, and do not require precise mathematical models of the system, making conventional PID controllers still a very popular and effective control tool in various industrial and engineering applications. However, during the application process, its PID parameters do not change adaptively with changes in the external environment and the internal environment of the machine. They are very dependent on the engineer's familiarity with the system model and the mastery of the machine. This undoubtedly greatly increases production costs and reduces production efficiency, which runs counter to the rapid development of the industry. And it may perform poorly when dealing with nonlinear or event systems, especially when the nonlinear characteristics of the system are strong. In the case of improper parameter settings, the traditional PID controller will have excessive control output adjustment or be unable to respond quickly or maintain good control performance. In addition, because its PID parameters are immutable and lack adaptive capabilities, it performs poorly when dealing with systems with strong nonlinear characteristics or time-varying systems. Manual parameter adjustment is often required, and it cannot provide the best control effect in some complex systems and scenarios requiring high precision.

[0004] The PID parameters of the prior art fuzzy PID control method are variable, which can overcome the shortcomings of conventional PID controllers that cannot adapt to nonlinear and time-varying systems due to the inability to adjust parameters, and greatly enhance the stability and robustness of the system. The prior art invention patent with publication number CN111221245A proposes a PSO-based fuzzy PID tension control parameter self-tuning method for a setting machine, comprising the following steps: S1: According to the characteristics of the tension control system of the setting machine, a discrete PID control algorithm is selected; S2: A fuzzy control regulation with an adjustment factor is set, and the adjustment factor is optimized using a particle swarm algorithm; S3: The global optimal position is obtained, and the corresponding PID value is obtained, and this value is substituted into the discrete PID control algorithm to control the tension of the setting machine. This scheme has certain disadvantages and limitations, and is overly dependent on randomness, and cannot guarantee that an ideal solution can be obtained every time it is run. At the same time, its performance is also highly dependent on its parameter settings, such as the number of particles, inertia weight, learning factor, etc. Improper parameter selection may lead to slow convergence and fall into the problem of local optimality. Summary of the invention

[0005] In order to overcome the problems in the prior art, especially the insufficient control accuracy of the existing PID control algorithm, and the slow convergence speed and falling into the local optimum problem caused by inappropriate parameter selection, the present invention provides a coating equipment servo motion control method and system based on optimized fuzzy PID.

[0006] The primary purpose of the present invention is to solve the above technical problems. The technical solution of the present invention is as follows:

[0007] The first aspect of the present invention provides a coating equipment servo motion control method based on optimized fuzzy PID, comprising the following steps:

[0008] Build a virtual simulation platform, construct a motion model on the simulation platform, input the external environment data of the PI coating equipment system into the motion model, output the target rotation speed of the rubber-distributing roller of the PI coating equipment; measure the actual rotation speed of the rubber-distributing roller on the PI coating equipment;

[0009] The target speed is subtracted from the actual speed to obtain a speed deviation, and the speed deviation is derived with respect to time to obtain a deviation change rate;

[0010] The speed deviation and the deviation change rate are input into the fuzzy PID controller, and the fuzzy PID algorithm combined with the adaptive particle swarm optimization algorithm is used to calculate and obtain the PID control parameters. The PID control parameters are input into the PID controller for adjustment so that the actual speed of the rubber roller reaches the target speed.

[0011] Furthermore, the method for measuring the actual rotation speed of the rubber-distributing roller on the PI coating device is to use a grating measurement system for measurement.

[0012] Furthermore, when constructing the virtual simulation platform, the characteristics that need to be considered include: viscous damping, viscous friction and adhesion effect; when constructing the motion model, the process links that need to be analyzed include: cleaning, gluing and drying.

[0013] Furthermore, the PID control parameters include: proportional gain, integral gain and differential gain.

[0014] Furthermore, respective fuzzy rule bases are formulated for the PID control parameters, and the establishment of the fuzzy rule base is based on the following principles:

[0015] The value of the proportional gain is adjusted according to the current speed deviation. When the speed deviation is large, the proportional gain fuzzy value should be set to a larger value to reduce the deviation quickly. When the speed deviation is moderate, if the speed deviation change rate is large, the proportional gain fuzzy value should be appropriately increased to respond to the change quickly. If the speed deviation change rate is small, the proportional gain fuzzy value should be appropriately reduced to improve system stability. When the speed deviation is small, the proportional gain fuzzy value should be set to a smaller value to avoid system over-adjustment.

[0016] The integral gain adjusts the output through the accumulation of speed deviation. When the accumulated deviation is large, the integral gain fuzzy value should be appropriately increased to speed up the error elimination speed; when the accumulated deviation is moderate, if the speed deviation change rate is large, the integral gain fuzzy value should be reduced to prevent integral saturation; if the speed deviation change rate is small, the integral gain fuzzy value should be appropriately increased to improve the steady-state accuracy; when the accumulated deviation is small, the integral gain fuzzy value should be set to a small value to prevent system overshoot or oscillation;

[0017] The differential gain is adjusted by predicting the speed deviation change rate. When the speed deviation change rate is large, the differential gain fuzzy amount should be appropriately increased to suppress system overshoot and reduce oscillation. When the speed deviation change rate is moderate, if the speed deviation is large, the differential gain fuzzy amount should be kept high to stabilize the adjustment. If the speed deviation is small, the differential gain fuzzy amount should be appropriately reduced to avoid overreaction. When the speed deviation change rate is small, the differential gain fuzzy amount should take a smaller value to ensure the stability of the system adjustment process.

[0018] Further, the PID control parameters are calculated using a fuzzy PID algorithm combined with an adaptive particle swarm optimization algorithm, including the following steps:

[0019] Quantifying the speed deviation and the deviation change rate respectively to obtain a fuzzy quantized value of the speed deviation and a fuzzy quantized value of the deviation change rate;

[0020] The fuzzy quantized value of the speed deviation and the fuzzy quantized value of the deviation change rate are respectively input into the membership function, and the membership degree of the speed deviation and the membership degree of the deviation change rate are output;

[0021] Determine a fuzzy subset of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate in combination with a fuzzy rule base of the corresponding control parameter, and calculate the comprehensive membership of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate according to the fuzzy subset;

[0022] The controller adjustment amount is calculated by using the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount in combination with the center of gravity method to obtain the controller adjustment amount;

[0023] The PID control parameters are calculated by using the controller adjustment amount and the current PID control parameters combined with the particle swarm optimization algorithm.

[0024] Furthermore, the membership function is a triangular membership function.

[0025] Furthermore, the center of gravity method is used to calculate the controller adjustment amount, and the formula is as follows:

[0026]

[0027] Among them, ΔK is the controller adjustment amount, A i is the comprehensive membership of the controller adjustment quantity, F i is the fuzzy quantization value, and n is the number of fuzzy subsets.

[0028] Furthermore, the controller adjustment amount and the current PID control parameters are used in combination with the particle swarm optimization algorithm to calculate the PID control parameters. The formula is as follows:

[0029] K(t)=K(t-1)+ΔK*α (1)

[0030] Among them, K(t) is the PID control parameter currently output, K(t-1) is the PID control parameter output of the previous iteration, t is the number of iterations, ΔK is the controller adjustment amount, α is the adaptive quantization factor, and the expression of the adaptive quantization factor is as follows:

[0031]

[0032] Where t is the number of iterations, e(t)-e(t-1) represents the change in the speed deviation between the current iteration and the previous iteration, ec(t)-ec(t-1) represents the change in the deviation change rate between the current iteration and the previous iteration, X is the minimum value of the adaptive quantization factor, Y and Z are the scaling factors of the adaptive quantization factor function, and the scaling factor is optimized using the particle swarm optimization algorithm. The specific steps for optimization are:

[0033] Initialize the parameters Y and Z to a group of particles with a group size of N and a position of x i , the speed is v i, and assigns each particle an initial position, velocity, inertia weight, and learning factor;

[0034] Calculate the current inertia weight ω, the formula is as follows:

[0035]

[0036] Among them, t is the number of iterations, T is the maximum number of iterations, ω max and ω min They are the maximum and minimum values ​​of the preset inertia weight respectively;

[0037] Using the particle's current velocity v i (t), position x i (t), individual best historical position pbest i , the group's global best position gbest and inertia weight ω(t), calculate the updated velocity v of each particle i (t+1), the formula is as follows:

[0038] v i (t+1)=ω(t)+v i (t)+c1(t)*rand()*(pbest i -x i (t))

[0039] +c2(t)*rand()*(gbest-x i (t)) (3)

[0040] Among them, v i (t) represents the speed of the i-th particle before updating, rand() is a random number, pbest i Then represents the personal best position of the i-th particle, gbest represents the global optimal position, c1(t) and c2(t) are the current learning factors;

[0041] Using the updated particle velocity v i (t+1) and the current particle position x i (t), calculate the new position x of the particle i (t+1), the formula is as follows:

[0042] x i (t+1)=x i (t)+v i (t+1)

[0043] The particle's current position x i (t+1) Input the fitness function, combine the system's stability time and gain margin, and calculate the fitness value f(x i), the formula is as follows:

[0044]

[0045] Among them, T s (x i ) is the position x i The stabilization time, G m (x i ) is the position x i Gain margin of

[0046] Compare the fitness value of the particle with its historical best fitness value, update the particle's personal best position, and compare the personal best positions of all particles with the global best position to obtain the updated global best position;

[0047] According to the particle and the global best position gbest and the individual historical best position pbest i The distance between the two variables is dynamically adjusted, and the learning factors c1 and c2 are adjusted dynamically. The formula is as follows:

[0048]

[0049] Among them, c 1,base and c 2,base are the preset basic values ​​of c1 and c2, d i (t) represents the current position of the i-th particle, |pbest i -d i (t)| is the best position of the i-th particle and the individual in history pbest i The distance between |gbest-d i (t)| is the distance between the ith particle and the global best position gbest, and t is the number of iterations;

[0050] Substitute the updated learning factor into formula (3) to adjust the particle speed;

[0051] Repeat the above steps until the maximum number of iterations is reached or the global optimal fitness value meets the predetermined threshold, and output the optimized scaling factors Y and Z;

[0052] Substituting the scaling factors Y and Z into formula (2), we get the adaptive quantization factor;

[0053] Substituting the adaptive quantization factor into formula (1), we can get the PID control parameters.

[0054] The second aspect of the present invention provides a coating equipment servo motion control system based on optimized fuzzy PID, the system comprising: a memory and a processor, the memory comprising a coating equipment servo motion control method program based on optimized fuzzy PID, the coating equipment servo motion control method program based on optimized fuzzy PID, when executed by the processor, implements the steps of a coating equipment servo motion control method based on optimized fuzzy PID.

[0055] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0056] A method for optimizing the adaptive quantization scaling factor in a fuzzy PID controller through an adaptive particle swarm optimization algorithm is proposed. A fuzzy rule table is established according to system characteristics. The characteristic of the fuzzy PID controller that can change parameters according to system characteristics is utilized to ensure that the actual speed of the coating equipment can perfectly fit the target speed obtained by the virtual simulation platform, enhance the adaptive ability of the coating equipment system, improve the system control accuracy, enhance the stability and robustness of the system, realize accurate monitoring and feedback control of parameters such as the position and speed of the coating equipment, and provide the best control effect in some high-precision scenarios; at the same time, the adaptive particle swarm optimization algorithm optimizes parameters such as the inertia weight and learning factor in the algorithm by feeding back the speed and position of the particles, prevents the particle swarm from falling into the local optimum, ensures that the particle swarm can converge to the ideal optimal solution in the shortest time, solves the problems of slow convergence speed and falling into the local optimum caused by inappropriate parameter selection, and improves the stability of the particle swarm optimization algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to make the purpose and technical solution of the present invention clearer, the present invention provides the following drawings and descriptions:

[0058] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0059] Figure 2 A triangular membership function diagram provided by an embodiment of the present invention;

[0060] Figure 3 This is a rendering of the PI coating equipment when the adaptive particle swarm optimization algorithm is not used according to an embodiment of the present invention;

[0061] Figure 4 The effect of PI coating equipment when using the adaptive particle swarm optimization algorithm provided by the embodiment of the present invention;

[0062] Figure 5 A proportional gain fuzzy rule table provided by an embodiment of the present invention;

[0063] Figure 6 An integral gain fuzzy rule table provided in an embodiment of the present invention;

[0064] Figure 7 A differential gain fuzzy rule table provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0067] Embodiment 1:

[0068] The present invention provides a coating equipment servo motion control method based on optimized fuzzy PID, such as Figure 1 The figure shows a flow chart of a coating equipment servo motion control method based on optimized fuzzy PID. The specific steps are as follows:

[0069] S1: Analyze the scheduling method and main parameter requirements of the process links, consider non-ideal characteristics, build a virtual simulation platform, analyze the process of colloid from the slit die to the glass substrate, construct a motion model and parameter constraints on the simulation platform, input the external environment data of the PI coating equipment system into the motion model, and output the target speed of the PI coating equipment's rubber roller; obtain the actual rotation angle of the PI coating equipment's rubber roller per unit time through real-time monitoring technology, and calculate the actual speed of the current PI coating equipment's rubber roller.

[0070] More specifically, the method for measuring the actual rotation speed of the glue roller on the PI coating equipment is to use a grating measurement system for measurement. The characteristics that need to be considered in building the virtual simulation platform include: viscous damping, viscous friction and adhesion effect; the process links that need to be analyzed in building the motion model include: cleaning, gluing and drying.

[0071] S2: Subtract the target speed from the actual speed to obtain a speed deviation e, and use the speed deviation e to derive the time to obtain a deviation change rate ec.

[0072] S3: Input the speed deviation e and the deviation change rate ec into the fuzzy PID controller, and use the fuzzy PID algorithm combined with the adaptive particle swarm optimization algorithm to calculate and obtain the real-time PID parameters, which are integrated and adjusted with the PID initial parameters to obtain the PID control parameters. The PID control parameters are input into the PID controller for adjustment so that the actual speed of the rubber roller reaches the target speed. The adaptive particle swarm optimization algorithm optimizes the inertia weight and learning factor and other parameters in the algorithm by feeding back the speed and position of the particles to prevent the particle swarm from falling into the local optimum and ensure that the particle swarm can converge to the ideal optimal solution in the shortest time. It solves the problems of slow convergence speed and falling into the local optimum caused by inappropriate parameter selection and improves the stability of the particle swarm optimization algorithm.

[0073] More specifically, the PID control parameters include: proportional gain Kp, integral gain Ki and differential gain Kd. The PID control parameters need to formulate their own fuzzy rule bases. The establishment of the fuzzy rule base is based on the following principles:

[0074] The value of the proportional gain is adjusted according to the current speed deviation. When the speed deviation is large, the proportional gain fuzzy value should be set to a larger value to quickly reduce the deviation. When the speed deviation is moderate, if the speed deviation change rate is large, the proportional gain fuzzy value should be appropriately increased to quickly respond to the change. If the speed deviation change rate is small, the proportional gain fuzzy value should be appropriately reduced to improve system stability. When the speed deviation is small, the proportional gain fuzzy value should be set to a smaller value to avoid over-regulation of the system. The fuzzy rule table is as follows: Figure 5 As shown;

[0075] The integral gain adjusts the output through the accumulation of speed deviation. When the accumulated deviation is large, the integral gain fuzzy value should be appropriately increased to speed up the error elimination speed. When the accumulated deviation is moderate, if the speed deviation change rate is large, the integral gain fuzzy value should be reduced to prevent integral saturation. If the speed deviation change rate is small, the integral gain fuzzy value should be appropriately increased to improve the steady-state accuracy. When the accumulated deviation is small, the integral gain fuzzy value should be set to a small value to prevent the system from overshooting or oscillation. The obtained fuzzy rule table is as follows: Figure 6 As shown;

[0076] The differential gain is adjusted by predicting the speed deviation change rate. When the speed deviation change rate is large, the differential gain fuzzy value should be appropriately increased to suppress system overshoot and reduce oscillation. When the speed deviation change rate is moderate, if the speed deviation is large, the differential gain fuzzy value should be kept high to stabilize the adjustment. If the speed deviation is small, the differential gain fuzzy value should be appropriately reduced to avoid overreaction. When the speed deviation change rate is small, the differential gain fuzzy value should take a small value to ensure the stability of the system adjustment process. The fuzzy rule table is as follows: Figure 7 shown.

[0077] Furthermore, the calculation method of the PID control parameters includes the following steps:

[0078] The speed deviation and the deviation change rate are quantified respectively to obtain the fuzzy quantization value of the speed deviation and the fuzzy quantization value of the deviation change rate. The formula is as follows:

[0079] f(e)=6*e / (L max -L min )

[0080] f(ec)=6*ec / [2*(L max -L min )]

[0081] Among them, L max and L min are the maximum and minimum values ​​of the speed of the rubber roller of the PI coating equipment, L max Take 20m / min, L min Take 1m / min.

[0082] The fuzzy quantized value of the speed deviation and the fuzzy quantized value of the deviation change rate are input into the triangular membership function respectively, and the membership degree of the speed deviation and the membership degree of the deviation change rate are output. The triangular membership function is as follows: Figure 2 As shown;

[0083] Determine a fuzzy subset of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate in combination with a fuzzy rule base of the corresponding control parameter, and calculate the comprehensive membership of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate according to the fuzzy subset;

[0084] The controller adjustment amount ΔK is obtained by using the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount in combination with the centroid method. The formula is as follows:

[0085]

[0086] Among them, A i is the comprehensive membership of the controller adjustment quantity, F i is the fuzzy quantization value, n is the number of fuzzy subsets;

[0087] Using the controller adjustment amount and the current PID control parameters, combined with the particle swarm optimization algorithm, the PID control parameters are calculated. The formula is as follows:

[0088] K(t)=K(t-1)+ΔK*α (1)

[0089] Among them, K(t) is the PID control parameter currently output, K(t-1) is the PID control parameter output of the previous iteration, t is the number of iterations, ΔK is the controller adjustment amount, α is the adaptive quantization factor, and the expression of the adaptive quantization factor is as follows:

[0090]

[0091] Where t is the number of iterations, e(t)-e(t-1) represents the change in the speed deviation between the current iteration and the previous iteration, ec(t)-ec(t-1) represents the change in the deviation change rate between the current iteration and the previous iteration, X is the minimum value of the adaptive quantization factor, which is 0.23, and Y and Z are the scaling factors of the adaptive quantization factor function, which are used to control the scaling range of the adaptive quantization factor. The particle swarm optimization algorithm is used to optimize the scaling factor. The specific steps of the optimization are as follows:

[0092] Initialize the parameters Y and Z to a group of particles with a group size of N and a position of x i , the speed is v i , and assigns each particle an initial position, velocity, inertia weight, and learning factor based on experience or the characteristics of the problem;

[0093] Calculate the current inertia weight ω, the formula is as follows:

[0094]

[0095] Among them, t is the number of iterations, T is the maximum number of iterations, ω max and ω min are the maximum and minimum values ​​of the preset inertia weight, ω max Take 0.9, ω min Take 0.3, and according to the formula, the inertia weight will decrease as the number of iterations increases;

[0096] Using the particle's current velocity v i (t), position x i (t), individual best historical position pbest i , the group's global best position gbest and inertia weight ω(t), calculate the updated velocity v of each particle i (t+1), the formula is as follows:

[0097] v i (t+1)=ω(t)+v i (t)+c1(t)*rand()*(pbest i -x i (t))

[0098] +c2(t)*rand()*(gbest-x i (t)) (3)

[0099] Among them, v i (t) represents the speed of the i-th particle before updating, rand() is a random number, pbest i Then represents the personal best position of the i-th particle, gbest represents the global optimal position, c1(t) and c2(t) are the current learning factors;

[0100] Using the updated particle velocity v i (t+1) and the current particle position x i (t), calculate the new position x of the particle i (t+1), the formula is as follows:

[0101] x i (t+1)=x i (t)+v i (t+1)

[0102] The particle's current position x i (t+1) Input the fitness function, combine the system's stability time and gain margin, and calculate the fitness value f(x i ), the formula is as follows:

[0103]

[0104] Among them, T s (x i ) is the position x i The stabilization time, G m (x i ) is the position x i Gain margin of

[0105] The fitness value of the particle is compared with its historical best fitness value, the personal best position of the particle is updated, and the personal best positions of all particles are compared with the global best position to obtain the updated global best position.

[0106] According to the particle and the global best position gbest and the individual historical best position pbest i The distance between the two variables is dynamically adjusted, and the learning factors c1 and c2 are adjusted dynamically. The formula is as follows:

[0107]

[0108] Among them, c 1,base and c 2,base are the preset basic values ​​of c1 and c2, both of which are 2.15, d i(t) represents the current position of the i-th particle, |pbest i -d i (t)| is the best position of the i-th particle and the individual in history pbest i The distance between |gbest-d i (t)| is the distance between the ith particle and the global best position gbest, and t is the number of iterations;

[0109] Substitute the updated learning factor into formula (1) to adjust the particle speed;

[0110] Repeat the above steps until the maximum number of iterations is reached or the global optimal fitness value meets the predetermined threshold, and output the optimized scaling factors Y and Z.

[0111] Substituting the scaling factors Y and Z into formula (2), we get the adaptive quantization factor;

[0112] Substituting the adaptive quantization factor into formula (1), we can get the PID control parameters.

[0113] A virtual simulation platform was built in Matlab, and the motion model and parameter constraints were constructed. The system temperature of the simulated PI coating equipment was 27°C. The effect of the PI coating equipment was as follows when the adaptive particle swarm optimization algorithm was not used: Figure 3 As shown in Figure 2, the effect of PI coating equipment when using the adaptive particle swarm optimization algorithm is as follows Figure 4 As shown, red is the target speed and green is the actual speed of the rubber roller. It can be seen from the figure that the use of the adaptive particle swarm optimization algorithm can ensure that the actual speed of the coating equipment can perfectly fit the target speed obtained by the virtual simulation platform, enhance the adaptive ability of the coating equipment system, improve the system control accuracy, enhance the stability and robustness of the system, and realize accurate monitoring and feedback control of parameters such as the position and speed of the coating equipment to obtain the best control effect.

[0114] Embodiment 2:

[0115] The present embodiment provides a coating equipment servo motion control system based on optimized fuzzy PID, comprising a memory and a processor, wherein the memory comprises a coating equipment servo motion control method program based on optimized fuzzy PID, and when the coating equipment servo motion control method program based on optimized fuzzy PID is executed by the processor, the steps of the coating equipment servo motion control method based on optimized fuzzy PID as described in Example 1 are implemented.

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A coating equipment servo motion control method based on optimized fuzzy PID, characterized in that: The steps include: Build a virtual simulation platform, construct a motion model on the simulation platform, input the external environment data of the PI coating equipment system into the motion model, output the target rotation speed of the rubber-distributing roller of the PI coating equipment; measure the actual rotation speed of the rubber-distributing roller on the PI coating equipment; The target speed is subtracted from the actual speed to obtain a speed deviation, and the speed deviation is derived with respect to time to obtain a deviation change rate; The speed deviation and the deviation change rate are input into the fuzzy PID controller, and the fuzzy PID algorithm combined with the adaptive particle swarm optimization algorithm is used to calculate and obtain the PID control parameters. The PID control parameters are input into the PID controller for adjustment so that the actual speed of the rubber roller reaches the target speed.

2. According to the method of servo motion control of coating equipment based on optimized fuzzy PID in claim 1, it is characterized in that: The method for measuring the actual rotation speed of the rubber-distributing roller on the PI coating device is to use a grating measurement system for measurement.

3. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 1 is characterized in that: When building the virtual simulation platform, the characteristics that need to be considered include: viscous damping, viscous friction and adhesion effect; when building the motion model, the process links that need to be analyzed include: cleaning, gluing and drying.

4. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 1 is characterized in that: The PID control parameters include: proportional gain, integral gain and differential gain.

5. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 4 is characterized in that: A fuzzy rule base is established for each of the PID control parameters. The establishment of the fuzzy rule base is based on the following principles: The value of proportional gain is adjusted according to the current speed deviation. When the speed deviation is large, the proportional gain fuzzy value should be set to a larger value to reduce the deviation quickly. When the speed deviation is moderate, if the speed deviation change rate is large, the proportional gain fuzzy value should be appropriately increased to respond to the change quickly. If the speed deviation change rate is small, the proportional gain fuzzy value should be appropriately reduced to improve system stability. When the speed deviation is small, the proportional gain fuzzy value should be small to avoid system over-regulation; The integral gain adjusts the output through the accumulation of speed deviation. When the accumulated deviation is large, the integral gain fuzzy value should be appropriately increased to speed up the error elimination speed; when the accumulated deviation is moderate, if the speed deviation change rate is large, the integral gain fuzzy value should be reduced to prevent integral saturation; if the speed deviation change rate is small, the integral gain fuzzy value should be appropriately increased to improve the steady-state accuracy; when the accumulated deviation is small, the integral gain fuzzy value should be set to a small value to prevent system overshoot or oscillation; The differential gain is adjusted by predicting the speed deviation change rate. When the speed deviation change rate is large, the differential gain fuzzy amount should be appropriately increased to suppress system overshoot and reduce oscillation. When the speed deviation change rate is moderate, if the speed deviation is large, the differential gain fuzzy amount should be kept high to stabilize the adjustment. If the speed deviation is small, the differential gain fuzzy amount should be appropriately reduced to avoid overreaction. When the speed deviation change rate is small, the differential gain fuzzy amount should take a smaller value to ensure the stability of the system adjustment process.

6. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 1, characterized in that: The PID control parameters are calculated using the fuzzy PID algorithm combined with the adaptive particle swarm optimization algorithm, including the following steps: Quantifying the speed deviation and the deviation change rate respectively to obtain a fuzzy quantized value of the speed deviation and a fuzzy quantized value of the deviation change rate; The fuzzy quantized value of the speed deviation and the fuzzy quantized value of the deviation change rate are respectively input into the membership function, and the membership degree of the speed deviation and the membership degree of the deviation change rate are output; Determine a fuzzy subset of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate in combination with a fuzzy rule base of the corresponding control parameter, and calculate the comprehensive membership of the controller adjustment amount by using the membership of the speed deviation and the membership of the deviation change rate according to the fuzzy subset; The controller adjustment amount is calculated by using the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount in combination with the center of gravity method to obtain the controller adjustment amount; The PID control parameters are calculated by using the controller adjustment amount and the current PID control parameters combined with the particle swarm optimization algorithm.

7. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 6 is characterized in that: The membership function is a triangular membership function.

8. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 6, characterized in that: The controller adjustment is calculated using the center of gravity method. The formula is as follows: Among them, ΔK is the controller adjustment amount, A i is the comprehensive membership of the controller adjustment quantity, F i is the fuzzy quantization value, and n is the number of fuzzy subsets.

9. The coating equipment servo motion control method based on optimized fuzzy PID according to claim 8, characterized in that: Using the controller adjustment amount and the current PID control parameters, combined with the particle swarm optimization algorithm, the PID control parameters are calculated. The formula is as follows: K(t)=K(t-1)+ΔK*α (1) Among them, K(t) is the PID control parameter currently output, K(t-1) is the PID control parameter output of the previous iteration, t is the number of iterations, ΔK is the controller adjustment amount, α is the adaptive quantization factor, and the expression of the adaptive quantization factor is as follows: Where t is the number of iterations, e(t)-e(t-1) represents the change in the speed deviation between the current iteration and the previous iteration, ec(t)-ec(t-1) represents the change in the deviation change rate between the current iteration and the previous iteration, X is the minimum value of the adaptive quantization factor, Y and Z are the scaling factors of the adaptive quantization factor function, and the scaling factor is optimized using the particle swarm optimization algorithm. The specific steps for optimization are: Initialize the parameters Y and Z to a group of particles with a group size of N and a position of x i , the speed is v i , and assigns each particle an initial position, velocity, inertia weight, and learning factor; Calculate the current inertia weight ω, the formula is as follows: Among them, t is the number of iterations, T is the maximum number of iterations, ω max and ω min They are the maximum and minimum values ​​of the preset inertia weight respectively; Using the particle's current velocity v i (t), position x i (t), individual best historical position pbest i , the group's global best position gbest and inertia weight ω(t), calculate the updated velocity v of each particle i (t+1), the formula is as follows: v i (t+1)=ω(t)+v i (t)+c1(t)*rand()*(pbest i -x i (t))+c2(t)*rand()*(gbest-x i (t))(3) Among them, v i (t) represents the speed of the i-th particle before updating, rand() is a random number, pbest i Then represents the personal best position of the i-th particle, gbest represents the global optimal position, c1(t) and c2(t) are the current learning factors; Using the updated particle velocity v i (t+1) and the current particle position x i (t), calculate the new position x of the particle i (t+1), the formula is as follows: x i (t+1)=x i (t)+v i (t+1) The particle's current position x i (t+1) Input the fitness function, combine the system's stability time and gain margin, and calculate the fitness value f(x i ), the formula is as follows: Among them, T s (x i ) is the position x i The stabilization time, G m (x i ) is the position x i Gain margin of Compare the fitness value of the particle with its historical best fitness value, update the particle's personal best position, and compare the personal best positions of all particles with the global best position to obtain the updated global best position; According to the particle and the global best position gbest and the individual historical best position pbest i The distance between the two variables is dynamically adjusted, and the learning factors c1 and c2 are adjusted dynamically. The formula is as follows: Among them, c 1,base and c 2,base are the preset basic values ​​of c1 and c2, d i (t) represents the current position of the i-th particle, |pbest i -d i (t)| is the best position of the i-th particle and the individual in history pbest i The distance between |gbest-d i (t)| is the distance between the ith particle and the global best position gbest, and t is the number of iterations; Substitute the updated learning factor into formula (3) to adjust the particle speed; Repeat the above steps until the maximum number of iterations is reached or the global optimal fitness value meets the predetermined threshold, and output the optimized scaling factors Y and Z; Substituting the scaling factors Y and Z into formula (2), we get the adaptive quantization factor; Substituting the adaptive quantization factor into formula (1), we can get the PID control parameters.

10. A coating equipment servo motion control system based on optimized fuzzy PID, characterized in that: The system includes: a memory and a processor, wherein the memory includes a coating equipment servo motion control method program based on optimized fuzzy PID, and when the coating equipment servo motion control method program based on optimized fuzzy PID is executed by the processor, the steps of a coating equipment servo motion control method based on optimized fuzzy PID as described in any one of claims 1 to 9 are implemented.

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