Model parameter identification method for hysteresis characteristic of giant magnetostrictive material

Abstract

The invention discloses a model parameter identification method for hysteresis characteristics of a giant magnetostrictive material, and the method comprises the steps: describing the hysteresis characteristics through employing a nonlinear Jiles-Atherton model, and obtaining a hysteresis loop of the magnetostrictive material through constructing a mathematical model; the method comprises the following steps: performing parameter identification on hysteresis characteristics of a giant magnetostrictive material by using a longitudinal and transverse crossing strategy-based improved north pole sea-sparrow optimization algorithm, and improving the north pole sea-sparrow optimization algorithm by using a longitudinal and transverse crossing strategy to improve global optimization and local optimization capabilities of the algorithm; the model parameter identification method provided by the invention is simple in design and high in iteration efficiency, can replace a large number of manual parameter setting tests, can quickly obtain the optimal solution of the hysteresis model, and provides theoretical guidance for engineering application of the giant magnetostrictive material.

Description

Technical Field

[0001] The invention relates to the technical field of testing systems, and in particular to a model parameter identification method for hysteresis characteristics of giant magnetostrictive materials. Background Art

[0002] Giant magnetostrictive materials are a new type of functional material, among which the most representative ones are iron-gallium alloy and terbium-dysprosium-iron alloy. Giant magnetostrictive materials can produce large deformations under the action of a magnetic field, and can convert electromagnetic energy into mechanical energy or acoustic energy. They are widely used in electroacoustic transducers, hydroacoustic transducers, intelligent robots, sensors, marine exploration, and automation. However, the current research on the hysteresis characteristics of magnetostrictive materials is incomplete and there are few theoretical descriptions, so it is impossible to accurately identify the parameters of their hysteresis model. Jia Zhenyuan established a hysteresis model of a giant magnetostrictive actuator based on the Priesach theory, but due to the large number of parameters in the Priesach theory model, it is difficult to identify its accurate model parameters. Weng Ling introduced a mathematical model of magnetic energy loss under a small hysteresis loop and measured the high-frequency hysteresis loop of magnetostrictive materials, but the physical meaning of the model is unclear. Jiles and Atherton proposed a nonlinear hysteresis model with fewer parameters and clear physical meaning. This hysteresis model has good applications in the field of permanent magnet modeling. However, there is no corresponding theoretical research on how to apply the Jiles-Atherton model to giant magnetostrictive materials and use the improved Arctic Puffin optimization algorithm based on the crisscross strategy for fast and accurate parameter identification. Summary of the invention

[0003] The purpose of the present invention is to provide a method for identifying model parameters of hysteresis characteristics of giant magnetostrictive materials.

[0004] To achieve the above object, the technical solution of the present invention is as follows: A method for identifying model parameters of hysteresis characteristics of giant magnetostrictive materials comprises the following steps: Step 1: Set the input current to obtain the magnetic field strength H， Get the actual measured hysteresis curve of giant magnetostrictive material ； Step 2: Mathematically describe the Jiles-Atherton model and determine five parameters to be determined: saturation magnetization M s , shape parameters of the hysteresis-free magnetization curve , containment coefficient k , reversible susceptibility c , magnetic domain internal coupling parameters The physical meaning of the Jiles-Atherton type parameter identification model is constructed: In Simulink, the identification model of Jiles-Atherton type parameters is established according to the mathematical description of Jiles-Atherton model; Step 3, adjusting the five undetermined parameters by using the improved Arctic Puffin optimization algorithm based on the crisscross strategy until the predicted hysteresis curve output by the Jiles-Atherton type parameter identification model is closest to the actual measured hysteresis curve, that is, obtaining the optimized Jiles-Atherton type parameter identification model; The improved Arctic puffin optimization algorithm based on the crisscross strategy adopts the behavior conversion factor K beh , used to balance the global search stage and the local development stage; the global search stage includes two strategies: aerial search and diving predation. The aerial search strategy is used by the Arctic puffins to find potential food while keeping vigilant against nearby natural enemies. The diving predation strategy is used by the Arctic puffins to quickly change the flight direction to speed up food capture and avoid competition from other predators; the local development stage includes three strategies: cooperation mechanism, enhanced search, and avoidance of natural enemies. The cooperation mechanism is used for collective foraging to improve the efficiency and success rate of predation. The enhanced search is used for the Arctic puffins to flexibly adapt to the distribution of food in the environment to improve adaptability and optimization ability. Avoidance of natural enemies is used to warn other individuals to leave the dangerous area to jump out of the local optimum; Step 4: The magnetic field strength of the giant magnetostrictive material to be tested H The hysteresis curve of the giant magnetostrictive material to be predicted is obtained by inputting the optimized Jiles-Atherton type parameter identification model.

[0005] Further improvement, in step 1, the magnetic field strength H Including magnetic field strength H The amplitude, frequency, phase and size of the bias magnetic field.

[0006] As a further improvement, in step 2, the mathematical description of the Jiles-Atherton model is as follows: The magnetic induction intensity It is expressed as: (1) in, is the absolute magnetic permeability of vacuum; is the magnetic field strength; is the magnetization intensity; Magnetization It is expressed as: (2) in, is the hysteresis-free magnetization; is the direction coefficient, when the magnetic field strength When increasing, ,when constant, ,when When decreasing, ; is the loss coefficient; is the irreversible magnetization component; is the effective magnetic field strength; represents the derivation; Irreversible magnetization component The expression is: (3) in, is the reversible magnetic susceptibility; Effective magnetic field strength The expression is: (4) in, is the internal coupling parameter of magnetic domains; Hysteresis-free magnetization The expression is: (5) in, is the saturation magnetization; is the shape parameter of the hysteresis-free magnetization curve.

[0007] As a further improvement, in step 3, when the objective function is minimum, it is determined that the predicted hysteresis curve is closest to the actual measured hysteresis curve; wherein the objective function is as follows:

[0008] in, is the objective function; is the actual measured magnetic induction intensity B The value of is the magnetic induction intensity obtained by CSAPO identification The value of is the number of data points.

[0009] As a further improvement, in step 3, the objective function is optimized by using the CSAPO algorithm based on the vertical and horizontal cross strategy to minimize the objective function. The specific method is as follows: according to the upper and lower limits of the search space, the population individuals of CSAPO are initialized, that is:

[0010] in, ; Represents the current iteration number; Represents the number of individuals in a population; Indicates Arctic Puffin it The position of the iteration; is a random number between 0 and 1; and Respectively represent the upper and lower bounds of the parameter value; In order to balance CSAPO's global search and local development capabilities, a behavior conversion factor is introduced ,Right now:

[0011] in, Represents the number of iterations; express; CSAPO's aerial search strategy is expressed as:

[0012] in, New populations generated for aerial search strategies; 1 to Random numbers between excluding ; N Represents the number of individuals in a population; represents a candidate solution randomly selected from the population; Represents the random number generated by flying through "Levi"; is the population dimension; is a random number that follows a normal distribution; () means that the value in brackets is rounded off.

[0013] The diving predation strategy is expressed as:

[0014] in, new populations generated for the dive-hunting strategy; is the velocity coefficient, used to adjust the displacement; In order to obtain the best solution for the aerial search and diving predation strategies, the aerial search strategy and the diving predation strategy are combined to merge the candidate solutions into a new population, and the top solutions are selected by sorting the fitness. N Arctic puffins form a new population, and the specific relationship is as follows:

[0015] in, for the combined population; is the new population after sorting; for The front A new population formed by 100 Arctic Puffin individuals; () means to arrange the elements in the matrix in ascending order; Arctic puffins use a cooperative mechanism to collaborate in predation, which improves the efficiency and success rate of predation. The position update description is:

[0016] in, is the updated position; It is a cooperative factor that regulates the predation behavior of Arctic puffins; For the exception In addition, 1 to A random number between , , is a candidate solution randomly selected from the current population; and satisfies ; As predation progresses, it enters the intensive search phase, and the position update is described as:

[0017] in, To adjust the adaptive factor of the Arctic Puffin's underwater position; is the updated position; is the current iteration number; When in danger, the Arctic puffins adopt the third strategy to avoid predators. The position update is described as follows:

[0018] in, is the updated position; is a random number uniformly distributed between 0 and 1; The optimal solution is obtained by combining three different strategies: cooperation mechanism, enhanced search, and avoiding natural enemies. The equation is described as follows:

[0019] in, The population after merging three different strategies; is the new population after sorting; for The front A new population of 1,000 Arctic puffins was formed.

[0020] Further improvement, in order to improve the global optimization ability and local optimization ability of the Arctic Puffin algorithm, the vertical and horizontal crossover strategy is used to improve the Arctic Puffin algorithm. The horizontal crossover strategy is based on the crossover operation of all dimensions between two individuals, which is described as:

[0021] in, and For the parent and Offspring produced by horizontal crossover; and is a random number uniformly distributed between 0 and 1; and is a random number uniformly distributed between -1 and 1; for dimension; The vertical crossover strategy is a crossover operation in two different dimensions between all individuals, described as:

[0022] in and For the parents of two different dimensions; for and Offspring produced by vertical crossover; is a random number uniformly distributed between 0 and 1.

[0023] The advantages of the present invention are as follows: The present invention also considers the excitation magnetic field H The amplitude, frequency, phase and bias magnetic field of the giant magnetostrictive material are taken into consideration, and the improved Arctic Puffin optimization algorithm based on the crisscross strategy is used to identify the parameters of the hysteresis model of the giant magnetostrictive material, providing theoretical guidance for the engineering application of the giant magnetostrictive material. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a flow chart of using the Jiles-Atherton model based on CSAPO to perform parameter identification on the hysteresis loop of giant magnetostrictive material.

[0025] Figure 2 It is the iteration curve of CSAPO of the present invention.

[0026] Figure 3 This is a comparison diagram of the fitting results between the Jiles-Atherton model based on CSAPO of the present invention and the actual giant magnetostrictive material. DETAILED DESCRIPTION

[0027] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification are only for describing specific implementation purposes and are not intended to limit the present invention.

[0029] A method for identifying model parameters of hysteresis characteristics of giant magnetostrictive materials, the specific steps are as follows: Step 1: Set the magnetic field strength H , namely, parameters such as amplitude, phase, frequency, and bias; Step 2, clarify the physical meaning of the Jiles-Atherton model and establish the hysteresis model of giant magnetostrictive materials; The Jiles-Atherton model is mathematically described as follows: Magnetic induction intensity It can be expressed as:

[0030] in, is the absolute magnetic permeability of vacuum; is the magnetic field strength; is the magnetization intensity.

[0031] Magnetization Given by:

[0032] in, is the hysteresis-free magnetization; is the direction coefficient, when the magnetic field strength When increasing, ,when constant, ,when When decreasing, ; is the loss coefficient; is the irreversible magnetization component; is the effective magnetic field strength.

[0033] Irreversible magnetization component The expression is:

[0034] in, is the reversible magnetic susceptibility.

[0035] Effective magnetic field strength The expression is:

[0036] in, is the internal coupling parameter of the magnetic domain.

[0037] Hysteresis-free magnetization The expression is:

[0038] in, is the saturation magnetization; is the shape parameter of the hysteresis-free magnetization curve.

[0039] From the above description of the Jiles-Atherton model, the total saturation magnetization is , shape parameters of the hysteresis-free magnetization curve , containment coefficient , reversible susceptibility , magnetic domain internal coupling parameters These five parameters have clear physical meanings; the above expressions are mathematically modeled in Simulink, and the hysteresis loop can be obtained by solving them.

[0040] Step 3, such as Figure 1 As shown, the model parameters of the hysteresis characteristics of the giant magnetostrictive material are identified, and the Arctic Puffin Optimization Algorithm improved by the criss-cross strategy (CSAPO for short) is used to optimize the five model parameters of the Jiles-Atherton model of the giant magnetostrictive material. The specific method is as follows: The model parameters are identified based on the hysteresis loop obtained from actual measurements, and the objective function is optimized using CSAPO to obtain the hysteresis characteristic curve of the magnetostrictive material, where the objective function is as follows:

[0041] in, is the objective function; is the actual measured magnetic induction intensity B The value of is the magnetic induction intensity obtained by CSAPO identification The value of is the number of data points.

[0042] According to the upper and lower limits of the search space, the population individuals of CSAPO are initialized, namely:

[0043] in, ; Represents the current iteration number; Represents the number of individuals in a population; Indicates Arctic Puffin The position of the iteration; is a random number between 0 and 1; and They represent the upper and lower bounds of the parameter value respectively.

[0044] In order to balance CSAPO's global search and local development capabilities, a behavior conversion factor is introduced ,Right now:

[0045] Among them, when When it is greater than 0.5, global search is performed, otherwise, local development is performed; Represents the number of iterations.

[0046] The Arctic puffins rely on a unique aerial search and swooping predation strategy for global search, where the aerial search strategy is expressed as:

[0047] in, New populations generated for aerial search strategies; 1 to Random numbers between excluding ; Represents the number of individuals in a population; represents a candidate solution randomly selected from the population; Represents the random number generated by Levy flight; is the population dimension; is a random number that follows a normal distribution.

[0048] The diving predation strategy is expressed as:

[0049] in, new populations generated for the dive-hunting strategy; It is the velocity coefficient, which is used to adjust the displacement and plays a key role in the speed and direction of flight.

[0050] In order to obtain the best solution for the aerial search and diving predation strategies, the above two strategies are combined and the candidate solutions are merged into a new population. N Arctic puffins form a new population, and the specific relationship is as follows:

[0051] in, for the combined population; is the new population after sorting; for The front A new population of 1,000 Arctic puffins was formed.

[0052] The Arctic puffins use three strategies for local development: cooperation mechanism, enhanced search, and avoidance of natural enemies. The cooperation mechanism is described as:

[0053] in, is the updated position; It is a cooperative factor that regulates the predation behavior of Arctic puffins; For the exception In addition, 1 to A random number between , , is a candidate solution randomly selected from the current population; and satisfies .

[0054] As predation progresses, it enters intensive search, and the position update can be described as:

[0055] in, To adjust the adaptive factor of the Arctic Puffin's underwater position; is the updated position.

[0056] When encountering danger, in order to avoid predators, the Arctic puffins adopt the third strategy, and the position update can be described as:

[0057] in, is the updated position; is a random number uniformly distributed between 0 and 1.

[0058] By combining the three different strategies of collaborative search and predation, enhanced search, and avoiding natural enemies, a better solution can be obtained to a certain extent. The equation is described as follows:

[0059] in, is the population after the three strategies are combined; is the new population after sorting; for The front A new population of 1,000 Arctic puffins was formed.

[0060] In order to improve the global and local optimization capabilities of the Arctic Puffin algorithm, the vertical and horizontal crossover strategy is used to improve the Arctic Puffin algorithm. The horizontal crossover strategy is based on the crossover operation of all dimensions between two individuals, which is described as:

[0061] in, and For the parent and Offspring produced by horizontal crossover; and is a random number uniformly distributed between 0 and 1; and A random number uniformly distributed between -1 and 1.

[0062] The vertical crossover strategy is a crossover operation in two different dimensions between all individuals, described as:

[0063] in, and For the parents of two different dimensions; for and Offspring produced by vertical crossover; is a random number uniformly distributed between 0 and 1.

[0064] The model parameter identification results of this implementation are as follows Figure 2 and Figure 3 As shown. Among them, Figure 2 is the iteration curve of the adaptation value, Figure 3 The actual measurement curve of the hysteresis characteristics of giant magnetostrictive materials and the model identification curve using CSAPO. Figure 2 and Figure 3 It can be seen that this method has fast convergence speed and high identification accuracy, and can provide theoretical guidance for the practical application of giant magnetostrictive materials.

[0065] The technical features of the above embodiments may be combined arbitrarily. For the sake of brevity, not all combinations are described. However, as long as the combination of the above technical features does not conflict, it should be considered to be within the scope of this description.

[0066] The above is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several improvements and variations can be made without departing from the concept of the present invention, and these improvements and variations are within the protection scope of the present invention.

Claims

1. A method for identifying model parameters of hysteresis characteristics of giant magnetostrictive materials, characterized in that: The steps include: Step 1: Set the input current to obtain the magnetic field strength H， Get the actual measured hysteresis curve of giant magnetostrictive material ； Step 2: Mathematically describe the Jiles-Atherton model and determine five parameters to be determined: saturation magnetization M s , shape parameters of the hysteresis-free magnetization curve , containment coefficient k , reversible susceptibility c , magnetic domain internal coupling parameters The physical meaning of the Jiles-Atherton type parameter identification model is constructed: In Simulink, the identification model of Jiles-Atherton type parameters is established according to the mathematical description of Jiles-Atherton model; Step 3, adjusting the five undetermined parameters by using the improved Arctic Puffin optimization algorithm based on the crisscross strategy until the predicted hysteresis curve output by the Jiles-Atherton type parameter identification model is closest to the actual measured hysteresis curve, that is, obtaining the optimized Jiles-Atherton type parameter identification model; The improved Arctic puffin optimization algorithm based on the crisscross strategy adopts the behavior conversion factor K beh , used to balance the global search stage and the local development stage; the global search stage includes two strategies: aerial search and diving predation. The aerial search strategy is used by the Arctic puffins to find potential food while keeping vigilant against nearby natural enemies. The diving predation strategy is used by the Arctic puffins to quickly change the flight direction to speed up food capture and avoid competition from other predators; the local development stage includes three strategies: cooperation mechanism, enhanced search, and avoidance of natural enemies. The cooperation mechanism is used for collective foraging to improve the efficiency and success rate of predation. The enhanced search is used for the Arctic puffins to flexibly adapt to the distribution of food in the environment to improve adaptability and optimization ability. Avoidance of natural enemies is used to warn other individuals to leave the dangerous area to jump out of the local optimum; Step 4: The magnetic field strength of the giant magnetostrictive material to be tested H The hysteresis curve of the giant magnetostrictive material to be predicted is obtained by inputting the optimized Jiles-Atherton type parameter identification model.

2. The method for identifying model parameters of hysteresis characteristics of giant magnetostrictive materials according to claim 1, characterized in that: In the step 1, the magnetic field strength H Including magnetic field strength H The amplitude, frequency, phase and size of the bias magnetic field.

3. The method for identifying model parameters of hysteresis characteristics of giant magnetostrictive material according to claim 1, characterized in that: In step 2, the mathematical description of the Jiles-Atherton model is as follows: The magnetic induction intensity It is expressed as: （1） in, is the absolute magnetic permeability of vacuum; is the magnetic field strength; is the magnetization intensity; Magnetization It is expressed as: （2） in, is the hysteresis-free magnetization; is the direction coefficient, when the magnetic field strength H When increasing, ,when constant, ,when When decreasing, ; is the loss coefficient; is the irreversible magnetization component; is the effective magnetic field strength; represents the derivation; Irreversible magnetization component The expression is: （3） in, is the reversible magnetic susceptibility; Effective magnetic field strength The expression is: （4） in, is the internal coupling parameter of magnetic domains; Hysteresis-free magnetization The expression is: （5） in, is the saturation magnetization; is the shape parameter of the hysteresis-free magnetization curve.

4. The method for identifying model parameters of hysteresis characteristics of giant magnetostrictive material according to claim 1, characterized in that: In step 3, when the objective function is minimum, it is determined that the predicted hysteresis curve is closest to the actual measured hysteresis curve; wherein the objective function is as follows: ； in, is the objective function; is the actual measured magnetic induction intensity The value of is the magnetic induction intensity obtained by CSAPO identification The value of is the number of data points.

5. The method for identifying model parameters of hysteresis characteristics of giant magnetostrictive material according to claim 4, characterized in that: In the step 3, the objective function is optimized by using the improved Arctic Puffin Optimization Algorithm CSAPO based on the crisscross strategy to minimize the objective function. The specific method is as follows: according to the upper and lower limits of the search space, the population individuals of CSAPO are initialized, that is: ； in, ; Represents the current iteration number; Represents the number of individuals in the population; Indicates Arctic Puffin The position of the iteration; is a random number between 0 and 1; and Respectively represent the upper and lower bounds of the parameter value; In order to balance CSAPO's global search and local development capabilities, a behavior conversion factor is introduced ,Right now: ； in, Represents the number of iterations; express; CSAPO's aerial search strategy is expressed as: ； in, New populations generated for aerial search strategies; 1 to Random numbers between excluding ; N Represents the number of individuals in the population; represents a candidate solution randomly selected from the population; Represents the random number generated by flying through "Levi"; is the population dimension; is a random number that follows a normal distribution; () indicates that the values ​​in brackets are rounded off; The diving predation strategy is expressed as: ； in, new populations generated for the dive-hunting strategy; is the velocity coefficient, used to adjust the displacement; In order to obtain the best solution for the aerial search and diving predation strategies, the aerial search strategy and the diving predation strategy are combined to merge the candidate solutions into a new population, and the top solutions are selected by sorting the fitness. N Arctic puffins form a new population, and the specific relationship is as follows: ； in, for the combined population; is the new population after sorting; for The front A new population formed by 100 Arctic Puffin individuals; () means to arrange the elements in the matrix in ascending order; Arctic puffins use a cooperative mechanism to collaborate in predation, which improves the efficiency and success rate of predation. The position update description is: ； in, is the updated position; It is a cooperative factor that regulates the predation behavior of Arctic puffins; For the exception In addition, 1 to A random number between , , is a candidate solution randomly selected from the current population; and satisfies ; As predation progresses, it enters the intensive search phase, and the position update is described as: ； in, To adjust the adaptive factor of the Arctic Puffin's underwater position; is the updated position; is the current iteration number; When in danger, the Arctic puffins adopt the third strategy to avoid predators. The position update is described as follows: ； in, is the updated position; is a random number uniformly distributed between 0 and 1; The optimal solution is obtained by combining three different strategies: cooperation mechanism, enhanced search, and avoiding natural enemies. The equation is described as follows: ； in, The population after merging three different strategies; is the new population after sorting; for The front A new population of 1,000 Arctic puffins was formed.

6. The method for identifying model parameters of hysteresis characteristics of giant magnetostrictive material according to claim 5, characterized in that: In step 3, in order to improve the global optimization ability and local optimization ability of the Arctic Puffin algorithm, the vertical and horizontal crossover strategy is used to improve the Arctic Puffin algorithm. The horizontal crossover strategy is based on the crossover operation of all dimensions between two individuals, which is described as: ； in, and For the parent and Offspring produced by horizontal crossover; and is a random number uniformly distributed between 0 and 1; and is a random number uniformly distributed between -1 and 1; for dimension; The vertical crossover strategy is a crossover operation in two different dimensions between all individuals, described as: ； in and , are parents of two different dimensions; for and Offspring produced by vertical crossover; is a random number uniformly distributed between 0 and 1.

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