A rotating speed control method and device, electronic equipment and storage medium

Abstract

The application relates to the technical field of motor control, and discloses a rotating speed control method and device, electronic equipment and a storage medium, the method comprising the following steps: acquiring a rotating speed error and a rotating speed error change amount of a controlled motor at a current moment; calculating a hesitating fuzzy number of the rotating speed error and the rotating speed error change amount, applying a time weight coefficient to the hesitating fuzzy number to obtain a time sequence hesitating fuzzy number of the rotating speed error and the rotating speed error change amount; establishing a fuzzy rule table, performing fuzzy reasoning on the time sequence hesitating fuzzy number according to a lookup table function of the fuzzy rule table to obtain a parameter adjustment amount; and performing defuzzification processing on the parameter adjustment amount, and performing filtering processing on the parameter adjustment amount after the defuzzification processing to obtain a parameter increment value. By constructing the hesitating fuzzy number and applying the time weight coefficient, the influence of interference at different moments on control parameter adjustment can be reflected, the uncertainty caused by the interference can be limited, and control output jitter caused by membership degree fluctuation can be effectively inhibited.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically to a speed control method, device, electronic equipment, and storage medium. Background Technology

[0002] Proportional-integral (PI) control has become the mainstream solution for industrial motor speed control due to its simple structure, clear physical meaning of parameters, and ease of engineering implementation. However, the parameters of traditional PI controllers are fixed once tuned. When the motor faces complex disturbances such as sudden load changes, torque pulsation, parameter perturbation, or friction fluctuations, the control law with fixed parameters is difficult to adapt to the dynamic changes in operating conditions, resulting in increased overshoot, prolonged settling time, and even oscillation instability.

[0003] To overcome the limitations of fixed-parameter control, the industry has proposed improved schemes such as sliding mode control, disturbance observer compensation control, and active disturbance rejection control, which have improved the system's anti-interference capability to some extent. However, the above methods all focus on making control decisions based on the current state information and lack the utilization of the evolution of disturbances over time, that is, they do not consider time as an independent dimension for adjusting control parameters. Summary of the Invention

[0004] This invention provides a speed control method, device, electronic device, and storage medium to solve the problem of lack of timing information in existing speed control methods.

[0005] In a first aspect, the present invention provides a speed control method, comprising: Obtain the speed error and speed error change of the controlled motor at the current moment; Calculate the hesitant fuzzy numbers of speed error and speed error change, apply time weighting coefficients to the hesitant fuzzy numbers to obtain the temporal hesitant fuzzy numbers of speed error and speed error change; A fuzzy rule table is established, and fuzzy inference is performed on the temporal hesitant fuzzy numbers based on the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; The parameter adjustment amount is deblurred, and the deblurred parameter adjustment amount is filtered to obtain the parameter increment value. The controller's control parameters are updated based on the parameter increment values, and the speed control signal is output to the controlled motor according to the updated control parameters.

[0006] In one optional implementation, calculating the hesitant fuzzy number of the rotational speed error and the change in rotational speed error includes: The membership center value of the speed error or the change in speed error is calculated according to the first preset formula, wherein the first preset formula is:

[0007] in, The membership center value of the speed error or the change in speed error. This refers to the speed error or the change in speed error. This represents the maximum amplitude of the speed error or the change in speed error. The degree of hesitation is calculated based on the second preset formula, the membership center value, and the hesitation coefficient. The second preset formula is as follows:

[0008] in, For hesitation level, Let be the hesitation coefficient, and ; The hesitant fuzzy number is constructed based on the third preset formula, the membership center value, and the hesitancy degree. The third preset formula is:

[0009] in, The membership center value, This is the lower bound of the membership degree. This is the upper bound of the membership degree. For hesitant and ambiguous numbers.

[0010] In one alternative implementation, the formula for constructing the temporal hesitant ambiguity number is:

[0011] in, For time-series hesitant fuzzy numbers, This is the time weighting coefficient.

[0012] In one optional implementation, the parameter adjustment amount includes a lower bound, a center value, and an upper bound, and the formula for calculating the parameter adjustment amount is:

[0013] in, This is the lower bound of the parameter adjustment value. For parameter adjustment center value, This is the upper limit of the parameter adjustment value. For table lookup functions, , , Respectively, speed error The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. , , These are the changes in rotational speed error. The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. The lower bound of the parameter adjustment value in the fuzzy rule table line, number The output quantization value corresponding to the column, The center value of the parameter adjustment quantity in the fuzzy rule table line, number The output quantization value corresponding to the column, The upper bound of the parameter adjustment amount is in the fuzzy rule table. line, number The output quantization value corresponding to the column.

[0014] In one optional implementation, the parameter adjustment amount is deblurred, including: The parameter adjustment amount is defuzzified according to the fourth preset formula, which is:

[0015] in, This represents the parameter adjustment amount after deblurring.

[0016] In one optional implementation, the parameter adjustment amount after deblurring is filtered to obtain the parameter increment value, including: The parameter increment value is obtained by filtering the deblurred parameter adjustment amount according to the fifth preset formula, whereby the fifth preset formula is:

[0017] in, For the current moment The parameter increment value, for The parameter increment value at time t. These are the smoothing filter coefficients.

[0018] In one optional implementation, updating the controller's control parameters based on parameter increment values ​​includes: According to the sixth preset formula, and the incremental values ​​of the proportional gain parameter and the integral gain parameter, the proportional gain reference parameter and the integral gain reference parameter of the controller are updated. The parameter increment values ​​include the incremental values ​​of the proportional gain parameter and the integral gain parameter, and the control parameters include the proportional gain reference parameter and the integral gain reference parameter. The sixth preset formula is:

[0019] in, , These are the updated proportional gain reference parameters and integral gain reference parameters at the current time. , These are the proportional gain reference parameters and integral gain reference parameters before the update. , These are the incremental values ​​of the proportional gain parameter and the integral gain parameter, respectively.

[0020] In a second aspect, the present invention provides a speed control device, the device comprising: The data acquisition module is used to acquire the speed error and speed error change of the controlled motor at the current moment; The temporal hesitant fuzzy number calculation module is used to calculate the hesitant fuzzy number of speed error and speed error change, and apply a time weighting coefficient to the hesitant fuzzy number to obtain the temporal hesitant fuzzy number of speed error and speed error change. The parameter adjustment calculation module is used to establish a fuzzy rule table, and to perform fuzzy inference on the temporal hesitant fuzzy numbers based on the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; The parameter increment value calculation module is used to defuzzify the parameter adjustment amount, and then filter the defuzzified parameter adjustment amount to obtain the parameter increment value. The control parameter update module is used to update the controller's control parameters based on the parameter increment value, and output a speed control signal to the controlled motor according to the updated control parameters.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the speed control method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the speed control method of the first aspect or any corresponding embodiment thereof.

[0023] The present invention has at least the following beneficial effects: This invention provides a speed control method, device, electronic device, and storage medium. By constructing hesitant fuzzy numbers for the speed error and the change in speed error and applying time weighting coefficients, the impact of disturbances at different times on the adjustment of control parameters can be characterized differently. By making full use of historical time series information and establishing a fuzzy rule table, fuzzification processing, and filtering processing, the uncertainty caused by disturbances can be limited, effectively suppressing the control output jitter caused by membership fluctuations, and realizing the smooth online update of PI parameters. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic flowchart of a speed control method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structural framework of a speed control device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structural framework of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The archiving method of this invention can be executed by a terminal device, which may be a smartphone, tablet computer, laptop computer, handheld computer, desktop computer, game console, smart TV, smart wearable device, etc.

[0029] This embodiment provides a speed control method, such as... Figure 1 As shown, it includes: Step S101: Obtain the speed error and speed error change of the controlled motor at the current moment; Specifically, the actual speed and the given speed of the controlled motor at the current moment are first obtained. The given speed is the speed command value of the controlled motor, and the actual speed can be obtained through a sensor installed on the controlled motor. The speed error is the difference between the actual speed and the given speed, and the change in speed error is the difference between the speed error at the current moment and the speed error at the previous moment.

[0030] Step S102: Calculate the hesitant fuzzy numbers of the speed error and the change in speed error, apply a time weighting coefficient to the hesitant fuzzy numbers to obtain the temporal hesitant fuzzy numbers of the speed error and the change in speed error; Specifically, since there are uncertainties such as measurement noise, load disturbance, and parameter perturbation during the actual operation of the motor, and these uncertainties are not definite values, it is necessary to construct hesitant fuzzy numbers to confine the uncertainties within a range.

[0031] Hesitant fuzzy numbers are constructed based on the rotational speed error and the change in rotational speed error. The hesitant fuzzy numbers are ordered combinations of several fuzzy membership degrees. In this embodiment, the fuzzy number includes three membership values, which are the lower bound values ​​of the membership degree. Membership center value and membership degree upper bound value The three membership values ​​confine the uncertainty caused by the disturbance within a single interval. After calculating the hesitant fuzzy number, a time-weighted coefficient is applied to obtain the temporal hesitant fuzzy number. The time-weighted coefficient reflects the degree of influence of input information at different times on the current control decision.

[0032] Step S103: Establish a fuzzy rule table, and perform fuzzy inference on the temporal hesitant fuzzy number according to the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; Specifically, a fuzzy rule table is pre-established. A lookup function is used to infer and search for the three membership values ​​of the temporal hesitant fuzzy number, and the corresponding output value is found in the fuzzy rule table. This output value corresponds to the parameter adjustment amount. The fuzzy rules in this embodiment are as follows: ,in, For speed error, This represents the change in rotational speed error. The first of the speed error A set of fuzzy numbers The first of the changes in rotational speed error A set of fuzzy numbers For the fuzzy rule table, the first... Line number The parameter adjustment amount corresponding to the column.

[0033] Step S104: Deblur the parameter adjustment amount, and then filter the deblurred parameter adjustment amount to obtain the parameter increment value. Specifically, after the temporal hesitant fuzzy number looks up the fuzzy rule table, the lower bound, center value, and upper bound each correspond to a parameter adjustment amount, i.e., the obtained parameter adjustment amount is a triplet. This triplet needs to be transformed into a single, definite value. After defuzzifying the parameter adjustment amount, a clear value is obtained. The clear value is then filtered to obtain the parameter increment value, making the parameter adjustment process smoother and preventing high-frequency abrupt changes in the parameter adjustment amount due to instantaneous disturbances.

[0034] Step S105: Update the controller's control parameters based on the parameter increment value, and output a speed control signal to the controlled motor according to the updated control parameters.

[0035] Specifically, the controller adopts a proportional-integral (PI) controller, which includes two control parameters: proportional gain and integral gain. The control parameters are dynamically updated by applying incremental values ​​of the parameters to the proportional gain and integral gain.

[0036] This invention constructs hesitant fuzzy numbers for both the rotational speed error and its variation, and applies time-weighted coefficients to introduce time-series information into the fuzzy inference process. This allows the impact of disturbances at different times on the control parameters to be reflected. Compared to control methods that rely solely on time-state information, this approach fully utilizes historical time-series information for control decisions. By obtaining parameter increment values ​​through defuzzification and filtering, and updating the PI controller's control parameters online based on these increment values, dynamic adaptive adjustment of the control parameters is achieved.

[0037] In one embodiment, step S102, calculating the hesitant fuzzy number of the rotational speed error and the change in rotational speed error, includes: Step S1021: Calculate the membership center value of the speed error or the change in speed error according to the first preset formula, wherein the first preset formula is:

[0038] in, The membership center value of the speed error or the change in speed error. This refers to the speed error or the change in speed error. This represents the maximum amplitude of the speed error or the change in speed error. Specifically, the membership center value, or deterministic membership, reflects the most reliable degree of membership under the current speed error or the change in speed error. The input variables are normalized to the interval [0, 1] using the first preset formula.

[0039] Step S1022: Calculate the degree of hesitation based on the second preset formula, the membership center value, and the hesitation coefficient. The second preset formula is:

[0040] in, For hesitation level, Let be the hesitation coefficient, and ; Specifically, a hesitation coefficient is introduced to characterize the range of uncertainty in the membership degree of the input variable. The larger the hesitation coefficient, the stronger the interference under the current operating condition, the greater the measurement noise, and the greater the uncertainty in the membership degree. The hesitation coefficient is preset by the staff based on the objective interference.

[0041] Step S1023: Construct a hesitant fuzzy number based on the third preset formula, the membership center value, and the hesitancy degree, wherein the third preset formula is:

[0042] in, The membership center value, This is the lower bound of the membership degree. This is the upper bound of the membership degree. For hesitant and ambiguous numbers.

[0043] Specifically, by combining the membership center value with the hesitation degree, a hesitant fuzzy number in the form of a triple containing a lower bound, a center value, and an upper bound is constructed. This hesitant fuzzy number confines the uncertainty caused by interference within an interval. This hesitant fuzzy number can be simplified as follows: .

[0044] In this embodiment of the invention, the hesitant fuzzy number of rotational speed error and rotational speed error change is calculated by the first preset formula, the second preset formula and the third preset formula. By calculating the membership center value and combining the membership center value with the hesitancy, the uncertainty of membership degree is quantitatively characterized. The final calculated hesitant fuzzy number can close the uncertainty caused by interference within an interval, providing a data basis for subsequent calculations.

[0045] In one embodiment, step S102, the formula for constructing the temporal hesitant ambiguity number is:

[0046] in, For time-series hesitant fuzzy numbers, This is the time weighting coefficient.

[0047] Specifically, the time weighting coefficient reflects the degree of influence of input information at different times on the current control decision. Information closer to the current time has a greater impact on the current control and is given a larger time weight; historical information further away from the current time has a gradually weakening impact on the current control and is given a smaller time weight. The temporal residual fuzzy number is obtained by multiplying the three elements of the residual fuzzy number at each time step by the corresponding time weighting coefficient.

[0048] This invention, through the introduction of a time weighting coefficient into the hesitant fuzzy number, gives greater time weight to input information closer to the current moment, and the influence of historical information gradually weakens as the time distance increases, thus demonstrating the impact of changes in timing parameters on system control.

[0049] In one embodiment, step S103, the parameter adjustment amount includes a lower bound value, a center value, and an upper bound value, and the calculation formula for the parameter adjustment amount is:

[0050] in, This is the lower bound of the parameter adjustment value. For parameter adjustment center value, This is the upper limit of the parameter adjustment value. For table lookup functions, , , Respectively, speed error The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. , , These are the changes in rotational speed error. The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. The lower bound of the parameter adjustment value in the fuzzy rule table line, number The output quantization value corresponding to the column, The center value of the parameter adjustment quantity in the fuzzy rule table line, number The output quantization value corresponding to the column, The upper bound of the parameter adjustment amount is in the fuzzy rule table. line, number The output quantization value corresponding to the column.

[0051] Specifically, the lower bound, center value, and upper bound of the temporal hesitant fuzzy number of the rotational speed error and the change in rotational speed error are used as inputs to the lookup table function. The corresponding output values ​​are searched in the fuzzy rule table to obtain three corresponding values ​​of the parameter adjustment amount. The lower bound, center value, and upper bound of the temporal hesitant fuzzy number correspond to the lower bound, center value, and upper bound of the parameter adjustment amount.

[0052] This invention, through table lookup mapping of the temporal hesitant fuzzy number input into the fuzzy rule table, can find the corresponding lower bound, center value, and upper bound value of the parameter adjustment amount based on the calculated lower bound, center value, and upper bound value of the temporal hesitant fuzzy number. This realizes the transformation from the uncertainty of the input membership degree to the uncertainty of the output parameter adjustment amount, ensuring the consistency and coordination of the fuzzy inference results.

[0053] In one embodiment, step S104, which involves deblurring the parameter adjustment amount, includes: The parameter adjustment amount is defuzzified according to the fourth preset formula, which is:

[0054] in, This represents the parameter adjustment amount after deblurring.

[0055] Specifically, the values ​​of the parameter adjustment triples are transformed into a single, definite value through deblurring. In this embodiment, the deblurring uses a weighted average method, where the center value of the triple has the highest confidence, followed by the lower and upper bound values, which are assigned weights of 1:2:1 respectively. The single value obtained after weighted averaging is the clear value of the parameter adjustment.

[0056] It should be understood that the above weighting ratio is only a preferred embodiment of the present invention. In other embodiments, the weighting ratio can be adjusted according to actual control requirements, for example, 1:3:1 or 1:4:1, all of which are within the scope of protection of the present invention.

[0057] This invention, through weighted average defuzzification of the parameter adjustment triplet, assigns the maximum weight to the deterministic membership degree of the center value and assigns smaller values ​​to the lower and upper bounds. This transforms the triplet containing uncertainty into a single deterministic value, preserving the determinism of the center value while taking into account the upper and lower bounds of the uncertainty range. This achieves a good balance between suppressing the influence of input noise and maintaining response sensitivity.

[0058] In one embodiment, step S104, filtering the deblurred parameter adjustment amount to obtain the parameter increment value, includes: The parameter increment value is obtained by filtering the deblurred parameter adjustment amount according to the fifth preset formula, whereby the fifth preset formula is:

[0059] in, For the current moment The parameter increment value, for The parameter increment value at time t. These are the smoothing filter coefficients.

[0060] Specifically, this embodiment uses a first-order hysteresis filter to process the clear value of the parameter adjustment. The smoothing filter coefficient ranges from 0 to 1 and can be adjusted according to the motor's operating state. When the smoothing filter coefficient is 1, the parameter increment value does not undergo a smooth change. When the smoothing filter coefficient approaches 0, the parameter increment value remains at its historical value. The result of filtering the clear value of the parameter adjustment is the parameter increment value.

[0061] This invention achieves a smooth transition of parameter adjustment by filtering the parameter adjustment amount after defuzzification and using a smoothing filter coefficient to average the current input value and the filtering result of the previous time step, effectively avoiding high-frequency abrupt changes in control parameters caused by fluctuations in membership intervals or measurement noise.

[0062] In one embodiment, step S105, updating the controller's control parameters based on the parameter increment value, includes: According to the sixth preset formula, and the incremental values ​​of the proportional gain parameter and the integral gain parameter, the proportional gain reference parameter and the integral gain reference parameter of the controller are updated. The parameter increment values ​​include the incremental values ​​of the proportional gain parameter and the integral gain parameter, and the control parameters include the proportional gain reference parameter and the integral gain reference parameter. The sixth preset formula is:

[0063] in, , These are the updated proportional gain reference parameters and integral gain reference parameters at the current time. , These are the proportional gain reference parameters and integral gain reference parameters before the update. , These are the incremental values ​​of the proportional gain parameter and the integral gain parameter, respectively.

[0064] Specifically, the proportional gain reference parameters and integral gain reference parameters before the update can be obtained through offline tuning. The incremental values ​​of the proportional gain parameter and integral gain parameter are calculated by the aforementioned embodiments. The incremental values ​​of the proportional gain parameter and integral gain parameter are dynamically updated with each control cycle. Therefore, the proportional gain reference parameters and integral gain reference parameters are dynamically adjusted, enabling the parameters of the PI controller to adaptively adjust with changes in the motor's operating state.

[0065] This invention utilizes incremental values ​​to update the control parameters of the PI controller, ensuring the basic control performance of the motor under ideal operating conditions while using incremental values ​​to cope with various disturbances in system operation, thus achieving online adaptive adjustment of control parameters.

[0066] This embodiment provides a speed control device, such as... Figure 2 As shown, the device includes: The data acquisition module 201 is used to acquire the speed error and speed error change of the controlled motor at the current moment; The temporal hesitant fuzzy number calculation module 202 is used to calculate the hesitant fuzzy number of the speed error and the change in speed error, and to apply a time weighting coefficient to the hesitant fuzzy number to obtain the temporal hesitant fuzzy number of the speed error and the change in speed error. The parameter adjustment amount calculation module 203 is used to establish a fuzzy rule table, and to perform fuzzy inference on the temporal hesitant fuzzy number according to the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; The parameter increment value calculation module 204 is used to defuzzify the parameter adjustment amount and then filter the defuzzified parameter adjustment amount to obtain the parameter increment value. The control parameter update module 205 is used to update the controller's control parameters based on the parameter increment value, and output a speed control signal to the controlled motor according to the updated control parameters.

[0067] In one embodiment, the temporal hesitation fuzzy number calculation module 202 includes: The membership center value calculation unit is used to calculate the membership center value of the speed error or the change in speed error according to the first preset formula. The hesitation calculation unit is used to calculate the hesitation degree based on the second preset formula, the membership center value, and the hesitation coefficient. The hesitant fuzzy number calculation unit is used to construct hesitant fuzzy numbers based on the third preset formula, membership center values, and hesitancy degree.

[0068] In one embodiment, the temporal hesitation fuzzy number calculation module 202 includes: The temporal hesitant fuzzy number calculation unit is used to construct temporal hesitant fuzzy numbers.

[0069] In one embodiment, the parameter adjustment calculation module 203 includes: The parameter adjustment calculation unit is used to calculate the parameter adjustment amount, which includes the lower limit value, the center value, and the upper limit value.

[0070] In one embodiment, the parameter increment value calculation module 204 includes: The deblurring unit is used to deblur the parameter adjustment amount according to the fourth preset formula.

[0071] In one embodiment, the parameter increment value calculation module 204 includes: The filtering unit is used to filter the parameter adjustment amount after deblurring according to the fifth preset formula to obtain the parameter increment value.

[0072] In one embodiment, the control parameter update module 205 includes: The control parameter update unit is used to update the proportional gain reference parameter and integral gain reference parameter of the controller according to the sixth preset formula, as well as the incremental value of the proportional gain parameter and the incremental value of the integral gain parameter.

[0073] The speed control device provided in this embodiment of the invention can execute the speed control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0074] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0075] The following is a detailed reference. Figure 3 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0076] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0077] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the speed control method of the embodiments of the present invention.

[0078] Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0079] This embodiment also provides a computer-readable storage medium. The methods described above according to embodiments of the present invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the speed control method shown in the above embodiments is implemented.

[0080] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A speed control method, characterized in that, include: Obtain the speed error and speed error change of the controlled motor at the current moment; Calculate the hesitant fuzzy numbers of the rotational speed error and the change in rotational speed error, apply a time weighting coefficient to the hesitant fuzzy numbers, and obtain the temporal hesitant fuzzy numbers of the rotational speed error and the change in rotational speed error; A fuzzy rule table is established, and fuzzy inference is performed on the temporal hesitant fuzzy number according to the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; The parameter adjustment amount is deblurred, and the deblurred parameter adjustment amount is filtered to obtain the parameter increment value; The controller's control parameters are updated based on the incremental values ​​of the parameters, and a speed control signal is output to the controlled motor according to the updated control parameters.

2. The method according to claim 1, characterized in that, Calculating the hesitant fuzzy number of the rotational speed error and the change in rotational speed error includes: The membership center value of the rotational speed error or the change in rotational speed error is calculated according to a first preset formula, wherein the first preset formula is: in, The membership center value is the value of the rotational speed error or the change in rotational speed error. The speed error or the change in speed error is mentioned. The maximum amplitude of the speed error or the change in the speed error; The degree of hesitation is calculated based on the second preset formula, the membership center value, and the hesitation coefficient. The second preset formula is: in, The degree of hesitation, Let be the hesitation coefficient, and ; A hesitant fuzzy number is constructed based on the third preset formula, the membership center value, and the hesitancy degree, wherein the third preset formula is: in, The membership center value is... This is the lower bound of the membership degree. This is the upper bound of the membership degree. For hesitant and ambiguous numbers.

3. The method according to claim 2, characterized in that, The formula for constructing the temporal hesitant fuzzy number is as follows: in, For the time-series hesitant fuzzy number, The time weighting coefficient is mentioned.

4. The method according to claim 3, characterized in that, The parameter adjustment amount includes a lower bound, a center value, and an upper bound. The formula for calculating the parameter adjustment amount is: in, This is the lower bound of the parameter adjustment amount. Adjust the center value of the parameter. This is the upper limit value of the parameter adjustment amount. For the table lookup function, , , The respective speed errors The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. , , These are the changes in the rotational speed error, respectively. The temporal membership lower bound, temporal membership center, and temporal membership upper bound of the temporal hesitant fuzzy number. The lower bound of the parameter adjustment amount is the value in the fuzzy rule table. line, number The output quantization value corresponding to the column, The center value of the parameter adjustment amount is in the fuzzy rule table. line, number The output quantization value corresponding to the column, The upper bound of the parameter adjustment amount is the value in the fuzzy rule table. line, number The output quantization value corresponding to the column.

5. The method according to claim 4, characterized in that, The parameter adjustment amount is deblurred, including: The parameter adjustment amount is defuzzified according to the fourth preset formula, wherein the fourth preset formula is: in, This refers to the parameter adjustment amount after deblurring.

6. The method according to claim 5, characterized in that, The parameter adjustment amount after deblurring is filtered to obtain the parameter increment value, including: The parameter adjustment amount after deblurring is filtered according to the fifth preset formula to obtain the parameter increment value, wherein the fifth preset formula is: in, For the current moment The parameter increment value, for The parameter increment value at time , These are the smoothing filter coefficients.

7. The method according to claim 6, characterized in that, Updating the controller's control parameters based on the parameter increment values ​​includes: According to the sixth preset formula, and the incremental values ​​of the proportional gain parameter and the integral gain parameter, the proportional gain reference parameter and the integral gain reference parameter of the controller are updated. The parameter increment values ​​include the incremental values ​​of the proportional gain parameter and the integral gain parameter, and the control parameters include the proportional gain reference parameter and the integral gain reference parameter. The sixth preset formula is: in, , These are the proportional gain reference parameters and the integral gain reference parameters updated at the current time, respectively. , These are the proportional gain reference parameters and the integral gain reference parameters before the update, respectively. , These are the incremental values ​​of the proportional gain parameter and the integral gain parameter, respectively.

8. A speed control device, characterized in that, The device includes: The data acquisition module is used to acquire the speed error and speed error change of the controlled motor at the current moment; The temporal hesitant fuzzy number calculation module is used to calculate the hesitant fuzzy number of the rotational speed error and the change in rotational speed error, and apply a time weighting coefficient to the hesitant fuzzy number to obtain the temporal hesitant fuzzy number of the rotational speed error and the change in rotational speed error; The parameter adjustment amount calculation module is used to establish a fuzzy rule table, and perform fuzzy inference on the temporal hesitant fuzzy number according to the lookup function of the fuzzy rule table to obtain the parameter adjustment amount; The parameter increment value calculation module is used to deblur the parameter adjustment amount, and then filter the deblurred parameter adjustment amount to obtain the parameter increment value. The control parameter update module is used to update the controller's control parameters based on the parameter increment value, and output a speed control signal to the controlled motor according to the updated control parameters.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the speed control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the speed control method according to any one of claims 1 to 7.

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