A coating equipment servo motion control method and system based on optimized fuzzy PID

Abstract

The application discloses a kind of based on optimization fuzzy PID's coating equipment servo motion control method and system.Method includes: build virtual simulation platform, motion model is constructed on the simulation platform, PI coating equipment system external environment data is input into motion model, and the target rotating speed of PI coating equipment even glue roller is output;The actual rotating speed of even glue roller on PI coating equipment is measured;Difference is obtained between target rotating speed and actual rotating speed, and the change rate of bias is obtained by derivation of the rotating speed bias with respect to time;Rotating speed bias and bias change rate are input into fuzzy PID controller, and the PID control parameter is obtained by using fuzzy PID algorithm combined with adaptive particle swarm optimization algorithm, and PID control parameter is input into PID controller to adjust, so that the actual rotating speed of even glue roller reaches target rotating speed.The application can effectively improve the control precision of PID control algorithm, and improve the stability of PID control algorithm.

Description

Technical Field

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

[0002] Polyimide (PI) coating equipment primarily coats an alignment film onto TFT & CF substrates, laying the foundation for the alignment of liquid crystal molecules on the film. Therefore, the control precision requirements for PI coating equipment are extremely high, and currently, the motion control method for PI coating equipment mainly employs the "conventional PID controller" approach.

[0003] Conventional PID controllers employ traditional PID closed-loop control. Their simple structure, long history of development, ease of implementation, and lack of a precise mathematical model make them a popular and effective control tool in various industrial and engineering applications. However, during application, their PID parameters do not adaptively change with external or internal machine environments. They heavily rely on the engineer's familiarity with the system model and their mastery of the machine, significantly increasing production costs and reducing efficiency, which contradicts the rapid pace of industrial development. Furthermore, they may perform poorly when handling nonlinear or time-varying systems, especially those with strong nonlinear characteristics. Improper parameter settings in traditional PID controllers can lead to over-adjustment of the control output or an inability to respond quickly or maintain good control performance. Because their PID parameters are immutable and lack adaptive capability, they perform poorly in systems with strong nonlinear characteristics or time-varying features, often requiring manual parameter adjustment. In complex systems and scenarios requiring high precision, they fail to provide optimal control results.

[0004] The existing fuzzy PID control method uses variable PID parameters, which overcomes the shortcomings of conventional PID controllers that cannot adapt to nonlinear and time-varying systems due to the inability to adjust parameters. This greatly enhances the stability and robustness of the system. A patent with publication number CN111221245A proposes a PSO-based fuzzy PID tension control parameter self-tuning method for a stenter, including the following steps: S1: Select a discrete PID control algorithm based on the characteristics of the stenter tension control system; S2: Set a fuzzy control rule with an adjustment factor and use the particle swarm optimization algorithm to optimize the adjustment factor; S3: Obtain the global optimal position and the corresponding PID value, and substitute this value into the discrete PID control algorithm to control the stenter tension. This scheme has certain drawbacks and limitations. It relies excessively on randomness and cannot guarantee that an ideal solution will be obtained in each run. Furthermore, its performance is highly dependent on its parameter settings, such as the number of particles, inertia weight, and learning factor. Inappropriate parameter selection may lead to slow convergence and getting trapped in local optima. Summary of the Invention

[0005] To overcome the problems in the prior art, especially the insufficient control accuracy of existing PID control algorithms, as well as the slow convergence speed and getting trapped in local optima caused by inappropriate parameter selection, this invention provides a servo motion control method and system for coating equipment based on optimized fuzzy PID.

[0006] The primary objective of this invention is to solve the aforementioned technical problems. The technical solution of this invention is as follows:

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

[0008] A virtual simulation platform is built, and a motion model is constructed on the simulation platform. The external environment data of the PI coating equipment system is input into the motion model, and the target rotation speed of the PI coating equipment's leveling roller is output. The actual rotation speed of the leveling roller on the PI coating equipment is measured.

[0009] The difference between the target speed and the actual speed is used to obtain the speed deviation. The rate of change of the deviation is obtained by differentiating the speed deviation with respect to time.

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

[0011] Furthermore, the method for measuring the actual rotational speed of the uniform roller on the PI coating equipment is to use a grating measurement system.

[0012] Furthermore, the characteristics to be considered when building the virtual simulation platform include viscous damping, viscous friction, and adhesion effects; the process steps to be analyzed when constructing the motion model include cleaning, gluing, and drying.

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

[0014] Furthermore, fuzzy rule bases are established for the PID control parameters, and the establishment of the fuzzy rule bases 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 larger to reduce the deviation quickly. When the speed deviation is moderate, if the rate of change of the speed deviation is large, the proportional gain fuzzy value should be increased appropriately to respond quickly to the change. If the rate of change of the speed deviation is small, the proportional gain fuzzy value should be decreased appropriately to improve system stability. When the speed deviation is small, the proportional gain fuzzy value should be smaller to avoid system over-adjustment.

[0016] The integral gain adjusts the output by accumulating the speed deviation. When the accumulated deviation is large, the integral gain fuzzy value should be increased appropriately to speed up the error elimination. When the accumulated deviation is moderate, if the speed deviation change rate is large, the integral gain fuzzy value should be decreased to prevent integral saturation. If the speed deviation change rate is small, the integral gain fuzzy value should be increased appropriately to improve 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 adjusts the output by predicting the rate of change of speed deviation. When the rate of change of speed deviation is large, the fuzzy value of the differential gain should be increased appropriately to suppress system overshoot and reduce oscillation. When the rate of change of speed deviation is moderate, if the speed deviation is large, the fuzzy value of the differential gain should be kept high to stabilize the adjustment. If the speed deviation is small, the fuzzy value of the differential gain should be decreased appropriately to avoid overreaction. When the rate of change of speed deviation is small, the fuzzy value of the differential gain should be taken as small to ensure the smoothness of the system adjustment process.

[0018] Furthermore, the PID control parameters are calculated using a fuzzy PID algorithm incorporating adaptive particle swarm optimization, including the following steps:

[0019] The speed deviation and the rate of change of deviation are quantized separately to obtain the fuzzy quantized values ​​of the speed deviation and the rate of change of deviation.

[0020] Input the fuzzy quantized values ​​of the speed deviation and the deviation change rate into the membership function, and output the membership degree of the speed deviation and the membership degree of the deviation change rate.

[0021] The fuzzy subset of the controller adjustment amount is determined by combining the membership degree of the speed deviation and the membership degree of the deviation change rate with the fuzzy rule base of the corresponding control parameters. Based on the fuzzy subset, the comprehensive membership degree of the controller adjustment amount is calculated using the membership degree of the speed deviation and the membership degree of the deviation change rate.

[0022] The controller adjustment amount is calculated by combining the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount with the centroid method.

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

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

[0025] Furthermore, the controller adjustment is calculated using the center of gravity method, as shown in the following formula:

[0026]

[0027] Where ΔK is the controller adjustment amount, A i F represents the overall membership degree of the controller adjustment. i is the fuzzy quantization value, and n is the number of fuzzy subsets.

[0028] Furthermore, using the controller adjustment and the current PID control parameters, combined with the particle swarm optimization algorithm, the PID control parameters are calculated, as shown in the following formula:

[0029] K(t)=K(t-1)+ΔK*α (1)

[0030] Where K(t) is the current output PID control parameter, K(t-1) is the output PID control parameter of the previous iteration, t is the iteration number, ΔK is the controller adjustment, and α is the adaptive quantization factor, the expression of which is shown below:

[0031]

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

[0033] Initialize parameters Y and Z to form a swarm of particles with a population size of N and a position of x. i The speed is v iAnd assign an initial position, velocity, inertial weight, and learning factor to each particle;

[0034] The current inertia weight ω is calculated using the following formula:

[0035]

[0036] Where t is the number of iterations, T is the maximum number of iterations, and ω max and ω min These are the maximum and minimum values ​​of the preset inertia weights, respectively;

[0037] Using the particle's current velocity v i (t), position x i (t), individual historical best position pbest i The global optimal position gbest and the inertial weight ω(t) are used to 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 velocity of the i-th particle before the update, rand() is a random number, and pbest i Then, it represents the individual best position of the i-th particle, gbest represents the global best position, and 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] Set the particle's current position x i (t+1) Input the fitness function, and calculate the fitness value f(x) for each particle by combining the system's settling time and gain margin. iThe formula is as follows:

[0044]

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

[0046] The particle's fitness value is compared with its historical best fitness value to update the particle's personal best position. The personal best positions of all particles are then compared with the global best position to obtain the updated global best position.

[0047] Based on the particle's global best position (gbest) and its individual historical best position (pbest) i The distance is used to dynamically adjust the learning factors c1 and c2, as shown in the following formula:

[0048]

[0049] Among them, c 1,base and c 2,base These are the preset base values ​​for c1 and c2, respectively, and d i (t) represents the position of the current i-th particle, |pbest i -d i (t)| represents the best historical position of the i-th particle, pbest. i The distance between them, |gbest-d i (t)| is the distance between the i-th particle and the global best position gbest, where t is the number of iterations;

[0050] The particle velocity is adjusted by substituting the updated learning factor into formula (3);

[0051] Repeat the above steps until the maximum number of iterations is reached or the global best 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 obtain the adaptive quantization factor;

[0053] Substituting the adaptive quantization factor into formula (1) yields the PID control parameters.

[0054] A second aspect of the present invention provides a servo motion control system for a coating equipment based on optimized fuzzy PID. The system includes a memory and a processor. The memory includes a program for a servo motion control method for a coating equipment based on optimized fuzzy PID. When the program for the servo motion control method for a coating equipment based on optimized fuzzy PID is executed by the processor, it implements the steps of a servo motion control method for a coating equipment based on optimized fuzzy PID.

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

[0056] This paper proposes a method to optimize the adaptive quantization scaling factor in a fuzzy PID controller using an adaptive particle swarm optimization algorithm. A fuzzy rule table is established based on system characteristics. Leveraging the ability of the fuzzy PID controller to adjust parameters according to system features, this method ensures that the actual rotational speed of the coating equipment perfectly matches the target rotational speed obtained from the virtual simulation platform. This enhances the adaptive capability of the coating equipment system, improves system control accuracy, stability, and robustness, and enables precise monitoring and feedback control of parameters such as the position and speed of the coating equipment, providing optimal control performance in high-precision scenarios. Simultaneously, the adaptive particle swarm optimization algorithm optimizes parameters such as inertia weights and learning factors by feeding back the velocity and position of particles, preventing the particle swarm from getting trapped in local optima and ensuring that the particle swarm converges to the ideal optimal solution in the shortest possible time. This solves the problem of slow convergence speed and local optima trapping caused by inappropriate parameter selection, thus improving the stability of the particle swarm optimization algorithm. Attached Figure Description

[0057] To make the objectives and technical solutions of this invention clearer, the following drawings are provided and described:

[0058] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0059] Figure 2 A triangular membership function diagram provided for embodiments of the present invention;

[0060] Figure 3 The image shows the effect of a PI coating device without using the adaptive particle swarm optimization algorithm, as provided in an embodiment of the present invention.

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

[0062] Figure 5 A proportional gain fuzzy rule table provided for embodiments of the present invention;

[0063] Figure 6 The integral gain fuzzy rule table provided for embodiments of the present invention;

[0064] Figure 7 The differential gain fuzzy rule table provided for embodiments of the present invention. Detailed Implementation

[0065] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0067] Example 1:

[0068] This invention provides a servo motion control method for coating equipment based on optimized fuzzy PID, such as... Figure 1 The diagram shows a servo motion control method for a coating equipment based on optimized fuzzy PID control. The specific steps are as follows:

[0069] S1: Analyze the scheduling method and main parameter requirements of the process steps, consider non-ideal characteristics, build a virtual simulation platform, analyze the process of the colloid from the slit die head 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 rotation speed of the PI coating equipment's leveling roller; obtain the actual rotation angle of the leveling roller on the PI coating equipment per unit time through real-time monitoring technology, and calculate the actual rotation speed of the current PI coating equipment's leveling roller.

[0070] More specifically, the method for measuring the actual rotational speed of the leveling roller on the PI coating equipment is to use a grating measurement system. The characteristics to be considered when building the virtual simulation platform include viscous damping, viscous friction, and adhesion effects; the process steps to be analyzed when constructing the motion model include cleaning, coating, and drying.

[0071] S2: Subtract the target speed from the actual speed to obtain the speed deviation e, and use the derivative of the speed deviation e with respect to time to obtain the deviation change rate ec.

[0072] S3: Input the rotational speed deviation e and the rate of change of deviation ec into the fuzzy PID controller. Calculate the real-time PID parameters using a fuzzy PID algorithm incorporating adaptive particle swarm optimization. Integrate and adjust these parameters with the initial PID parameters to obtain the PID control parameters. Input these PID control parameters into the PID controller for adjustment, ensuring the actual rotational speed of the equalizing roller reaches the target speed. The adaptive particle swarm optimization algorithm optimizes parameters such as inertia weight and learning factor by feeding back the velocity and position of particles, preventing the particle swarm from getting trapped in local optima. This ensures the particle swarm converges to the ideal optimal solution in the shortest possible time, solving the problems of slow convergence speed and getting trapped in local optima caused by inappropriate parameter selection, thus improving the stability of the particle swarm optimization algorithm.

[0073] More specifically, the PID control parameters include: proportional gain Kp, integral gain Ki, and derivative gain Kd. Each PID control parameter needs to have its own fuzzy rule base established, and the establishment of the fuzzy rule base is based on the following principles:

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

[0075] The integral gain adjusts the output based on the accumulation of speed deviation. When the accumulated deviation is large, the integral gain fuzzy value should be increased appropriately to accelerate error elimination. When the accumulated deviation is moderate, if the rate of change of speed deviation is large, the integral gain fuzzy value should be decreased to prevent integral saturation. If the rate of change of speed deviation is small, the integral gain fuzzy value should be increased appropriately to improve 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 resulting fuzzy rule table is as follows: Figure 6 As shown;

[0076] The differential gain adjusts the output based on the prediction of the rate of change of speed deviation. When the rate of change of speed deviation is large, the fuzzy value of the differential gain should be appropriately increased to suppress system overshoot and reduce oscillation. When the rate of change of speed deviation is moderate, if the speed deviation is large, the fuzzy value of the differential gain should be kept high for stable adjustment; if the speed deviation is small, the fuzzy value of the differential gain should be appropriately decreased to avoid overreaction. When the rate of change of speed deviation is small, the fuzzy value of the differential gain should be small to ensure the smoothness of the system adjustment process. The resulting fuzzy rule table is shown below. Figure 7 As shown.

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

[0078] The speed deviation and the rate of change of deviation are quantized separately to obtain the fuzzy quantized values ​​of the speed deviation and the rate of change of deviation, as shown in the following formulas:

[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 These represent the maximum and minimum rotational speeds of the leveling roller in the PI coating equipment, respectively. max Take 20 m / min, L min Take 1 m / min.

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

[0083] The fuzzy subset of the controller adjustment amount is determined by combining the membership degree of the speed deviation and the membership degree of the deviation change rate with the fuzzy rule base of the corresponding control parameters. Based on the fuzzy subset, the comprehensive membership degree of the controller adjustment amount is calculated using the membership degree of the speed deviation and the membership degree of the deviation change rate.

[0084] The controller adjustment amount ΔK is calculated using the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount combined with the centroid method, as shown in the following formula:

[0085]

[0086] Among them, A i F represents the overall membership degree of the controller adjustment. i Here, n represents the fuzzy quantization value, and n is the number of fuzzy subsets.

[0087] The PID control parameters are calculated using the controller adjustment and the current PID control parameters, combined with the particle swarm optimization algorithm, as shown in the following formula:

[0088] K(t)=K(t-1)+ΔK*α (1)

[0089] Where K(t) is the current output PID control parameter, K(t-1) is the output PID control parameter of the previous iteration, t is the iteration number, ΔK is the controller adjustment, and α is the adaptive quantization factor, the expression of which is shown below:

[0090]

[0091] Where t is the iteration number, e(t)-e(t-1) represents the change in rotational speed deviation between the current iteration and the previous iteration, ec(t)-ec(t-1) represents the change in the rate of change of deviation 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 both scaling factors of the adaptive quantization factor function, used to control the scaling range of the adaptive quantization factor. The scaling factor is optimized using the particle swarm optimization algorithm. The specific steps of the optimization are as follows:

[0092] Initialize parameters Y and Z to form a swarm of particles with a population size of N and a position of x. i The speed is v i Furthermore, based on experience or the characteristics of the problem, each particle is assigned an initial position, velocity, inertial weight, and learning factor.

[0093] The current inertia weight ω is calculated using the following formula:

[0094]

[0095] Where t is the number of iterations, T is the maximum number of iterations, and ω max and ω min These are the maximum and minimum values ​​of the preset inertia weights, ω. max Take 0.9, ω min We set it to 0.3, and according to the formula, the inertia weight decreases continuously as the number of iterations increases.

[0096] Using the particle's current velocity v i (t), position x i (t), individual historical best position pbest i The global optimal position gbest and the inertial weight ω(t) are used to 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 velocity of the i-th particle before the update, rand() is a random number, and pbest i Then, it represents the individual best position of the i-th particle, gbest represents the global best position, and 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] Set the particle's current position x i (t+1) Input the fitness function, and calculate the fitness value f(x) for each particle by combining the system's settling time and gain margin. i The formula is as follows:

[0103]

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

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

[0106] Based on the particle's global best position (gbest) and its individual historical best position (pbest) i The distance is used to dynamically adjust the learning factors c1 and c2, as shown in the following formula:

[0107]

[0108] Among them, c 1,base and c 2,base These are the preset base values ​​for c1 and c2, both set to 2.15, and d. i(t) represents the position of the current i-th particle, |pbest i -d i (t)| represents the best historical position of the i-th particle, pbest. i The distance between them, |gbest-d i (t)| is the distance between the i-th particle and the global best position gbest, where t is the number of iterations;

[0109] The particle velocity is adjusted by substituting the updated learning factor into formula (1);

[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 obtain the adaptive quantization factor;

[0112] Substituting the adaptive quantization factor into formula (1) yields the PID control parameters.

[0113] A virtual simulation platform was built in MATLAB, and a motion model and parameter constraints were constructed. The simulated PI coating equipment system temperature was 27℃. The effect of the PI coating equipment without using the adaptive particle swarm optimization algorithm was as follows: Figure 3 As shown, the effect of the PI coating equipment when using the adaptive particle swarm optimization algorithm is as follows: Figure 4 As shown in the figure, red represents the target rotational speed and green represents the actual rotational speed of the coating roller. It can be seen from the figure that the adaptive particle swarm optimization algorithm can ensure that the actual rotational speed of the coating equipment can perfectly fit the target rotational speed obtained by the virtual simulation platform, enhance the adaptive capability of the coating equipment system, improve the system control accuracy, enhance the system stability and robustness, realize the accurate monitoring and feedback control of parameters such as the position and speed of the coating equipment, and obtain the best control effect.

[0114] Example 2:

[0115] This embodiment provides a servo motion control system for a coating equipment based on optimized fuzzy PID, including a memory and a processor. The memory includes a program for a servo motion control method for a coating equipment based on optimized fuzzy PID. When the processor executes the program for the servo motion control method for a coating equipment based on optimized fuzzy PID, it implements the steps of the servo motion control method for a coating equipment based on optimized fuzzy PID as described in Embodiment 1.

[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 implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A servo motion control method for coating equipment based on optimized fuzzy PID, characterized in that, Includes the following steps: A virtual simulation platform is built, and a motion model is constructed on the simulation platform. The external environment data of the PI coating equipment system is input into the motion model, and the target rotation speed of the PI coating equipment's leveling roller is output. The actual rotation speed of the leveling roller on the PI coating equipment is measured. The difference between the target speed and the actual speed is used to obtain the speed deviation. The rate of change of the deviation is obtained by differentiating the speed deviation with respect to time. The rotational speed deviation and the rate of change of deviation are input into the fuzzy PID controller. The fuzzy PID algorithm, which combines adaptive particle swarm optimization algorithm, is used to calculate and obtain the PID control parameters. The PID control parameters are then input into the PID controller for adjustment so that the actual rotational speed of the equalizing roller reaches the target rotational speed. The speed deviation and the rate of change of deviation are input into the fuzzy PID controller. The PID control parameters are calculated using the controller adjustment and the current PID control parameters, combined with the particle swarm optimization algorithm, as shown in the following formula: （1） in, These are the currently output PID control parameters. Here, t represents the PID control parameters output from the previous iteration, and t represents the iteration number. Adjust the amount for the controller. The adaptive quantization factor is expressed as follows: （2） Where t is the number of iterations. This represents the change in rotational speed deviation between the current iteration and the previous iteration. X represents the change in the rate of change of the deviation between the current iteration and the previous iteration, X is the minimum value of the adaptive quantization factor, and Y and Z are both scaling factors of the adaptive quantization factor function. The scaling factor is optimized using the particle swarm optimization algorithm. The specific steps for optimization are as follows: Initialize parameters Y and Z to form a swarm of particles with a population size of N, located at... The speed is And assign an initial position, velocity, inertial weight, and learning factor to each particle; Calculate the current inertia weight The formula is shown below: Where t is the number of iterations, and T is the maximum number of iterations. and These are the maximum and minimum values ​​of the preset inertia weights, respectively; Using the particle's current velocity ,Location The optimal position of the i-th particle Group global optimal position With inertia weight Calculate the updated velocity of each particle. The formula is shown below: in, This represents the velocity of the i-th particle before the update, and rand() is a random number. This represents the optimal position for the i-th particle. This indicates the optimal position of the group globally. and The current learning factor; Utilizing the updated particle velocity and current particle position Calculate the new position of the particle The formula is shown below: The current position of the particle Input the fitness function, and calculate the fitness value for each particle by considering the system's settling time and gain margin. The formula is shown below: in, For position The stabilization time For position Gain margin; The particle's fitness value is compared with its historical best fitness value to update the particle's personal best position. The personal best positions of all particles are then compared with the global best position to obtain the updated global best position. Based on particles and global optimal position And the individual optimal position of the i-th particle The distance, dynamically adjust the learning factor and The formula is shown below: in, and They are and The preset basic value It is the first i The optimal position of each particle and the i-th particle. The distance between them It is the first i individual particles and The distance between them, where t is the number of iterations; The particle velocity is adjusted by substituting the updated learning factor into formula (3); Repeat the above steps until the maximum number of iterations is reached or the global best 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 obtain the adaptive quantization factor; Substituting the adaptive quantization factor into formula (1) yields the PID control parameters.

2. The servo motion control method for coating equipment based on optimized fuzzy PID according to claim 1, characterized in that, The method for measuring the actual rotational speed of the uniform roller on the PI coating equipment is to use a grating measurement system.

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

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

5. The servo motion control method for coating equipment based on optimized fuzzy PID according to claim 1, characterized in that, The PID control parameters are calculated using a fuzzy PID algorithm that incorporates adaptive particle swarm optimization, including the following steps: The speed deviation and the rate of change of deviation are quantized separately to obtain the fuzzy quantized values ​​of the speed deviation and the rate of change of deviation. Input the fuzzy quantized values ​​of the speed deviation and the deviation change rate into the membership function, and output the membership degree of the speed deviation and the membership degree of the deviation change rate. The fuzzy subset of the controller adjustment amount is determined by combining the membership degree of the speed deviation and the membership degree of the deviation change rate with the fuzzy rule base of the corresponding control parameters. Based on the fuzzy subset, the comprehensive membership degree of the controller adjustment amount is calculated using the membership degree of the speed deviation and the membership degree of the deviation change rate. The controller adjustment amount is calculated by combining the comprehensive membership degree and fuzzy quantization value of the controller adjustment amount with the centroid method. The PID control parameters are calculated using the controller adjustment and the current PID control parameters, combined with the particle swarm optimization algorithm.

6. The servo motion control method for coating equipment based on optimized fuzzy PID according to claim 5, characterized in that, The membership function is a trigonometric membership function.

7. The servo motion control method for coating equipment based on optimized fuzzy PID according to claim 5, characterized in that, The controller adjustment amount is calculated using the center of gravity method, as shown in the following formula: in, Adjustment amount for controller, The overall membership degree of the controller adjustment amount, is the fuzzy quantization value, and n is the number of fuzzy subsets.

8. A servo motion control system for a coating equipment based on optimized fuzzy PID, characterized in that, The system includes a memory and a processor. The memory includes a program for a servo motion control method for a coating equipment based on optimized fuzzy PID. When the processor executes the program for the servo motion control method for a coating equipment based on optimized fuzzy PID, it implements the steps of a servo motion control method for a coating equipment based on optimized fuzzy PID as described in any one of claims 1 to 7.

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