Resonance online suppression method, system and device based on cascaded sliding mode observer and storage medium

Abstract

The application discloses a kind of based on cascade sliding mode observer's resonance online suppression method, system, equipment and storage medium, comprising: the dynamics model of double-inertia system is analyzed, and the key parameters to be identified that influence resonance frequency are identified, and the key parameters to be identified include stiffness coefficient and load side rotational inertia;Based on the dynamics model, according to stiffness coefficient and load side rotational inertia, the cascade sliding mode observer is constructed by the step decoupling identification method of saturation function approach law;The steady-state estimated value of the identified parameter is obtained by the cascade sliding mode observer;According to the steady-state estimated value of the identified parameter, the center frequency of the notch filter is updated, and the online suppression of mechanical resonance is realized.The application reduces the chattering of sliding mode in the process of parameter identification by decoupling online identification of stiffness coefficient and load side rotational inertia, improves the stability of identification result and the accuracy of resonance frequency calculation, and then adjusts the parameters of the notch filter according to the identification result, to realize the real-time suppression of mechanical resonance.

Description

Technical Field

[0001] This invention relates to the field of online resonance suppression technology, and in particular to an online resonance suppression method, system, device and storage medium based on a cascaded sliding mode observer. Background Technology

[0002] In high-precision servo drive systems, the motor and load are often connected by flexible components such as couplings, reducers, and ball screws, forming a typical dual-inertia system. Under conditions of high-speed start-stop, variable load, variable stiffness, and long-distance transmission, the system is prone to mechanical resonance, leading to speed fluctuations, torque oscillations, and decreased positioning accuracy. In severe cases, it can cause mechanical structural resonance and damage the equipment.

[0003] Existing resonance suppression methods for dual-inertia systems still have significant limitations. Hardware solutions typically require additional detection equipment such as torque and acceleration sensors, increasing system cost and on-site debugging complexity. Regarding control algorithms, traditional fixed-parameter notch filters cannot adaptively adjust to changes in mechanical parameters such as stiffness and inertia, easily leading to notch frequency mismatch. To achieve adaptive parameter adjustment, existing technologies often employ identification methods based on dynamic models, such as least squares, model reference adaptive identification, and Kalman filtering. However, these methods struggle to effectively handle the coupling relationship between stiffness coefficients and load-side rotational inertia, exhibiting slow convergence speed, low online accuracy, and poor dynamic adaptability, affecting resonance frequency calculation and suppression effectiveness. Therefore, how to achieve parameter decoupling identification and improve the reliability of adaptive resonance suppression has become an urgent problem to be solved. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, device, and storage medium for online resonance suppression based on a cascaded sliding mode observer, aiming to solve the technical problem of how to ensure the compliance and security of the converted CBF file.

[0005] To achieve the above objectives, the present invention provides an online resonance suppression method based on a cascaded sliding mode observer, the online resonance suppression method based on a cascaded sliding mode observer comprising: Construct a dynamic model of a dual-inertia system; The dynamic model is analyzed to identify key parameters that affect the resonant frequency, including the stiffness coefficient and the load-side moment of inertia. Based on the dynamic model, a cascaded sliding mode observer is constructed using the step-by-step decoupling identification method based on the stiffness coefficient and the load-side moment of inertia through the saturation function approaching law. The identified steady-state estimates of the parameters are obtained through the cascaded sliding mode observer; The center frequency of the notch filter is updated based on the identified steady-state parameter estimates to achieve online suppression of mechanical resonance.

[0006] Optionally, the dynamic model of the dual-inertia system is:

[0007] In the formula, For the moment of inertia of the motor, The moment of inertia on the load side. This refers to the motor speed. For load speed, For electromagnetic torque, For load torque, This is the stiffness coefficient. For the twist angle, It is the derivative.

[0008] Optionally, the analysis of the dynamic model to identify key parameters affecting the resonant frequency includes: The dynamic model is derived to obtain the motor-side transfer function; The natural resonant frequency of the dual-inertia system is obtained when the gain of the transfer function on the motor side is at its maximum. Dynamic changes in mechanical properties are analyzed on the natural resonant frequency to obtain key parameters that affect the resonant frequency.

[0009] Optionally, the step of constructing a cascaded sliding mode observer based on the dynamic model, according to the stiffness coefficient and the load-side moment of inertia, through a step-by-step decoupling identification method using the saturation function reaching law, includes: Based on the aforementioned dynamic model, a first-level sliding surface and a second-level sliding surface are defined, and the dynamic model is converted into a rotational speed differential function; Saturation functions are constructed based on the first-level sliding surface and the second-level sliding surface, and the saturation functions are used as the reaching laws. Based on the approaching law, the rotational speed differential function, and the Lyapunov function, a cascaded sliding mode observer is constructed using a step-by-step decoupling identification method.

[0010] Optionally, the construction of the cascaded sliding mode observer based on the reaching law, the rotational speed differential function, and the Lyapunov function using a step-by-step decoupling identification method includes: Based on the approaching law and the rotational speed differential function, a first-level state estimation equation is constructed, and a first-level parameter adaptive law is obtained through the Lyapunov function. The first-level sliding mode observer is constructed based on the first-level state estimation equation and the first-level parameter adaptive law; Based on the stiffness estimate output by the first-stage sliding mode observer, the second-stage state estimation equation is constructed according to the rotational speed differential function and the approaching law, and the second-stage parameter adaptive law is obtained through the Lyapunov function. A second-level sliding mode observer is constructed based on the second-level state estimation equation and the second-level parameter adaptive law; By integrating the first-stage sliding mode observer and the second-stage sliding mode observer, a cascaded sliding mode observer is obtained.

[0011] Optionally, the cascaded sliding mode observer is:

[0012] In the formula, For the estimated speed of the motor, For electromagnetic torque, For the first-stage sliding mode observer, For the second-stage sliding mode observer, This is an estimate of the stiffness. For the twist angle, For the moment of inertia of the motor, and For sliding mode gain, and For adaptive gain, and The boundary layer thickness is a saturation function. It is a saturation function. For load torque, To reconstruct the torsional moment, This is an estimate of the derivative of the load inertia. The sliding surface of the first-stage sliding mode observer. For the sliding surface of the second-stage sliding mode observer, For the estimated speed of the load, It is the derivative.

[0013] Optionally, updating the center frequency of the notch filter based on the identified steady-state parameter estimate to achieve online suppression of mechanical resonance includes: The stiffness estimate and the load inertia estimate are determined based on the identified steady-state parameter estimates. The current resonant frequency of the dual-inertia system is calculated based on the stiffness estimate and the load inertia estimate, and the current resonant frequency is used as the center frequency of the notch filter. The transfer function of the notch filter is constructed based on the center frequency to achieve online suppression of mechanical resonance.

[0014] Furthermore, to achieve the above objectives, this invention also proposes an online resonance suppression system based on a cascaded sliding mode observer, wherein the online resonance suppression system based on a cascaded sliding mode observer comprises: Modules for building dynamic models of dual-inertia systems; The analysis module is used to analyze the dynamic model to identify key parameters that affect the resonant frequency, including the stiffness coefficient and the load-side moment of inertia. The construction module is also used to construct a cascaded sliding mode observer based on the dynamic model, according to the stiffness coefficient and the load-side moment of inertia, through a step-by-step decoupling identification method using the saturation function approaching law; A calculation module is used to obtain the identified steady-state estimates of the parameters through the cascaded sliding mode observer; The suppression module is used to update the center frequency of the notch filter based on the identified steady-state parameter estimates in order to achieve online suppression of mechanical resonance.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes an online resonance suppression device based on a cascaded sliding mode observer. The device includes: a memory, a processor, and an online resonance suppression program based on a cascaded sliding mode observer stored in the memory and executable on the processor. The online resonance suppression program based on a cascaded sliding mode observer is configured to implement the steps of the online resonance suppression method based on a cascaded sliding mode observer as described above.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing an online resonance suppression program based on a cascaded sliding mode observer. When the online resonance suppression program based on the cascaded sliding mode observer is executed by a processor, it implements the steps of the online resonance suppression method based on the cascaded sliding mode observer described above.

[0017] This invention first constructs a dynamic model of a dual-inertia system, then analyzes the dynamic model to identify key parameters affecting the resonant frequency. These key parameters include the stiffness coefficient and the load-side moment of inertia. Based on the dynamic model, a cascaded sliding mode observer is constructed using a step-by-step decoupling identification method based on the saturation function reaching law, according to the stiffness coefficient and the load-side moment of inertia. Finally, the steady-state estimates of the identified parameters are obtained through the cascaded sliding mode observer. The center frequency of the notch filter is updated based on the identified steady-state estimates of the parameters to achieve online suppression of mechanical resonance. To address the shortcomings of existing technologies, such as poor time-varying adaptability of mechanical parameters, difficulty in identifying the coupling between stiffness coefficient and load-side moment of inertia, chattering in identification results easily caused by traditional sliding mode sign function approaching laws, and insufficient stability guarantee due to reliance on empirical settings for parameter adaptive laws, this invention achieves decoupling online identification of stiffness coefficient and load-side moment of inertia through a resonance online suppression method based on cascaded sliding mode observers. This reduces sliding mode chattering during parameter identification, improves the stability of identification results and the accuracy of resonant frequency calculation, and adaptively adjusts notch filter parameters based on identification results to achieve real-time suppression of mechanical resonance. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of the online resonance suppression device based on a cascaded sliding mode observer in the hardware operating environment of the embodiment of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the online resonance suppression method based on a cascaded sliding mode observer according to the present invention. Figure 3 This is a schematic diagram of the first embodiment of the online resonance suppression method based on a cascaded sliding mode observer of the present invention. Figure 4 This is a simulation experiment result diagram of the stiffness coefficient identification process of the first embodiment of the resonance online suppression method based on cascaded sliding mode observer of the present invention; Figure 5 This is a simulation experiment result of the load inertia identification process in the first embodiment of the online resonance suppression method based on cascaded sliding mode observers of the present invention. Figure 6 This is a simulation experiment comparison of rotational speeds before and after online resonance suppression in the first embodiment of the resonance online suppression method based on cascaded sliding mode observers of the present invention. Figure 7 This is a structural block diagram of the first embodiment of the online resonance suppression system based on a cascaded sliding mode observer according to the present invention.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0021] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an online resonance suppression device based on a cascaded sliding mode observer, which is part of the hardware operating environment of the embodiment of the present invention.

[0022] like Figure 1 As shown, the resonance online suppression device based on a cascaded sliding mode observer may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0023] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the online resonance suppression device based on the cascaded sliding mode observer, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0024] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a resonance online suppression program based on a cascaded sliding mode observer.

[0025] exist Figure 1In the online resonance suppression device based on a cascaded sliding mode observer shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the online resonance suppression device based on the cascaded sliding mode observer of the present invention can be set in the online resonance suppression device based on the cascaded sliding mode observer. The online resonance suppression device based on the cascaded sliding mode observer calls the online resonance suppression program based on the cascaded sliding mode observer stored in the memory 1005 through the processor 1001, and executes the online resonance suppression method based on the cascaded sliding mode observer provided in the embodiment of the present invention.

[0026] This invention provides an online resonance suppression method based on a cascaded sliding mode observer, referring to... Figure 2 and Figure 3 , Figure 2 This is a flowchart illustrating the first embodiment of the online resonance suppression method based on a cascaded sliding mode observer according to the present invention. Figure 3 This is a schematic diagram of the first embodiment of the online resonance suppression method based on a cascaded sliding mode observer according to the present invention.

[0027] In this embodiment, the online resonance suppression method based on a cascaded sliding mode observer includes the following steps: S1, construct the dynamic model of the dual-inertia system.

[0028] It is easy to understand that the execution subject of this embodiment can be a resonance online suppression system with functions such as data processing, network communication and program execution, or other computer devices with similar functions. This embodiment does not limit it.

[0029] It should be noted that the dynamic model of the two-inertia system is as follows: (1) In the formula, For the moment of inertia of the motor, The moment of inertia on the load side. This refers to the motor speed. For load speed, For electromagnetic torque, For load torque, This is the stiffness coefficient. For the twist angle, It is the derivative.

[0030] S2, Analyze the dynamic model to identify key parameters that affect the resonant frequency. These key parameters include the stiffness coefficient and the load-side moment of inertia.

[0031] Furthermore, the dynamic model is analyzed to identify the key parameters affecting the resonant frequency. The process involves: deriving the dynamic model to obtain the motor-side transfer function; obtaining the natural resonant frequency of the dual-inertia system when the gain of the motor-side transfer function is at its maximum; and performing dynamic change analysis of the mechanical characteristics of the natural resonant frequency to obtain the key parameters affecting the resonant frequency.

[0032] In the specific implementation, the transfer function on the motor side is derived from equation (1): (2) Setting the denominator in equation (2) to zero, the gain of the transfer function is at its maximum, and the natural resonant frequency of the system, i.e., the resonance point, can be obtained as follows: (3) Among them, the resonant frequency and , and related, These are the inherent parameters of the motor itself, which remain basically unchanged. However, with the dynamic changes in mechanical characteristics, and The changes are key parameters affecting the resonant frequency that need to be identified. S3. Based on the dynamic model, a cascaded sliding mode observer is constructed using the step-by-step decoupling identification method based on the stiffness coefficient and the load-side moment of inertia through the saturation function approaching law.

[0033] Furthermore, a first-level sliding surface and a second-level sliding surface are defined based on the dynamic model, and the dynamic model is converted into a rotational speed differential function. Saturation functions are constructed based on the first-level and second-level sliding surfaces, and these saturation functions are used as reaching laws. A first-level state estimation equation is constructed based on the reaching law and the rotational speed differential function, and a first-level parameter adaptive law is obtained through a Lyapunov function. A first-level sliding mode observer is constructed based on the first-level state estimation equation and the first-level parameter adaptive law. Based on the stiffness estimate output by the first-level sliding mode observer, a second-level state estimation equation is constructed based on the rotational speed differential function and the reaching law, and a second-level parameter adaptive law is obtained through a Lyapunov function. A second-level sliding mode observer is constructed based on the second-level state estimation equation and the second-level parameter adaptive law. The first-level and second-level sliding mode observers are integrated to obtain a cascaded sliding mode observer.

[0034] In practical implementation, the dynamic equation of (1) is written in differential form with respect to rotational speed, and the rotational speed differential function is as follows: (4) The dynamic equations on the motor side include stiffness coefficients. The dynamic equations on the load side include stiffness coefficients. and load inertia The first-stage observer uses the stiffness coefficient as the observed quantity. A sliding mode observer is constructed for the state variables; the second-stage observer uses the stiffness estimates obtained from the first-stage observer. Using load inertia as the observable and Construct sliding mode observers for the state variables: (5) in Represented as The estimated value (i.e., the estimated value of the derivative of the load inertia). For the estimated speed of the motor, For electromagnetic torque, For the first-stage sliding mode observer, For the second-stage sliding mode observer, This is an estimate of the stiffness. For the twist angle, For the moment of inertia of the motor, and For sliding mode gain, and For adaptive gain, and The boundary layer thickness is a saturation function. It is a saturation function. For load torque, To reconstruct the torsional moment, The sliding surface of the first-stage sliding mode observer. For the sliding surface of the second-stage sliding mode observer, For the estimated speed of the load, It is the derivative.

[0035] To ensure that the designed parameter adaptive law can theoretically guarantee the asymptotic stability of the observer error system, the following is the forward derivation process of the two-stage observer parameter adaptive law.

[0036] Based on the dynamic equations on the motor side in equation (1), the sliding surface is defined as follows: (6) in, This is the estimated speed of the motor.

[0037] The state estimation equations for the first-level sliding mode observer (i.e., the first-level state estimation equations) are as follows: (7) To suppress sliding mode jitter, the reaching law uses a saturation function: (8) in, , The saturation function is defined as: (9) From equations (1) and (7), the error dynamic equation can be obtained: (10) in, This represents the estimation error of the stiffness coefficient.

[0038] Constructing Lyapunov functions: (11) in, , The derivative with respect to time is: (12) Substituting equation (10) into the equation, we get: (13) In order to make It needs to be eliminated. Given the uncertainty of the derivative sign, setting the expression within the parentheses to zero, we obtain the parametric adaptive law (i.e., the first-order parametric adaptive law): (14) Substituting equation (14) into equation (13), we get: (15) when hour, ,at this time ,when hour, ,at this time Therefore, the saturation function satisfies ,and then And only in The time is zero. According to Lyapunov's stability theorem, It converges asymptotically to zero. After entering sliding mode motion, the sliding mode equivalent control principle can be used to obtain... It also converges to zero, at which point the first-stage observer is asymptotically stable.

[0039] Stiffness estimates output by the first-stage observer Reconstructing torsional moment Define the sliding surface The state estimation equations for the second-level sliding mode observer (i.e., the second-level state estimation equations) are as follows: (16) in, , , .

[0040] Ignoring higher-order minor quantities and utilizing the convergence results of the first-level observer, the error dynamic equation is obtained based on equations (1) and (16): (17) Constructing Lyapunov functions: (18) in, For the undetermined adaptive gain, The derivative with respect to time is: (19) Substituting equation (17) into the equation, we get: (20) In order to make It needs to be eliminated. Given the uncertainty of the derivative sign, setting the expression within the parentheses to zero, we obtain the parametric adaptive law (i.e., the second-order parametric adaptive law): (twenty one) Substituting equation (21) into equation (20), we get: (twenty two) when hour, ,at this time ,when hour, ,at this time Therefore, the saturation function satisfies ,and then And only in The time is zero. According to Lyapunov's stability theorem, Converging to zero, according to the sliding mode equivalent control principle, we can obtain... Approaching zero, that is Converging to the true value .

[0041] The discrete representation of a cascaded observer is as follows: (twenty three) in, and Indicates the first Time and the Data values ​​at any given time Indicates a time interval.

[0042] S4, obtain the identified steady-state estimates of the parameters through the cascaded sliding mode observer; It should be noted that the identified steady-state estimates of the parameters are stiffness coefficients. and load inertia .

[0043] S5. Update the center frequency of the notch filter based on the identified steady-state parameter estimates to achieve online suppression of mechanical resonance.

[0044] Furthermore, the stiffness estimate and load inertia estimate are determined based on the identified steady-state parameter estimates; the current resonant frequency of the dual-inertia system is calculated based on the stiffness estimate and load inertia estimate, and the current resonant frequency is used as the center frequency of the notch filter; the transfer function of the notch filter is constructed based on the center frequency to achieve online suppression of mechanical resonance.

[0045] In practical implementation, based on the identified stiffness coefficient and load inertia Calculate the current resonant frequency of the system: (twenty four) Real-time resonant frequency As the center frequency of the second-order notch filter, the transfer function of the constructed notch filter is: (25) in, , Controlling the notch depth, Control the notch width. Connect the notch filter between the speed loop output and the current loop input to filter the torque command, thereby suppressing resonant frequency components in real time.

[0046] In this embodiment, the first-stage observer provides an estimate for the second-stage observer, forming a cascaded observer. The parameters identified by the cascaded observer are then used to update the center frequency of the notch filter online, thereby achieving online resonance suppression.

[0047] To verify the feasibility of the online resonance suppression technology provided by this invention, a corresponding simulation model was built for verification. The relevant simulation parameters are set as follows: motor inertia... Load inertia stiffness coefficient The speed command is set to 1000 rpm, and the sliding mode gain is selected. , Parameter adaptive gain , Boundary layer thickness , notch depth notch width .

[0048] refer to Figure 4 and Figure 5 , Figure 4 This is a simulation experiment result diagram of the stiffness coefficient identification process of the first embodiment of the resonance online suppression method based on cascaded sliding mode observer of the present invention. Figure 5 This is a simulation experiment result of the load inertia identification process in the first embodiment of the online resonance suppression method based on cascaded sliding mode observers of the present invention. Figure 4 and Figure 5 The results of the identification process for the stiffness coefficient and the load moment of inertia are shown below. The identification results are 300.25 Nm / rad and 0.0006328 kg·m², respectively, with identification error rates of 0.08% and 0.02%, indicating high accuracy in the identification. (Reference) Figure 6 , Figure 6 This is a simulation experiment comparison of rotational speeds before and after online resonance suppression in the first embodiment of the online resonance suppression method based on a cascaded sliding mode observer according to the present invention. The theoretical resonant frequency is... The resonant frequency of the system is calculated from the estimated values ​​identified by the cascaded observers. With an error rate of 0.01%, the center frequency of the notch filter is updated in real time, and then the input of the current loop is notched, resulting in a 91.7% reduction in the peak value of the rotational speed fluctuation, thus achieving online suppression of resonance and verifying the effectiveness of the online resonance suppression method based on the cascaded sliding mode observer.

[0049] Reference Figure 7 , Figure 7 This is a structural block diagram of the first embodiment of the online resonance suppression system based on a cascaded sliding mode observer according to the present invention.

[0050] like Figure 7 As shown, the online resonance suppression system based on a cascaded sliding mode observer proposed in this embodiment of the invention includes: Module 7001 is used to construct the dynamic model of a dual-inertia system. Analysis module 7002 is used to analyze the dynamic model to identify key parameters that affect the resonant frequency, including stiffness coefficient and load-side moment of inertia. The construction module 7001 is also used to construct a cascaded sliding mode observer based on the dynamic model, according to the stiffness coefficient and the load-side moment of inertia, through a step-by-step decoupling identification method using the saturation function approaching law. The calculation module 7003 is used to obtain the identified steady-state estimates of the parameters through the cascaded sliding mode observer; The suppression module 7004 is used to update the center frequency of the notch filter based on the identified steady-state estimate of the parameters, so as to achieve online suppression of mechanical resonance.

[0051] Other embodiments or specific implementations of the online resonance suppression system based on cascaded sliding mode observers of the present invention can be found in the above-described method embodiments, and will not be repeated here.

[0052] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0053] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0054] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0055] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for online suppression of resonance based on a cascaded sliding mode observer, characterized in that, The method includes the following steps: Construct a dynamic model of a dual-inertia system; The dynamic model is analyzed to identify key parameters that affect the resonant frequency, including the stiffness coefficient and the load-side moment of inertia. Based on the dynamic model, a cascaded sliding mode observer is constructed using the step-by-step decoupling identification method based on the stiffness coefficient and the load-side moment of inertia through the saturation function approaching law. The identified steady-state estimates of the parameters are obtained through the cascaded sliding mode observer; The center frequency of the notch filter is updated based on the identified steady-state parameter estimates to achieve online suppression of mechanical resonance.

2. The method as described in claim 1, characterized in that, The dynamic model of the dual-inertia system is as follows: In the formula, For the moment of inertia of the motor, The moment of inertia on the load side. This refers to the motor speed. For load speed, For electromagnetic torque, For load torque, This is the stiffness coefficient. For the twist angle, It is the derivative.

3. The method as described in claim 1, characterized in that, The analysis of the dynamic model to identify key parameters affecting the resonant frequency includes: The dynamic model is derived to obtain the motor-side transfer function; The natural resonant frequency of the dual-inertia system is obtained when the gain of the transfer function on the motor side is at its maximum. Dynamic changes in mechanical properties are analyzed on the natural resonant frequency to obtain key parameters that affect the resonant frequency.

4. The method as described in claim 1, characterized in that, The construction of a cascaded sliding mode observer based on the dynamic model, according to the stiffness coefficient and the load-side moment of inertia, using a step-by-step decoupling identification method based on the saturation function reaching law, includes: Based on the aforementioned dynamic model, a first-level sliding surface and a second-level sliding surface are defined, and the dynamic model is converted into a rotational speed differential function; Saturation functions are constructed based on the first-level sliding surface and the second-level sliding surface, and the saturation functions are used as the reaching laws. Based on the approaching law, the rotational speed differential function, and the Lyapunov function, a cascaded sliding mode observer is constructed using a step-by-step decoupling identification method.

5. The method as described in claim 4, characterized in that, The construction of a cascaded sliding mode observer based on the reaching law, the rotational speed differential function, and the Lyapunov function using a step-by-step decoupling identification method includes: Based on the approaching law and the rotational speed differential function, a first-level state estimation equation is constructed, and a first-level parameter adaptive law is obtained through the Lyapunov function. The first-level sliding mode observer is constructed based on the first-level state estimation equation and the first-level parameter adaptive law; Based on the stiffness estimate output by the first-stage sliding mode observer, the second-stage state estimation equation is constructed according to the rotational speed differential function and the approaching law, and the second-stage parameter adaptive law is obtained through the Lyapunov function. A second-level sliding mode observer is constructed based on the second-level state estimation equation and the second-level parameter adaptive law; By integrating the first-stage sliding mode observer and the second-stage sliding mode observer, a cascaded sliding mode observer is obtained.

6. The method as described in claim 5, characterized in that, The cascaded sliding mode observer is: In the formula, For the estimated speed of the motor, For electromagnetic torque, For the first-stage sliding mode observer, For the second-stage sliding mode observer, This is an estimate of the stiffness. For the twist angle, For the moment of inertia of the motor, and For sliding mode gain, and For adaptive gain, and The boundary layer thickness is a saturation function. It is a saturation function. For load torque, To reconstruct the torsional moment, This is an estimate of the derivative of the load inertia. For the sliding surface of the first-stage sliding mode observer, For the sliding surface of the second-stage sliding mode observer, The estimated rotational speed for the load, It is the derivative.

7. The method as described in claim 1, characterized in that, The step of updating the center frequency of the notch filter based on the identified steady-state parameter estimates to achieve online suppression of mechanical resonance includes: The stiffness estimate and the load inertia estimate are determined based on the identified steady-state parameter estimates. The current resonant frequency of the dual-inertia system is calculated based on the stiffness estimate and the load inertia estimate, and the current resonant frequency is used as the center frequency of the notch filter. The transfer function of the notch filter is constructed based on the center frequency to achieve online suppression of mechanical resonance.

8. A resonance online suppression system based on a cascaded sliding mode observer, characterized in that, The system includes: Modules for building dynamic models of dual-inertia systems; The analysis module is used to analyze the dynamic model to identify key parameters that affect the resonant frequency, including the stiffness coefficient and the load-side moment of inertia. The construction module is also used to construct a cascaded sliding mode observer based on the dynamic model, according to the stiffness coefficient and the load-side moment of inertia, through a step-by-step decoupling identification method using the saturation function approaching law; A calculation module is used to obtain the identified steady-state estimates of the parameters through the cascaded sliding mode observer; The suppression module is used to update the center frequency of the notch filter based on the identified steady-state parameter estimates in order to achieve online suppression of mechanical resonance.

9. A resonance online suppression device based on a cascaded sliding mode observer, characterized in that, The device includes: a memory, a processor, and an online resonance suppression program based on a cascaded sliding mode observer stored in the memory and executable on the processor, the online resonance suppression program based on a cascaded sliding mode observer being configured to implement the steps of the online resonance suppression method based on a cascaded sliding mode observer as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores an online resonance suppression program based on a cascaded sliding mode observer, which, when executed by a processor, implements the steps of the online resonance suppression method based on a cascaded sliding mode observer as described in any one of claims 1 to 7.

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