A disturbance-resistant underwater joint motor control method and related device

Abstract

This invention provides a disturbance-resistant underwater articulated motor control method and related apparatus, comprising: acquiring target torque, real-time current, and speed angle information; preprocessing the information to obtain a DC control quantity in a rotating coordinate system; using the DC control quantity in the rotating coordinate system as input data for a current controller; using a disturbance observer to estimate the current and disturbance quantity for the next time step; then inputting the DC control quantity in the rotating coordinate system into a deadbeat current control model; calculating the motor's right-angle axis voltage control quantity for the next time step based on the voltage equation model of the system in the rotating coordinate system; updating the deadbeat current control model online using parameter identification; and converting the right-angle axis voltage control quantity into a three-phase pulse width modulation signal through inverse Park transform and SVPWM module conversion. The identification method of this invention introduces the Kalman filter method and the H-infinity method into motor parameter identification, combining the advantages of both by fusing the AEKF and H-infinity estimation methods to achieve higher accuracy.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to a disturbance-resistant underwater joint motor control method and related device. Background Technology

[0002] In the field of marine equipment research, underwater robot technology is constantly developing, becoming increasingly intelligent and automated, with improvements in the richness, stability, and flexibility of its movements. Bionic underwater robots have multiple joints, each equipped with a drive motor, which drives the robot to perform its actions. However, considering the underwater robot's motion environment, the torque output of these joint motors suffers from slow response speed, poor disturbance rejection performance, and low control precision, affecting the robot's overall motion performance. Summary of the Invention

[0003] The purpose of this invention is to provide an anti-disturbance underwater articulated motor control method and related device to solve the problems of slow response speed, poor anti-disturbance performance and low control accuracy of the torque output of the articulated motor.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a disturbance-resistant underwater joint motor control method, comprising:

[0006] The target torque, real-time current, and speed angle information are acquired, and the DC control quantity in the rotating coordinate system is obtained after preprocessing.

[0007] The DC control quantity in the rotating coordinate system is used as the input data of the current controller. The disturbance observer is used to predict the current and disturbance quantity of the next time step, and then input into the deadbeat current control model. The motor direct-axis voltage control quantity at the next moment is calculated according to the voltage equation model of the system in the rotating coordinate system. The deadbeat current control model is updated online by parameter identification.

[0008] The direct-axis voltage control signal is converted into a three-phase pulse width modulation signal through inverse Park transform and SVPWM module conversion.

[0009] Furthermore, the target torque, real-time current, and speed angle information are acquired, and after preprocessing, the DC control quantity in the rotating coordinate system is obtained:

[0010] Initialize the control program parameters, obtain the target torque, collect the three-phase current of the test motor and read the encoder angle information; then, use a digital filter to perform low-pass filtering on the collected three-phase current; then, use Clark transform and Park transform to reconstruct the three-phase current into two-phase current, and convert the AC quantity into DC control quantity in the rotating coordinate system.

[0011] Furthermore, the DC control quantity in the rotating coordinate system is used as the input data for the current controller, and the disturbance observer is used to predict the current and disturbance quantity in the next time step:

[0012] The DC control quantity in the rotating coordinate system is used as the input data of the current controller. The current and disturbance quantity in the next control cycle are observed using LESO and SMO observers, respectively. Then, a linear combination of the outputs of LESO and SMO is obtained through a hysteresis switching scheme based on the weighting method. The specific calculation formula is as follows:

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] in: and These are the lower and upper limits of the estimated current error during the switching process, respectively. and These are the lower and upper limits of the disturbance during the handover process, respectively. and The observed currents are for SMO and LESO, respectively; and These are the observational perturbations for SMO and LESO, respectively.

[0019] Furthermore, the input is fed into the deadbeat current control model, and the motor's right-angle axis voltage control quantity at the next moment is calculated based on the voltage equation model of the system in the rotating coordinate system: through hysteresis switching, the observer's estimated current and disturbance are solved, and substituted into DPCC to obtain the output voltage under the PMSM predicted current.

[0020] Furthermore, parameter identification is used to update the deadbeat current control model online:

[0021] Step 1): Initialize the stator inductance Ls, stator resistance Rs, and rotor flux linkage Ψf in the parameters to be identified, as well as the error covariance matrix P and noise covariance matrix Q and R parameters in the EKF and H infinity identification methods; then, acquire the three-phase current and voltage of the test motor and read the encoder angle information.

[0022] Step 2): Based on the data collected in Step 1), the EKF and H-infinity methods are used to perform state prediction, error covariance prediction, and gain coefficient calculation, respectively.

[0023] Step 3) Update the gain coefficients of the parameter identification method for the hybrid extended Kalman filter. Using weight allocation to synthesize the two methods from Step 2), set the steady-state Kalman filter gain as K. EKF Meanwhile, the steady-state H∞ filter gain is expressed as K∞, and the gain of the hybrid filter is constructed in the following form:

[0024]

[0025] in, The equation for calculating the weight α(k-j+1) is as follows:

[0026]

[0027]

[0028] In the formula: j=1,2,…,p; M(k-j+1) is the innovation at time j in M(p,k); α(k-j+1) is the weight of the innovation at time j; σ2 is the noise variance;

[0029] The weight α(k-j+1) of the standardized innovation at time j:

[0030]

[0031] Step 4) Update the error covariance matrix in EKF and H infinity, calculated using the following formula:

[0032] EKF error covariance matrix update:

[0033]

[0034] Update the error covariance matrix of H infinity:

[0035]

[0036] Step 5) Update the noise covariance matrix, the calculation formula is as follows:

[0037] Define new information as

[0038]

[0039] Process noise It is expressed as:

[0040]

[0041] The noise covariance matrix can be updated in real time according to the above formula:

[0042]

[0043] Where, The specific expression form of is:

[0044]

[0045] In the above formula, b is the forgetting factor, 0 < b < 1, and the usually selected range is 0.95 - 0.99;

[0046] Step 6) Update the relevant identification parameters according to the gain coefficient solved in step 3); Its calculation formula is as follows:

[0047] .

[0048] Furthermore, the relevant calculation formulas of EKF are as follows:

[0049] State prediction:

[0050]

[0051] Error covariance prediction:

[0052]

[0053] Gain coefficient calculation:

[0054]

[0055] The relevant calculation formulas of H-infinity are as follows:

[0056] State prediction:

[0057]

[0058] Error covariance prediction:

[0059]

[0060] Expansion Filter gain:

[0061] .

[0062] Furthermore, the direct-axis voltage control signal is converted into a three-phase pulse width modulation signal through inverse Park transform and SVPWM module conversion:

[0063] The obtained quadrature axis voltage is subjected to inverse Park transformation to be converted into a control voltage in the stationary coordinate system. Then, the control voltage is converted into a three-phase pulse width modulation signal through the SVPWM pulse width modulation module. The controller's control level is then output to the pre-drive circuit to control the shutdown of the three-phase inverter bridge and control the motor rotation.

[0064] In a second aspect, the present invention provides an anti-disturbance underwater joint motor control system, comprising:

[0065] The data acquisition module is used to acquire target torque, real-time current and speed angle information, and obtain DC control quantities in the rotating coordinate system after preprocessing.

[0066] The voltage control output module is used to take the DC control quantity in the rotating coordinate system as the input data of the current controller, use the disturbance observer to predict the current and disturbance quantity of the next time step, and then input it into the deadbeat current control model. Based on the voltage equation model of the system in the rotating coordinate system, the motor direct-axis voltage control quantity of the next moment is calculated; the deadbeat current control model is updated online by parameter identification.

[0067] The modulation signal output module is used to convert the direct-axis voltage control quantity into a three-phase pulse width modulation signal through inverse Park transformation and SVPWM module conversion.

[0068] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an anti-disturbance underwater joint motor control method.

[0069] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a disturbance-resistant underwater joint motor control method.

[0070] Compared with the prior art, the present invention has the following technical effects:

[0071] This invention first improves the design of the motor controller based on the basic motor vector control model, employing a model-based deadbeat current predictive control method to achieve precise current control. Simultaneously, a disturbance observer is introduced to correct the control model, using a disturbance observer for compensation and correction to reduce the steady-state error of the system control, thereby improving the system's disturbance rejection capability and control accuracy. Combining the performance characteristics of linear disturbance observers and sliding diaphragm disturbance observers, a switching algorithm is proposed, leveraging the advantages of both to improve the system's control performance.

[0072] The identification method proposed in this invention introduces the Kalman filter method and the H-infinity method into motor parameter identification. Combining the advantages of both, the estimation methods of AEKF and H-infinity are integrated to achieve higher accuracy and stronger robustness. Attached Figure Description

[0073] Figure 1 This is a flowchart of the present invention.

[0074] Figure 2 The control block diagram for switching the disturbance rejection controller.

[0075] Figure 3 Flowchart for switching the disturbance rejection controller algorithm.

[0076] Figure 4 The control block diagram for the parameter identification method.

[0077] Figure 5 Flowchart of parameter identification method.

[0078] Figure 6 This is a schematic diagram of the installation of the present invention.

[0079] In the diagram: 1. Tension controller; 2. Magnetic powder brake; 3. Torque sensor; 4. Test motor; 5. Coupling. Detailed Implementation

[0080] The present invention will be further described below with reference to the accompanying drawings:

[0081] Please see Figure 1 This invention provides a disturbance-resistant underwater joint motor control method, comprising:

[0082] The target torque, real-time current, and speed angle information are acquired, and the DC control quantity in the rotating coordinate system is obtained after preprocessing.

[0083] The DC control quantity in the rotating coordinate system is used as the input data of the current controller. The disturbance observer is used to predict the current and disturbance quantity of the next time step, and then input into the deadbeat current control model. The motor direct-axis voltage control quantity at the next moment is calculated according to the voltage equation model of the system in the rotating coordinate system. The deadbeat current control model is updated online by parameter identification.

[0084] The direct-axis voltage control signal is converted into a three-phase pulse width modulation signal through inverse Park transform and SVPWM module conversion.

[0085] For details, please refer to Figure 1 This invention provides a disturbance-resistant underwater joint motor control method:

[0086] Step 1): Initialize the control program parameters, obtain the target torque, acquire the three-phase current of the test motor, and read the encoder angle information. Then, perform low-pass filtering on the acquired three-phase current using a digital filter. Next, reconstruct the three-phase current into a two-phase current using Clark and Park transforms. Finally, convert the AC quantity into a DC control quantity in a rotating coordinate system.

[0087] Step 2) Based on the data processing in Step 1), the sampled data is used as input data for the current controller. For example... Figure 3 As shown in a), the current and disturbance in the next control cycle are observed using LESO and SMO observers respectively. Then, a linear combination of the outputs of LESO and SMO is obtained through a hysteresis switching scheme based on the weighting method. The specific calculation formula is as follows:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] in: and These are the lower and upper limits of the estimated current error during the switching process, respectively. and These are the lower and upper limits of the disturbance during the handover process, respectively. and The observed currents are for SMO and LESO, respectively; and These are the observational perturbations for SMO and LESO, respectively.

[0094] By using hysteresis switching, the estimated current and disturbance of the observer are solved, and then substituted into the DPCC to obtain the output voltage under the predicted current of the PMSM. Its control block diagram is as follows: Figure 3 As shown in b).

[0095] Step 3) Perform an inverse Park transform on the orthogonal axis voltage obtained in Step 2) to convert it into a control voltage in the stationary coordinate system. Then, use the SVPWM pulse width modulation module to convert the control voltage into a three-phase pulse width modulation signal. Finally, output the controller's control level to the pre-drive circuit to control the shutdown of the three-phase inverter bridge, thereby controlling the motor rotation.

[0096] The traditional current loop controller is replaced by deadbeat control. A predictive model is established based on the mathematical model of the permanent magnet synchronous motor, and optimization calculations and error compensation corrections are performed within the rolling time window.

[0097] ;

[0098] The disturbance observer is introduced to modify the control model, where the linear disturbance observer (LESO) is calculated as follows:

[0099]

[0100] In the formula, , , , These are the d-axis currents. Tracking value, total disturbance along the d-axis The observed values, q-axis current Tracking value and total q-axis disturbance The observed values. represents the discrete gain coefficient in the discrete LESO state equation.

[0101] The sliding membrane disturbance observer is designed to account for the nonlinear characteristics of the control system, and its calculation formula is as follows:

[0102]

[0103] in, , This refers to system disturbances caused by parameter changes. , for , The estimated value, , for , The estimated value, , For the slug control function, , These are the synovial control parameters.

[0104] For the parameter identification method, please refer to [link / reference]. Figures 3 to 5 :

[0105] Step 1): Initialize the stator inductance Ls, stator resistance Rs, and rotor flux linkage Ψf in the parameters to be identified, as well as the error covariance matrix P, noise covariance matrix Q, and R parameters in the EKF and H infinity identification methods. Then, acquire the three-phase current and voltage of the test motor and read the encoder angle information.

[0106] Step 2): Based on the data collected in Step 1), the EKF and H-infinity methods are used to perform state prediction, error covariance prediction, and gain coefficient calculation, respectively. The relevant calculation formula for EKF is as follows:

[0107] State prediction:

[0108]

[0109] Error covariance prediction:

[0110]

[0111] Gain coefficient calculation:

[0112]

[0113] The relevant formulas for calculating H infinity are as follows:

[0114] State prediction:

[0115]

[0116] Error covariance prediction:

[0117]

[0118] Extend Filter gain:

[0119]

[0120] Step 3) Update the gain coefficients of the parameter identification method for the hybrid extended Kalman filter, using weight allocation to combine the advantages of the two methods in Step 2). The steady-state Kalman filter gain is defined as K. EKF Meanwhile, the steady-state H∞ filter gain is expressed as K∞, and the gain of the hybrid filter is constructed in the following form:

[0121]

[0122] in, This hybrid filter gain is a convex combination of Kalman and H∞, which is a balance between the root mean square and the worst performance. The calculation equation for the weight α(k-j+1) is as follows:

[0123]

[0124]

[0125] In the formula: j = 1, 2, …, p; M(k-j+1) is the innovation at time j in M(p,k); α(k-j+1) is the weight of the innovation at time j; σ2 is the noise variance.

[0126] To obtain the weight of each innovation, normalize the weight value α(k-j+1) of the innovation at time j:

[0127]

[0128] Step 4) Update the error covariance matrices in EKF and H-infinity, and the calculation formulas are as follows:

[0129] Update of the error covariance matrix of EKF:

[0130]

[0131] Update of the error covariance matrix of H-infinity:

[0132]

[0133] Step 5) Update the noise covariance matrix to achieve adaptive adjustment of the algorithm, and its calculation formula is as follows:

[0134] Define the innovation as

[0135]

[0136] Process noise can be expressed as:

[0137]

[0138] The noise covariance matrix can be updated in real time according to the above formula:

[0139] [[ID=5​​​​​​​​​​​​

[0143] Step 6) Update the relevant identification parameters based on the gain coefficients obtained in Step 3). The calculation formula is as follows:

[0144] .

[0145] Please see Figure 6 :

[0146] Tension controller 1: A controller used to control the load generated by the magnetic powder brake.

[0147] Magnetic powder brake 2: generates load torque by inputting control current.

[0148] Torque sensor 3: A sensor used to measure the motor's output torque, speed, and output power in real time.

[0149] Test motor 4: Mounted and fixed on the test bench as the component under test.

[0150] In another embodiment of the present invention, a disturbance-resistant underwater articulated motor control system is provided, which can be used to implement the above-described disturbance-resistant underwater articulated motor control method. Specifically, the system includes:

[0151] The data acquisition module is used to acquire target torque, real-time current and speed angle information, and obtain DC control quantities in the rotating coordinate system after preprocessing.

[0152] The voltage control output module is used to take the DC control quantity in the rotating coordinate system as the input data of the current controller, use the disturbance observer to predict the current and disturbance quantity of the next time step, and then input it into the deadbeat current control model. Based on the voltage equation model of the system in the rotating coordinate system, the motor direct-axis voltage control quantity of the next moment is calculated; the deadbeat current control model is updated online by parameter identification.

[0153] The modulation signal output module is used to convert the direct-axis voltage control quantity into a three-phase pulse width modulation signal through inverse Park transformation and SVPWM module conversion.

[0154] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0155] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of an anti-disturbance underwater joint motor control method.

[0156] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the disturbance-resistant underwater articulated motor control method in the above embodiments.

[0157] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A disturbance-resistant underwater joint motor control method, characterized in that, Including: Obtain the target torque, real-time current and rotational speed angle information, and obtain the DC control quantity in the rotating coordinate system after preprocessing; Take the DC control quantity in the rotating coordinate system as the input data of the current controller, use a disturbance observer to estimate the current and disturbance quantity at the next time step, and then input it into the deadbeat current control model, and calculate the direct and quadrature axis voltage control quantities of the motor at the next moment according to the voltage equation model of the system in the rotating coordinate system; Adopt parameter identification to update the deadbeat current control model online; Convert the direct and quadrature axis voltage control quantities into three-phase pulse width modulation signals through inverse Park transformation and SVPWM module conversion; Adopt parameter identification to update the deadbeat current control model online: Step 1): Initialize the stator inductance Ls, stator resistance Rs, rotor flux Ψf in the measurement parameters to be identified, as well as the error covariance matrix P, noise covariance matrix Q and R parameters in the EKF and H-infinity identification methods; then, collect the three-phase current and voltage of the test motor and read the angle information of the encoder; Step 2): Based on the data collected in Step 1), use the EKF and H-infinity methods to perform state prediction, error covariance prediction, and gain coefficient calculation respectively; Step 3) Update the gain coefficients of the parameter identification method for the hybrid extended Kalman filter. Using weight allocation to synthesize the two methods from Step 2), set the steady-state Kalman filter gain as K. EKF Meanwhile, the steady-state H∞ filter gain is expressed as K∞, and the gain of the hybrid filter is constructed in the following form: in, The equation for calculating the weight α(k-j+1) is as follows: In the formula: j = 1, 2, ..., p; m(k-j+1) is the innovation at time j in M(p,k); α(k-j+1) is the weight of the innovation at time j; σ 2 For noise variance; Standardize the weight value α(k - j + 1) of the innovation at time j: Step 4) Update the error covariance matrix in EKF and H-infinity, and the calculation formula is as follows: Update of the error covariance matrix of EKF: Update of the error covariance matrix of H-infinity: Step 5) Update the noise covariance matrix, and its calculation formula is as follows: Define the innovation as Process noise Represented as: The noise covariance matrix can be updated in real time according to the above formula: in, The specific form of expression is: b in the above formula is the forgetting factor, 0 < b < 1, and the selected range is 0.95 - 0.99; Step 6) Update the relevant identification parameters according to the gain coefficient solved in Step 3); its calculation formula is as follows: ； The relevant calculation formulas of EKF are as follows: State prediction: Error covariance prediction: Gain coefficient calculation: The relevant calculation formulas of H-infinity are as follows: State prediction: Error covariance prediction: Extend Filter gain: 。 2. The method for controlling an underwater joint motor against disturbances according to claim 1, characterized in that, Obtain the target torque, real-time current and rotational speed angle information, and obtain the DC control quantity in the rotating coordinate system after preprocessing: Initialize the control program parameters, obtain the target torque, collect the three-phase current of the test motor and read the angle information of the encoder; then, perform low-pass filtering on the collected three-phase current through a digital filter; then, reconstruct and convert the three-phase current into two-phase current through Clark transformation and Park transformation, and convert the alternating quantity into the DC control quantity in the rotating coordinate system.

3. The method for controlling an underwater joint motor against disturbances according to claim 1, characterized in that, Take the DC control quantity in the rotating coordinate system as the input data of the current controller, and use a disturbance observer to estimate the current and disturbance quantity at the next time step: The DC control quantity in the rotating coordinate system is used as the input data of the current controller. Respectively use the LESO observer and the SMO observer to observe the current and disturbance quantity in the next control cycle, and then obtain the linear combination of the outputs of LESO and SMO through the hysteresis switching scheme based on the weighting method. The specific calculation formula is as follows: in: and These are the lower and upper limits of the estimated current error during the switching process, respectively. and These are the lower and upper limits of the disturbance during the handover process, respectively. and The observed currents are for SMO and LESO, respectively; and These are the observational perturbations for SMO and LESO, respectively.

4. The method for controlling an underwater joint motor against disturbances according to claim 1, characterized in that, The input is fed into the deadbeat current control model, and the motor's right-angle axis voltage control quantity at the next moment is calculated based on the voltage equation model of the system in the rotating coordinate system. By switching the hysteresis loop, the observer's estimated current and disturbance are solved, and the output voltage under the predicted current of PMSM is obtained by substituting them into DPCC.

5. The method for controlling an underwater joint motor against disturbances according to claim 1, characterized in that, The direct-axis voltage control signal is converted into a three-phase pulse width modulation signal through inverse Park transform and SVPWM module conversion: The obtained quadrature axis voltage is subjected to inverse Park transformation to be converted into a control voltage in the stationary coordinate system. Then, the control voltage is converted into a three-phase pulse width modulation signal through the SVPWM pulse width modulation module. The controller's control level is then output to the pre-drive circuit to control the shutdown of the three-phase inverter bridge and control the motor rotation.

6. A disturbance-resistant underwater joint motor control system, characterized in that, The underwater joint motor control method based on claim 1 includes: The data acquisition module is used to acquire target torque, real-time current and speed angle information, and obtain DC control quantities in the rotating coordinate system after preprocessing. The voltage control output module is used to take the DC control quantity in the rotating coordinate system as the input data of the current controller, use the disturbance observer to predict the current and disturbance quantity of the next time step, and then input it into the deadbeat current control model. Based on the voltage equation model of the system in the rotating coordinate system, the motor direct-axis voltage control quantity of the next moment is calculated; the deadbeat current control model is updated online by parameter identification. The modulation signal output module is used to convert the direct-axis voltage control quantity into a three-phase pulse width modulation signal through inverse Park transformation and SVPWM module conversion.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the disturbance-resistant underwater joint motor control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the disturbance-resistant underwater joint motor control method as described in any one of claims 1 to 5.

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