Temperature control methods, devices, equipment, and media for adaptive sliding mode observers

Abstract

This application provides a temperature control method, apparatus, device, and medium for an adaptive sliding mode observer. The method includes: first, acquiring a target temperature and an actual temperature; then, determining a feedforward compensation control quantity based on the target temperature and an unknown disturbance estimation term; next, determining a fuzzy proportional-integral feedback control quantity based on the target temperature, the actual temperature, a variable proportional gain, and a variable integral gain; finally, determining a laser power based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and controlling the solder paste temperature based on the laser power. This method can effectively eliminate the error between the solder joint temperature and the target temperature, improve the temperature control accuracy during the welding process, and can be widely applied in the field of welding control technology.

Description

Technical Field

[0001] This application relates to the field of welding control technology, and in particular to a temperature control method, device, equipment and medium for an adaptive sliding mode observer. Background Technology

[0002] Laser soldering is a method of efficient welding that uses a laser as a heat source. During the welding process, a high-energy-density laser beam is focused on the welding point to heat the welding materials, thereby melting and joining them. The welding material is usually a filler metal, which has a low melting point and can melt and wet the surfaces of the materials to be welded at a relatively low temperature.

[0003] Laser soldering technology, due to its high efficiency, precision, rapid heating, and localized heating capabilities, is commonly used for soldering tiny and delicate components. It can complete the soldering task without damaging surrounding materials, effectively addressing the increasing difficulty of fabricating and soldering microelectronic components, and meeting the demands of miniaturization, structural complexity, and functional integration in microelectronic devices. Temperature control during the laser soldering heating process is crucial to product quality; excessively high or low temperatures will severely affect the final solder joint quality. Therefore, employing effective temperature control methods to reduce or eliminate the error between the solder paste temperature and the target temperature is an indispensable step in the laser soldering process.

[0004] However, the most commonly used temperature control method in laser soldering heating is the well-tuned proportional-integral (PI) control method. But with the increasing miniaturization and complexity of microelectronic components, general laser soldering temperature control technology can no longer meet the requirements of current processes. In laser soldering, single-laser welding often suffers from rapid cooling, which leads to a lag in the output power control for periodically sampled temperatures.

[0005] In addition, temperature sampling is sensitive to disturbances and noise in the soldering process, and the thermodynamic parameters in the soldering process are highly time-varying. Summary of the Invention

[0006] The main objective of this application is to propose a temperature control method, apparatus, device, and medium for an adaptive sliding mode observer, which can effectively eliminate the error between the solder joint temperature and the target temperature.

[0007] To achieve the above objectives, one aspect of this application proposes a temperature control method for an adaptive sliding mode observer, the method comprising:

[0008] Obtain the target temperature and the actual temperature;

[0009] Based on the target temperature and the unknown disturbance estimation term, the feedforward compensation control quantity is determined; wherein, the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer;

[0010] Based on the target temperature, the actual temperature, the variable proportional gain, and the variable integral gain, the fuzzy proportional-integral feedback control quantity is determined; wherein, the variable proportional gain and the variable integral gain are obtained through a fuzzy proportional-integral algorithm.

[0011] The laser power is determined based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and the solder paste temperature is controlled based on the laser power.

[0012] In some embodiments, the expression for the feedforward compensation control quantity is:

[0013]

[0014] Among them, P obc T is the feedforward compensation control variable. d The target temperature is T0, the ambient temperature is L. OB For unknown disturbance estimation terms, α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, and h is the surface heat transfer coefficient;

[0015] The expression for the fuzzy proportional-integral feedback control quantity is:

[0016]

[0017] Among them, P fpi For fuzzy proportional-integral feedback control, e(t) is the difference between the actual temperature and the target temperature, i.e., e(t) = T. p (t)-T d (t), T p T represents the actual temperature. d For the target temperature, K p For variable scaling gain, K i This is the variable integral gain.

[0018] In some embodiments, the unknown perturbation estimate is obtained through an expression of an adaptive sliding mode observer, including the following steps:

[0019] Based on the Lyanov stability principle and the adaptive gain algorithm, and according to the error expression between the thermodynamic model temperature and the actual temperature and the sliding mode function, the expression of the adaptive sliding mode observer is determined.

[0020] The adaptive gain of the sliding surface is determined based on the relationship between the absolute values ​​of the boundary layer and the sliding mode variables.

[0021] The unknown disturbance estimation term is determined based on the error between the thermodynamic model temperature and the actual temperature, the adaptive gain of the sliding surface, the sliding variables, the surface area of ​​the solder paste, the surface heat transfer coefficient, the solder paste mass and the specific heat capacity of the solder paste, and the expression of the adaptive sliding observer.

[0022] The expression for the adaptive sliding mode observer is:

[0023]

[0024] Where, ∈≥γ,

[0025] Among them, L OB For the unknown disturbance estimate, c1 is the normal value, s is the sliding mode variable, and T is the variable value. e The error between the thermodynamic model temperature and the actual temperature, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, ∈ and ζ are positive constants, and γ is the maximum value of the system disturbance. Let sgn(s) be the adaptive gain of the sliding surface, sgn(s) be the sign function of s, |s| be the absolute value of the sliding variable, and η be a boundary layer.

[0026] In some embodiments, the error expression between the thermodynamic model temperature and the actual temperature is obtained through the following steps:

[0027] Construct the first mathematical relationship between laser power and thermodynamic model temperature, and the second mathematical relationship between laser power and actual temperature;

[0028] Based on the first mathematical relation and the second mathematical relation, determine the error expression between the thermodynamic model temperature and the actual temperature.

[0029] In some embodiments, determining the error expression between the thermodynamic model temperature and the actual temperature based on the first mathematical relation and the second mathematical relation includes the following steps:

[0030] Calculate the error between the thermodynamic model temperature in the first mathematical relation and the actual temperature in the second mathematical relation, and determine the error expression between the thermodynamic model temperature and the actual temperature.

[0031] The expression for the first mathematical relation is:

[0032]

[0033] Among them, T mWhere is the thermodynamic model temperature, t is time, α is the absorptivity of the solder paste to the laser, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, P is the laser power, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, T0 is the ambient temperature, and L is the laser power. OB For unknown disturbance estimation terms;

[0034] The second mathematical relation is:

[0035]

[0036] Among them, T p Let t be the actual temperature, P be the laser power, T0 be the ambient temperature, ω be the total disturbance during the laser welding process, |ω|≤γ, γ be a positive constant, and d(t) be the unknown disturbance. α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, Δa is the unknown error of a, and Δb is the unknown error of b.

[0037] The error expression between the thermodynamic model temperature and the actual temperature is as follows:

[0038]

[0039] Among them, T e T represents the error between the thermodynamic model temperature and the actual temperature. e =T m -T p T m T is the temperature in the thermodynamic model. p The actual temperature is L, t is time, and L is the actual temperature. OB Here, ω represents the total disturbance in the laser welding process, |ω|≤γ, and γ is a positive constant. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, and c is the specific heat capacity of the solder paste.

[0040] In some embodiments, the sliding mode function is obtained through the following steps:

[0041] Based on the order of the error expression between the thermodynamic model temperature and the actual temperature, and the error between the thermodynamic model temperature and the actual temperature, determine the corresponding sliding mode function;

[0042] The expression for the sliding mode function is as follows:

[0043]

[0044] Where s is the sliding mode variable, c1 is a positive constant, and T e This represents the error between the temperature in the thermodynamic model and the actual temperature.

[0045] In some embodiments, the variable scaling gain and variable integral gain are obtained through a fuzzy scaling-integral algorithm, including the following steps:

[0046] The first adjustment amount and the second adjustment amount of the proportional-integral controller gain are determined by the fuzzy proportional-integral algorithm.

[0047] The variable proportional gain is obtained based on the first adjustment amount and the first initial gain of the proportional-integral controller;

[0048] The variable integral gain is obtained based on the second adjustment amount and the second initial gain of the proportional-integral controller;

[0049] The expressions for the variable proportional gain and the variable integral gain are as follows:

[0050]

[0051] Among them, K p For variable scaling gain, K i For variable integral gain, K p * K is the first initial gain of the proportional-integral controller. i * ΔK is the second initial gain of the proportional-integral controller. p ΔK is the first adjustment amount for the gain of the proportional-integral controller. i This is the second adjustment amount for the gain of the proportional-integral controller.

[0052] To achieve the above objectives, another aspect of this application provides a temperature control device for an adaptive sliding mode observer, the device comprising:

[0053] The first module is used to obtain the target temperature and the actual temperature;

[0054] The second module is used to determine the feedforward compensation control quantity based on the target temperature and the unknown disturbance estimation term; wherein the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer;

[0055] The third module is used to determine the fuzzy proportional-integral feedback control quantity based on the target temperature, the actual temperature, the variable proportional gain, and the variable integral gain; wherein the variable proportional gain and the variable integral gain are obtained through the fuzzy proportional-integral principle.

[0056] The fourth module is used to determine the laser power based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and to control the solder paste temperature based on the laser power.

[0057] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0058] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0059] The embodiments of this application include at least the following beneficial effects: This application provides a temperature control method, apparatus, device, and medium using an adaptive sliding mode observer. This scheme determines the feedforward compensation control quantity through an adaptive sliding mode observer, which can improve the accuracy of the original thermodynamic model and the control effect of feedforward compensation. Compared with the original well-tuned proportional-integral control algorithm, it has higher response speed and control accuracy. In addition, by determining the fuzzy proportional-integral feedback control quantity based on the fuzzy proportional-integral control algorithm, the error between the actual temperature and the target temperature in the measurement can be further reduced, and the accuracy of temperature control can be improved. It also has strong robustness to residual disturbances and uncertainties existing in the welding process. Attached Figure Description

[0060] Figure 1 This is a flowchart of the temperature control method for the adaptive sliding mode observer provided in the embodiments of this application;

[0061] Figure 2 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application;

[0062] Figure 3 This is a structural schematic diagram of a laser soldering equipment;

[0063] Figure 4 This is a membership function graph of fuzzification and defuzzification provided in the embodiments of this application;

[0064] Figure 5 The pad radius provided in this embodiment is 2×10 -3 A comparative diagram showing the experimental temperature curves at a laser power of 8.2W, the temperature simulation curves of the thermodynamic model with an adaptive sliding mode observer, and the temperature simulation curves of the thermodynamic model without an adaptive sliding mode observer.

[0065] Figure 6 The pad radius provided in this embodiment is 1.5 × 10. -3 A schematic diagram showing the temperature error comparison curves between the temperature control method of the adaptive sliding mode observer provided in this application embodiment and the traditional well-tuned proportional-integral control method at m;

[0066] Figure 7 The pad radius provided in this embodiment is 2×10 -3 A schematic diagram showing the temperature error comparison curves between the temperature control method of the adaptive sliding mode observer provided in this application embodiment and the traditional well-tuned proportional-integral control method at m;

[0067] Figure 8 The pad radius provided in this embodiment is 2.5 × 10. -3 A schematic diagram showing the temperature error comparison curves between the temperature control method of the adaptive sliding mode observer provided in this application embodiment and the traditional well-tuned proportional-integral control method at m;

[0068] Figure 9 This is a schematic diagram of the error simulation curve obtained by applying Gaussian white noise based on the temperature control method of the adaptive sliding mode observer provided in the embodiments of this application. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0070] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0072] Please see Figure 1 , Figure 1This is an optional flowchart of the temperature control method for the adaptive sliding mode observer provided in the embodiments of this application. The method of the embodiments of this application can be applied to laser soldering equipment. Figure 1 The temperature control method for the adaptive sliding mode observer includes, but is not limited to, steps S101 to S104.

[0073] Step S101: Obtain the target temperature and the actual temperature.

[0074] Step S102: Determine the feedforward compensation control quantity based on the target temperature and the unknown disturbance estimation term; wherein, the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer.

[0075] Step S103: Determine the fuzzy proportional-integral feedback control quantity based on the target temperature, actual temperature, variable proportional gain, and variable integral gain; wherein, the variable proportional gain and variable integral gain are obtained through the fuzzy proportional-integral algorithm.

[0076] Step S104: Determine the laser power based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and control the solder paste temperature based on the laser power.

[0077] In some embodiments, step S101, the target temperature is determined based on the physical properties of the welding material, practical engineering experience, and the requirements of different stages in the welding heating process. These physical properties include, but are not limited to, the specific heat capacity, thermal conductivity, and melting point of the welding material.

[0078] For example, when the melting point of the welding material is known, the target temperature during the preheating stage can be set to the melting point temperature of the welding material. Once the welding material reaches its melting point, the laser soldering heating process enters the activation stage, at which point the phase of the welding material begins to change. Based on practical engineering experience, the temperature continues to rise during this stage, but relatively slowly. Therefore, the target temperature can be set to the temperature at which the welding material completely melts. After the welding material reaches the temperature at which it completely melts, the laser soldering heating process enters the final holding stage. At this point, the target temperature needs to be determined based on practical engineering experience as the temperature at which the weld joint forms.

[0079] Specifically, the heating time for each stage is determined by factors including, but not limited to, the thermal conductivity, mass, and surface area of ​​the welding materials. Generally, the better the thermal conductivity of the welding materials, the shorter the required heating time; while the greater the mass and surface area of ​​the welding materials, the longer the required heating time. The specific heating time needs to be determined in advance through open-loop experiments under preset engineering conditions.

[0080] In some embodiments, step S102, the expression for the feedforward compensation control quantity is:

[0081]

[0082] Among them, P obc T is the feedforward compensation control variable. d The target temperature is T0, the ambient temperature is L. OB For unknown disturbance estimation terms, α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, and h is the surface heat transfer coefficient.

[0083] Furthermore, the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer, which may include, but is not limited to, steps S201 to S203:

[0084] Step S201: Based on the Lyanov stability principle and the adaptive gain algorithm, and according to the error expression between the thermodynamic model temperature and the actual temperature and the sliding mode function, determine the expression of the adaptive sliding mode observer.

[0085] Step S202: Determine the adaptive gain of the sliding surface based on the relationship between the absolute values ​​of the boundary layer and the sliding mode variables.

[0086] Step S203: Based on the error between the thermodynamic model temperature and the actual temperature, the adaptive gain of the sliding surface, the sliding variables, the surface area of ​​the solder paste, the surface heat transfer coefficient, the solder paste mass and the specific heat capacity of the solder paste, and the expression combined with the adaptive sliding observer, determine the unknown disturbance estimation term.

[0087] In some embodiments, step S201, the error expression between the thermodynamic model temperature and the actual temperature is obtained through the following steps, which may include, but are not limited to, steps S301 to S302:

[0088] Step S301: Construct the first mathematical relationship between laser power and thermodynamic model temperature, and the second mathematical relationship between laser power and actual temperature.

[0089] Step S302: Determine the error expression between the thermodynamic model temperature and the actual temperature based on the first mathematical relation and the second mathematical relation.

[0090] In some embodiments, prior to step S301, the temperature control method for the adaptive sliding mode observer may include, but is not limited to, the following steps:

[0091] A thermodynamic model is established using laser power as input and thermodynamic model temperature as output; the thermodynamic model is used to describe the heating process of laser soldering.

[0092] By introducing an unknown perturbation estimation term into the thermodynamic model, the first mathematical relationship between laser power and thermodynamic model temperature is obtained.

[0093] The thermodynamic model is expressed as follows:

[0094]

[0095] Among them, T m α is the thermodynamic model temperature, t is time, α is the absorption rate of the solder paste to the laser, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, P is the laser power, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, and T0 is the ambient temperature.

[0096] In some embodiments, in step S301, the expression of the first mathematical relation is:

[0097]

[0098] Among them, T m Where is the thermodynamic model temperature, t is time, α is the absorptivity of the solder paste to the laser, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, P is the laser power, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, T0 is the ambient temperature, and L is the laser power. OB For unknown disturbance estimation terms;

[0099] The second mathematical relation is:

[0100]

[0101] Among them, T p Let t be the actual temperature, P be the laser power, T0 be the ambient temperature, ω be the total disturbance during the laser welding process, |ω|≤γ, γ be a positive constant, and d(t) be the unknown disturbance. α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, Δa is the unknown error of a, and Δb is the unknown error of b.

[0102] In some embodiments, step S302 involves calculating the error between the thermodynamic model temperature in the first mathematical relationship and the actual temperature in the second mathematical relationship, thereby determining the expression for the error between the thermodynamic model temperature and the actual temperature, as follows:

[0103] The expression for the error between the thermodynamic model temperature and the actual temperature is:

[0104]

[0105] Among them, T e T represents the error between the thermodynamic model temperature and the actual temperature. e =T m -T p T mT is the temperature in the thermodynamic model. p The actual temperature is L, t is time, and L is the actual temperature. OB Here, ω represents the total disturbance in the laser welding process, |ω|≤γ, and γ is a positive constant. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, and c is the specific heat capacity of the solder paste.

[0106] Furthermore, in some embodiments, step S201, the sliding mode function is obtained through the following steps:

[0107] Based on the order of the error expression between the thermodynamic model temperature and the actual temperature, and the error between the thermodynamic model temperature and the actual temperature, determine the corresponding sliding mode function;

[0108] The expression for the sliding mode function is:

[0109]

[0110] Where s is the sliding mode variable, c1 is a positive constant, and T e This represents the error between the temperature in the thermodynamic model and the actual temperature.

[0111] In some embodiments, in steps S202 to S203, the unknown disturbance estimation term is determined by an expression of an adaptive sliding mode observer, wherein the expression of the adaptive sliding mode observer is:

[0112]

[0113] Where, ∈≥γ,

[0114] Among them, L OB For the unknown disturbance estimate, c1 is the normal value, s is the sliding mode variable, and T is the variable value. e The error between the thermodynamic model temperature and the actual temperature, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, ∈ and ζ are positive constants, and γ is the maximum value of the system disturbance. Let sgn(s) be the adaptive gain of the sliding surface, sgn(s) be the sign function of s, |s| be the absolute value of the sliding variable, and η be a boundary layer.

[0115] In steps S201 to S203 of the embodiments of this application, the expression of the adaptive sliding mode observer conforms to the Lyapunov stability principle, and the verification process is as follows:

[0116] Take the Lyapunov function:

[0117]

[0118] in, k * for The optimal value of (adaptive gain of the sliding surface), i.e. Adaptive gain for sliding surfaces With the optimal value k * The difference between them, where V is a positive definite function.

[0119] Differentiating formula (8) and substituting formulas (5), (6), and (7) into the differentiated formula (8), we get:

[0120]

[0121]

[0122] Right now

[0123] From formula (9), we can see that When the unknown disturbance estimate term L OB When it is formula (7), if let Then s≡0 must be true, that is Since it is a negative definite function, formula (7) conforms to the Lyapunov stability principle.

[0124] According to Lyapunov's stability principle, the closed-loop system asymptotically converges, that is, when t→∞, as shown by formula (6), s→0, T e →0. That is, the adaptive sliding mode observer of this invention can significantly improve the accuracy of the thermodynamic model and reduce the error between the thermodynamic model temperature and the actual temperature (see details). Figure 5 , Figure 5 The model curves with an observer and the experimental curves have a higher degree of overlap compared to the original model curves and the experimental curves without an observer. This means that the accuracy of the thermodynamic model can be significantly improved by using an observer, thereby reducing the error between the model and the experimental curves.

[0125] In some embodiments, in step S103, since the actual disturbance cannot be fully estimated by the adaptive sliding mode observer, the embodiments of the present invention employ a fuzzy proportional-integral algorithm to reduce the residual disturbance that is not estimated by the adaptive sliding mode observer, and further obtain the fuzzy proportional-integral feedback control quantity according to the fuzzy proportional-integral algorithm, which can effectively and quickly eliminate the temperature error in the laser soldering process.

[0126] Specifically, the expression for the fuzzy proportional-integral feedback control quantity is:

[0127]

[0128] Among them, P fpiFor fuzzy proportional-integral feedback control, e(t) is the difference between the actual temperature and the target temperature, i.e., e(t) = T. p (t)-T d (t), T p T represents the actual temperature. d For the target temperature, K p For variable scaling gain, K i This is the variable integral gain.

[0129] Furthermore, the variable proportional gain and the variable integral gain are obtained through a fuzzy proportional-integral algorithm, which may include, but is not limited to, steps S401 to S403:

[0130] Step S401: Determine the first adjustment amount and the second adjustment amount of the proportional-integral controller gain using the fuzzy proportional-integral algorithm.

[0131] Step S402: Obtain the variable proportional gain based on the first adjustment amount and the first initial gain of the proportional-integral controller.

[0132] Step S403: Obtain the variable integral gain based on the second adjustment amount and the second initial gain of the proportional-integral controller.

[0133] The expressions for the variable proportional gain and the variable integral gain are as follows:

[0134]

[0135] Among them, K p For variable scaling gain, K i For variable integral gain, K p * K is the first initial gain of the proportional-integral controller. i * ΔK is the second initial gain of the proportional-integral controller. p ΔK is the first adjustment amount for the gain of the proportional-integral controller. i This is the second adjustment amount for the gain of the proportional-integral controller.

[0136] In some embodiments, in step S401, the fuzzy proportional-integral controller uses the error e and its derivative. As input. Let the actual domain of the fuzzy proportional-integral controller input be: n im =[-n a n a The input fuzzy domain is: m im =[-m a m a ]. Where, n im Represents e or m imThis represents the normalized temperature error E or the rate of change EC of the normalized temperature error. Specifically, n im and m im The relationship between them is as follows:

[0137]

[0138] Where, k in It is k e or k ec , respectively representing mapping e to E and mapping E to E. The input quantization factor mapped to EC. The actual domain of the fuzzy proportional-integral controller output, i.e., the first adjustment of the proportional-integral controller gain and the second adjustment of the proportional-integral controller gain, i.e., ΔK. p and ΔK i Let W and Z be the corresponding fuzzy domain values, respectively. The relationship between the output actual domain and fuzzy domain is as follows:

[0139]

[0140] Where, k Δkp This represents mapping W to ΔK. p The output scaling factor, k Δki This represents mapping Z to ΔK. i The output scaling factor, ΔK p ΔK is the first adjustment amount for the gain of the proportional-integral controller. i W is the second adjustment amount of the proportional-integral controller gain, where W is ΔK. p The corresponding fuzzy threshold value, Z, is ΔK. i The corresponding fuzzy domain value.

[0141] Please refer to the membership functions for the blurred and deblurred interfaces. Figure 4 Let the fuzzy domain intervals of E, EC, W, and Z be the same, defined as: Un = [-1, -0.5, 0, 0.5, 1]. The fuzzy subsets are: [NM, NS, ZO, PS, PM], representing negative middle, negative small, zero, positive small, and positive middle, respectively.

[0142] Based on the tuning experience and rules of proportional-integral controllers, ΔK p and ΔK i The fuzzy control rules for the corresponding fuzzy domains are shown in Table 1 and Table 2, respectively:

[0143] Table 1: ΔK p Fuzzy control rule table corresponding to the fuzzy domain

[0144]

[0145] Table 2: ΔK i Fuzzy control rule table corresponding to the fuzzy domain

[0146]

[0147] This invention employs the centroid method as a deblurring method, combined with formula (13), ΔK p and ΔK i The expressions for can be shown in formulas (14) and (15) respectively:

[0148]

[0149]

[0150] Where ΔK p ΔK is the first adjustment amount for the gain of the proportional-integral controller. i is the second adjustment of the proportional-integral controller gain, j is the number of quantization levels in the output, and μ w (W n ) for W n The fuzzy membership function value, μ z (Z n ) is Z n The fuzzy membership function value, k Δkp This represents mapping W to ΔK. p The output scaling factor, k Δki This represents mapping Z to ΔK. i The output scaling factor, W, is ΔK. p The corresponding fuzzy threshold value, Z, is ΔK. i The corresponding fuzzy domain value.

[0151] In some embodiments, in steps S402 to S403, as can be seen from formula (11), the variable proportional gain can be obtained by adding the first initial gain of the proportional-integral controller and the first adjustment amount of the proportional-integral controller gain; the variable integral gain can be obtained by adding the second initial gain of the proportional-integral controller and the second adjustment amount of the proportional-integral controller gain.

[0152] Steps S401 to S403 as shown in the embodiments of this application obtain the first adjustment amount and the second adjustment amount of the proportional-integral controller gain through the fuzzy proportional-integral algorithm, and further obtain the variable proportional gain and the variable integral gain, laying the foundation for obtaining the fuzzy proportional-integral feedback control quantity, and reducing the residual disturbances that are not estimated by the adaptive sliding mode observer.

[0153] In some embodiments, step S104, based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, the overall control calculation formula can be obtained, that is, the overall control calculation formula is:

[0154] P = Pobc +P fpi (16)

[0155] Where P is the laser power, P obc P is the feedforward compensation control quantity (obtained according to formula (1)). fpi It is the fuzzy proportional-integral feedback control quantity (obtained according to formula (10)).

[0156] Steps S101 to S104, as illustrated in the embodiments of this application, determine the feedforward compensation control quantity through an adaptive sliding mode observer. This improves the accuracy of the original thermodynamic model and the control effect of the feedforward compensation, exhibiting higher response speed and control accuracy compared to the original well-tuned proportional-integral control algorithm. Furthermore, by determining the fuzzy proportional-integral feedback control quantity based on the fuzzy proportional-integral control algorithm, the error between the actual temperature and the target temperature during measurement can be further reduced, demonstrating strong robustness.

[0157] Furthermore, the application scenarios and beneficial effects of the methods of the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0158] Please see Figure 3 The temperature control method of the adaptive sliding mode observer in this embodiment of the invention can be applied to... Figure 3 The laser soldering equipment includes a laser emitter 1000, an optical focusing system 1001, an infrared temperature measurement system 1003, an error detection system (not marked in the figure), a control system 1004, and a power supply 1002.

[0159] Specifically, the workflow of laser soldering equipment is as follows:

[0160] First, the worktable 1006 is controlled by the motor controller in the control system to move the solder joint directly below the laser emitter. Then, the defocusing amount of the laser emitter is adjusted by the optical focusing system so that the laser beam emitted by the laser emitter is focused on the solder joint of the workpiece 1005. Next, the actual temperature of the solder joint is periodically measured by the infrared temperature measurement system. Then, the error detection system determines whether there is an error between the actual temperature and the target temperature of the solder joint in the current cycle. If so, the control system outputs the laser power based on the error, combined with the actual temperature and the target temperature of the solder joint in the current cycle, and through the total control calculation formula (i.e., formula (16)) in the temperature control method of adaptive sliding mode observation in this embodiment of the invention. Finally, the laser emitter is driven to perform laser heating according to the laser power, and the actual temperature of the solder joint in the new cycle is measured until the error between the actual temperature and the target temperature of the solder joint is eliminated to ensure that the solder paste reaches the target temperature.

[0161] Please see Figures 6-8 , Figures 6-8The error curve is obtained by subtracting the actual temperature from the target temperature under the control method. In the figure, "the control method" refers to the temperature control method of the adaptive sliding mode observer provided in this embodiment of the invention; "the well-tuned PI control method" refers to the conventional well-tuned proportional-integral control method; ±1℃ is only used as the temperature boundary line in the figure.

[0162] Depend on Figures 6-8 As can be seen, the overall trend of the curves corresponding to "the control method" is close to 0, indicating that the temperature control method of the adaptive sliding mode observer provided by the embodiment of the present invention has a better temperature control effect than the traditional proportional-integral control method after good tuning. In addition, the temperature control method of the adaptive sliding mode observer in the embodiment of the present invention still has a good temperature control effect when the pad radius is different, indicating that the embodiment of the present invention has strong applicability and can be used in different parameter environments.

[0163] Please see Figure 9 , Figure 9 The error simulation curve is obtained by applying Gaussian white noise based on the temperature control method of the adaptive sliding mode observer provided in the embodiments of this application. Figure 9 It can be seen that after applying Gaussian white noise, the temperature error is within ±5℃ (the actual engineering error range requirement is ±5℃). This indicates that under strong interference, the temperature control method of the adaptive sliding mode observer provided in this application embodiment can still ensure that the temperature error is within the range required in the actual engineering. In other words, the temperature control method of the adaptive sliding mode observer provided in this application embodiment has a certain robustness.

[0164] This application embodiment also provides a temperature control device for an adaptive sliding mode observer, which can implement the above method. The device includes:

[0165] The first module is used to obtain the target temperature and the actual temperature;

[0166] The second module is used to determine the feedforward compensation control quantity based on the target temperature and the unknown disturbance estimation term; wherein, the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer;

[0167] The third module is used to determine the fuzzy proportional-integral feedback control quantity based on the target temperature, the actual temperature, the variable proportional gain, and the variable integral gain; wherein, the variable proportional gain and the variable integral gain are obtained through the fuzzy proportional-integral principle.

[0168] The fourth module is used to determine the laser power based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and to control the solder paste temperature based on the laser power.

[0169] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0170] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the temperature control method of the adaptive sliding mode observer described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0171] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0172] Please see Figure 2 , Figure 2 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0173] The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0174] The memory 502 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called and executed by the processor 501 to execute the temperature control method of the adaptive sliding mode observer in the embodiments of this application.

[0175] The input / output interface 503 is used to implement information input and output;

[0176] The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0177] Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504);

[0178] The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0179] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the temperature control method of the adaptive sliding mode observer described above.

[0180] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0181] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0182] The temperature control method, device, electronic equipment, and storage medium for the adaptive sliding mode observer provided in this application, based on a thermodynamic model, introduce an estimation term for unknown disturbances to further calculate the error expression between the thermodynamic model temperature and the actual temperature and the corresponding sliding mode function. Based on the Lyapunov stability principle and adaptive gain algorithm, the adaptive sliding mode observer is determined. Simultaneously, the feedforward compensation control quantity is determined based on the adaptive sliding mode observer, thereby improving the accuracy of the original thermodynamic model and the control effect of the feedforward compensation. Compared with the original well-tuned proportional-integral control algorithm, it has higher response speed and control accuracy. Furthermore, this application embodiment also determines the fuzzy proportional-integral feedback control quantity based on the fuzzy proportional-integral control algorithm, which can further reduce the error between the actual temperature and the target temperature in the measurement, improve the accuracy of temperature control, and has strong robustness to residual disturbances and uncertainties existing in the welding process.

[0183] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0184] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0185] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0186] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0187] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A temperature control method for an adaptive sliding mode observer, characterized in that, The method includes the following steps: Obtain the target temperature and the actual temperature; Based on the target temperature and the unknown disturbance estimation term, the feedforward compensation control quantity is determined; wherein, the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer; Based on the target temperature, the actual temperature, the variable proportional gain, and the variable integral gain, the fuzzy proportional-integral feedback control quantity is determined; wherein, the variable proportional gain and the variable integral gain are obtained through a fuzzy proportional-integral algorithm. The laser power is determined based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and the solder paste temperature is controlled based on the laser power.

2. The method according to claim 1, characterized in that, The expression for the feedforward compensation control quantity is: Among them, P obc T is the feedforward compensation control variable. d The target temperature is T0, the ambient temperature is L. OB For unknown disturbance estimation terms, α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, and h is the surface heat transfer coefficient; The expression for the fuzzy proportional-integral feedback control quantity is: Among them, P fpi For fuzzy proportional-integral feedback control, e is the difference between the actual temperature and the target temperature, i.e., e(t) = T. p (t)-T d (t), T p T represents the actual temperature. d For the target temperature, K p For variable scaling gain, K i This is the variable integral gain.

3. The method according to claim 1, characterized in that, The unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer, including the following steps: Based on the Lyanov stability principle and the adaptive gain algorithm, and according to the error expression between the thermodynamic model temperature and the actual temperature and the sliding mode function, the expression of the adaptive sliding mode observer is determined. The adaptive gain of the sliding surface is determined based on the relationship between the absolute values ​​of the boundary layer and the sliding mode variables. The unknown disturbance estimation term is determined based on the error between the thermodynamic model temperature and the actual temperature, the adaptive gain of the sliding surface, the sliding variables, the surface area of ​​the solder paste, the surface heat transfer coefficient, the solder paste mass and the specific heat capacity of the solder paste, and the expression of the adaptive sliding observer. The expression for the adaptive sliding mode observer is: in, Among them, L OB For the unknown disturbance estimate, c1 is the normal value, s is the sliding mode variable, and T is the variable value. e The error between the thermodynamic model temperature and the actual temperature, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, ∈ and ζ are positive constants, and γ is the maximum value of the system disturbance. Let sgn(s) be the adaptive gain of the sliding surface, sgn(s) be the sign function of s, |s| be the absolute value of the sliding variable, and η be a boundary layer.

4. The method according to claim 3, characterized in that, The error expression between the thermodynamic model temperature and the actual temperature is obtained through the following steps: Construct the first mathematical relationship between laser power and thermodynamic model temperature, and the second mathematical relationship between laser power and actual temperature; Based on the first mathematical relation and the second mathematical relation, determine the error expression between the thermodynamic model temperature and the actual temperature.

5. The method according to claim 4, characterized in that, The step of determining the error expression between the thermodynamic model temperature and the actual temperature based on the first mathematical relation and the second mathematical relation includes the following steps: Calculate the error between the thermodynamic model temperature in the first mathematical relation and the actual temperature in the second mathematical relation, and determine the error expression between the thermodynamic model temperature and the actual temperature. The expression for the first mathematical relation is: Among them, T m Where is the thermodynamic model temperature, t is time, α is the absorptivity of the solder paste to the laser, m is the mass of the solder paste, c is the specific heat capacity of the solder paste, P is the laser power, A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, T0 is the ambient temperature, and L is the laser power. oB For unknown disturbance estimation terms; The second mathematical relation is: Among them, T p Let t be the actual temperature, P be the laser power, T0 be the ambient temperature, ω be the total disturbance during the laser welding process, |ω|≤γ, γ be a positive constant, and d(t) be the unknown disturbance. α represents the laser absorptivity of the solder paste, m represents the mass of the solder paste, and c represents the specific heat capacity of the solder paste. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, Δa is the unknown error of a, and Δb is the unknown error of b. The error expression between the thermodynamic model temperature and the actual temperature is as follows: Among them, T e T represents the error between the thermodynamic model temperature and the actual temperature. e =T m -T p T m T is the temperature in the thermodynamic model. p The actual temperature is L, t is time, and L is the actual temperature. OB Here, ω represents the total disturbance in the laser welding process, |ω|≤γ, and γ is a positive constant. A is the surface area of ​​the solder paste, h is the surface heat transfer coefficient, and c is the specific heat capacity of the solder paste.

6. The method according to claim 3, characterized in that, The sliding mode function is obtained through the following steps: Based on the order of the error expression between the thermodynamic model temperature and the actual temperature, and the error between the thermodynamic model temperature and the actual temperature, determine the corresponding sliding mode function; The expression for the sliding mode function is as follows: Where s is the sliding mode variable, c1 is a positive constant, and T e This represents the error between the temperature in the thermodynamic model and the actual temperature.

7. The method according to claim 1, characterized in that, The variable proportional gain and variable integral gain are obtained through a fuzzy proportional-integral algorithm, including the following steps: The first adjustment amount and the second adjustment amount of the proportional-integral controller gain are determined by the fuzzy proportional-integral algorithm. The variable proportional gain is obtained based on the first adjustment amount and the first initial gain of the proportional-integral controller; The variable integral gain is obtained based on the second adjustment amount and the second initial gain of the proportional-integral controller; The expressions for the variable proportional gain and the variable integral gain are as follows: Among them, K p For variable scaling gain, K i For variable integral gain, K p * K is the first initial gain of the proportional-integral controller. i * ΔK is the second initial gain of the proportional-integral controller. p ΔK is the first adjustment amount for the gain of the proportional-integral controller. i This is the second adjustment amount for the gain of the proportional-integral controller.

8. A temperature control device for an adaptive sliding mode observer, characterized in that, The device includes: The first module is used to obtain the target temperature and the actual temperature; The second module is used to determine the feedforward compensation control quantity based on the target temperature and the unknown disturbance estimation term; wherein the unknown disturbance estimation term is obtained through the expression of the adaptive sliding mode observer; The third module is used to determine the fuzzy proportional-integral feedback control quantity based on the target temperature, the actual temperature, the variable proportional gain, and the variable integral gain; wherein the variable proportional gain and the variable integral gain are obtained through the fuzzy proportional-integral principle. The fourth module is used to determine the laser power based on the feedforward compensation control quantity and the fuzzy proportional-integral feedback control quantity, and to control the solder paste temperature based on the laser power.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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