IGWO Fuzzy PID Automatic Regulation Vacuum Sucker Control Method

Abstract

The present invention discloses a control method for automatically adjusting a vacuum chuck based on IGWO fuzzy PID. An air pump delivers the air in the cylinder to a solenoid valve. The gas introduced into the solenoid valve causes a vacuum generator to generate a vacuum negative pressure. Then, the atmospheric pressure enables the vacuum chuck to hold the workpiece. A vacuum gauge measures the vacuum degree of the vacuum chuck in real time and transmits the measured value to an industrial control computer for comparison with the ideal value. After calculation by an IGWO fuzzy PID calculator, a control instruction is sent to the solenoid valve, thereby controlling the on / off of the solenoid valve and the magnitude of the pressure to reduce the difference. The present invention uses a vacuum gauge as the feedback input and then controls the vacuum degree of the vacuum chuck to reach the desired value through fuzzy PID. The optimization of the fuzzy controller parameters is based on the gray wolf search algorithm, and its nonlinear adjustment convergence factor and position update method are improved. Compared with the basic gray wolf algorithm, the optimization ability of the algorithm is greatly improved, and the algorithm is prevented from falling into a local optimum.

Description

Technical Field

[0001] The present invention relates to the field of ultra-precision machine tool automation, and specifically to a control method for automatically adjusting a vacuum chuck based on IGWO fuzzy PID. Background Technique

[0002] Machine tools have always been known as the "mother machines of industry", which are machines for manufacturing machines, and their importance is beyond doubt. Machine tools are the core production foundation of the entire equipment manufacturing industry. In particular, top machine tools such as ultra-high-precision machine tools and five-axis high-grade CNC machine tools directly reflect the overall competitiveness of a country's manufacturing industry.

[0003] No field of manufacturing can be separated from high-precision machine tools. Whether it is key components of national defense weapons, aerospace, aircraft carriers, or small components such as watch gears and various precision instruments, it is the same. In recent years, with the rise of intelligent manufacturing in China, the rapid development of the aviation industry, independent aircraft carriers, and high-tech industries, every leap in the manufacturing industry is inseparable from the improvement of the manufacturing accuracy of machine tools. In today's industrial production, new standards such as flexible manufacturing, high efficiency, and precision are continuously pushed to new heights, which put higher requirements on ultra-high-precision machine tools.

[0004] Since ultra-precision machine tools need to process workpieces of different sizes, that is, the processed workpieces have diversity, the vacuum chuck needs to provide different load suction forces according to specific workpieces to meet the processing requirements. How to accurately control the load suction force is particularly important. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method for automatically adjusting a vacuum chuck based on IGWO fuzzy PID to solve at least one of the above technical problems.

[0006] The present invention realizes the above purpose through the following technical solutions: A control method for automatically adjusting a vacuum chuck based on IGWO fuzzy PID, including a cylinder, an air pump, a one-way valve, a solenoid valve, a vacuum generator, a vacuum gauge, a vacuum chuck, and an industrial control computer;

[0007] The air pump transports the air in the cylinder to the solenoid valve. The one-way valve prevents the gas from flowing reversely. The solenoid valve controls the on-off and pressure magnitude of the gas path. The gas introduced into the solenoid valve causes the vacuum generator to generate a vacuum negative pressure. Then, the atmospheric pressure can make the vacuum chuck hold the workpiece. The vacuum gauge measures the vacuum degree of the vacuum chuck in real time, and then transmits the measured value to the industrial control computer for comparison with the ideal value. After calculation by the IGWO fuzzy PID calculator, a control instruction is sent to the solenoid valve to control the on-off and pressure magnitude of the solenoid valve; among them, the calculation method based on IGWO fuzzy PID is as follows:

[0008] Step 1: Initialize the position X of each wolf pack and the maximum number of iterations N max, the current iteration number t, convergence factor a, coefficient vectors A and C, and substitute the optimization parameters into the fitness function to calculate the objective fitness;

[0009] Step 2: Iterate once, update the position X of the current gray wolf, convergence factor a, coefficient vectors A and C;

[0010] Step 3: Calculate the fitness values of each population, update the positions (optimal values) of the three wolves Alpha (α), Beta (β), and Delta (δ), and the current iteration number t + 1;

[0011] Step 4: Compare the current iteration number t with the maximum iteration number N max If the current iteration number t has not reached the maximum iteration number N max , then continue to update the iteration, and if it has reached, exit the loop;

[0012] Step 5: Transmit the real-time error and error change rate to the fuzzy logic controller, and find the corresponding rule in the fuzzy rule base;

[0013] Step 6: Substitute the PID optimal values obtained by the improved gray wolf optimization algorithm IGWO into the fuzzy logic controller. Under the specific rules of the fuzzy rule base, after fuzzification, fuzzy inference, and defuzzification, finally output three PID parameters;

[0014] Step 7: After the real-time error passes through the PID controller, the control signal is given to the solenoid valve, and then the pressure of the valve port is controlled to reach the preset target value.

[0015] The control method of the PID controller is:

[0016]

[0017] Among them, K p , K i and K d are the three parameters of the PID controller respectively, e(t) is the difference between the ideal vacuum degree and the actual vacuum degree, and u(t) is the output of the PID controller;

[0018] The calculation formulas for the three parameters of the fuzzy PID controller are:

[0019]

[0020] Among them, Kp, Ki, and Kd are the initial three PID parameters, and △K p , △K i and △K d are the output quantities of the fuzzy controller.

[0021] The Improved Grey Wolf Optimization (IGWO) algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves, namely Alpha (α), Beta (β), Delta (δ), and Omega (ω), are used to simulate the leadership hierarchy. In addition, three main steps of hunting are implemented: searching for prey, surrounding prey, and attacking prey. To mathematically model the social hierarchy of grey wolves when designing the GWO algorithm, we consider the optimal solution as α.

[0022] Therefore, the second and third best solutions are named β and δ respectively; the remaining candidate solutions are assumed to be ω. In the GWO algorithm, the hunting process is guided by α, β, and δ, and ω wolves follow these three wolves.

[0023] During the hunting process, the behavior of grey wolves surrounding prey is defined as follows:

[0024] D = |C × X p (t) - X(t) |

[0025] X(t + 1) = X o (t) - A × D

[0026] where t is the current iteration number, and A and C are coefficient vectors; X o is the position vector of the prey, and X represents the position vector of the wolf; the coefficient vectors are expressed as:

[0027] A = 2a × r1 - a

[0028] C = 2r2

[0029] where r1 and r2 are random vectors in the range from 1 to 0, and the vector a is expressed as

[0030]

[0031] During the hunting stage, the best positions of α, β, and δ are used to find the best direction of the wolves; the next location where the wolves surround the prey is:

[0032] D α = |C1 × X α (t) - X(t)

[0033] D β = |C2 × X β (t) - X(t)

[0034] D δ = |C3 × X δ (t) - X(t)

[0035] X1 = X α - A1 × D α

[0036] X2 = X β -A2 × D β

[0037] X3 = X δ -A3 × D δ

[0038]

[0039]

[0040] Where X α , X β and X δ are the position vectors of Alpha, Beta, and Delta respectively. X1, X2, and X3 represent the distance and direction of the ω wolf towards the α, β, and δ wolves; X p (t) is the current position vector of the ω wolf;

[0041] When the prey stops moving, the gray wolf completes the hunting process by attacking;

[0042] To simulate the approaching prey, when the value of a linearly decreases from 2 to 0, its corresponding a also varies within the interval [-a, a];

[0043] When the value of A is within the range [-A, A], the next position of the gray wolf can be anywhere between its current position and the prey's position;

[0044] When |A| < 1, the wolf attacks the prey;

[0045] When |A| > 1, the gray wolf separates from the prey and hopes to find a more suitable prey; the position of the wolf is updated after each iteration, and the positions of the α, β, and δ wolves are recalculated to capture the prey.

[0046] The convergence factor a changes as: The position update method becomes:

[0047]

[0048] Where, m1, m2, w i and w f are constants.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] 1. Use a vacuum gauge as the feedback input, and then control the vacuum degree of the vacuum chuck to the desired value through fuzzy PID control. The optimization of the fuzzy controller parameters is based on the grey wolf search algorithm, which improves its non-linear adjustment convergence factor and position update method. Compared with the basic grey wolf algorithm, the optimization ability of the algorithm is greatly improved, and the algorithm is prevented from falling into local optimum.

[0051] 2. Utilize the information integration ability and strong learning efficiency of the intelligent optimization algorithm to optimize the three parameters of the fuzzy control system, so that the obtained fuzzy PID after optimization has better adaptability, ensuring the robustness of the entire system, meeting the processing requirements of different workpieces, and improving the automation degree and processing efficiency of the ultra-precision CNC lathe. Brief Description of the Drawings

[0052] Figure 1 It is the framework diagram of the automatic control system of the vacuum chuck of the present invention;

[0053] Figure 2 It is the schematic diagram of the automatic control system of the vacuum chuck of the present invention;

[0054] Figure 3 It is the flow chart of the IGWO fuzzy PID control method of the present invention;

[0055] Figure 4 It is the design diagram of the fuzzy rule base of the present invention;

[0056] Figure 5 It is the diagram of the fuzzy PID control method of the present invention;

[0057] Figure 6 It is the rule table of Kp input in MATLAB of the present invention;

[0058] Figure 7 It is the rule table of Ki input in MATLAB of the present invention;

[0059] Figure 8 It is the rule table of Kd input in MATLAB of the present invention;

[0060] In the figure: 1. Cylinder, 2. Air pump, 3. Check valve, 4. Solenoid valve, 5. Vacuum generator, 6. Vacuum gauge, 7. Vacuum chuck, 8. Industrial control computer. Detailed Embodiment

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] As Figure 1 , Figure 2 , Figure 5 shown, the present invention is a control method for automatically adjusting a vacuum chuck based on an IGWO fuzzy PID, including a cylinder 1, an air pump 2, a check valve 3, a solenoid valve 4, a vacuum generator 5, a vacuum gauge 6, a vacuum chuck 7, and an industrial control computer 8;

[0063] The air pump 2 delivers the air in the cylinder 1 to the solenoid valve 4. The check valve 3 prevents the gas from flowing reversely. The solenoid valve 4 controls the on / off of the gas path and the magnitude of the pressure. The gas introduced into the solenoid valve 4 causes the vacuum generator 5 to generate a vacuum negative pressure. Then, the atmospheric pressure enables the vacuum chuck 7 to hold the workpiece. The vacuum gauge 6 measures the vacuum degree of the vacuum chuck 7 in real time, and then transmits the measured value to the industrial control computer 8 for comparison with the ideal value. After calculation by an IGWO fuzzy PID calculator, a control command is sent to the solenoid valve 4 to control the on / off and the magnitude of the pressure of the solenoid valve 4. Among them, as Figure 3 shown: The calculation method based on IGWO fuzzy PID is as follows:

[0064] Step 1: Initialize the position X of each wolf pack, the maximum number of iterations N max , the current number of iterations t, the convergence factor a, the coefficient vectors A and C, and substitute the optimization parameters into the fitness function to calculate the objective fitness;

[0065] Step 2: Iterate once to update the position X of the current gray wolf, the convergence factor a, the coefficient vectors A and C;

[0066] Step 3: Calculate the fitness value of each population, update the positions (optimal values) of the three wolves Alpha (α), Beta (β), and Delta (δ), and the current number of iterations t + 1;

[0067] Step 4: Compare the current number of iterations t with the maximum number of iterations N max . If the current number of iterations t does not reach the maximum number of iterations N max , then continue to update the iteration. If it reaches, then exit the loop;

[0068] Step 5: Transmit the real-time error and error change rate to the fuzzy logic controller, and find the corresponding rule in the fuzzy rule base;

[0069] Step 6: Substitute the PID optimal value obtained by the improved gray wolf optimization algorithm IGWO into the fuzzy logic controller. Under the specific rules of the fuzzy rule base, after fuzzification, fuzzy inference, and defuzzification, finally output three PID parameters;

[0070] Step 7: The real-time error passes through the PID controller and then the control signal is given to the solenoid valve (4) to control the magnitude of the valve port pressure and reach the preset target value.

[0071] Among them, the convergence factor a changes as follows:

[0072]

[0073] The position update method becomes:

[0074]

[0075] , where m1, m2, w i and w f are constants.

[0076] Such as Figure 2 、 Figure 4 and Figure 5 As shown: The control method of the PID controller is:

[0077]

[0078] Among them, K p 、K i and K d are the three parameters of the PID controller respectively, e(t) is the difference between the ideal vacuum degree and the actual vacuum degree, and u(t) is the output of the PID controller;

[0079] The calculation formulas for the three parameters of the fuzzy PID controller are:

[0080]

[0081] Among them, Kp, Ki, and Kd are the initial three PID parameters, see Figures 6 - 8 specifically, △K p 、△K i and △K d are the output quantities of the fuzzy controller, see Figure 4 .

[0082] The Grey Wolf Optimization Algorithm, i.e., the IGWO algorithm, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves, such as Alpha (α), Beta (β), Delta (δ), and Omega (ω), are used to simulate the leadership hierarchy; in addition, three main steps of hunting are implemented: searching for prey, surrounding prey, and attacking prey; in order to mathematically model the social hierarchy of grey wolves when designing the GWO algorithm, we take the optimal solution as α;

[0083] Therefore, the second and third best solutions are named β and δ respectively; the remaining candidate solutions are assumed to be ω; in the GWO algorithm, the hunting process is guided by α, β, and δ, and ω wolves follow these three wolves;

[0084] During the hunting process, the behavior of gray wolves surrounding their prey is defined as follows:

[0085] D = |C × X p (t) - X(t) |

[0086] X(t + 1) = X o (t) - A × D

[0087] where t is the current iteration number, and A and C are coefficient vectors; X o is the position vector of the prey, and X represents the position vector of the wolf; the coefficient vectors are expressed as:

[0088] A = 2a × r1 - a

[0089] C = 2r2

[0090] where r1 and r2 are random vectors in the range from 1 to 0, and the vector a is expressed as

[0091]

[0092] During the hunting stage, the best positions of α, β, and δ are used to find the best orientation of the wolves; the next location where the wolves surround the prey is:

[0093] D α = |C1 × X α (t) - X(t) |

[0094] D β = |C2 × X β (t) - X(t) |

[0095] D δ = |C3 × X δ (t) - X(t) |

[0096] X1 = X α - A1 × D α

[0097] X2 = X β - A2 × D β

[0098] X3 = X δ - A3 × D δ

[0099]

[0100]

[0101] where X α, X β and X δ are the position vectors of Alpha, Beta, and Delta respectively. X1, X2, and X3 represent the distances and directions of the omega wolf towards the alpha, beta, and delta wolves respectively; X p (t) is the current position vector of the omega wolf;

[0102] When the prey stops moving, the grey wolf completes the hunting process by attacking;

[0103] To simulate the approaching prey, when the value of a linearly decreases from 2 to 0, its corresponding a also varies within the interval [-a, a];

[0104] When the value of A is within the range [-A, A], the next position of the grey wolf can be anywhere between its current position and the prey's position;

[0105] When |A| < 1, the wolf attacks the prey;

[0106] When |A| > 1, the grey wolf separates from the prey in the hope of finding a more suitable prey; the position of the wolf is updated after each iteration, and the positions of the alpha, beta, and delta wolves are recalculated to capture the prey.

[0107] Using a vacuum gauge as the feedback input, and then controlling the vacuum degree of the vacuum chuck to reach the desired value through fuzzy PID control. The parameter optimization of the fuzzy controller is based on the grey wolf search algorithm, improving its nonlinear adjustment convergence factor and position update method. Compared with the basic grey wolf algorithm, it greatly improves the algorithm optimization ability and avoids the algorithm falling into local optimum.

[0108] Utilizing the information integration ability and strong learning efficiency of the intelligent optimization algorithm to optimize the three parameters of the fuzzy control system, so that the optimized fuzzy PID has better adaptive ability, ensuring the robustness of the entire system, meeting the processing requirements of different workpieces, and improving the automation degree and processing efficiency of the ultra-precision CNC lathe.

[0109] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0110] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A control method for an automatically adjusting vacuum suction cup based on IGWO fuzzy PID, characterized in that, It includes a cylinder (1), an air pump (2), a check valve (3), a solenoid valve (4), a vacuum generator (5), a vacuum gauge (6), a vacuum chuck (7), and an industrial control computer (8); The air pump (2) delivers the air from the cylinder (1) to the solenoid valve (4). The check valve (3) prevents the reverse flow of gas. The solenoid valve (4) controls the on / off of the gas path and the magnitude of the pressure. The gas introduced into the solenoid valve (4) causes the vacuum generator (5) to generate a vacuum negative pressure. Then, the atmospheric pressure enables the vacuum chuck (7) to hold the workpiece. The vacuum gauge (6) measures the vacuum degree of the vacuum chuck (7) in real time and then transmits the measured value to the industrial control computer (8) for comparison with the ideal value. After calculation by an IGWO fuzzy PID calculator, a control instruction is sent to the solenoid valve (4) to control the on / off and the magnitude of the pressure of the solenoid valve (4). Among them, the calculation method based on IGWO fuzzy PID is as follows: Step 1: Initialize the position X of each wolf pack, the maximum number of iterations N max , the current iteration number t, the convergence factor a, the coefficient vectors A and C, and substitute the optimization parameters into the fitness function to calculate the objective fitness; Step 2: Iterate once to update the current position X of the gray wolf, the convergence factor a, the coefficient vectors A and C; Step 3: Calculate the fitness value of each population, update the positions (optimal values) of the three wolves Alpha (α), Beta (β), and Delta (δ), and the current iteration number t + 1; Step 4: The current iteration number t and the maximum iteration number N max Compare. If the current iteration number t has not reached the maximum iteration number N max , then continue to update the iteration. If it has reached, then exit the loop; Step 5: Transmit the real-time error and error change rate into the fuzzy logic controller, and find the corresponding rule in the fuzzy rule base; Step 6: Substitute the PID optimal value obtained by the improved gray wolf optimization algorithm IGWO into the fuzzy logic controller. Under the specific rules of the fuzzy rule base, after fuzzification, fuzzy inference, and defuzzification, finally output three PID parameters; Step 7: The real-time error passes through the PID controller and then the control signal is given to the solenoid valve (4) to control the magnitude of the valve port pressure and reach the preset target value.

2. The control method for an automatically adjusting vacuum suction cup based on IGWO fuzzy PID according to claim 1, characterized in that, The control method of the PID controller is: Among them, K p , K i and K d are respectively the three parameters of the PID controller, e(t) is the difference between the ideal vacuum degree and the actual vacuum degree, and u(t) is the output of the PID controller; And the calculation formulas for the three parameters of the fuzzy PID controller are: where Kp, Ki, and Kd are the initial three PID parameters, and △K p , △K i , and △K d are the output quantities of the fuzzy controller.

3. The control method for an automatically adjusting vacuum suction cup based on IGWO fuzzy PID according to claim 1, characterized in that, The gray wolf optimization algorithm, i.e., the IGWO algorithm, simulates the leadership hierarchy and hunting mechanism of gray wolves in nature. Four types of gray wolves, such as Alpha (α), Beta (β), Delta (δ), and Omega (ω), are used to simulate the leadership hierarchy. In addition, three main steps of hunting are implemented: searching for prey, surrounding the prey, and attacking the prey. To mathematically model the social hierarchy of gray wolves when designing the GWO algorithm, we take the optimal solution as α; Therefore, the second and third best solutions are named β and δ respectively; the remaining candidate solutions are assumed to be ω; in the GWO algorithm, the hunting process is guided by α, β, and δ, and ω wolves follow these three wolves; During the hunting process, the behavior of gray wolves surrounding the prey is defined as follows: D = |C × X p (t) - X(t)| X(t + 1) = X o (t) - A × D where t is the current iteration number, A and C are coefficient vectors; X o is the position vector of the prey, and X represents the position vector of the wolf; the coefficient vectors are expressed as: A = 2a × r1 - a C＝2r2 where r1 and r2 are random vectors in the range from 1 to 0, and the expression of vector a is During the hunting stage, the best positions of α, β, and δ are used to find the best orientation of the wolves. The next location where the wolves surround the prey is: D α = |C1 × X α (t) - X(t)| D β = |C2 × X β (t) - X(t)| D δ = |C3 × X δ (t) - X(t)| X1 = X α -A1 × D α X2 = X β -A2 × D β X3 = X δ -A3 × D δ where X α 、X β and X δ are the position vectors of Alpha, Beta, and Delta respectively; X1, X2, and X3 represent the distances and directions of the omega wolf towards the alpha, beta, and delta wolves respectively; X p (t) is the current position vector of the omega wolf; When the prey stops moving, the gray wolves complete the hunting process by attacking; To simulate approaching the prey, when the value of a linearly decreases from 2 to 0, its corresponding a also changes in the interval [-a, a]; When the value of A is within the range of [-A, A], the next position of the grey wolf can be anywhere between its current position and the prey's position; When |A| < 1, the wolf attacks the prey; When |A| > 1, the grey wolf separates from the prey and hopes to find a more suitable prey; the position of the wolf is updated after each iteration, and the positions of the α, β, and δ wolves are recalculated to capture the prey.

4. According to the method for automatically adjusting the control of a vacuum chuck based on IGWO fuzzy PID described in claim 1 or 3, it is characterized in that, Its convergence factor a changes to: The position update method changes to: where m1, m2, w i and w f are constants.

Images