Motor feasible task design method based on motor performance parameters and controller structure

Abstract

The application belongs to the technical field of motor system control, and discloses a motor feasible task design method based on motor performance parameters and controller structure, which comprises the following steps: first, determining two types of motor performance parameters of the maximum torque and the peak power of the selected motor; second, based on Euler-Lagrange equation, establishing motor dynamics differential equation containing unknown rotational inertia, unknown friction resistance, and describing the characteristics of motor generalized speed; third, designing an adaptive controller to realize speed tracking control and online estimation of unknown rotational inertia, unknown friction resistance and unknown external disturbance; fourth, based on motor performance parameters and adaptive controller structure, establishing task feasibility constraint inequality, and determining motor feasible task in combination with the task load condition. The application can realize the analysis and determination of motor feasible task based on the control strategy and the maximum speed and the maximum torque of the motor without experimental test.

Description

Technical Field

[0001] This invention belongs to the field of motor system control technology, and in particular relates to a method for designing feasible tasks for a motor based on motor performance parameters and controller structure. Background Technology

[0002] In industrial applications, motors need to be loaded with different loads to perform different tasks. To address this requirement, engineers need to determine in advance the range of tasks a given motor can perform under a given load and optimize the task design to ensure safe and smooth execution. Typically, this process requires initial judgment based on experience, followed by extensive testing to finalize the task. However, this process consumes considerable testing time and engineering resources, placing a workload on engineers and posing certain safety hazards.

[0003] Furthermore, it is worth noting that different control strategies will impart different dynamic performances to the motor, thus having varying impacts on the design of feasible tasks. Therefore, when designing a motor speed tracking task under load, the influence of the controller structure must be additionally considered.

[0004] In summary, developing a theoretically supported feasible task design method for motors based on motor performance parameters and control strategies can effectively reduce the workload of engineers and has strong engineering application value. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a theoretically supported method for designing feasible motor tasks based on motor performance parameters and control strategies.

[0006] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: A method for designing feasible tasks for motors based on motor performance parameters and controller structure includes the following steps: The first step is to determine the two types of motor performance parameters for the selected motor: maximum torque and peak power. The second step is to establish a differential equation for motor dynamics based on the Euler-Lagrange equations, which includes unknown moment of inertia and unknown frictional resistance, to describe the characteristics of the generalized speed of the motor. The third step is to design an adaptive controller to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. The fourth step is to establish a task feasibility constraint inequality based on the motor performance parameters and the adaptive controller structure, and determine the feasible tasks for the motor in combination with the task load.

[0007] Furthermore, the first step specifically involves defining the maximum torque as the maximum output torque that the motor can achieve; the peak power as the maximum power that the motor can reach within a certain time period; and introducing the following variables to characterize the maximum torque of the motor. Peak power .

[0008] Furthermore, in the second step, based on the Euler-Lagrange equations, a differential equation for motor dynamics is established, incorporating unknown moment of inertia and unknown frictional resistance, to describe the characteristics of the motor's generalized speed as follows:

[0009] in It refers to the generalized speed of the electric motor. It is the control torque of the motor. It is the unknown but bounded moment of inertia of the motor under load. It is the unknown but bounded coefficient of friction resistance of the motor under load. The external noise has known boundaries, and the initial state of the differential equation is: .

[0010] Furthermore, the third step specifically includes: S3.1, Record the load in the task. Friction resistance coefficient External noise The range of values ​​for:

[0011] in, , , , , and For positive constants; design adaptive estimates , and Regarding the moment of inertia respectively The ratio of frictional resistance to moment of inertia The ratio of unknown external noise to rotational inertia Make an estimate; based on the load Friction resistance coefficient External noise The range of values ​​is determined , and The range of values ​​for; S3.2, the controller is designed as follows:

[0012] in, These are positive controller parameters. , It is the desired generalized speed of the motor. It is the desired generalized acceleration of the motor; S3.3, Design Adaptive Estimation , and The update law is:

[0013] in, , and These are positive controller parameters.

[0014] Furthermore, the fourth step specifically includes: S4.1, based on the motor performance parameters and the adaptive controller structure, the following task feasibility constraint inequalities are established:

[0015] S4.2, Define the task load The range of values ​​and the corresponding friction resistance coefficient The range of values ​​is used to evaluate external noise in the working environment. The range of values ​​for which the value is determined. upper bound of values and lower bound of values , upper bound of values and lower bound of values , upper bound of values and lower bound of values ; S4.3, Define the expected speed of the tracking task. upper bound of values and lower bound of values and acceleration upper bound of values and lower bound of values ; S4.4, regarding variables Let its value be or Regarding variables , , , Let its value be its upper bound or its lower bound; By iterating through all possible values ​​of the five variables and substituting each value into the task feasibility constraint inequality, if all values ​​make the inequality true, then the designed tracking task with load is feasible for the selected motor and the selected controller structure; if there is a violation of the inequality constraint, then the designed tracking task with load is not feasible for the selected motor and the selected controller structure.

[0016] The present invention has the following advantages: 1. This invention designs a feasible task design method for motors based on motor performance parameters and controller structure, realizing the analysis and optimization design of the selection range of feasible tasks for motors; 2. The method proposed in this invention does not require precise measurement of the physical parameters of the motor and the load, and has broad application prospects; 3. The motor selection method proposed in this invention can analyze and determine the feasibility of a motor task based on the control strategy and the maximum speed and torque of the motor without conducting tests, effectively reducing the loss of manpower and material resources caused by tests. 4. The adaptive controller proposed in this invention can reduce the adverse effects of resistance and noise on tracking error by increasing the control parameters. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method. Figure 2 This is a schematic diagram of speed tracking error; Figure 3 This is a schematic diagram of the adaptive estimate; Figure 4 This is a schematic diagram of the motor torque. Figure 5 This is a schematic diagram of the motor power. Figure 6 This is a schematic diagram of the speed tracking error after the control parameters are reduced. Detailed Implementation

[0018] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0019] This invention aims to propose a feasible task design method for motors based on motor performance parameters and controller structure. The specific technical solution is as follows: it includes the following steps: The first step is to determine the two types of motor performance parameters for the selected motor: maximum torque and peak power. The second step is to establish a differential equation for motor dynamics based on the Euler-Lagrange equations, which includes unknown moment of inertia and unknown frictional resistance, to describe the characteristics of the generalized speed of the motor. The third step is to design an adaptive controller to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. The fourth step is to establish a task feasibility constraint inequality based on the motor performance parameters and the adaptive controller structure, and determine the feasible tasks for the motor in combination with the task load.

[0020] Furthermore, in the first step, two types of motor performance parameters are determined: maximum torque and peak power. Specifically, maximum torque is defined as the maximum output torque that the motor can achieve; peak power is the maximum power that the motor can reach within a certain time period, which can be specified according to the motor manufacturer's performance manual. Based on the above definitions, the following variables are introduced to characterize the maximum torque of the motor. Peak power ; In the second step, based on the Euler-Lagrange equations, a differential equation for motor dynamics is established, which includes unknown moment of inertia and unknown frictional resistance. The characteristics of the generalized speed of the motor are described as follows:

[0021] in, It refers to the generalized speed of the electric motor. It is the control torque of the motor. It is the unknown but bounded moment of inertia of the motor under load. It is the unknown but bounded coefficient of friction resistance of the motor under load. The external noise has known boundaries, and the initial state of the differential equation is: ; In the third step, an adaptive controller is designed to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. Specifically, S1, Determine the load in the task. Friction resistance coefficient External noise The range of values ​​for:

[0022] in, , , , , and For positive constants. Design an adaptive estimate. , and Regarding the moment of inertia respectively The ratio of frictional resistance to moment of inertia The ratio of unknown external noise to rotational inertia Make an estimate. Based on the load. Friction resistance coefficient External noise The range of values ​​is determined , and The range of values ​​for .

[0023] S2, the adaptive controller is designed as follows:

[0024] in, It is a positive control parameter. , It is the desired generalized speed of the motor. It is the expected generalized acceleration of the motor.

[0025] S3, Design Adaptive Estimator , and The update law is:

[0026] in, , and These are positive controller parameters.

[0027] The fourth step involves establishing a task feasibility constraint inequality based on motor performance parameters and the adaptive controller structure, and determining feasible tasks for the motor by considering the task load. Specifically: S1. Based on the motor performance parameters and the adaptive controller structure, the following task feasibility constraint inequality is established:

[0028] S2, Define the task load The range of values ​​and the corresponding friction resistance coefficient The range of values ​​is used to evaluate external noise in the working environment. The range of values ​​for which the value is determined. upper bound of values and lower bound of values , upper bound of values and lower bound of values , upper bound of values and lower bound of values .

[0029] S3, specify the expected speed of the tracking task. upper bound of values and lower bound of values and acceleration upper bound of values and lower bound of values .

[0030] S4, regarding variables Let its value be or Regarding variables , , , Let the value be its upper bound or its lower bound. Iterate through all possible values ​​of the five variables and substitute each value into the task feasibility constraint inequality. If all values ​​make the inequality true, then the designed load-tracking task is feasible for the selected motor and controller structure. If there is a violation of the inequality constraint, then the designed load-tracking task is not feasible for the selected motor and controller structure.

[0031] Example 1 The following section, using specific parameters, further explains the motor feasibility task design method based on motor performance parameters and controller structure provided in this embodiment. A flowchart of the method is shown below. Figure 1 As shown, it includes the following steps: The first step is to determine the two types of motor performance parameters for the selected motor: maximum torque and peak power. The second step is to establish a differential equation for motor dynamics based on the Euler-Lagrange equations, which includes unknown moment of inertia and unknown frictional resistance, to describe the characteristics of the generalized speed of the motor. The third step is to design an adaptive controller to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. The fourth step is to establish a task feasibility constraint inequality based on the motor performance parameters and the adaptive controller structure, and determine the feasible tasks for the motor in combination with the task load.

[0032] In the first step, the maximum torque of the selected motor is determined. and peak power .

[0033] In the second step, based on the Euler-Lagrange equations, a differential equation for motor dynamics is established, which includes unknown moment of inertia and unknown frictional resistance. The characteristics of the generalized speed of the motor are described as follows:

[0034] The initial state is , Friction resistance coefficient and external noise They are respectively:

[0035] In the third step, an adaptive controller is designed to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. Specifically, S1, Determine the load in the task. Friction resistance coefficient External noise The range of values ​​for:

[0036] S2, the adaptive controller is designed as follows:

[0037] Among them, control parameters .

[0038] S3, Design Adaptive Estimator , and The update law is:

[0039] Among them, the update law parameters , , .

[0040] The fourth step involves establishing a task feasibility constraint inequality based on motor performance parameters and the adaptive controller structure, determining feasible tasks for the motor based on task load conditions, and optimizing these tasks. Specifically, S1. Based on the motor performance parameters and the adaptive controller structure, the following task feasibility constraint inequality is established:

[0041] S2, based on task load Friction resistance coefficient External noise The range of values ​​for can be determined. The upper bound of its value is 2 and the lower bound is 1. The upper bound of its value is 0.2 and the lower bound is 0.1. The upper bound of the value is 0.2 and the lower bound is -0.2.

[0042] S3, the tracking task is designed as follows:

[0043] The expected speed of the tracking task can be clearly defined. The upper bound of the value is 2 and the lower bound is -2, and the acceleration The upper bound of the value is 0.2 and the lower bound is -0.2.

[0044] S4, regarding variables Let its value be or Regarding variables , , , Let the value be its upper bound or its lower bound. Iterate through all possible values ​​of the five variables and substitute each value into the task feasibility constraint inequality. It can be concluded that the inequality holds for all the above values; therefore, the designed load-bearing tracking task is feasible.

[0045] To further verify the above tracking task, simulations were performed using MATLAB. The differential equations were solved numerically using the fourth-order Runge-Kutta method, with a calculation step size of 0.005 seconds and a simulation duration of 40 seconds. The simulation results are attached. Figures 2-6 As shown.

[0046] Appendix Figure 2 This is a schematic diagram of the tracking error generated by the proposed method. It can be seen that under the influence of disturbances, the controller can ensure rapid convergence of the unknown tracking error. (Attached) Figure 3 This is a schematic diagram of the adaptive estimate. It can be seen that the adaptive law of the designed adaptive estimate ensures that the estimate is bounded. (Attached) Figure 4 and attached Figure 5 These are schematic diagrams of motor torque and motor power, respectively. It can be seen that for the designed motor task, the actual motor performance does not trigger its performance boundaries. (See attached diagram.) Figure 2 , 4 As shown in section 5, the tracking task with load designed based on this method is feasible for the selected motor. (Appendix) Figure 6 This is a schematic diagram of the speed tracking error after the control parameters are reduced. With attachment Figure 2 The comparison shows that the parameters After the disturbance is reduced, its impact on tracking performance increases. The simulation results above verify the effectiveness and beneficial effects of this method.

[0047] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art will be able to make various modifications and improvements without departing from the principles of the present invention, and these modifications and improvements should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for designing feasible tasks for a motor based on motor performance parameters and controller structure, characterized in that, Includes the following steps: The first step is to determine the two types of motor performance parameters for the selected motor: maximum torque and peak power. The second step is to establish a differential equation for motor dynamics based on the Euler-Lagrange equations, which includes unknown moment of inertia and unknown frictional resistance, to describe the characteristics of the generalized speed of the motor. The third step is to design an adaptive controller to achieve speed tracking control and online estimation of unknown moment of inertia, unknown frictional resistance, and unknown external disturbances. The fourth step is to establish a task feasibility constraint inequality based on the motor performance parameters and the adaptive controller structure, and determine the feasible tasks for the motor in combination with the task load.

2. The motor feasibility task design method based on motor performance parameters and controller structure according to claim 1, characterized in that, The first step specifically involves defining the maximum torque as the maximum output torque that the motor can achieve; the peak power as the maximum power that the motor can reach within a certain time period; and introducing the following variables to characterize the maximum torque of the motor. Peak power .

3. The motor feasibility task design method based on motor performance parameters and controller structure according to claim 2, characterized in that, In the second step, based on the Euler-Lagrange equations, a differential equation for motor dynamics is established, which includes unknown moment of inertia and unknown frictional resistance. The characteristics of the generalized speed of the motor are described as follows: in It refers to the generalized speed of the electric motor. It is the control torque of the motor. It is the unknown but bounded moment of inertia of the motor under load. It is the unknown but bounded coefficient of friction resistance of the motor under load. The external noise has known boundaries, and the initial state of the differential equation is: .

4. The motor feasibility task design method based on motor performance parameters and controller structure according to claim 3, characterized in that, The third step specifically involves: S3.1, Record the load in the task. Friction resistance coefficient External noise The range of values ​​for: in, , , , , and For positive constants; design adaptive estimates , and Regarding the moment of inertia respectively The ratio of frictional resistance to moment of inertia The ratio of unknown external noise to rotational inertia Make an estimate; based on the load Friction resistance coefficient External noise The range of values ​​is determined , and The range of values ​​for; S3.2, the controller is designed as follows: in, These are positive controller parameters. , It is the desired generalized speed of the motor. It is the desired generalized acceleration of the motor; S3.3, Design Adaptive Estimation , and The update law is: in, , and These are positive controller parameters.

5. The motor feasibility task design method based on motor performance parameters and controller structure according to claim 4, characterized in that, The fourth step specifically involves: S4.1, based on the motor performance parameters and the adaptive controller structure, the following task feasibility constraint inequalities are established: S4.2, Define the task load The range of values ​​and the corresponding friction resistance coefficient The range of values ​​is used to evaluate external noise in the working environment. The range of values ​​for which the value is determined. upper bound of values and lower bound of values , upper bound of values and lower bound of values , upper bound of values and lower bound of values ; S4.3, Define the expected speed of the tracking task. upper bound of values and lower bound of values and acceleration upper bound of values and lower bound of values ; S4.4, regarding variables Let its value be or Regarding variables , , , Let its value be its upper bound or its lower bound; By iterating through all possible values ​​of the five variables and substituting each value into the task feasibility constraint inequality, if all values ​​make the inequality true, then the designed tracking task with load is feasible for the selected motor and the selected controller structure; if there is a violation of the inequality constraint, then the designed tracking task with load is not feasible for the selected motor and the selected controller structure.

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