Position control method and device of planar motor, equipment and medium

Abstract

The invention discloses a position control method and device of a planar motor, equipment and a medium. The method comprises the steps that a position prediction model is determined according to a discrete state space model of a target planar motor and current moment position information, and a speed prediction model is determined according to the position prediction model and preset discrete sampling time; determining a position control optimization model according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; converting the position control optimization model into a quadratic programming model, and determining an output equation and a state equation of a dual neural network according to a dual model of the quadratic programming model; and solving the position control optimization model to obtain a target control thrust, and performing position control on the target planar motor according to the target control thrust. According to the scheme, the hard constraint optimization problem in the operation process of the motor can be quickly solved by using the dual neural network, and high-precision position control of the planar motor is realized.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and in particular to a position control method, device, equipment and medium for a planar motor. Background Art

[0002] Planar motors are motors that can directly convert electromagnetic energy into planar motion. Due to their high-precision positioning capabilities, they are widely used in industrial automation, medical equipment, aerospace, and other fields. Model predictive control methods have the ability to handle system nonlinearity, multi-input and multi-output, and constraint problems. If model predictive control methods are applied to planar motor position control, it is expected to further improve the precision of planar motor position control.

[0003] The rapid solution of optimization problems with hard constraints is a research hotspot and difficulty in model predictive control methods. The commonly used solution methods for hard-constrained optimization problems in model predictive control have disadvantages such as weak constraint ability, long solution time, and high algorithm complexity. Therefore, when the model predictive control method is applied to the planar motor position system, there are problems such as the inability to strictly constrain the control input, complex algorithm, poor real-time performance and poor control effect. Summary of the invention

[0004] The present invention provides a planar motor position control method, device, equipment and medium, applies a dual neural network to the planar motor position control, uses the created dual neural network to complete the rapid solution of the hard-constrained optimization problem during the real-time operation of the motor position system, and considers the motor position error and speed error simultaneously in the solution process, so that the planar motor tracks the reference position at the speed corresponding to the reference position information, and can achieve high-precision and efficient control of the planar motor position under the premise of ensuring the safe operation of the planar motor.

[0005] According to one aspect of the present invention, a position control method for a planar motor is provided, the method comprising:

[0006] Determine a position prediction model of the target planar motor according to a discrete state space model of the target planar motor and the current position information, and determine a speed prediction model according to the position prediction model and a preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor;

[0007] Determine a position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor;

[0008] The position control optimization model is converted into a quadratic programming model, and the output equation and state equation of the dual neural network are determined according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables;

[0009] The position control optimization model is solved based on the output equation and the state equation of the dual neural network to obtain the target control thrust, and the position of the target planar motor is controlled according to the target control thrust.

[0010] According to another aspect of the present invention, a position control device for a planar motor is provided, the device comprising:

[0011] A prediction model determination module, used to determine a position prediction model of the target planar motor according to a discrete state space model of the target planar motor and the current position information, and to determine a speed prediction model according to the position prediction model and a preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor;

[0012] A position control optimization model determination module, used to determine the position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor;

[0013] A dual neural network determination module, used to convert the position control optimization model into a quadratic programming model, and determine the output equation and state equation of the dual neural network according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables;

[0014] The motor position control module is used to solve the position control optimization model based on the output equation and state equation of the dual neural network to obtain the target control thrust, and perform position control on the target plane motor according to the target control thrust.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and,

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the position control method of the planar motor described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the position control method of a planar motor described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention determines the position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, and determines the speed prediction model according to the position prediction model and the preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor; the position control optimization model of the target planar motor is determined according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor; the position control optimization model is converted into a quadratic programming model, and the output equation and state equation of the dual neural network are determined according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables; the position control optimization model is solved based on the output equation and state equation of the dual neural network to obtain the target control thrust, and the position of the target planar motor is controlled according to the target control thrust. This technical solution applies dual neural networks to planar motor position control, and uses the created dual neural network to quickly solve the hard-constrained optimization problem during the real-time operation of the motor position system. In addition, the target cost function of the hard-constrained optimization problem is established based on the position error and speed error of the planar motor. When solving the hard-constrained optimization problem, the position error and speed error are taken into account at the same time, so that the planar motor tracks the reference position at the speed corresponding to the reference position information. High-precision and efficient control of the planar motor position can be achieved under the premise of ensuring the safe operation of the planar motor.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a position control method of a planar motor provided according to the first embodiment of the present invention;

[0024] Figure 2is a flow chart of a position control method of a planar motor provided according to a second embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of position control of a planar switched reluctance motor provided according to a second embodiment of the present invention;

[0026] Figure 4 is a schematic structural diagram of a position control device for a planar motor provided according to a third embodiment of the present invention;

[0027] Figure 5 It is a structural schematic diagram of an electronic device for implementing a position control method of a planar motor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a planar motor position control method provided in the first embodiment of the present invention. This embodiment is applicable to the situation where the position of a planar motor is controlled with high precision and high efficiency. The method can be executed by a planar motor position control device. The planar motor position control device can be implemented in the form of hardware and / or software. The planar motor position control device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0032] S110, determining a position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, and determining a speed prediction model according to the position prediction model and a preset discrete sampling time.

[0033] Among them, the target planar motor may refer to a planar motor that needs to be position controlled. Exemplary planar motors may include variable reluctance planar motors, permanent magnet synchronous planar motors, and stepping planar motors. The state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor. The position prediction model can be used to predict the position of the planar motor. The speed prediction model can be used to predict the speed of the planar motor. The preset discrete sampling time may refer to the sampling period corresponding to the discrete state space model pre-set according to actual needs, which is the same as the outer loop control period of the target planar motor.

[0034] In this embodiment, optionally, before determining the position prediction model of the target planar motor based on the discrete state space model of the target planar motor and the current position information, it also includes: determining the continuous state space model of the target planar motor; discretizing the continuous state space model based on a preset discrete sampling time to obtain a discrete state space model.

[0035] Specifically, the continuous state space model can be expressed as formula (1): .in, is a continuous state variable, for The derivative of is the continuous input control variable, is a continuous output variable, is the continuous motor system matrix, is a continuous input matrix, is the output matrix. It should be noted that the input control variable is the control thrust, the state variables include the motor position and the motor speed, and the output variable is the motor position. Since this solution focuses on the motor position control, the output matrix can be set to act on the state variable so that the output variable is the motor position. Furthermore, if you want to achieve motor speed control, you can also set the output matrix to act on the state variable so that the output variable is the motor speed.

[0036] In this embodiment, the discrete Euler method can be used to discretize formula (1) to obtain a discrete state space model of the planar motor, which can be specifically expressed as formula (2): .in, is the discrete state variable at time k+1, is the discrete input control variable, is a discrete output variable, is the discrete motor system matrix, is the discrete input matrix, and Represent the current sampling time and the next sampling time respectively. Among them, is a two-dimensional matrix containing the motor position and motor speed.

[0037] After determining the discrete state space model of the target planar motor, the position prediction model of the target planar motor can be determined based on the discrete state space model and the current position information. Specifically, within a limited time domain, assuming that the prediction time domain The prediction step length is , control time domain The control step length is ,in, , then the position prediction model of the target plane motor It can be expressed as the following formula (3): It should be noted that The motor position in is the current position information. Among them:

[0038] ;

[0039] ;

[0040] ;

[0041] .

[0042] in, A position prediction model for a target planar motor, used to predict position information of the target planar motor; It is a control quantity matrix from k to (k+m-1), which is equivalent to the control variables input into the model.

[0043] After determining the position prediction model of the target planar motor, the speed prediction model can be determined based on the position prediction model difference of adjacent discrete sampling moments and the preset discrete sampling time. Specifically, the speed prediction model of the target planar motor It can be expressed as the following formula (4): .in:

[0044] ;

[0045] ;

[0046] ;

[0047] in, A speed prediction model for a target planar motor, used to predict the speed of the target planar motor; is the identity matrix; Indicates that all elements of the matrix are divided by the preset discrete sampling time .

[0048] S120, determining a position control optimization model of the target planar motor according to the reference position information, position prediction model and speed prediction model of the target planar motor; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current operating characteristics of the target planar motor.

[0049] Among them, the reference position information can be used to describe the position to which the motor is desired to run, and can be flexibly set according to actual needs. Exemplarily, the reference position information can be used to describe the motor running trajectory in sine or cosine form. The position error can be used to represent the difference between the reference position information and the position prediction model, and the speed error can be used to represent the difference between the reference speed information and the speed prediction model, wherein the reference speed information can be determined based on the ratio of the reference position information difference at adjacent discrete sampling moments to the preset discrete sampling time. The position control optimization model includes the target cost function and the control thrust constraint.

[0050] Among them, the target cost function can be used to describe the difference between the reference position information and the predicted position information. The control thrust constraint can be used to describe the normal working range of the control thrust, for example, it can be characterized by the upper limit and lower limit of the control thrust. Among them, the control thrust constraint is determined based on the current working characteristics of the target planar motor, and the current working characteristics can be used to describe the normal working range of the current. It should be noted that there is a certain conversion relationship between the current and the control thrust. Therefore, based on the known normal working range of the current, the conversion relationship between the two can be used to obtain the corresponding normal working range of the control thrust, and the normal working range of the control thrust can be used as the control thrust constraint, so as to ensure the safe operation of the motor.

[0051] In this embodiment, after determining the position prediction model and the speed prediction model, the position control optimization model of the target planar motor can be determined according to the reference position information, the position prediction model and the speed prediction model of the target planar motor. Optionally, the position control optimization model of the target planar motor is determined according to the reference position information, the position prediction model and the speed prediction model of the target planar motor, including: determining the reference speed information according to the reference position information and the preset discrete sampling time; determining the position error matrix according to the difference between the reference position information and the position prediction model, and determining the first parameter matrix according to the product of the first weight matrix and the position error matrix; determining the second parameter matrix according to the product of the second weight matrix and the control thrust matrix; determining the speed error matrix according to the difference between the reference speed information and the speed prediction model, and determining the third parameter matrix according to the product of the third weight matrix and the speed error matrix; determining the target cost function according to the first parameter matrix, the second parameter matrix and the third parameter matrix; determining the control thrust constraint according to the current operating range of the target planar motor and the current-control thrust conversion relationship; determining the position control optimization model of the target planar motor according to the target cost function and the control thrust constraint.

[0052] The first weight matrix can be used to characterize the weight of the position error matrix, the second weight matrix can be used to characterize the weight of the control thrust matrix, and the third weight matrix can be used to characterize the weight of the speed error matrix. The current-control thrust conversion relationship can be used to describe the mapping relationship between the current and the control thrust of the target planar motor.

[0053] Exemplarily, the position control optimization model can be expressed as the following formula (5):

[0054] .

[0055] in, ;

[0056] ;

[0057] , , .

[0058] in, is the target cost function, is the reference location information, is the reference speed information, is the position error matrix, is the velocity error matrix, To control the thrust matrix, is the first weight matrix, is the first parameter matrix, is the second weight matrix, is the second parameter matrix, is the third weight matrix, is the third parameter matrix, and are the minimum and maximum values ​​of the control thrust, To control the thrust constraint (i.e. hard constraint), the position control optimization model can be regarded as an optimization problem with hard constraints.

[0059] S130, converting the position control optimization model into a quadratic programming model, and determining the output equation and state equation of the dual neural network according to the dual model of the quadratic programming model.

[0060] The dual neural network may refer to a structure capable of solving the optimal control quantity for the position control optimization model. The state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables.

[0061] In this embodiment, formula (5) can be transformed into a quadratic programming problem to obtain a corresponding quadratic programming model, which can be specifically expressed as the following formula (6): .in, , , , , , is the identity matrix, Furthermore, we can get the dual model of formula (6), which is specifically expressed as formula (7): .in, and is the optimization variable of the dual problem, let According to the KKT (Karush-Kuhn-Tucker) condition of the convex optimization problem, formula (6) and formula (7) have the same solution as the following equation group (8), which can be specifically expressed as: According to the projection formula, equation (8) can be equivalently expressed as equation (9): .in, is a piecewise linear function, defined as: ; ; for The i-th element of the column vector, for The i-th element of the column vector, for The i-th element of the column vector.

[0062] because, is a reversible matrix. The output equation of the dual neural network can be obtained from the first equation in equation (9), which is specifically expressed as formula (10): Substituting formula (10) into the second formula in formula (9) yields formula (11): According to formula (11), the state equation of the dual neural network in continuous time can be established, which can be specifically expressed as formula (12): .in, is a parameter about the convergence speed of the dual neural network.

[0063] It should be noted that the dual neural network algorithm can be implemented using digital circuits, and the dual neural network construction and network parameter adjustment can be achieved through software programming. It is easy to implement and more suitable for quickly solving the position control optimization model of planar motors.

[0064] Furthermore, according to the Euler method, the differential term in formula (12) can be expressed as formula (13): .in, Complete an optimization variable for the dual neural network Update time, is the sampling time of the discrete dual neural network.

[0065] Substituting formula (13) into formula (12), we can get the state equation of the discrete-time dual neural network, which is specifically expressed as formula (14): .in, is the scaling parameter that determines the convergence speed of the discrete-time dual neural network. At the same time, the output equation of the discrete-time dual neural network can be obtained, expressed as formula (15): .

[0066] S140, solving the position control optimization model based on the output equation and the state equation of the dual neural network to obtain the target control thrust, and performing position control on the target planar motor according to the target control thrust.

[0067] In this embodiment, after determining the output equation and state equation of the dual neural network, the position control optimization model can be solved based on the output equation and state equation of the dual neural network to obtain the target control thrust. Optionally, solving the position control optimization model based on the output equation and state equation of the dual neural network to obtain the target control thrust includes: solving the position control optimization model according to the state equation of the dual neural network to obtain the target optimization variable value; determining the target control thrust according to the output equation and the target optimization variable value of the dual neural network.

[0068] Specifically, we can first solve formula (14) to obtain the target optimization variable value: , and then Substituting into formula (15) we can get the optimal control quantity , and then the optimal control quantity The first element of is used as the target control thrust at the current moment, that is .exist Repeat the above steps at any time to get The goal of every moment is to control the thrust.

[0069] After the target control thrust is obtained, the target plane motor can be position controlled according to the target control thrust. Optionally, the target plane motor is position controlled according to the target control thrust, including: determining a target current corresponding to the target control thrust according to a current-control thrust conversion relationship; inputting the target current into a current driver to obtain a target voltage, and position controlling the target plane motor according to the target voltage.

[0070] Among them, the target current includes a three-phase current, and correspondingly, the target voltage includes a three-phase voltage. Exemplarily, the current-control thrust conversion relationship may include a force-current conversion model and a force distribution function. Specifically, the target control thrust can be converted into a three-phase current as the target current according to the force-current conversion model and the force distribution function, and then the target current is input into the current driver, and the corresponding three-phase voltage is obtained as the target voltage, and then the target voltage can be applied to the target planar motor to achieve high-precision position control of the target planar motor through the target voltage.

[0071] The technical solution of the embodiment of the present invention determines the position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, and determines the speed prediction model according to the position prediction model and the preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor; the position control optimization model of the target planar motor is determined according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor; the position control optimization model is converted into a quadratic programming model, and the output equation and state equation of the dual neural network are determined according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables; the position control optimization model is solved based on the output equation and state equation of the dual neural network to obtain the target control thrust, and the position of the target planar motor is controlled according to the target control thrust. This technical solution applies dual neural networks to planar motor position control, and uses the created dual neural network to quickly solve the hard-constrained optimization problem during the real-time operation of the motor position system. In addition, the target cost function of the hard-constrained optimization problem is established based on the position error and speed error of the planar motor. When solving the hard-constrained optimization problem, the position error and speed error are taken into account at the same time, so that the planar motor tracks the reference position at the speed corresponding to the reference position information. High-precision and efficient control of the planar motor position can be achieved under the premise of ensuring the safe operation of the planar motor.

[0072] Embodiment 2

[0073] Figure 2 This is a flow chart of a position control method for a planar motor provided in the second embodiment of the present invention. This embodiment is optimized based on the above embodiment.

[0074] like Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0075] S210, determining a continuous state space model of the target planar motor, and discretizing the continuous state space model based on a preset discrete sampling time to obtain a discrete state space model.

[0076] S220, determining a position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, and determining a speed prediction model according to the position prediction model and a preset discrete sampling time.

[0077] The state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor.

[0078] S230, determining reference speed information according to the reference position information and a preset discrete sampling time.

[0079] S240, determining a position error matrix according to the difference between the reference position information and the position prediction model, and determining a first parameter matrix according to the product of the first weight matrix and the position error matrix.

[0080] S250, determining a second parameter matrix according to the product of the second weight matrix and the control thrust matrix.

[0081] S260, determining a speed error matrix according to the difference between the reference speed information and the speed prediction model, and determining a third parameter matrix according to the product of the third weight matrix and the speed error matrix.

[0082] S270, determining a target cost function according to the first parameter matrix, the second parameter matrix, and the third parameter matrix.

[0083] S280, determining a control thrust constraint condition according to a current operating range of the target planar motor and a current-control thrust conversion relationship.

[0084] S290, determining a position control optimization model for the target planar motor according to the target cost function and the control thrust constraint condition.

[0085] S2100, converting the position control optimization model into a quadratic programming model, and determining the output equation and state equation of the dual neural network according to the dual model of the quadratic programming model.

[0086] Among them, the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables.

[0087] S2110, solving the position control optimization model based on the output equation and state equation of the dual neural network to obtain the target control thrust, and performing position control on the target planar motor according to the target control thrust.

[0088] Figure 3 A schematic diagram of position control of a planar switched reluctance motor provided in the second embodiment of the present invention. Figure 3 As shown in the figure, taking the X-axis position control of the planar switched reluctance motor as an example, the target control thrust corresponding to the X-axis position of the planar switched reluctance motor can be predicted by the predictive position controller. Indicates a delay of one cycle, which can be used for updating status.

[0089] Specifically, firstly, the current position information of the planar switched reluctance motor is obtained through the X-axis position sensor. , and based on the discrete state space model of the planar switched reluctance motor and the current position information Get the location prediction model , and then according to the location prediction model and preset discrete sampling time to determine the speed prediction model Then, based on the reference position information , reference speed information , location prediction model And the speed prediction model Determine the position control optimization model with hard constraints , and use the dual neural network to optimize the position control model Solve to obtain the optimal control quantity , the optimal control quantity The first element is used as the target control thrust at the current moment Then, according to the force distribution function and the force-current conversion model, the target control thrust is Converted into target current, where the target current includes , and Then the target current is input into the current driver to obtain the target voltage, where the target voltage includes , and Finally, the target voltage is applied to the planar switched reluctance motor to control the motor movement, thereby obtaining the corresponding predicted position information. .

[0090] The technical solution of the embodiment of the present invention applies a dual neural network to the position control of a planar motor, and uses the created dual neural network to quickly solve the optimization problem with hard constraints during the real-time operation of the motor position system. In addition, a target cost function for the optimization problem with hard constraints is established based on the position error and speed error of the planar motor. When solving the optimization problem with hard constraints, the position error and speed error are simultaneously taken into account, so that the planar motor tracks the reference position at the speed corresponding to the reference position information, and can achieve high-precision and efficient control of the position of the planar motor while ensuring the safe operation of the planar motor.

[0091] Embodiment 3

[0092] Figure 4 This is a schematic diagram of the structure of a planar motor position control device provided in the third embodiment of the present invention. The device can execute the planar motor position control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 4 As shown, the device comprises:

[0093] A prediction model determination module 310 is used to determine a position prediction model of the target planar motor according to a discrete state space model of the target planar motor and the current position information, and to determine a speed prediction model according to the position prediction model and a preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor;

[0094] A position control optimization model determination module 320 is used to determine the position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor;

[0095] A dual neural network determination module 330 is used to convert the position control optimization model into a quadratic programming model, and determine the output equation and state equation of the dual neural network according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables;

[0096] The motor position control module 340 is used to solve the position control optimization model based on the output equation and state equation of the dual neural network to obtain the target control thrust, and perform position control on the target plane motor according to the target control thrust.

[0097] Optionally, the device further comprises: a state space model discretization module, configured to:

[0098] Before determining the position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, determining the continuous state space model of the target planar motor;

[0099] The continuous state space model is discretized based on the preset discrete sampling time to obtain a discrete state space model.

[0100] Optionally, the position control optimization model determination module is used to:

[0101] Determine reference speed information according to the reference position information and the preset discrete sampling time;

[0102] Determine a position error matrix according to the difference between the reference position information and the position prediction model, and determine a first parameter matrix according to the product of a first weight matrix and the position error matrix;

[0103] Determine a second parameter matrix according to the product of the second weight matrix and the control thrust matrix;

[0104] Determine a speed error matrix according to the difference between the reference speed information and the speed prediction model, and determine a third parameter matrix according to the product of a third weight matrix and the speed error matrix;

[0105] Determine a target cost function according to the first parameter matrix, the second parameter matrix and the third parameter matrix;

[0106] Determining a control thrust constraint condition according to a current operating range of the target planar motor and a current-control thrust conversion relationship;

[0107] A position control optimization model of the target planar motor is determined according to the target cost function and the control thrust constraint condition.

[0108] Optionally, the motor position control module 340 is used to:

[0109] Solving the position control optimization model according to the state equation of the dual neural network to obtain the target optimization variable value;

[0110] The target control thrust is determined according to the output equation of the dual neural network and the target optimization variable value.

[0111] Optionally, the motor position control module 340 is further used for:

[0112] Determining a target current corresponding to the target control thrust according to the current-control thrust conversion relationship;

[0113] The target current is input into a current driver to obtain a target voltage, and the position of the target planar motor is controlled according to the target voltage.

[0114] A position control device for a planar motor provided in an embodiment of the present invention can execute a position control method for a planar motor provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0115] Embodiment 4

[0116] Figure 5A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0117] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0118] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a position control method for a planar motor.

[0120] In some embodiments, the position control method of the planar motor may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the position control method of the planar motor described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the position control method of the planar motor in any other appropriate manner (e.g., by means of firmware).

[0121] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0123] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0124] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0125] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0126] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0127] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0128] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A position control method for a planar motor, characterized in that: The method comprises: Determine a position prediction model of the target planar motor according to a discrete state space model of the target planar motor and the current position information, and determine a speed prediction model according to the position prediction model and a preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor; Determine a position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor; The position control optimization model is converted into a quadratic programming model, and the output equation and state equation of the dual neural network are determined according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables; The position control optimization model is solved based on the output equation and the state equation of the dual neural network to obtain the target control thrust, and the position of the target planar motor is controlled according to the target control thrust.

2. The method according to claim 1, characterized in that Before determining the position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, the method further includes: Determining a continuous state space model of the target planar motor; The continuous state space model is discretized based on the preset discrete sampling time to obtain a discrete state space model.

3. The method according to claim 1 or 2, characterized in that: Determining a position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model, including: Determine reference speed information according to the reference position information and the preset discrete sampling time; Determine a position error matrix according to the difference between the reference position information and the position prediction model, and determine a first parameter matrix according to the product of a first weight matrix and the position error matrix; Determine a second parameter matrix according to the product of the second weight matrix and the control thrust matrix; Determine a speed error matrix according to the difference between the reference speed information and the speed prediction model, and determine a third parameter matrix according to the product of a third weight matrix and the speed error matrix; Determine a target cost function according to the first parameter matrix, the second parameter matrix and the third parameter matrix; Determining a control thrust constraint condition according to a current operating range of the target planar motor and a current-control thrust conversion relationship; A position control optimization model of the target planar motor is determined according to the target cost function and the control thrust constraint condition.

4. The method according to claim 3, characterized in that Solving the position control optimization model based on the output equation and the state equation of the dual neural network to obtain the target control thrust includes: Solving the position control optimization model according to the state equation of the dual neural network to obtain the target optimization variable value; The target control thrust is determined according to the output equation of the dual neural network and the target optimization variable value.

5. The method according to claim 4, characterized in that The target planar motor is position controlled according to the target control thrust, including: Determining a target current corresponding to the target control thrust according to the current-control thrust conversion relationship; The target current is input into a current driver to obtain a target voltage, and the position of the target planar motor is controlled according to the target voltage.

6. A position control device for a planar motor, characterized in that: The device comprises: A prediction model determination module, used to determine a position prediction model of the target planar motor according to a discrete state space model of the target planar motor and the current position information, and to determine a speed prediction model according to the position prediction model and a preset discrete sampling time; wherein the state space model is used to describe the relationship between the position of the motor and the control thrust, and the control thrust is used to control the movement of the motor; A position control optimization model determination module, used to determine the position control optimization model of the target planar motor according to the reference position information of the target planar motor, the position prediction model and the speed prediction model; wherein the position control optimization model includes a target cost function and a control thrust constraint condition, the target cost function is established based on the control thrust, position error and speed error of the target planar motor, and the control thrust constraint condition is determined based on the current working characteristics of the target planar motor; A dual neural network determination module, used to convert the position control optimization model into a quadratic programming model, and determine the output equation and state equation of the dual neural network according to the dual model of the quadratic programming model; wherein the state equation is used to describe the relationship between the optimization variables at adjacent moments in the dual model, and the output equation is used to describe the relationship between the control thrust and the optimization variables; The motor position control module is used to solve the position control optimization model based on the output equation and state equation of the dual neural network to obtain the target control thrust, and perform position control on the target plane motor according to the target control thrust.

7. The device according to claim 6, characterized in that The device also includes a state space model discrete module, which is used to: Before determining the position prediction model of the target planar motor according to the discrete state space model of the target planar motor and the current position information, determining the continuous state space model of the target planar motor; The continuous state space model is discretized based on the preset discrete sampling time to obtain a discrete state space model.

8. The device according to claim 6 or 7, characterized in that The position control optimization model determination module is used to: Determine reference speed information according to the reference position information and the preset discrete sampling time; Determine a position error matrix according to the difference between the reference position information and the position prediction model, and determine a first parameter matrix according to the product of a first weight matrix and the position error matrix; Determine a second parameter matrix according to the product of the second weight matrix and the control thrust matrix; Determine a speed error matrix according to the difference between the reference speed information and the speed prediction model, and determine a third parameter matrix according to the product of a third weight matrix and the speed error matrix; Determine a target cost function according to the first parameter matrix, the second parameter matrix and the third parameter matrix; Determining a control thrust constraint condition according to a current operating range of the target planar motor and a current-control thrust conversion relationship; A position control optimization model of the target planar motor is determined according to the target cost function and the control thrust constraint condition.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the position control method of the planar motor according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the position control method of the planar motor according to any one of claims 1 to 5 when executed.

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