A position tracking method and device for a linear direct-drive motor, an electronic device, and a storage medium

Abstract

The embodiment of the present application discloses a position tracking method for a linear direct drive motor. The method comprises: obtaining an error state prediction model and an input state prediction model pre-constructed for the linear direct drive motor, and determining a control law parameter solution space of a control law with respect to an error state, wherein the control law is a parameter to be determined in the error state prediction model and the input state prediction model; for each constraint term based on the linear direct drive motor, determining a target model corresponding to the constraint term in the error state prediction model and the input state prediction model, and determining a function expression of the constraint term based on the target model; and determining a target parameter of the control law from the control law parameter solution space based on all the determined function expressions, so as to track the position of the linear direct drive motor based on the target parameter. The technical scheme of the embodiment of the present application can improve the flexibility of position tracking of the linear direct drive motor.

Description

Technical Field

[0001] The present invention relates to the field of motion control, and more particularly to a position tracking method, apparatus, electronic device and storage medium for a linear direct drive motor. Background Technology

[0002] Position tracking using linear direct drive motors is widely used in cutting-edge fields requiring the highest speed and positioning accuracy, such as semiconductor lithography machines, precision measuring machines, high-precision machine tools, and high-end scientific instruments.

[0003] However, the position tracking methods used for linear direct drive motors are not very flexible and need improvement. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for position tracking of a linear direct drive motor, thereby improving the flexibility of position tracking for linear direct drive motors.

[0005] According to one aspect of the present invention, a position tracking method for a linear direct drive motor is provided, which may include:

[0006] Obtain the pre-constructed error state prediction model and input state prediction model for the linear direct drive motor, and determine the control law parameter solution space with respect to the error state, where the control law is a parameter to be determined in both the error state prediction model and the input state prediction model;

[0007] For each constraint term preset based on the linear direct drive motor, determine the target model corresponding to the constraint term in the error state prediction model and the input state prediction model, and determine the functional expression of the constraint term based on the target model;

[0008] Based on all the determined function expressions, the target parameters of the control law are determined from the solution space of the control law parameters, so as to track the position of the linear direct drive motor based on the target parameters.

[0009] According to another aspect of the present invention, a position tracking device for a linear direct drive motor is provided, comprising:

[0010] The control law parameter solution space determination module is used to obtain the pre-built error state prediction model and input state prediction model for the linear direct drive motor, and to determine the control law parameter solution space with respect to the error state, wherein the control law is a parameter to be determined in both the error state prediction model and the input state prediction model;

[0011] The function expression determination module is used to determine the target model corresponding to the constraint in the error state prediction model and the input state prediction model for each constraint item preset based on the linear direct drive motor, and to determine the function expression of the constraint item based on the target model.

[0012] The position tracking module is used to determine the target parameters of the control law from the solution space of the control law parameters based on all the determined function expressions, so as to track the position of the linear direct drive motor based on the target parameters.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by at least one processor to cause the at least one processor to perform any of the position tracking methods for linear direct drive motors provided in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon, which are configured to cause a processor to execute and implement any of the position tracking methods for linear direct drive motors provided in any embodiment of the present invention.

[0017] The technical solution of this invention obtains a pre-constructed error state prediction model and input state prediction model for a linear direct drive motor, and determines the control law parameter solution space with respect to the error state. The control law consists of parameters to be determined in both the error state prediction model and the input state prediction model. For each constraint term pre-set based on the linear direct drive motor, a target model corresponding to the constraint term is determined in both the error state prediction model and the input state prediction model. Based on the target model, the functional expression of the constraint term is determined to improve the flexibility of control law determination. Based on all determined functional expressions, the target parameters of the control law are determined from the control law parameter solution space to track the position of the linear direct drive motor, achieving a trade-off between error and energy consumption in the position tracking of the linear direct drive motor. This technical solution, by determining the target parameters of the control law through multiple determined functional expressions and tracking the position of the linear direct drive motor based on the target parameters, can improve the flexibility of position tracking for linear direct drive motors.

[0018] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a position tracking method for a linear direct drive motor provided according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of another position tracking method for a linear direct drive motor provided according to an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of another position tracking method for a linear direct drive motor provided according to an embodiment of the present invention;

[0023] Figure 4 This is a flowchart of solving the solution space of control law parameters in a specific example of a position tracking method for a linear direct drive motor provided by an embodiment of the present invention;

[0024] Figure 5 This is an algorithm flowchart of a specific example of a position tracking method for a linear direct drive motor provided according to an embodiment of the present invention;

[0025] Figure 6 This is a comparison result diagram of a specific example of a position tracking method for a linear direct drive motor provided according to an embodiment of the present invention;

[0026] Figure 7 This is a structural block diagram of a position tracking device for a linear direct drive motor according to an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device that implements the position tracking method for a linear direct drive motor according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. 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.

[0030] Figure 1 This is a flowchart illustrating a position tracking method for a linear direct drive motor according to an embodiment of the present invention. This embodiment is applicable to situations where the predicted position of a linear direct drive motor is controlled, and position tracking of the linear direct drive motor is performed. This method can be executed by a position tracking device for a linear direct drive motor provided in this embodiment of the invention. This device can be implemented in software and / or hardware and can be integrated into an electronic device, which can be various user terminals or servers.

[0031] See Figure 1 The method of this invention specifically includes the following steps:

[0032] S110. Obtain the pre-constructed error state prediction model and input state prediction model for the linear direct drive motor, and determine the control law parameter solution space with respect to the error state, wherein the control law is a parameter to be determined in both the error state prediction model and the input state prediction model.

[0033] The error state prediction model can be understood as a model that predicts the error between the expected position and speed and the actual position and speed of the linear direct drive motor. The input state prediction model can be understood as a model that predicts the input to the linear direct drive motor. Optionally, the error state prediction model and the input state prediction model can be constructed through the following steps:

[0034] Step 1: Construct the discrete state-space equations of the linear direct-drive motor:

[0035] (1)

[0036] in, For the state variables of a linear direct drive motor. For the input control variables of the linear direct drive motor, For the output variables of a linear direct drive motor, For the system matrix of linear direct drive motors, For the input matrix of the linear direct drive motor, is the output matrix of the linear direct drive motor, and k is the current sampling time.

[0037] Step 2: Construct a discrete mathematical model of the reference trajectory of the linear direct drive motor:

[0038] (2)

[0039] in, For the linear direct drive motor, This is the reference output variable for the linear direct drive motor. The system matrix is ​​the reference trajectory. The output matrix is ​​the reference trajectory.

[0040] Step 3: Construct an augmented discrete state-space model containing the reference trajectory using formulas (1) and (2):

[0041] (3)

[0042] in, , , , , This refers to the actual position, actual speed, reference position, and reference speed of the linear direct drive motor.

[0043] Step 4: Define the error state of the linear direct drive motor:

[0044] (4)

[0045] in, A two-dimensional column vector of position and velocity errors .

[0046] Step 5: Determine the error state prediction model and the input state prediction model:

[0047] The expression for the control law is: Combining formula (3) and formula (4) and The error state prediction model and the input state prediction model are obtained as follows:

[0048] (5)

[0049] in, , , , , ,

[0050] To predict the step size, To control the step size.

[0051] Since the control law is determined to be in the form of an error state feedback solution, the control law parameter solution space of the error state can be understood as the set of all feasible error state feedback gain matrices K under the given reference trajectory error state space (i.e., the deviation between the actual position of the linear direct drive motor and the reference trajectory) and dynamic constraints.

[0052] S120. For each constraint term preset based on the linear direct drive motor, determine the target model corresponding to the constraint term in the error state prediction model and the input state prediction model, and determine the functional expression of the constraint term based on the target model.

[0053] Among them, constraint terms can be understood as terms that constrain various aspects of the operation of the linear direct drive motor. For example, they may constrain the error state of the linear direct drive motor or limit the control input of the motor.

[0054] For each constraint, a target model corresponding to the constraint can be determined from the error state prediction model and the input state prediction model. Based on the target model, the functional expression of the constraint can be determined. For example, the constraint for predicting the error condition (i.e., the position tracking condition) of a linear direct drive motor can have its functional expression about the error state determined by the error state prediction model.

[0055] S130. Based on all the determined function expressions, determine the target parameters of the control law from the control law parameter solution space, so as to track the position of the linear direct drive motor based on the target parameters.

[0056] Here, the objective parameters can be understood as error state feedback solutions determined from multiple control law parameter solution spaces. Multi-objective constraint solving can be performed based on all determined function expressions, thereby determining the objective parameters of the control law from multiple control law parameter solution spaces.

[0057] After obtaining the target parameters, the position of the linear direct drive motor can be tracked using these parameters. First, the current motor position and speed can be determined, along with the error state between these values ​​and the reference position and speed on the reference trajectory. Then, the target parameters corresponding to this error state can be retrieved from the target parameters list. Finally, the position of the linear direct drive motor can be tracked based on these target parameters.

[0058] The technical solution of this invention obtains a pre-constructed error state prediction model and input state prediction model for a linear direct drive motor, and determines the control law parameter solution space with respect to the error state. The control law consists of parameters to be determined in both the error state prediction model and the input state prediction model. For each constraint term pre-set based on the linear direct drive motor, a target model corresponding to the constraint term is determined in both the error state prediction model and the input state prediction model. Based on the target model, the functional expression of the constraint term is determined to improve the flexibility of control law determination. Based on all determined functional expressions, the target parameters of the control law are determined from the control law parameter solution space to track the position of the linear direct drive motor, achieving a trade-off between error and energy consumption in the position tracking of the linear direct drive motor. This technical solution, by determining the target parameters of the control law through multiple determined functional expressions and tracking the position of the linear direct drive motor based on the target parameters, can improve the flexibility of position tracking for linear direct drive motors.

[0059] An optional technical solution for determining the control law parameter solution space of the control law with respect to the error state includes: obtaining a preset error state space for the control law, and dividing the error state space into multiple error state subspaces; for each error state subspace, performing the following two steps: determining the initial search solution space of the control law corresponding to the error state subspace based on the operating parameters of the linear direct drive motor; for each initial search solution in the initial search solution space, testing the initial search solution in a simulation system corresponding to the linear direct drive motor, using the initial search solution that stabilizes the simulation system, converges the error state, and satisfies the input constraints as the parameter solution, and determining the control law parameter solution space corresponding to the error state subspace based on the obtained parameter solutions; and determining the target parameters of the control law from the control law parameter solution space based on all determined function expressions, including: for each obtained control law parameter solution space, determining the target parameters of the control law from the control law parameter solution space based on all determined function expressions.

[0060] The error state space can be understood as an error state space that is pre-defined in shape based on the actual operating conditions of the motor.

[0061] For each error state subspace obtained based on the partitioning of the error state space, the initial search solution space for the error state feedback solution corresponding to the error state subspace can be determined based on the operating parameters of the linear direct drive motor, such as the rated voltage, maximum stroke, and preset reference trajectory of the linear direct drive motor at the time of manufacture.

[0062] The initial search solution can be understood as the solution that needs to be filtered within the initial search solution space. For each initial search solution in the initial search solution space, it needs to be filtered out. The initial search solutions are then tested in the simulation system corresponding to the linear direct drive motor. The initial search solution that makes the simulation system stable, the error state converges, and satisfies the input constraints is taken as the parameter solution. Optionally, to determine whether the simulation system is stable, the eigenvalues ​​of the closed-loop system composed of the initial search solution and the offline state-space equations of the linear direct drive motor can be calculated. If the eigenvalues ​​are within the unit circle, it indicates that the discrete system of the linear direct drive motor is stable under the error state feedback solution. Finally, based on the obtained parameter solutions, the parameter solution space of the control law corresponding to the error state subspace is determined.

[0063] The above technical solution can accurately obtain the solution space of control law parameters that meet the conditions, thereby improving the accuracy of position tracking of linear direct drive motors.

[0064] Optionally, based on the target parameters, the position of the linear direct drive motor can be tracked, including: determining the target subspace corresponding to the linear direct drive motor from the entire error state subspace according to the error state of the linear direct drive motor; and tracking the position of the linear direct drive motor according to the target parameters corresponding to the target subspace.

[0065] After obtaining the error state of the linear direct drive motor, the target subspace corresponding to the linear direct drive motor can be determined from the total error state subspace by looking up a table. Then, the corresponding target parameters can be found from the target subspace, and the position of the linear direct drive motor can be tracked through the target parameters.

[0066] The above technical solution can achieve accurate tracking of the position of a linear direct drive motor by using an online lookup table.

[0067] Figure 2 This is a flowchart of another position tracking method for a linear direct drive motor provided in this embodiment of the invention. This embodiment is based on and optimized from the above-described technical solutions. Optionally, this embodiment further includes: obtaining a preset multi-objective optimization function for the linear direct drive motor; determining the functional expression of the constraint terms based on the target model, including: determining the target optimization function corresponding to the constraint terms in the multi-objective optimization function; and determining the functional expression of the constraint terms based on the target model and the target optimization function. Explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0068] See Figure 2 The method in this embodiment may specifically include the following steps:

[0069] S210. Obtain the pre-constructed error state prediction model and input state prediction model for the linear direct drive motor, and determine the control law parameter solution space with respect to the error state, wherein the control law is a parameter to be determined in both the error state prediction model and the input state prediction model.

[0070] S220: Obtain the preset multi-objective optimization function for the linear direct drive motor.

[0071] The multi-objective optimization function can be understood as a function that minimizes the position tracking error and the energy consumption of a linear direct drive motor. The minimum position tracking error and the minimum energy consumption of a linear direct drive motor can be represented by a function.

[0072] S230. For each constraint term preset based on the linear direct drive motor, determine the target model corresponding to the constraint term in the error state prediction model and the input state prediction model, and determine the target optimization function corresponding to the constraint term in the multi-objective optimization function. Based on the target model and the target optimization function, determine the functional expression of the constraint term.

[0073] After obtaining the multi-objective optimization function, for each constraint term, the corresponding objective optimization function can be determined from the multi-objective optimization function. Finally, the functional expression of the constraint term can be determined together based on the objective model and the objective optimization function.

[0074] S240. Based on all the determined function expressions, determine the target parameters of the control law from the control law parameter solution space, so as to track the position of the linear direct drive motor based on the target parameters.

[0075] The technical solution of this invention can determine an accurate function expression for each constraint, and then determine the accurate target parameters through the function expression, thereby improving the accuracy of position tracking of the linear direct drive motor.

[0076] An optional technical solution includes a multi-objective optimization function comprising minimizing an error cost function, minimizing an energy consumption cost function, and input constraints. The input constraints are constraints specific to the input of the linear direct-drive motor within the error cost function. When the constraint term is an error constraint, the objective model is an error state prediction model, and the objective optimization function minimizes the error cost function. Alternatively, when the constraint term is an energy consumption constraint, the objective model is an input state prediction model, and the objective optimization function minimizes the energy consumption cost function. And / or, when the constraint term is a constraint violation, the objective model is an input state prediction model, and the objective optimization function is the input constraint.

[0077] The multi-objective optimization function includes minimizing the error cost function and input constraints. The input constraints are used to constrain the input of the linear direct drive motor, as shown in the following equation:

[0078] (6)

[0079] in, Optimize the function for the objective. To predict the step size, This is the error weight coefficient matrix. This is the energy consumption weighting coefficient matrix. and They are respectively and , To input the minimum thrust, Enter the maximum thrust value.

[0080] When the constraint is an error constraint, the corresponding objective model is an error state prediction model, and the objective optimization function is to minimize the error cost function. When the constraint is an energy consumption constraint, the corresponding objective model is an input state prediction model, and the objective optimization function is to minimize the energy consumption cost function. When the constraint is a constraint violation, the corresponding objective model is an input state prediction model, and the objective optimization function is the input constraint condition. The specific function expressions are as follows:

[0081] (7)

[0082] in, For the first individual Error constraint term at time, For the first individual Energy consumption constraints at time t. For the first The sum of the degree to which each individual violates the constraints Constraint violations within the time frame, For simulation time, For the simulation cycle, Pick Integer multiples of, For the first The sum of error constraint terms within the individual simulation time. For the first The sum of energy consumption constraints for each individual simulation time. For the first The sum of constraint violations within the simulation time of each individual.

[0083] The above technical solution constrains the linear direct drive motor by minimizing error, minimizing energy consumption, and input constraints. It can track the position of the linear direct drive motor according to actual needs, thus improving the flexibility of position tracking.

[0084] Figure 3 This is a flowchart of another position tracking method for a linear direct drive motor provided in this embodiment of the invention. This embodiment is based on and optimized from the above-mentioned technical solutions. In this embodiment, optionally, the target parameters of the control law are determined from the control law parameter solution space based on all determined function expressions, including: generating an initial population based on the control law parameter solution space, and determining a boundary function based on the control law parameter solution space, wherein the boundary function is used to characterize the boundary of the control law parameter solution space; for each initial individual in the initial population, based on the boundary function and the initial individual, obtaining a target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor, and calculating the function value of each function expression in all function expressions based on the target individual; determining the Pareto optimal solution set based on all obtained function values; and determining the target parameters of the control law from the control law parameter solution space based on the Pareto optimal solution set.

[0085] The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0086] See Figure 3 The method in this embodiment may specifically include the following steps:

[0087] S310. Obtain the pre-constructed error state prediction model and input state prediction model for the linear direct drive motor, and determine the control law parameter solution space with respect to the error state, wherein the control law is a parameter to be determined in both the error state prediction model and the input state prediction model.

[0088] S320. For each constraint term preset based on the linear direct drive motor, determine the target model corresponding to the constraint term in the error state prediction model and the input state prediction model, and determine the functional expression of the constraint term based on the target model.

[0089] S330. Based on the solution space of the control law parameters, generate an initial population and determine the boundary function based on the solution space of the control law parameters, wherein the boundary function is used to characterize the boundary of the solution space of the control law parameters.

[0090] The initial population can be understood as a parameter solution randomly generated in the solution space of the control law parameter, ensuring that the initial population is uniformly distributed in the solution space of the control law parameter.

[0091] A boundary function can be understood as a function that limits the boundary of the solution space of the control law parameters. Optionally, all parameter solutions on the boundary in the solution space of the control law parameters can be normalized to obtain the boundary function.

[0092] S340. For each initial individual in the initial population, based on the boundary function and the initial individual, obtain the target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor, and based on the target individual, calculate the function value of each function expression in all function expressions respectively.

[0093] Specifically, target individuals satisfying the system stability conditions and input constraints corresponding to the linear direct drive motor can be selected from the initial population based on the boundary function. Optionally, the initial population can be directly filtered using the boundary function to remove those outside the boundary function. Alternatively, the initial population can be adjusted using the boundary function to ensure it is within the boundary function.

[0094] S350. Based on all the obtained function values, determine the Pareto optimal solution set.

[0095] The process involves using all obtained function values ​​as the first-generation population, performing dominance sorting and crowding calculation; obtaining a second-generation population through crossover and mutation selection, and combining it with the previous generation population to form a larger population, thus determining the optimal population size; and finally, through multiple iterations of a predetermined number of iterations, obtaining the Pareto optimal solution set.

[0096] S360: Based on the Pareto optimal solution set, the target parameters of the control law are determined from the solution space of the control law parameters, so as to track the position of the linear direct drive motor based on the target parameters.

[0097] The technical solution of this invention can determine the target parameters that meet the requirements to the greatest extent possible, thereby improving the precision of linear direct drive motor position tracking.

[0098] An optional technical solution, based on boundary functions and initial individuals, obtains target individuals that satisfy the system stability conditions and input constraints corresponding to the linear direct drive motor, including:

[0099] Based on the boundary function, it is determined whether the initial individual is located within the boundary. If the initial individual is located within the boundary, it is used as the target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor. If the initial individual is not located within the boundary, it is adjusted based on the boundary function to obtain the target individual.

[0100] For each initial individual, a boundary function can be used to determine whether the initial individual lies within the boundary. If the initial individual lies within the boundary, it is directly used as the target individual. If the initial individual does not lie within the boundary, it can be adjusted based on the boundary function to obtain the target individual. Optionally, values ​​in the initial individual that exceed the boundary can be adjusted to boundary values ​​to obtain the target individual.

[0101] The above technical solution can retain target individuals that meet the conditions to the greatest extent, thereby improving the precision of linear direct drive motor position tracking.

[0102] To better understand the various technical solutions described above, a specific example is provided below. In this specific example, the steps are as follows:

[0103] Step 1: Pre-built error state prediction model and input state prediction model for linear direct drive motor.

[0104] Step 2: Divide the preset control law parameter solution space into multiple error state subspaces (i.e., error state spaces). Based on the operating parameters of the linear direct drive motor, determine the initial search solution space of the control law corresponding to the error state subspace. For each initial search solution in the initial search solution space, test it in the simulation system corresponding to the linear direct drive motor. The initial search solution that makes the simulation system stable (i.e., satisfies the stability condition), the error state converges (i.e., makes the points in the error state space converge to 0), and satisfies the input constraints is taken as the parameter solution. Based on the obtained parameter solutions, determine the control law parameter solution space corresponding to the error state subspace. The specific flowchart is as follows: Figure 4 As shown.

[0105] Step 3: Construct the function expression for multiple constraint terms.

[0106] The objective optimization function is determined as follows: when the constraint is an error constraint, the functional expression of the error constraint is determined based on the error state prediction model and the minimization of the error cost function; when the constraint is an energy consumption constraint, the functional expression of the energy consumption constraint is determined based on the input state prediction model and the minimization of the error cost function; when the constraint is a constraint violation, the functional expression of the constraint violation is determined based on the input state prediction model and the input constraints.

[0107] Step 4: For the multiple control law parameter solution spaces obtained in Step 2, encode each control law parameter solution into an individual, and divide the control law parameter solution space into 7 spaces, each subspace corresponding to one control law parameter solution. Since the error state is 2-dimensional, the individual dimension is 2 × 7 = 14. Then, generate an initial population and adjust it using boundary functions. Each dimension has a different range. Use the three function expressions as the three fitness functions of the algorithm, and use the Pareto optimal solution set obtained by the multi-objective constraint optimization algorithm to select a series of control law parameter solutions that meet the expectations. The algorithm flowchart is as follows. Figure 5 As shown. Finally, after obtaining the parametric solution of the control law, online control is performed based on the parametric solution. A comparison experiment of the intelligent explicit model predictive controller designed in this invention and the explicit model predictive controller designed by MPT3 was conducted to compare the single-axis position tracking control of the motor. The comparison results are as follows. Figure 6 As shown. The explicit model predictive controller has 28 partitions, while the error state partitions of this invention are 7, a reduction of 75%; the online computation time of the explicit model predictive controller is 32.2 ~ 33.3 μs, while the online computation time of this invention is 31.6 ~ 32.7 μs, a reduction of 1.8%; the maximum starting absolute position error of the explicit model predictive control is 0.7203 mm, and the maximum steady-state absolute position error is 0.1802 mm, while the maximum starting absolute position error of this invention is 0.6443 mm, and the maximum steady-state absolute position error is 0.0131 mm, representing reductions of 10.5% and 92.7% respectively compared to the former.

[0108] The above specific examples can improve the flexibility of position tracking for linear direct drive motors.

[0109] Figure 7 This is a structural block diagram of a position tracking device for a linear direct drive motor provided in an embodiment of the present invention. This device is used to execute the position tracking method for a linear direct drive motor provided in any of the above embodiments. This device and the position tracking method for a linear direct drive motor in the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the position tracking device for a linear direct drive motor can be found in the embodiments of the position tracking method for a linear direct drive motor described above. See also... Figure 7 The device may specifically include: a control law parameter solution space determination module 410, a function expression determination module 420, and a position tracking module 430.

[0110] The control law parameter solution space determination module 410 is used to obtain the error state prediction model and input state prediction model pre-built for the linear direct drive motor, and to determine the control law parameter solution space with respect to the error state, wherein the control law is a parameter to be determined in both the error state prediction model and the input state prediction model.

[0111] The function expression determination module 420 is used to determine the target model corresponding to the constraint in the error state prediction model and the input state prediction model for each constraint item preset based on the linear direct drive motor, and to determine the function expression of the constraint item based on the target model.

[0112] The position tracking module 430 is used to determine the target parameters of the control law from the control law parameter solution space based on all determined function expressions, so as to track the position of the linear direct drive motor based on the target parameters.

[0113] An optional addition includes:

[0114] The multi-objective optimization function acquisition module is used to acquire a preset multi-objective optimization function for the linear direct drive motor;

[0115] Function expression determination module 420 includes:

[0116] The objective optimization function determination submodule is used to determine the objective optimization function corresponding to the constraint terms in the multi-objective optimization function.

[0117] The function expression determination submodule is used to determine the function expressions of the constraint terms based on the target model and the target optimization function.

[0118] Based on this, the optional multi-objective optimization function includes minimizing the error cost function, minimizing the energy consumption cost function, and input constraints, wherein the input constraints are the constraints on the input of the linear direct drive motor in the error cost function.

[0119] When the constraint term is an error constraint term, the objective model is an error state prediction model, and the objective optimization function is to minimize the error cost function; and / or,

[0120] When the constraint is an energy consumption constraint, the objective model is an input state prediction model, and the objective optimization function is to minimize the energy consumption cost function; and / or,

[0121] When the constraint term is a constraint violation term, the objective model is the input state prediction model, and the objective optimization function is the input constraint condition.

[0122] Alternatively, the control law parameter solution space determination module 410 includes:

[0123] The error state subspace partitioning submodule is used to obtain the error state space preset for the control law, and divide the error state space into multiple error state subspaces. For each error state subspace, the following two steps are performed:

[0124] The initial search solution space determination submodule is used to determine the initial search solution space of the control law corresponding to the error state subspace based on the operating parameters of the linear direct drive motor.

[0125] The control law parameter solution space determination submodule is used to test each initial search solution in the initial search solution space by substituting the initial search solution into the simulation system corresponding to the linear direct drive motor. The initial search solution that makes the simulation system stable, the error state converges, and satisfies the input constraints is used as the parameter solution. Based on the obtained parameter solutions, the control law parameter solution space corresponding to the error state subspace is determined.

[0126] Position tracking module 430 includes:

[0127] The target parameter determination submodule is used to determine the target parameters of the control law from the solution space of the control law parameters for each obtained control law parameter solution space, based on all the determined function expressions.

[0128] Based on this, optionally, the position tracking module 430 includes:

[0129] The target subspace determination submodule is used to determine the target subspace corresponding to the linear direct drive motor from all error state subspaces based on the error state of the linear direct drive motor.

[0130] The position tracking submodule is used to track the position of the linear direct drive motor based on the target parameters corresponding to the target subspace.

[0131] Another optional location tracking module 430 includes:

[0132] The boundary function determination submodule is used to generate an initial population based on the solution space of the control law parameters, and to determine the boundary function based on the solution space of the control law parameters, wherein the boundary function is used to characterize the boundary of the solution space of the control law parameters;

[0133] The function value calculation submodule is used to obtain the target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor for each initial individual in the initial population, based on the boundary function and the initial individual, and to calculate the function value of each function expression in all function expressions based on the target individual;

[0134] The Pareto optimal solution set determination submodule is used to determine the Pareto optimal solution set based on all obtained function values;

[0135] The target parameter acquisition submodule is used to determine the target parameters of the control law from the solution space of the control law parameters based on the Pareto optimal solution set.

[0136] Based on this, the optional function value calculation submodule includes:

[0137] The initial individual determination unit is used to determine whether the initial individual is located within the boundary based on the boundary function;

[0138] The target individual determination unit is used to determine the initial individual as the target individual that satisfies the system stability conditions and input constraints corresponding to the linear direct drive motor, provided that the initial individual is located within the boundary.

[0139] The target individual is obtained by using a unit that adjusts the initial individual based on a boundary function when the initial individual is not located within the boundary, thereby obtaining the target individual.

[0140] The position tracking device for a linear direct drive motor provided in this invention uses a control law parameter solution space determination module to obtain a pre-constructed error state prediction model and input state prediction model for the linear direct drive motor, and determines the control law parameter solution space for the error state. The control law consists of parameters to be determined in both the error state prediction model and the input state prediction model. A function expression determination module determines the target model corresponding to each constraint term in the error state prediction model and the input state prediction model for each pre-defined constraint term based on the linear direct drive motor, and determines the function expression of the constraint term based on the target model, thereby improving the flexibility of control law determination. A position tracking module determines the target parameters of the control law from the control law parameter solution space based on all determined function expressions, and tracks the position of the linear direct drive motor based on these target parameters, achieving a trade-off between error and energy consumption in the position tracking of the linear direct drive motor. This device, by determining the target parameters of the control law through multiple determined function expressions and tracking the position of the linear direct drive motor based on these target parameters, can improve the flexibility of position tracking for linear direct drive motors.

[0141] The position tracking device for linear direct drive motors provided in the embodiments of the present invention can execute the position tracking method for linear direct drive motors provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0142] It is worth noting that in the above embodiments of the position tracking device for linear direct drive motors, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0143] Figure 8 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0144] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0145] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0146] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the position tracking method for a linear direct drive motor.

[0147] In some embodiments, the position tracking method for a linear direct drive motor may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the position tracking method for a linear direct drive motor described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the position tracking method for a linear direct drive motor by any other suitable means (e.g., by means of firmware).

[0148] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0150] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0151] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0152] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0153] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0154] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0155] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A position tracking method for a linear direct drive motor, characterized in that, include: Obtain a pre-constructed error state prediction model and input state prediction model for a linear direct drive motor, and determine the control law parameter solution space with respect to the error state. The control law is a parameter to be determined in both the error state prediction model and the input state prediction model. The error state prediction model and the input state prediction model are obtained through the following methods: Construct an augmented discrete state-space model containing the reference trajectory of the linear direct drive motor. ; in, , Let be the state variable of the linear direct drive motor. This is the reference state variable for the linear direct drive motor. , , , The actual position, actual speed, reference position, and reference speed of the linear direct drive motor are given. The system matrix of the linear direct drive motor is... This is the input matrix for the linear direct drive motor. Let k be the output matrix of the linear direct drive motor, and k be the current sampling time. The system matrix is ​​the reference trajectory. The output matrix of the reference trajectory; The error state of the linear direct drive motor is defined as follows: ； in, Let be a two-dimensional column vector representing the position error and speed error of the linear direct drive motor. ; The expression for the control law is: Based on the augmented discrete state-space model, the expression for the error state, and the expression for the control law, the error state prediction model and the input state prediction model are obtained as follows: ； in, , , , , , To predict the step size, To control the step size, For the input control variables of the linear direct drive motor; For each constraint term preset based on the linear direct drive motor, a target model corresponding to the constraint term is determined in the error state prediction model and the input state prediction model, and a functional expression for the constraint term is determined based on the target model; Based on all the determined function expressions, the target parameters of the control law are determined from the solution space of the control law parameters, so as to track the position of the linear direct drive motor based on the target parameters.

2. The method according to claim 1, characterized in that, Also includes: Obtain a preset multi-objective optimization function for the linear direct drive motor; The step of determining the functional expression of the constraint term based on the target model includes: Determine the objective optimization function corresponding to the constraint term in the multi-objective optimization function; Based on the target model and the target optimization function, the functional expression of the constraint term is determined.

3. The method according to claim 2, characterized in that, The multi-objective optimization function includes a minimum error cost function, a minimum energy consumption cost function, and input constraints, wherein the input constraints are the constraints on the linear direct drive motor input in the minimum error cost function. When the constraint term is an error constraint term, the objective model is the error state prediction model, and the objective optimization function is the function that minimizes the error cost; and / or, When the constraint is an energy consumption constraint, the objective model is the input state prediction model, and the objective optimization function is the function that minimizes the energy consumption cost; and / or, When the constraint term is a constraint violation term, the objective model is the input state prediction model, and the objective optimization function is the input constraint condition.

4. The method according to claim 1, characterized in that, The solution space of the control law parameters with respect to the error state, which determines the control law, includes: Obtain the error state space preset for the control law, and divide the error state space into multiple error state subspaces. For each error state subspace, perform the following two steps: Based on the operating parameters of the linear direct drive motor, determine the initial search solution space of the control law corresponding to the error state subspace; For each initial search solution in the initial search solution space, the initial search solution is substituted into the simulation system corresponding to the linear direct drive motor for testing. The initial search solution that makes the simulation system stable, the error state converges, and the input constraint conditions are satisfied is taken as the parameter solution. Based on the obtained parameter solutions, the control law parameter solution space corresponding to the error state subspace is determined. The step of determining the target parameters of the control law from the solution space of the control law parameters based on all the determined function expressions includes: For each of the obtained control law parameter solution spaces, the target parameters of the control law are determined from the control law parameter solution space based on all the determined function expressions.

5. The method according to claim 4, characterized in that, The step of tracking the position of the linear direct drive motor based on the target parameters includes: Based on the error state of the linear direct drive motor, determine the target subspace corresponding to the linear direct drive motor from all the error state subspaces; The position of the linear direct drive motor is tracked based on the target parameters corresponding to the target subspace.

6. The method according to claim 1, characterized in that, The step of determining the target parameters of the control law from the solution space of the control law parameters based on all the determined function expressions includes: Based on the solution space of the control law parameters, an initial population is generated, and based on the solution space of the control law parameters, a boundary function is determined, wherein the boundary function is used to characterize the boundary of the solution space of the control law parameters. For each initial individual in the initial population, based on the boundary function and the initial individual, a target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor is obtained, and based on the target individual, the function value of each of the function expressions in all the function expressions is calculated respectively; Based on all the obtained function values, determine the Pareto optimal solution set; Based on the Pareto optimal solution set, the target parameters of the control law are determined from the solution space of the control law parameters.

7. The method according to claim 6, characterized in that, The process of obtaining the target individual that satisfies the system stability conditions and input constraints corresponding to the linear direct drive motor based on the boundary function and the initial individual includes: Based on the boundary function, determine whether the initial individual is located within the boundary; When the initial individual is located within the boundary, the initial individual is taken as the target individual that satisfies the system stability condition and input constraint condition corresponding to the linear direct drive motor; If the initial individual is not located within the boundary, the initial individual is adjusted based on the boundary function to obtain the target individual.

8. A position tracking device for a linear direct drive motor, characterized in that, include: The control law parameter solution space determination module is used to obtain a pre-constructed error state prediction model and input state prediction model for a linear direct drive motor, and to determine the control law parameter solution space with respect to the error state. The control law is a parameter to be determined in both the error state prediction model and the input state prediction model. The error state prediction model and the input state prediction model are obtained through the following methods: Construct an augmented discrete state-space model containing the reference trajectory of the linear direct drive motor. ; in, , Let be the state variable of the linear direct drive motor. This is the reference state variable for the linear direct drive motor. , , , The actual position, actual speed, reference position, and reference speed of the linear direct drive motor. The system matrix of the linear direct drive motor is... This is the input matrix for the linear direct drive motor. Let k be the output matrix of the linear direct drive motor, and k be the current sampling time. The system matrix is ​​the reference trajectory. The output matrix of the reference trajectory; The error state of the linear direct drive motor is defined as follows: ； in, Let be a two-dimensional column vector representing the position error and speed error of the linear direct drive motor. ; The expression for the control law is: Based on the augmented discrete state-space model, the expression for the error state, and the expression for the control law, the error state prediction model and the input state prediction model are obtained as follows: ； in, , , , , , To predict the step size, To control the step size, For the input control variables of the linear direct drive motor; The function expression determination module is used to determine the target model corresponding to the constraint in the error state prediction model and the input state prediction model for each constraint term preset based on the linear direct drive motor, and to determine the function expression of the constraint term based on the target model. The position tracking module is used to determine the target parameters of the control law from the solution space of the control law parameters based on all the determined function expressions, so as to track the position of the linear direct drive motor based on the target parameters.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to cause the at least one processor to perform the position tracking method for a linear direct drive motor as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the position tracking method for a linear direct drive motor as described in any one of claims 1-7.

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