Magnetic drive transport line position control method based on variable universe fuzzy iterative learning

Abstract

The invention provides a magnetic drive transport line position control method based on variable universe fuzzy iterative learning, which is applied to a position control system of a magnetic drive transport line, relates to the technical field of permanent magnet synchronous motor control, and specifically comprises the following steps: constructing a mathematical model of a permanent magnet synchronous linear motor used by the magnetic drive transport line; designing a closed-loop iterative learning rate; designing a main fuzzy controller; designing a secondary fuzzy controller to generate a telescopic factor; designing a variable universe fuzzy closed loop iterative learning rate; designing an adaptive forgetting factor to improve an iterative learning rate; designing a smooth switching strategy and a slow strategy to improve the adaptive rate of a forgetting factor; designing a variable universe fuzzy iterative learning control rate with an improved adaptive forgetting factor; a simulation model of the permanent magnet synchronous linear motor used by the magnetic drive conveying line is built, the control strategy designed by the invention is applied to a position ring of a permanent magnet synchronous linear motor control system, position control of the motor is completed, and the production capacity of the production line is improved.

Description

Technical Field

[0001] The invention relates to the relevant field of permanent magnet synchronous linear motor control, and in particular to a magnetic drive transport line position control method based on variable universe fuzzy iterative learning. Background Art

[0002] Industrial automation discrete intelligent manufacturing equipment is one of the key support directions of the national industrial policy, but the current discrete manufacturing system has low operating efficiency and is difficult to deploy and debug. Therefore, an intelligent flexible transmission line system that can achieve high-efficiency operation and deployment and debugging has emerged. The core change is to replace the traditional synchronous conveying system with a multi-motor linear motor.

[0003] The main components of the new generation of highly flexible maglev operation systems are: maglev motor conveying system + self-developed integrated control system. Among them, the core of the maglev motor conveying system is the magnetic drive transport line, which is completed by multiple independent linear motor movers. Therefore, it is important to design a reasonable permanent magnet synchronous linear motor position control strategy and improve the position tracking accuracy of the motor.

[0004] The motion of permanent magnet synchronous linear motor is repetitive, so iterative learning control (ILC) is suitable. However, the learning gain of traditional iterative learning control strategy is fixed, and the control system will be affected by load disturbance and parameter changes, which will reduce the control effect. A fuzzy controller is designed to change the iterative learning gain according to the control system status.

[0005] The design of fuzzy control is relatively complex and requires rich expert experience and long-term online adjustment, especially the size of the domain and the form of the membership function are difficult to choose. Variable domain fuzzy control can adjust the input and output information of the fuzzy controller according to the system error, and adjust the quantization factor and proportional factor in the fuzzy controller by designing the scaling factor to achieve the contraction and expansion of the domain. Some scholars have designed a scaling factor in the form of a function, but this method requires artificial determination of the parameter value in the formula, and has poor versatility.

[0006] The forgetting factor is often used to improve the iterative learning rate, but a fixed forgetting factor usually cannot take into account both convergence speed and accuracy. Some scholars have proposed designing an adaptive rate to achieve a variable forgetting factor, but the selection of the adaptive rate is a major difficulty.

[0007] Therefore, the above-mentioned technical problems need to be further solved. Summary of the invention

[0008] The purpose of the embodiment of the present invention is to provide a magnetic drive transport line position control method based on variable universe fuzzy iterative learning, which aims to improve the position tracking accuracy of the permanent magnet synchronous linear motor used in the magnetic drive transport line.

[0009] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0010] The present invention provides a magnetic drive transport line position control method based on variable universe fuzzy iterative learning, comprising the following steps:

[0011] S10. Construct a mathematical model of a permanent magnet synchronous linear motor used in a magnetic drive transport line;

[0012] S20. Design closed-loop iterative learning rate;

[0013] S30. Design the main fuzzy controller;

[0014] S40. Design a two-level fuzzy controller to generate a telescoping factor;

[0015] S50. Design variable domain fuzzy closed-loop iterative learning rate;

[0016] S60. Design an adaptive forgetting factor to improve iterative learning rate;

[0017] S70. Design smooth switching strategy and slow-step strategy to improve the adaptive rate of forgetting factor;

[0018] S80 designs variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor;

[0019] S90. Build a simulation model of the permanent magnet synchronous linear motor used in the magnetic drive transport line, apply the control strategy designed in the present invention to the position loop of the permanent magnet synchronous linear motor control system, and complete the position control of the motor.

[0020] In some modified implementations of the first aspect of the present invention, in step S10, the mathematical model of the permanent magnet synchronous linear motor used in the magnetic drive transport line is constructed as follows:

[0021]

[0022] Where M is the mass of the linear motor rotor, x is the position of the rotor, and K e is the electromagnetic thrust constant, i q is the current of the q axis in the dq coordinate system, F l is the load disturbance, B v is the load disturbance coefficient, and v is the rotor speed.

[0023] Assume that the state variables of the system are:

[0024]

[0025] Then the state equation of the system is:

[0026]

[0027] Then we can get the general expression of the system:

[0028]

[0029] Among them, t is the system running time, k is the number of iterations, and x k (t) is the state vector of the system, u k (t) is the control vector of the system, y k (t) is the output vector of the system, f(t,x k (t)) is the system perturbation, A, B, C are matrices of corresponding dimensions.

[0030] In some modified implementations of the first aspect of the present invention, in step S20, the designed closed-loop iterative learning rate is:

[0031]

[0032] Among them, u k+1 (t) is the output of the k+1th iteration system, u k (t) is the output of the k-th iteration system, e k+1 (t) is the tracking error of the system at the k+1th iteration, e k+1 (t) = y d (t)-y k+1 (t), y d (t) is the expected position of the system, k p , k i , k d are the proportional coefficient, integral coefficient and differential coefficient respectively, and τ is the time variable in the integral term.

[0033] In some modified implementations of the first aspect of the present invention, in step S30, the designed main fuzzy controller is:

[0034] The input of the main fuzzy controller is error e and error change ec. The output is Δk p , Δk i , Δk d The domains of the input variables e and ec are both [-6,6], and the output variable Δk p , Δk i The domain of discourse is [-6,6], Δk d The domain of e, ec, Δk is [-1,1]. p , Δk i , Δk d Select the same fuzzy subset

[0035] {NB, NM, NS, ZO, PS, PM, PB}, {NM, NS, ZO, PS, PM} all use triangular membership functions, NB uses the Z-type membership curve, and PB uses the S-type membership curve.

[0036] Δk p , Δk i , Δk d The fuzzy rules are shown in Table 1-3.

[0037] Table 1Δk p Fuzzy rules

[0038]

[0039] Table 2Δk i Fuzzy rules

[0040]

[0041] Table 3Δk d Fuzzy rules

[0042]

[0043] In some modified implementations of the first aspect of the present invention, in step S40, the designed two-stage fuzzy controller for generating the scaling factor is:

[0044] The input variables of the secondary fuzzy controller are the system error e and the error change rate ec, and the output variables are the expansion factors α and β. α is used to adjust the domain size of the input variables e and ec in the main fuzzy controller, and β is used to adjust the output variable Δk in the main fuzzy controller. p , Δk i , Δk d The fuzzy rules of α are shown in Table 4, and the fuzzy rules of β are α plus a sufficiently small positive number ε, multiplied by different coefficients.

[0045] Table 4 Fuzzy rules of α

[0046]

[0047] In some modified implementations of the first aspect of the present invention, in step S50, the designed variable domain fuzzy iterative learning rate is:

[0048]

[0049] Among them, k p1 =k p +Δk p , k i1 =k i +Δk i , kd1 =k d +Δk d , Δk p , Δk i , Δk d is the correction value output by the main fuzzy controller, k p1 , k i1 , k d1 is the corrected iterative learning gain.

[0050] In some modified implementations of the first aspect of the present invention, the iterative learning rate with the adaptive forgetting factor in step S60 is:

[0051]

[0052] Among them, λ(k) is the designed adaptive forgetting factor, which is expressed as:

[0053]

[0054] Where k is the number of iteration cycles.

[0055] In some modified implementations of the first aspect of the present invention, in step S70, the designed adaptive rate of the forgetting factor with smooth switching strategy is:

[0056]

[0057] Among them, S f is the smoothing factor, which changes with time and number of cycles, and its expression is:

[0058]

[0059] The adaptive rate when k=2 is redesigned by adding a slow-step strategy:

[0060]

[0061] Among them, λ(t,k) is the redesigned forgetting factor, t f is the slow-step factor designed in this paper, θ is the smoothing interval. When the smoothing factor of the second iteration cycle is greater than the smoothing interval, an exponential decay function is used for smooth transition. f The expression is:

[0062]

[0063] Among them, t w is the width of the step to be designed, and t w ≤θ.

[0064] The adaptive rate of the forgetting factor after update is:

[0065]

[0066] In some modified implementations of the first aspect of the present invention, in step S80, the designed variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor is:

[0067]

[0068] In some modified implementations of the first aspect of the present invention, in step S80, the designed variable domain fuzzy iterative learning control strategy with an improved adaptive forgetting factor is applied to the position loop of the motor control system used in the magnetic drive transport line to complete the position control of the permanent magnet synchronous linear motor.

[0069] The present invention constructs a mathematical model of a permanent magnet synchronous linear motor used in a magnetic drive transport line, sets a state equation of the system, and obtains a general expression of a motor control system.

[0070] The present invention designs a variable domain fuzzy iterative learning controller, which can change the gain of iterative learning in real time through fuzzy reasoning to improve the accuracy of the control system. The secondary fuzzy controller can generate a telescoping factor to adjust the quantization factor and proportional factor of the main fuzzy controller, so that the domain can automatically shrink or expand according to the input amount, thereby improving the control effect without increasing the number of fuzzy rules.

[0071] The present invention designs an exponential adaptive forgetting factor, so that the forgetting factor can converge quickly with a larger value in the early stage of iteration, and perform precise adjustment through a smaller forgetting factor when the convergence accuracy is high in the later stage.

[0072] The present invention designs a smooth switching strategy and a slow-step strategy to act on the adaptive rate of the forgetting factor. The exponential forgetting factor does not change smoothly enough when the iterative cycle switches. The smooth switching strategy can reduce the position error generated when the cycle switches, and the slow-step strategy can solve the problem of the forgetting factor changing too fast in the second cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments are briefly introduced below.

[0074] Figure 1 An integrated architecture of a magnetic drive transport line provided by an embodiment of the present invention;

[0075] Figure 2 A physical picture of a mover used in a magnetic drive transport line provided in an embodiment of the present invention;

[0076] Figure 3A block diagram of a variable domain fuzzy iterative learning control with an improved adaptive forgetting factor provided by an embodiment of the present invention;

[0077] Figure 4 A flow chart of a variable universe fuzzy iterative learning control method with an improved adaptive forgetting factor provided by an embodiment of the present invention;

[0078] Figure 5 A block diagram of a three-loop control system of a permanent magnet linear synchronous motor provided in an embodiment of the present invention;

[0079] Figure 6 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a traditional iterative learning control strategy when a sinusoidal position signal is inputted is provided in an embodiment of the present invention.

[0080] Figure 7 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a fuzzy iterative learning control strategy when a sinusoidal position signal is inputted is provided in an embodiment of the present invention.

[0081] Figure 8 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a variable universe fuzzy iterative learning control strategy when a sinusoidal position signal is input is provided in an embodiment of the present invention.

[0082] Fig. 9 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a variable universe fuzzy iterative learning control strategy when a cosine position signal is input is provided in an embodiment of the present invention.

[0083] Fig.10 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a variable universe fuzzy iterative learning control strategy with an adaptive forgetting factor when a cosine position signal is input is provided in an embodiment of the present invention.

[0084] Fig.11 A schematic diagram of the position error of a permanent magnet linear synchronous motor based on a variable universe fuzzy iterative learning control strategy with an improved adaptive forgetting factor when a cosine position signal is input is provided in an embodiment of the present invention.

[0085] In the figure: 100, annular guide rail; 200, motor module. DETAILED DESCRIPTION

[0086] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0088] like Figure 3 and Figure 4 As shown, the magnetic drive transport line position control method based on variable universe fuzzy iterative learning provided by the embodiment of the present invention includes the following steps:

[0089] S10. Construct a mathematical model of a permanent magnet synchronous linear motor used in a magnetic drive transport line;

[0090] S20. Design closed-loop iterative learning rate;

[0091] S30. Design the main fuzzy controller;

[0092] S40. Design a two-level fuzzy controller to generate a telescoping factor;

[0093] S50. Design variable domain fuzzy closed-loop iterative learning rate;

[0094] S60. Design an adaptive forgetting factor to improve iterative learning rate;

[0095] S70. Design smooth switching strategy and slow-step strategy to improve the adaptive rate of forgetting factor;

[0096] S80 designs variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor;

[0097] S90. Build a simulation model of the permanent magnet synchronous linear motor used in the magnetic drive transport line, apply the control strategy designed in the present invention to the position loop of the permanent magnet synchronous linear motor control system, and complete the position control of the motor.

[0098] Furthermore, in step S10, the mathematical model of the permanent magnet synchronous linear motor used in the magnetic drive transport line is:

[0099]

[0100] Where M is the mass of the linear motor rotor, x is the position of the rotor, and K e is the electromagnetic thrust constant, i q is the current of the q axis in the dq coordinate system, F l is the load disturbance, B v is the load disturbance coefficient, and v is the rotor speed.

[0101] Assume that the state variables of the system are:

[0102]

[0103] Then the state equation of the system is:

[0104]

[0105] Then we can get the general expression of the system:

[0106]

[0107] Among them, t is the system running time, k is the number of iterations, and x k (t) is the state vector of the system, u k (t) is the control vector of the system, y k (t) is the output vector of the system, f(t,x k (t)) is the system perturbation, A, B, C are matrices of corresponding dimensions.

[0108] Furthermore, in step S20, the designed closed-loop iterative learning rate is:

[0109]

[0110] Among them, u k+1 (t) is the output of the k+1th iteration system, u k (t) is the output of the k-th iteration system, e k+1 (t) is the tracking error of the system at the k+1th iteration, e k+1 (t) = y d (t)-y k+1 (t), y d (t) is the expected position of the system, k p , k i , k d are the proportional coefficient, integral coefficient and differential coefficient respectively, and τ is the time variable in the integral term.

[0111] Furthermore, in step S30, the designed main fuzzy controller is:

[0112] The input of the main fuzzy controller is error e and error change ec. The output is Δk p , Δk i , Δk d The domains of the input variables e and ec are both [-6,6], and the output variable Δk p , Δk i The domain of discourse is [-6,6], Δk d The domain of e, ec, Δk is [-1,1]. p , Δk i , Δk d Select the same fuzzy subset

[0113] {NB, NM, NS, ZO, PS, PM, PB}, {NM, NS, ZO, PS, PM} all use triangular membership functions, NB uses the Z-type membership curve, and PB uses the S-type membership curve.

[0114] Δk p , Δk i , Δk d The fuzzy rules are shown in Table 1-3.

[0115] Table 1Δk p Fuzzy rules

[0116]

[0117] Table 2Δk i Fuzzy rules

[0118]

[0119] Table 3Δk d Fuzzy rules

[0120]

[0121] Furthermore, in step S40, the designed two-level fuzzy controller for generating the scaling factor is:

[0122] The input variables of the secondary fuzzy controller are the system error e and the error change rate ec, and the output variables are the expansion factors α and β. α is used to adjust the domain size of the input variables e and ec in the main fuzzy controller, and β is used to adjust the output variable Δk in the main fuzzy controller. p , Δk i , Δk d The fuzzy rules of α are shown in Table 4, and the fuzzy rules of β are α plus a sufficiently small positive number ε, multiplied by different coefficients.

[0123] Table 4 Fuzzy rules of α

[0124]

[0125] Furthermore, in step S50, the designed variable domain fuzzy iterative learning rate is:

[0126]

[0127] Among them, k p1 =k p +Δk p , k i1 =k i +Δk i , k d1 =k d +Δkd , Δk p , Δk i , Δk d is the correction value output by the main fuzzy controller, k p1 , k i1 , k d1 is the corrected iterative learning gain.

[0128] Furthermore, the iterative learning rate with the adaptive forgetting factor in step S60 is:

[0129]

[0130] Among them, λ(k) is the designed adaptive forgetting factor, which is expressed as:

[0131]

[0132] Where k is the number of iteration cycles.

[0133] Further, in step S70, the designed forgetting factor adaptive rate with smooth switching strategy is:

[0134]

[0135] Among them, S f is the smoothing factor, which changes with time and number of cycles, and its expression is:

[0136]

[0137] The adaptive rate when k=2 is redesigned by adding a slow-step strategy:

[0138]

[0139] Among them, λ(t,k) is the redesigned forgetting factor, t f is the slow-step factor designed in this paper, θ is the smoothing interval. When the smoothing factor of the second iteration cycle is greater than the smoothing interval, an exponential decay function is used for smooth transition. f The expression is:

[0140]

[0141] Among them, t w is the width of the step to be designed, and t w ≤θ.

[0142] The adaptive rate of the forgetting factor after update is:

[0143]

[0144] Further, in step S80, the designed variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor is:

[0145]

[0146] Furthermore, if Figure 3 As shown, in step S80, the designed variable universe fuzzy iterative learning control strategy with an improved adaptive forgetting factor is applied to the position loop of the motor control system to complete the position control of the permanent magnet synchronous linear motor used in the magnetic drive transport line.

[0147] Figure 6 , Figure 7 , Figure 8 Schematic diagram of the position tracking error of the motor within ten iteration cycles when the input signal of the motor is a sinusoidal signal x=0.5sin(2πt)m and the traditional iterative learning control strategy, fuzzy iterative learning control strategy and variable domain fuzzy iterative learning control strategy are adopted respectively.

[0148] By comparison Figure 6 , Figure 7 , Figure 8 It can be seen that when the input position signal is a sinusoidal signal with an initial position of 0, under the variable universe fuzzy iterative learning control strategy, the position tracking error of the motor is smaller than that of the other two strategies, which proves the effectiveness and superiority of the variable universe fuzzy iterative learning control strategy of the present invention.

[0149] Fig. 9 , Fig.10 , Fig.11 Schematic diagram of the position tracking error of the motor within ten iteration cycles when the input signal of the motor is a cosine signal x=0.5cos(2πt)m and the variable universe fuzzy iterative learning control strategy, the variable universe fuzzy iterative learning control strategy with an adaptive forgetting factor, and the variable universe fuzzy iterative learning control strategy with an improved adaptive forgetting factor are adopted respectively.

[0150] By comparison Fig. 9 , Fig.10 , Fig.11 It can be seen that when the input position signal is a cosine signal whose initial position is not 0, under the variable domain fuzzy iterative learning control strategy with an improved adaptive forgetting factor, the position tracking error of the motor is smaller than that of the other two strategies, especially when switching occurs in the iteration cycle, the error is significantly smaller than that of the other two strategies, which proves the effectiveness of the smooth switching strategy and the slow-step strategy designed by the present invention, as well as the effectiveness and superiority of the variable domain fuzzy iterative learning control strategy with an improved adaptive forgetting factor. The present invention can ensure the accuracy and flexibility required for automated conveying and assembly, improve the production capacity of the production line, and truly achieve speed increase and cost reduction.

[0151] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A magnetic drive transport line position control method based on variable universe fuzzy iterative learning, characterized in that: The steps include: S10. Construct a mathematical model of a permanent magnet synchronous linear motor used in a magnetic drive transport line; S20. Design closed-loop iterative learning rate; S30. Design the main fuzzy controller; S40. Design a two-level fuzzy controller to generate a telescoping factor; S50. Design variable domain fuzzy closed-loop iterative learning rate; S60. Design an adaptive forgetting factor to improve iterative learning rate; S70. Design smooth switching strategy and slow-step strategy to improve the adaptive rate of forgetting factor; S80 designs variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor; S90. Build a simulation model of the permanent magnet synchronous linear motor used in the magnetic drive transport line.

2. According to claim 1, a magnetic drive transport line position control method based on variable universe fuzzy iterative learning is characterized in that: In step S10, the mathematical model of the permanent magnet synchronous linear motor used in the magnetic drive transport line is: Where M is the mass of the linear motor rotor, x is the position of the rotor, and K e is the electromagnetic thrust constant, i q is the current of the q axis in the dq coordinate system, F l is the load disturbance, B v is the load disturbance coefficient, and v is the rotor speed.

3. A magnetic drive transport line position control method based on variable universe fuzzy iterative learning according to claim 2, characterized in that: In step S20, the designed closed-loop iterative learning rate is: Among them, u k+1 (t) is the output of the k+1th iteration system, u k (t) is the output of the k-th iteration system, e k+1 (t) is the tracking error of the system at the k+1th iteration, e k+1 (t) = y d (t)-y k+1 (t), y d (t) is the expected position of the system, k p , k i , k d are the proportional coefficient, integral coefficient and differential coefficient respectively, and τ is the time variable in the integral term.

4. The method for position control of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 3 is characterized in that: In step S30, the designed main fuzzy controller is: The input of the main fuzzy controller is error e, error change ec, and the output is Δk p , Δk i , Δk d , the domains of the input variables e and ec are both [-6,6], and the output variable Δk p , Δk i The domain of discourse is [-6,6], Δk d The domain of discourse is [-1,1], e, ec, Δk p , Δk i , Δk d Select the same fuzzy subset {NB, NM, NS, ZO, PS, PM, PB}, {NM, NS, ZO, PS, PM} all use triangular membership functions, NB uses the Z-type membership curve, and PB uses the S-type membership curve.

5. The method for controlling the position of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 4 is characterized in that: In step S40, the designed two-level fuzzy controller for generating the telescoping factor is: The input variables of the secondary fuzzy controller are the system error e and the error change rate ec, and the output variables are the expansion factors α and β. α is used to adjust the domain size of the input variables e and ec in the main fuzzy controller, and β is used to adjust the output variable Δk in the main fuzzy controller. p , Δk i , Δk d The size of the domain.

6. A magnetic drive transport line position control method based on variable universe fuzzy iterative learning according to claim 5, characterized in that: In step S50, the designed variable universe fuzzy iterative learning rate is: Among them, k p1 =k p +Δk p , k i1 =k i +Δk i , k d1 =k d +Δk d , Δk p , Δk i , Δk d is the correction value output by the main fuzzy controller, k p1 , k i1 , k d1 is the corrected iterative learning gain.

7. The method for controlling the position of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 6 is characterized in that: The iterative learning rate with the adaptive forgetting factor in step S60 is: Among them, λ(k) is the designed adaptive forgetting factor, which is expressed as: Where k is the number of iteration cycles.

8. The method for position control of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 7 is characterized in that: In step S70, the designed adaptive rate of the forgetting factor with smooth switching strategy is: Among them, S f is the smoothing factor, which changes with time and number of cycles, and its expression is: The adaptive rate when k=2 is redesigned by adding a slow-step strategy: Among them, λ(t,k) is the redesigned forgetting factor, t f is the slow-step factor designed in this paper, θ is the smoothing interval. When the smoothing factor of the second iteration cycle is greater than the smoothing interval, an exponential decay function is used for smooth transition. f The expression is: Among them, t w is the width of the step to be designed, and t w ≤θ. The adaptive rate of the forgetting factor after update is:

9. The method for controlling the position of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 8, characterized in that: In step S80, the designed variable universe fuzzy iterative learning control rate with improved adaptive forgetting factor is:

10. The method for controlling the position of a magnetically driven transport line based on variable universe fuzzy iterative learning according to claim 8, characterized in that: In step S80, the designed variable domain fuzzy iterative learning control strategy with an improved adaptive forgetting factor is applied to the position loop of the motor control system to complete the position control of the permanent magnet synchronous linear motor used in the magnetic drive transport line. The magnetic drive transport line adopts any combination of annular guide rails (100) and motor modules (200) and a programmable motor control system.

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