A linear motor speed control method and system based on energy efficiency optimization

Abstract

The present invention discloses a linear motor speed control method and system based on energy efficiency optimization, which relates to the field of motor control technology, including collecting linear motor energy efficiency optimization and speed control system data; building energy efficiency optimization strategies based on energy efficiency modeling; outputting optimal control theory through energy recovery strategies; building an optimization target mathematical model, and building weight coefficients based on the optimization target state and input. The linear motor speed control method based on energy efficiency optimization provided by the present invention is based on energy efficiency modeling and building energy efficiency optimization strategies, improving the overall energy efficiency of the system, outputting optimal control theories through energy recovery strategies, reducing energy waste, building optimization target mathematical models, and achieving comprehensive improvement of linear motor speed control and energy efficiency optimization, ensuring efficient operation and maximization of energy utilization under complex working conditions, and the present invention achieves better results in terms of accuracy, stability and energy utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a linear motor speed regulation control method and system based on energy efficiency optimization. Background Art

[0002] In the field of modern industrial automation and intelligent manufacturing, linear motors are widely used in many fields such as CNC machine tools, robots, semiconductor manufacturing equipment, and logistics and transportation systems due to their high precision, high speed, and high response characteristics. Linear motors have become an important choice to replace traditional rotary motors and mechanical transmission systems due to their advantages such as direct drive, gearless transmission, simple structure, and easy control. However, with the continuous deepening of industrial applications, the requirements for the performance and energy efficiency of linear motors are also getting higher and higher. Traditional linear motor speed control methods mainly rely on classic PID control and simple vector control. These methods can meet basic needs in some application scenarios, but they still have obvious shortcomings in terms of high precision, high dynamic response, and energy efficiency optimization. In addition, advanced control methods developed in recent years, such as fuzzy control, neural network control, and adaptive control, have improved control accuracy and response speed to a certain extent, but there is still a lot of room for improvement in energy efficiency optimization.

[0003] The existing technology has many shortcomings in linear motor speed control and energy efficiency optimization. First, the traditional PID control method has limited performance when dealing with nonlinear, multivariable, and time-varying systems, and it is difficult to achieve global optimization. Secondly, the existing energy efficiency optimization strategies are mostly based on static models, which fail to fully consider the dynamically changing energy consumption characteristics during the operation of the motor, resulting in unsatisfactory energy efficiency optimization effects. In addition, the lack of energy recovery strategies is also a major bottleneck of the existing technology. Traditional systems often ignore the potential of energy recovery and fail to effectively utilize braking energy, resulting in energy waste. Furthermore, the existing optimization algorithms are mostly single algorithms, lacking multi-algorithm fusion and dynamic weight adjustment mechanisms, resulting in unstable optimization effects and difficulty in maintaining high-efficiency optimization control under complex working conditions. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing motor control technology method has the problems of low accuracy, high energy waste, low stability and poor adaptability.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a linear motor speed control method based on energy efficiency optimization, including collecting linear motor energy efficiency optimization and speed control system data; building energy efficiency optimization strategies based on energy efficiency modeling; outputting optimal control theory through energy recovery strategies; building an optimization target mathematical model, and building weight coefficients based on the optimization target state and input.

[0007] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the acquisition of linear motor energy efficiency optimization and speed control system data includes acquiring motor data and optimization algorithm data;

[0008] The motor data includes motor specification parameters, motor dynamic characteristics, voltage, current and power;

[0009] The optimization algorithm data includes genetic algorithm data, particle swarm optimization algorithm data, Kalman filter data, energy efficiency data and loss data.

[0010] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the energy efficiency modeling and energy efficiency optimization strategy construction includes constructing an energy efficiency model through input power and output power, which is expressed as:

[0011] ,

[0012] ,

[0013] ,

[0014] in, represents the input power, Indicates the input voltage, represents the input current, Indicates the output power, Represents the output force, Indicates speed, Indicates energy efficiency, Indicates the moment;

[0015] Considering resistance loss, iron loss, mechanical loss and eddy current loss, the power loss model is constructed through the energy efficiency model, which is expressed as:

[0016] ,

[0017] in, represents the power loss, Represents resistance, represents the iron loss coefficient, represents the magnetic flux density, represents the angular velocity, represents the friction force, Represents the eddy current loss coefficient.

[0018] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the energy efficiency-based modeling and energy efficiency optimization strategy construction includes constructing an optimization target model based on minimizing total energy loss, using variational method and Lagrange multiplier method, minimizing total energy loss within a time interval, optimizing current by variational method to achieve the goal of energy efficiency optimization, and adding constraints on the relationship between current and voltage;

[0019] The total energy loss is determined by the power loss model, including resistance loss, iron loss, mechanical loss, and eddy current loss;

[0020] Define the energy loss function in the time interval (0, T) as the optimization goal to minimize the energy loss of the motor under the constraints and optimize the input current , improve the energy efficiency of the motor, build an optimization target model, and set the input current and voltage The relationship between the inductance L and the resistance R is considered during the optimization process to ensure the optimization of the input current The physical feasibility of , and then introduce physical constraints, expressed as:

[0021] ,

[0022] ,

[0023] in, Indicates the upper limit of the time interval represents inductance;

[0024] The optimization objective and constraint conditions are combined, and the constraint conditions are handled by introducing the Lagrange multiplier λ. The loss term and constraint term are comprehensively considered, and the function I(t) of the Lagrange function extremization is used to obtain the optimal current distribution, which is expressed as:

[0025] ,

[0026] in, represents the Lagrangian function, combined with the objective function and constraints, represents the Lagrange multiplier, which is used to deal with constraints;

[0027] Based on solving the variational problem, the function I(t) that maximizes the Lagrangian function is output, and then the input current I(t) is output to minimize the energy loss in the time interval (0, T), optimize the current distribution, and output the mathematical model of the Lagrangian function extremization as follows:

[0028] ,

[0029] in, Represents the time derivative of the input current I(t).

[0030] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the output of the optimal control theory through the energy recovery strategy includes collecting the maximum recovered energy within the time interval, ensuring the maximum energy recovery efficiency through energy recovery, and constructing the optimal control theory model;

[0031] In the time interval ( ) is the objective function of maximizing the recovered energy, Represents two moments in the braking or deceleration process. As the optimization goal, in the energy recovery process, the energy recovery efficiency is maximized. By maximizing the recovered energy and the energy in the braking process, the energy efficiency of the system is improved. The optimal control theory model is constructed as:

[0032] ,

[0033] in, represents the recovered energy, represents the energy recovery efficiency, Indicates braking power.

[0034] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the output of the optimal control theory through the energy recovery strategy includes adopting Hamiltonian in the optimal control theory model, combining the energy recovery target and dynamic constraints, and processing the dynamic constraints by introducing Lagrange multiplier λ;

[0035] Constructing the Hamiltonian , combined with the energy recovery goal and dynamic constraints, the optimization goal of the system is expressed as:

[0036] ,

[0037] ,

[0038] in, Represent the Hamiltonian, combining the optimization objective and dynamic constraints;

[0039] Through the output , determine the optimal energy recovery strategy and the rate of change of the Lagrange multiplier λ, and then solve the optimal control problem to ensure that the system achieves optimal control under dynamic constraints.

[0040] As a preferred solution of the linear motor speed control method based on energy efficiency optimization described in the present invention, wherein: the construction of the optimization target mathematical model includes a comprehensive genetic algorithm, a particle swarm optimization algorithm, a gradient descent method and a Lagrange multiplier method, and the optimal control and energy efficiency optimization are achieved by dynamically adjusting the weight of the algorithm;

[0041] In the initial stage and when the global optimization demand increases, the weights of genetic algorithm and particle swarm optimization algorithm increase;

[0042] When the system stability and local optimization requirements increase, the weight of the gradient descent method and the constrained optimization part increases;

[0043] The optimization objective mathematical model is expressed as:

[0044] ,

[0045] in, represents the value of the optimization target mathematical model, Represents genetic algorithm The weight coefficient of represents the optimization result of genetic algorithm, represents the weight coefficient of the particle swarm optimization algorithm (PSO), represents the optimization result of the particle swarm optimization algorithm, represents the weight coefficient of the smoothing term based on the second-order derivative of power loss, represents the weight coefficient of the constrained optimization term based on the Lagrangian function, represents the current of the i-th node.

[0046] Another object of the present invention is to provide a linear motor speed control system based on energy efficiency optimization, which can output optimal control theory through energy recovery strategy, thereby solving the problem of high energy waste in current motor control technology methods.

[0047] As a preferred solution of the linear motor speed control system based on energy efficiency optimization described in the present invention, it includes: an initialization module, an energy efficiency optimization strategy construction module, an energy recovery module, and an optimization target mathematical model construction module; the initialization module is used to collect linear motor energy efficiency optimization and speed control system data; the energy efficiency optimization strategy construction module is used to dynamically adjust the current; the energy recovery module is used to recover energy through optimal control theory; the optimization target mathematical model construction module is used to achieve optimal control and energy efficiency optimization.

[0048] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a linear motor speed control method based on energy efficiency optimization.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a linear motor speed control method based on energy efficiency optimization.

[0050] Beneficial effects of the present invention: The linear motor speed control method based on energy efficiency optimization provided by the present invention is based on energy efficiency modeling and constructs an energy efficiency optimization strategy, realizes accurate modeling and optimization of the linear motor energy efficiency, improves the overall energy efficiency of the system, and outputs the optimal control theory through the energy recovery strategy. Combined with the Hamiltonian and the Pontryagin maximum principle, it ensures the optimal control under dynamic constraints, achieves the effect of maximizing energy recovery, reduces energy waste, constructs an optimization target mathematical model, relies on the optimization target state and input to construct the weight coefficient, realizes the comprehensive improvement of the linear motor speed control and energy efficiency optimization, ensures efficient operation under complex working conditions and maximizes the energy utilization rate. The present invention achieves better results in terms of accuracy, stability and energy utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use 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 ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0052] Figure 1 An overall flow chart of a linear motor speed control method based on energy efficiency optimization provided in the first embodiment of the present invention.

[0053] Figure 2 An overall flow chart of a linear motor speed control system based on energy efficiency optimization provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0055] Example 1

[0056] Reference Figure 1 , is an embodiment of the present invention, and provides a linear motor speed control method based on energy efficiency optimization, comprising:

[0057] S1: Collect linear motor energy efficiency optimization and speed control system data.

[0058] Furthermore, motor data and optimization algorithm data are collected;

[0059] The motor data includes motor specification parameters, motor dynamic characteristics, voltage, current and power;

[0060] The optimization algorithm data includes genetic algorithm data, particle swarm optimization algorithm data, Kalman filter data, energy efficiency data and loss data.

[0061] It should be noted that the linear motor energy efficiency optimization and speed control system data includes basic motor data, operating status data, control and optimization data, optimization algorithm data, energy efficiency analysis data, system response data, and environmental and external factor data;

[0062] Basic motor data includes motor specification parameters and motor dynamic characteristics;

[0063] Operating status data includes voltage and current, power and speed, and temperature;

[0064] Control and optimization data include control signals and power;

[0065] The optimization algorithm data includes genetic algorithm data, particle swarm optimization algorithm data and Kalman filter data;

[0066] Energy efficiency analysis data includes energy efficiency data and loss data;

[0067] System response data includes time response data and frequency response data;

[0068] Environmental and external factors data includes environmental conditions and external disturbances

[0069] Basic motor data describes the basic parameters and dynamic characteristics of the motor and is used to build a motor model and understand its operating characteristics.

[0070] The operating status data monitors the operating status of the motor in real time, including information such as voltage, current, power, speed and temperature, reflecting the actual working conditions of the motor.

[0071] Control and optimization data include control signals and power-related data output by the control system, which are used to analyze and optimize control strategies.

[0072] Optimization algorithm data Data related to optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, and Kalman filters) used to optimize system performance and improve energy efficiency.

[0073] Energy efficiency analysis data is used to evaluate system energy efficiency and analyze specific energy loss conditions, supporting energy efficiency optimization decisions.

[0074] System response data reflects the response characteristics of the system to various inputs and disturbances, including time response and frequency response data, which are used for system stability and performance analysis.

[0075] Environmental and external factors data records the impact of environmental conditions and external interference on the system, ensuring the reliability and stability of the system under various environmental conditions.

[0076] S2: Based on energy efficiency modeling and building energy efficiency optimization strategy.

[0077] Furthermore, the energy efficiency model is constructed by input power and output power, which is expressed as:

[0078] ,

[0079] ,

[0080] ,

[0081] in, represents the input power, Indicates the input voltage, represents the input current, Indicates the output power, Represents the output force, Indicates speed, Indicates energy efficiency, Indicates the moment;

[0082] Considering resistance loss, iron loss, mechanical loss and eddy current loss, the power loss model is constructed through the energy efficiency model, which is expressed as:

[0083] ,

[0084] in, represents the power loss, Represents resistance, represents the iron loss coefficient, represents the magnetic flux density, represents the angular velocity, represents the friction force, Represents the eddy current loss coefficient.

[0085] It should be noted that based on minimizing the total energy loss, the variational method and Lagrange multiplier method are used to construct the optimization target model, and the total energy loss is minimized within the time interval. The current is optimized by the variational method to achieve the goal of energy efficiency optimization, and the constraints on the relationship between current and voltage are added;

[0086] The total energy loss is determined by the power loss model, including resistance loss, iron loss, mechanical loss, and eddy current loss;

[0087] Define the energy loss function in the time interval (0, T) as the optimization goal to minimize the energy loss of the motor under the constraints and optimize the input current , improve the energy efficiency of the motor, build an optimization target model, and set the input current and voltage The relationship between the inductance L and the resistance R is considered during the optimization process to ensure the optimization of the input current The physical feasibility of , and then introduce physical constraints, expressed as:

[0088] ,

[0089] ,

[0090] in, Indicates the upper limit of the time interval represents inductance;

[0091] The optimization objective and constraint conditions are combined, and the constraint conditions are handled by introducing the Lagrange multiplier λ. The loss term and constraint term are comprehensively considered, and the function I(t) of the Lagrange function extremization is used to obtain the optimal current distribution, which is expressed as:

[0092] ,

[0093] in, represents the Lagrangian function, combined with the objective function and constraints, represents the Lagrange multiplier, which is used to deal with constraints;

[0094] Based on solving the variational problem, the function I(t) that maximizes the Lagrangian function is output, and then the input current I(t) is output to minimize the energy loss in the time interval (0, T), optimize the current distribution, and output the mathematical model of the Lagrangian function extremization as follows:

[0095] ,

[0096] in, Represents the time derivative of the input current I(t).

[0097] S3: Output optimal control theory through energy recovery strategy.

[0098] Furthermore, the maximum amount of recovered energy is collected within the time interval, and by recovering energy, the energy recovery efficiency is maximized, and an optimal control theory model is constructed;

[0099] In the time interval ( ) is the objective function of maximizing the recovered energy, Represents two moments in the braking or deceleration process. As the optimization goal, in the energy recovery process, the energy recovery efficiency is maximized. By maximizing the recovered energy and the energy in the braking process, the energy efficiency of the system is improved. The optimal control theory model is constructed as:

[0100] ,

[0101] in, represents the recovered energy, represents the energy recovery efficiency, Indicates braking power;

[0102] Hamiltonian is used in the optimal control theory model, combined with the energy recovery goal and dynamic constraints, and the dynamic constraints are handled by introducing the Lagrange multiplier λ;

[0103] Constructing the Hamiltonian , combined with the energy recovery goal and dynamic constraints, the optimization goal of the system is expressed as:

[0104] ,

[0105] ,

[0106] in, Represent the Hamiltonian, combining the optimization objective and dynamic constraints;

[0107] Through the output , determine the optimal energy recovery strategy and the rate of change of the Lagrange multiplier λ, and then solve the optimal control problem to ensure that the system achieves optimal control under dynamic constraints.

[0108] S4: Construct the optimization target mathematical model, which depends on the optimization target state and input construction weight coefficients.

[0109] Furthermore, the genetic algorithm, particle swarm optimization algorithm, gradient descent method and Lagrange multiplier method are integrated to achieve optimal control and energy efficiency optimization by dynamically adjusting the weights of the algorithms;

[0110] In the initial stage and when the global optimization demand increases, the weights of genetic algorithm and particle swarm optimization algorithm increase;

[0111] When the system stability and local optimization requirements increase, the weight of the gradient descent method and the constrained optimization part increases;

[0112] The optimization objective mathematical model is expressed as:

[0113] ,

[0114] in, represents the value of the optimization target mathematical model, Represents genetic algorithm The weight coefficient of represents the optimization result of genetic algorithm, represents the weight coefficient of the particle swarm optimization algorithm (PSO), represents the optimization result of the particle swarm optimization algorithm, represents the weight coefficient of the smoothing term based on the second-order derivative of power loss, represents the weight coefficient of the constrained optimization term based on the Lagrangian function, represents the current of the i-th node.

[0115] It should be noted that the dynamic weight adjustment ensures optimal control under different operating conditions, which can be expressed as:

[0116] ,

[0117] ,

[0118] ,

[0119] ,

[0120] in, , , , Indicates the impact of system status on different optimization algorithms, It controls the steepness of the Sigmoid function and adjusts the sensitivity of weight changes.

[0121] It should also be noted that genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are commonly used global optimization algorithms that can effectively search for the global optimal solution. They perform well in multi-modal functions and high-dimensional optimization problems. Gradient descent method is suitable for local optimization. By iteratively solving the gradient of the objective function, the local minimum can be quickly found. By combining these algorithms, the advantages of each can be utilized to improve the optimization efficiency and effect.

[0122] The weight coefficient is a dynamic function that depends on the system state, so that different optimization algorithms have different contributions in different states. Dynamic weight adjustment can improve the adaptability and flexibility of the system and ensure optimal control under different operating conditions.

[0123] The optimization objective function combines genetic algorithm, particle swarm optimization algorithm, gradient descent method and Lagrange multiplier method, and dynamically adjusts the weights of each algorithm to ensure optimal control and energy efficiency optimization under different operating conditions. Dynamic weight adjustment enables the system to adapt to changes and improve the overall optimization efficiency and effect.

[0124] Example 2

[0125] An embodiment of the present invention provides a linear motor speed control method based on energy efficiency optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0126] First, a linear motor model LMD-45 was selected for the experiment. The experimental preparation includes configuring the necessary sensors and data acquisition system to ensure that the various operating parameters of the motor can be monitored in real time. The experiment was divided into two groups, one group applied the energy efficiency optimization and speed control method of the present invention, and the other group, as a control group, used the traditional PID control method.

[0127] Configure a data acquisition system to collect linear motor energy efficiency optimization and speed control system data. The collected data include motor specifications (such as rated power, rated voltage), motor dynamic characteristics (such as magnetic flux density, angular velocity), voltage, current and power, etc. At the same time, record optimization algorithm data, including genetic algorithm data, particle swarm optimization algorithm data, Kalman filter data, energy efficiency data and loss data. The data acquisition system uses high-precision sensors, such as Hall sensors, photoelectric encoders and power meters, to ensure data accuracy.

[0128] After data collection is completed, energy efficiency modeling is performed and energy efficiency optimization strategies are constructed. The energy efficiency model is constructed through input power and output power, and the resistance loss, iron loss, mechanical loss and eddy current loss are considered to form a power loss model.

[0129] The optimal control theory is output through the energy recovery strategy. In the experiment, energy recovery data is collected during the braking process to build an optimal control theory model to ensure the maximization of energy recovery efficiency. The energy recovery strategy uses Hamiltonian combined with the Pontryagin maximum principle to optimize the energy recovery efficiency, build an optimization target mathematical model, and integrate genetic algorithm, particle swarm optimization algorithm, gradient descent method and Lagrange multiplier method to achieve optimal control and energy efficiency optimization by dynamically adjusting the weight coefficient. The weight coefficient adopts a dynamic adjustment mechanism and is adjusted according to the system state and input to ensure optimal control under different operating conditions.

[0130] During the experiment, the operating parameters of the motor were monitored and recorded in real time. By comparing the two groups of experimental data, the superiority of the method of the present invention in terms of accuracy, stability and energy utilization was verified.

[0131] Table 1 Experimental data table

[0132]

[0133] It can be seen from the data in the table that the method of the present invention is significantly superior to the traditional PID control method in many aspects.

[0134] First, the motor power of the experimental group is 450W, slightly lower than the 460W of the control group, indicating that the method of the present invention can achieve lower power consumption under the same input conditions. Further, from the perspective of input current, the experimental group is 2.05A and the control group is 2.10A, which further verifies the effectiveness of the method of the present invention in reducing current consumption.

[0135] Secondly, in terms of output power, the output power of the experimental group was 400W, while that of the control group was only 380W, indicating that the method of the present invention can achieve higher output efficiency. In terms of energy efficiency, the experimental group reached 88.9%, while the control group was only 82.6%, which shows that the method of the present invention has significant advantages in energy efficiency optimization.

[0136] In terms of energy recovery efficiency, the experimental group was 75.4%, significantly higher than the control group's 65.2%. This shows that the method of the present invention can effectively improve energy recovery efficiency, reduce energy waste, and improve the energy efficiency of the overall system.

[0137] In terms of power loss, the experimental group was 50W, while the control group was 80W, indicating that the method of the present invention performed well in reducing power loss. This was achieved by comprehensively considering resistance loss, iron loss, mechanical loss and eddy current loss, and adjusting them through optimization strategies.

[0138] Finally, from the perspective of system stability, the standard deviation (σ) of the experimental group was 0.03, while that of the control group was 0.07, indicating that the method of the present invention has higher stability during system operation. This is achieved by optimizing the target mathematical model and combining it with dynamically adjusting the weight coefficient to ensure that the system remains stable under different operating conditions.

[0139] In summary, the present invention achieves better results in terms of accuracy, stability and energy utilization.

[0140] Example 3

[0141] Reference Figure 2 , which is an embodiment of the present invention, provides a linear motor speed control system based on energy efficiency optimization, including an initialization module, an energy efficiency optimization strategy construction module, an energy recovery module, and an optimization target mathematical model construction module.

[0142] The initialization module is used to collect linear motor energy efficiency optimization and speed control system data, the energy efficiency optimization strategy construction module is used to dynamically adjust the current, the energy recovery module is used to recover energy through optimal control theory, and the optimization target mathematical model construction module is used to achieve optimal control and energy efficiency optimization.

[0143] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0145] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0147] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A linear motor speed control method based on energy efficiency optimization, characterized in that: include: Collect linear motor energy efficiency optimization and speed control system data, including motor data and optimization algorithm data; The motor data includes motor specification parameters, motor dynamic characteristics, voltage, current and power; The optimization algorithm data includes genetic algorithm data, particle swarm optimization algorithm data, Kalman filter data, energy efficiency data and loss data; Based on energy efficiency modeling and building energy efficiency optimization strategy; The energy efficiency model is constructed by input power and output power, which is expressed as: in, represents the input power, Indicates the input voltage, Indicates the input current, Indicates the output power, Represents the output force, Indicates speed, Indicates energy efficiency, Indicates the moment; Considering resistance loss, iron loss, mechanical loss and eddy current loss, the power loss model is constructed through the energy efficiency model, which is expressed as: in, represents the power loss, Represents resistance represents the iron loss coefficient, represents the magnetic flux density, represents the angular velocity, represents the friction force, represents the eddy current loss coefficient; Based on minimizing the total energy loss, the variational method and Lagrange multiplier method are used to build an optimization target model. The total energy loss is minimized within the time interval. The current is optimized by the variational method to achieve the goal of energy efficiency optimization, and the constraints on the relationship between current and voltage are added. The total energy loss is determined by the power loss model, including resistance loss, iron loss, mechanical loss, and eddy current loss; Define the energy loss function in the time interval (0, T) as the optimization goal to minimize the energy loss of the motor under the constraints and optimize the input current , improve the energy efficiency of the motor, build an optimization target model, and set the input current and voltage The relationship between the inductance L and the resistance R is considered during the optimization process to ensure the optimization of the input current The physical feasibility of , and then introduce physical constraints, expressed as: in, Indicates the upper limit of the time interval represents inductance; The optimization objective and constraint conditions are combined, and the constraint conditions are handled by introducing the Lagrange multiplier λ. The loss term and constraint term are comprehensively considered, and the function I(t) of the Lagrange function extremization is used to obtain the optimal current distribution, which is expressed as: in, represents the Lagrangian function, combined with the objective function and constraints, represents the Lagrange multiplier, which is used to deal with constraints; Based on solving the variational problem, the function I(t) that maximizes the Lagrangian function is output, and then the input current I(t) is output to minimize the energy loss in the time interval (0, T), optimize the current distribution, and output the mathematical model of the Lagrangian function extremization as follows: in, represents the time derivative of the input current I(t); Output optimal control theory through energy recovery strategy; Collect and maximize the recovered energy within the time interval, ensure the maximum energy recovery efficiency by recovering energy, and build the optimal control theory model; The objective function of maximizing the recovered energy in the time interval (t1, t2), t1, t2 represent two moments in the braking or deceleration process. As the optimization goal, the energy recovery efficiency is maximized during the energy recovery process. By maximizing the recovered energy and the energy during the braking process, the energy efficiency of the system is improved. The optimal control theory model is constructed as follows: in, represents the recovered energy, represents the energy recovery efficiency, Indicates braking power; Constructing the optimization target mathematical model, which depends on the optimization target state and the input construction weight coefficient; Hamiltonian is used in the optimal control theory model, combined with the energy recovery goal and dynamic constraints, and the dynamic constraints are handled by introducing the Lagrange multiplier λ; Constructing the Hamiltonian , combined with the energy recovery goal and dynamic constraints, the optimization goal of the system is expressed as: in, Represent the Hamiltonian, combining the optimization objective and dynamic constraints; Through the output , determine the optimal energy recovery strategy and the rate of change of the Lagrange multiplier λ, and then solve the optimal control problem to ensure that the system achieves optimal control under dynamic constraints; Combining genetic algorithm, particle swarm optimization algorithm, gradient descent method and Lagrange multiplier method, the optimal control and energy efficiency optimization are achieved by dynamically adjusting the weight of the algorithm; In the initial stage and when the global optimization demand increases, the weights of genetic algorithm and particle swarm optimization algorithm increase; When the system stability and local optimization requirements increase, the weight of the gradient descent method and the constrained optimization part increases; The optimization objective mathematical model is expressed as: in, represents the value of the optimization target mathematical model, Represents genetic algorithm The weight coefficient of represents the optimization result of genetic algorithm, represents the weight coefficient of the particle swarm optimization algorithm (PSO), represents the optimization result of the particle swarm optimization algorithm, represents the weight coefficient of the smoothing term based on the second-order derivative of power loss, represents the weight coefficient of the constrained optimization term based on the Lagrangian function, represents the current of the i-th node.

2. A system using the linear motor speed control method based on energy efficiency optimization as claimed in claim 1, characterized in that: It includes initialization module, energy efficiency optimization strategy building module, energy recovery module, and optimization target mathematical model building module; The initialization module is used to collect linear motor energy efficiency optimization and speed control system data; The energy efficiency optimization strategy building module is used to dynamically adjust the current; The energy recovery module is used to recover energy through optimal control theory; The optimization target mathematical model building module is used to achieve optimal control and energy efficiency optimization.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the linear motor speed control method based on energy efficiency optimization described in claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the linear motor speed control method based on energy efficiency optimization described in claim 1 are implemented.

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