An integrated electrical servo control method and system

Abstract

The application discloses an energy-saving integrated electric servo control method and system, wherein according to current rotating speed data, current current data and current position data, through control model comprehensive error value, error value total amount and error value change rate, the application obtains to-be-controlled rotating speed data, to-be-controlled current data and to-be-controlled position data, and then controls the energy-saving integrated servo motor. Through real-time current data, the application comprehensively considers three aspects to obtain to-be-controlled quantity, so that the to-be-controlled quantity changes in real time according to the error condition of the target quantity and the actual measured value, and solves the problem that the energy-saving integrated servo motor is in a constant power state in non-full power operation and electric stop state, thereby causing large energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor control, in particular to an integrated electric servo control method and system. BACKGROUND

[0002] The current energy-saving integrated electric servo control method uses the action of a certain component (such as a control lever) to make the state of the system reach or approach a certain predetermined value, and can compare the required state (required value) and the actual state, and adjust the control component according to their difference (sometimes the rate of change of this difference).

[0003] In the current energy-saving integrated electric servo control method, constant power is used within the rated power, and when the speed / torque exceeds the rated value, the power is overloaded for a short period. However, using the current energy-saving integrated electric servo control method, the energy-saving integrated servo motor is in a constant power state in the non-full power operation and the charged stop state, resulting in large energy consumption. SUMMARY

[0004] In view of the above problems in the prior art, the present application provides an integrated electric servo control method and system to solve the problem of large energy consumption caused by the energy-saving integrated servo motor being in a constant power state in the non-full power operation and the charged stop state using the current energy-saving integrated electric servo control method.

[0005] To achieve the above-mentioned application purposes, the technical scheme adopted by the present application is as follows: an integrated electric servo control method, comprising the following steps:

[0006] S1, sampling the rotor angle of the energy-saving integrated servo motor to obtain current rotor angle data;

[0007] S2, sampling the two-phase coil of the energy-saving integrated servo motor to obtain current current data;

[0008] S3, obtaining current rotation speed data of the energy-saving integrated servo motor and current position data of the energy-saving integrated servo motor according to the current rotor angle data;

[0009] S4, constructing a control model to calculate the controlled rotation speed data, the controlled current data and the controlled position data according to the current rotation speed data, the current current data and the current position data;

[0010] S5, controlling the energy-saving integrated servo motor according to the controlled rotation speed data, the controlled current data and the controlled position data.

[0011] Further, the formula for obtaining the current rotation speed data of the energy-saving integrated servo motor in S3 is:

[0012]

[0013] wherein, the current rotation speed data of the energy-saving integrated servo motor, the current rotor angle data, the current rotor angle data collected at the moment.

[0014] Further, the formula for obtaining the current position data of the energy-saving integrated servo motor in S3 is:

[0015]

[0016] wherein, the current position data of the energy-saving integrated servo motor, the current rotor angle data, the current rotor angle data collected at the moment.

[0017] Further, the expression of the control model in S4 is:

[0018]

[0019] wherein, the to-be-controlled quantity at the moment, the error value at the moment, the error value at the moment, the error value at the moment, the error value at the moment, the error value at the moment, the summation variable, the error value at the moment, the first weight coefficient, the second weight coefficient, the third weight coefficient. The above further scheme has the beneficial effect that the current error value, the total error value and the error value change rate are comprehensively considered to obtain the to-be-controlled quantity, so that the to-be-controlled quantity changes in real time according to the error between the target quantity and the actual measured value.

[0020] Further, the error value is the difference between the target rotation speed data and the current rotation speed data, or the difference between the target control current data and the current current data, or the difference between the target control position data and the current position data.

[0021] Further, the updating method of the weight coefficient in the control model is:

[0022] ​​​

[0023] A1. Construct a weight sequence using all weight coefficients as elements;

[0024] A2. Assign initial values ​​to the elements in the weight sequence;

[0025] A3. Substitute the elements in the current weight sequence into the control model to obtain the stable value of the model;

[0026] A4. Determine whether the stable value of the model is less than the stable threshold. If yes, the element value in the weight sequence is the required weight coefficient, and the process ends. If no, proceed to step A5.

[0027] A5. Update each element in the weight sequence based on the model's stable value, and then proceed to step A3.

[0028] The beneficial effect of the above further scheme is that in each iteration, the elements in the current weight sequence need to be brought into the control model. By testing the control model multiple times, the stable value of the model is obtained. If the stable value of the model is less than the stability threshold, it means that the control model system is stable, and the element value in the current weight sequence is the required weight coefficient.

[0029] Furthermore, the formula for calculating the model stability value in A3 is as follows:

[0030]

[0031] in, For the first The stable value of the model in the next iteration. In the first In the nth iteration The actual measured value, In the first In the nth iteration The target control quantity for this step. In the first The number of times the control model is tested in each iteration.

[0032] The beneficial effect of the above-mentioned further scheme is that the stability of the current model is measured by the difference between the target control quantity and the actual measured value after multiple tests. When the sum of the differences is small, it indicates that the control model is relatively stable.

[0033] Furthermore, the formula for updating each element in A5 is as follows:

[0034]

[0035] in, For the first The element value of the next iteration. As the initial value, For the first The stable value of the model in the next iteration. For the maximum model stability value, For the minimum model stability value, It is the arctangent function. This is the iteration speed adjustment factor.

[0036] The beneficial effect of the above further scheme is that during the iteration process, The iteration speed adjustment factor continues to increase. Used to restrict The degree of increase is determined by the iteration rate adjustment factor when it is necessary to increase the rate at which the adjustment element values ​​decrease. It can be set to a smaller value; when it is necessary to reduce the rate of decrease of the adjustment element value, the iteration rate adjustment factor is used. It can be set to a larger value; this is the iteration speed adjustment factor. The larger, The smaller, The slower the ascent rate, the more element values ​​are obtained, and the more complete the value selection; iteration speed adjustment factor The value is fixed, but its size can be adjusted at different stages of demand. A larger model stability value indicates a larger gap between the target control variable and the actual measured value, thus accelerating the descent rate. Conversely, a smaller model stability value indicates a smaller gap between the target control variable and the actual measured value, thus slowing the descent rate. This formula is more suitable for... When the value is large, for example, close to or equal to 1, it is assigned values ​​in descending order.

[0037] Furthermore, the formula for updating each element in A5 is as follows:

[0038]

[0039] in, For the first The element value of the next iteration. As the initial value, For the first The stable value of the model in the next iteration. For the maximum model stability value, For the minimum model stability value, It is the arctangent function. This is the iteration speed adjustment factor.

[0040] The beneficial effect of the above further scheme is that during the iteration process, The iteration speed adjustment factor continues to increase. Used to restrict The degree of increase is determined by the iteration rate adjustment factor when it is necessary to increase the rate of increase of the adjustment element value. Can be set to a smaller value, when the rising speed of the adjustment element value needs to be reduced, the iteration speed adjustment factor Can be set to a larger value, the iteration speed adjustment factor The larger, The smaller, The slower the rising speed, the more element values are obtained, and the values are more sufficient; the iteration speed adjustment factor A fixed specific value, but in different demand stages, the size can be adjusted, at the same time, when the model stable value is larger, it indicates that the target control quantity and the actual measured value are far apart, therefore, the rising speed can be accelerated, when the model stable value is smaller, it indicates that the target control quantity and the actual measured value are smaller, therefore, the rising speed can be reduced. The formula is more suitable for Smaller, for example, close to 0 or equal to 0, so that it is taken from small to large in turn.

[0041] An integrated electric servo control method system, comprising: a rotating magnetic field detection encoder, a current detector, a control unit and an energy-saving integrated servo motor;

[0042] The rotating magnetic field detection encoder is used for sampling the rotor angle of the energy-saving integrated servo motor to obtain current rotor angle data;

[0043] The current detector is used for sampling the two-phase coil of the energy-saving integrated servo motor to obtain current current data;

[0044] The control unit is used for obtaining current rotating speed data of the energy-saving integrated servo motor and current position data of the energy-saving integrated servo motor according to the current rotor angle data, and constructing a control model to calculate to-be-controlled rotating speed data, to-be-controlled current data and to-be-controlled position data according to the current rotating speed data, the current current data and the current position data, and controlling the energy-saving integrated servo motor through the to-be-controlled rotating speed data, the to-be-controlled current data and the to-be-controlled position data.

[0045] In summary, the beneficial effects of the present application are: according to the current rotating speed data, the current current data and the current position data, the control model is used to obtain the to-be-controlled rotating speed data, the to-be-controlled current data and the to-be-controlled position data by comprehensively considering the error value, the total amount of error values and the error value change rate, and then the energy-saving integrated servo motor is controlled. The present application comprehensively considers three aspects of real-time current data to obtain the to-be-controlled quantity, so that the to-be-controlled quantity changes in real time with the error between the target quantity and the actual measured value, and solves the problem that the energy-saving integrated servo motor is in a constant power state in the non-full power operation and the electric stop state, causing large energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1A flowchart of an integrated electrical servo control method;

[0047] Figure 2 This is a system block diagram of an integrated electrical servo control method. Detailed Implementation

[0048] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0049] like Figure 1 As shown, an integrated electrical servo control method includes the following steps:

[0050] S1. Sample the rotor angle of the energy-saving integrated servo motor to obtain the current rotor angle data;

[0051] S2. Sample the two-phase coils of the energy-saving integrated servo motor to obtain the current current data;

[0052] S3. Based on the current rotor angle data, obtain the current rotational speed data and the current position data of the energy-saving integrated servo motor;

[0053] The formula for obtaining the current rotational speed data of the energy-saving integrated servo motor in S3 is as follows:

[0054]

[0055] in, This is the current rotational speed data for the energy-efficient integrated servo motor. This is the current rotor angle data. for Rotor angle data collected at all times.

[0056] The formula for obtaining the current position data of the energy-saving integrated servo motor in S3 is as follows:

[0057]

[0058] in, This is the current position data for the energy-efficient integrated servo motor. This is the current rotor angle data. for Rotor angle data collected at all times.

[0059] The current position data of the energy-saving integrated servo motor and the current rotation speed data of the energy-saving integrated servo motor can be represented simultaneously.

[0060] S4, constructing a control model according to the current rotation speed data, the current current data and the current position data to calculate the to-be-controlled rotation speed data, the to-be-controlled current data and the to-be-controlled position data;

[0061] The expression of the control model in the S4 is:

[0062]

[0063] Wherein, is the to-be-controlled quantity at the i th moment, the to-be-controlled quantity is the to-be-controlled rotation speed data, the to-be-controlled current data or the to-be-controlled position data, is the error value at the i th moment, is the error value at the i th moment, is the error value at the i th moment, is the error value at the i th moment, is the summation variable, is the error value at the i th moment, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient. The control model in the step S4 can only process one type of data each time, for example, inputting the current rotation speed data, only the to-be-controlled rotation speed data can be obtained, inputting the current current data, only the to-be-controlled current data can be obtained, and inputting the current position data, only the to-be-controlled position data can be obtained.

[0064] The present application comprehensively considers the current error value, the total error value and the error value change rate to obtain the to-be-controlled quantity, so that the to-be-controlled quantity changes in real time according to the error between the target quantity and the actual measured value.

[0065] The error value is the difference between the target rotation speed data and the current rotation speed data, or the difference between the target control current data and the current current data, or the difference between the target control position data and the current position data.

[0066] The updating method of the weight coefficient in the control model is:

[0067] A1, constructing a weight sequence by taking all the weight coefficients as elements;

[0068] A2, assigning an initial value to the elements in the weight sequence;

[0069] A3, bringing the elements in the current weight sequence into the control model to obtain a model stable value;

[0070] A3, bringing the elements in the current weight sequence into the control model to obtain a model stable value;

[0071] ​A4. Determine whether the stable value of the model is less than the stable threshold. If yes, the element value in the weight sequence is the required weight coefficient, and the process ends. If no, proceed to step A5.

[0072] A5. Update each element in the weight sequence based on the model's stable value, and then proceed to step A3.

[0073] In each iteration, the elements in the current weight sequence must be fed into the control model. By testing the control model multiple times, the stable value of the model is obtained. If the stable value of the model is less than the stability threshold, it means that the control model system is stable, and the element values ​​in the current weight sequence are the required weight coefficients.

[0074] The formula for calculating the model stability value in A3 is as follows:

[0075]

[0076] in, For the first The stable value of the model in the next iteration. In the first In the nth iteration The actual measured value, In the first In the nth iteration The target control quantity for this step. In the first The number of times the control model is tested in each iteration.

[0077] The control model is tested multiple times, and the stability of the current model is measured by the difference between the target control variable and the actual measured value. If the sum of the differences is small, it indicates that the control model is relatively stable.

[0078] The formula for updating each element in A5 is as follows:

[0079]

[0080] in, For the first The element value of the next iteration. As the initial value, For the first The stable value of the model in the next iteration. For the maximum model stability value, For the minimum model stability value, It is the arctangent function. This is the iteration speed adjustment factor.

[0081] During the iteration process, The iteration speed adjustment factor continues to increase. Used to restrict The degree of increase is determined by the iteration rate adjustment factor when it is necessary to increase the rate at which the adjustment element values ​​decrease. It can be set to a smaller value; when it is necessary to reduce the rate of decrease of the adjustment element value, the iteration rate adjustment factor is used. It can be set to a larger value; this is the iteration speed adjustment factor. The larger, The smaller, The slower the ascent rate, the more element values ​​are obtained, and the more complete the value selection; iteration speed adjustment factor The value is fixed, but its size can be adjusted at different stages of demand. A larger model stability value indicates a larger gap between the target control variable and the actual measured value, thus accelerating the descent rate. Conversely, a smaller model stability value indicates a smaller gap between the target control variable and the actual measured value, thus slowing the descent rate. This formula is more suitable for... When the value is large, for example, close to or equal to 1, it is assigned values ​​in descending order.

[0082] The formula for updating each element in A5 is as follows:

[0083]

[0084] in, For the first The element value of the next iteration. As the initial value, For the first The stable value of the model in the next iteration. For the maximum model stability value, For the minimum model stability value, It is the arctangent function. This is the iteration speed adjustment factor.

[0085] During the iteration process, The iteration speed adjustment factor continues to increase. Used to restrict The degree of increase is determined by the iteration rate adjustment factor when it is necessary to increase the rate of increase of the adjustment element value. It can be set to a smaller value; when it is necessary to reduce the rate of increase of the adjustment element value, the iteration rate adjustment factor is used. It can be set to a larger value; this is the iteration speed adjustment factor. The larger, The smaller, The slower the ascent rate, the more element values ​​are obtained, and the more complete the value selection; iteration speed adjustment factor It is a fixed specific value, but the size can be adjusted in different demand stages, and at the same time, when the model stable value is large, it indicates that the target control quantity and the actual measured value are far apart, so the rising speed can be accelerated, and when the model stable value is small, it indicates that the target control quantity and the actual measured value are close, so the rising speed can be reduced. The formula is more suitable for the case that the target control quantity is close to the actual measured value. When the model stable value is small, for example, close to 0 or equal to 0, it is sequentially taken from small to large.

[0086] S5, according to the to-be-controlled rotating speed data, the to-be-controlled current data and the to-be-controlled position data, the energy-saving integrated servo motor is controlled.

[0087] As shown in Figure 2 , a system of an integrated electrical servo control method comprises a rotating magnetic field detection encoder, a current detector, a control unit and an energy-saving integrated servo motor.

[0088] The rotating magnetic field detection encoder is used for sampling the rotor angle of the energy-saving integrated servo motor to obtain current rotor angle data.

[0089] The current detector is used for sampling the two-phase coil of the energy-saving integrated servo motor to obtain current current data.

[0090] The control unit is used for obtaining current rotating speed data of the energy-saving integrated servo motor and current position data of the energy-saving integrated servo motor according to the current rotor angle data, and constructing a control model to calculate to-be-controlled rotating speed data, to-be-controlled current data and to-be-controlled position data according to the current rotating speed data, the current current data and the current position data, and controlling the energy-saving integrated servo motor through the to-be-controlled rotating speed data, the to-be-controlled current data and the to-be-controlled position data.

[0091] In summary, the beneficial effects of the present application are: according to the current rotating speed data, the current current data and the current position data, the to-be-controlled rotating speed data, the to-be-controlled current data and the to-be-controlled position data are obtained through the control model comprehensive error value, error value total amount and error value change rate, and then the energy-saving integrated servo motor is controlled. The present application comprehensively considers three aspects through real-time current data, obtains the to-be-controlled quantity, makes the to-be-controlled quantity change in real time with the error condition of the target quantity and the actual measured value, and solves the problem that the energy-saving integrated servo motor is in a constant power state in the non-full power operation and the electric stop state, causing large energy consumption.

Claims

1. An integrated electric servo control method, characterized by, The method comprises the following steps: S1, sampling a rotor angle of an integrated servo motor to obtain current rotor angle data; S2, sampling two-phase coils of the integrated servo motor to obtain current current data; S3, obtaining current rotation speed data of the integrated servo motor and current position data of the integrated servo motor according to the current rotor angle data; S4, constructing a control model according to the current rotation speed data, the current current data and the current position data to calculate to-be-controlled rotation speed data, to-be-controlled current data and to-be-controlled position data; The expression of the control model in the S4 is: wherein, is a first time, is a controlled variable at a first time, is a controlled variable at a second time, is an error value at a first time, is an error value at a second time, is an error value at a first time, is a summation variable, is an error value at a first time, is an error value at a second time, is a first weight coefficient, is a second weight coefficient, is a third weight coefficient. The error value is a difference between target rotation speed data and current rotation speed data, or a difference between target control current data and current current data, or a difference between target control position data and current position data; The updating method of the weight coefficient in the control model is: A1, constructing a weight sequence by taking all weight coefficients as elements; A2, assigning initial values to the elements in the weight sequence; A3, bringing the elements in the current weight sequence into the control model to obtain a model stable value; A4, determining whether the model stable value is less than a stable threshold value, if yes, the element values in the weight sequence are the required weight coefficients, and the process ends, if not, jumping to step A5; A5, updating each element in the weight sequence according to the model stable value, and jumping to step A3; The calculation formula of the model stable value in the A3 is: wherein, is the model stability value for the th iteration, is the actual measurement value for the th iteration, is the actual measurement value for the th iteration, is the target control value for the th iteration, is the target control value for the th iteration; and The formula for updating each element in the A5 is: wherein, is the element value for the first iteration, is the initial value, is the element value for the first iteration, is the initial value, is the model stability value for the first iteration, is the maximum model stability value, is the minimum model stability value, is the arctangent function, is the iteration speed adjustment factor; S5, controlling the integrated servo motor according to the to-be-controlled rotation speed data, the to-be-controlled current data and the to-be-controlled position data.

2. The integrated electric servo control method according to claim 1, wherein The formula for obtaining the current rotation speed data of the integrated servo motor in the S3 is: wherein, is current rotational speed data of the integrated servo motor, is current rotor angle data, is rotor angle data collected at the moment.

3. The integrated electrical servo control method of claim 1, wherein The formula for obtaining the current position data of the integrated servo motor in the S3 is: wherein is current position data of the integrated servo motor, is current rotor angle data, is is rotor angle data collected at the moment.

4. The integrated electrical servo control method of claim 1, wherein The formula for updating each element in the A5 is: in, For the first The element value of the next iteration. As the initial value, For the first The stable value of the model in the next iteration. For the maximum model stability value, For the minimum model stability value, It is the arctangent function. This is the iteration speed adjustment factor.

5. The system for integrated electric servo control method according to any one of claims 1 to 4, characterized in that, It comprises: A rotating magnetic field detection encoder, a current detector, a control unit and an integrated servo motor; The rotating magnetic field detection encoder is used for sampling a rotor angle of the integrated servo motor to obtain current rotor angle data; The current detector is used for sampling two-phase coils of the integrated servo motor to obtain current current data; The control unit is used for obtaining current rotation speed data of the integrated servo motor and current position data of the integrated servo motor according to the current rotor angle data, and constructing a control model according to the current rotation speed data, the current current data and the current position data to calculate to-be-controlled rotation speed data, to-be-controlled current data and to-be-controlled position data, and controlling the integrated servo motor through the to-be-controlled rotation speed data, the to-be-controlled current data and the to-be-controlled position data.

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