A mechanical system model self-updating method and system based on digital twin technology

Abstract

The present application relates to the field of mechanical system control, and particularly relates to a mechanical system model self-updating method and system based on digital twin technology, which comprises the following steps: calculating a sliding mode surface according to a digital twin system state and a state vector obtained in a mechanical system, and calculating a sliding mode switching variable and an iterative error estimation variable according to the sliding mode surface; judging whether an error between the digital twin system and the mechanical system is greater than a set requirement according to the sliding mode surface; if the error is greater than the set requirement, constructing an over-determined linear equation set according to the iterative error estimation variable, and calling a pseudo-inverse algorithm to update a target value of an internal coefficient of the digital twin system; and the digital twin system predicts the mechanical system according to the updated target value of the internal coefficient of the digital twin system and the sliding mode surface to calculate the sliding mode switching variable and the iterative error estimation variable. The error of the digital twin system can be robustly compensated in real time to achieve better observation, control and prediction performance.

Description

Technical Field

[0001] This invention relates to the field of mechanical system control, and in particular to a method and system for self-updating mechanical system models based on digital twin technology. Background Technology

[0002] With the development of aerospace, digital twins, advanced manufacturing, and other fields, higher standards are being placed on the control precision, response speed, and real-time performance of mechanical systems, leading to more diverse structures. However, the increased complexity of mechanical system structures also brings more complex dynamic models, posing greater challenges to the design of their control systems.

[0003] In dynamic modeling or control system design, uncertainties in modeling or time-varying parameters, such as parameter errors caused by unreasonable assumptions, slow changes in system characteristics during long-term operation of servo motors, and performance degradation of shape memory alloys after repeated use, can all lead to changes in the dynamic characteristics of the corresponding mechanical system, increasing the error of the digital twin system. This negatively impacts the system's tracking, prediction, and control. Therefore, observation algorithms can be introduced to capture system uncertainties based on the input of the mechanical system's state and feed them back into the digital twin system to reduce system errors.

[0004] In the field of control, changes in mechanical systems are expected to be observed in real time and updated in the corresponding digital twin system. However, unlike a host computer, the computational performance of hardware systems is typically relatively limited. Therefore, the design process of control systems needs to consider both the real-time performance of the algorithm and hardware feasibility, requiring the algorithm to achieve rapid updates and iterations with low time and computational costs. Thus, non-iterative update methods such as pseudo-inverse algorithms can be used to quickly establish overdetermined linear equations after capturing system uncertainties, approximating the internal coefficients of the mechanical system and ensuring the control system's rapid response capability and robustness.

[0005] How to adaptively calculate the error between the mechanical model and its digital twin system and update the parameters of the digital twin system is a problem that urgently needs to be solved in this field. Summary of the Invention

[0006] To reduce errors in the observation, control, and prediction performance of digital twin systems in mechanical systems such as drones, robotic arms, and spacecraft, this invention proposes a self-updating method for mechanical system models based on digital twin technology. The method involves constructing a mechanical system, considering system uncertainties, building a digital twin system for this mechanical system, using the digital twin system to predict the state changes of the mechanical system, tracking, observing, and predicting the mechanical system based on the predicted values, and controlling the mechanical system based on the prediction results. The synchronization of the mechanical system and the digital twin system specifically includes the following steps:

[0007] Construct a mechanical system and, considering system uncertainties, build a digital twin system for that mechanical system;

[0008] Obtain the real state vector from the mechanical system in real time, and obtain the predicted state vector from the digital twin system in real time;

[0009] The sliding surface is calculated based on the state of the digital twin system and the state vectors obtained from the mechanical system, and the sliding mode switching variables and iteration error estimation variables are calculated based on the sliding surface;

[0010] Determine whether the error between the digital twin system and the mechanical system exceeds the set requirements based on the sliding surface;

[0011] If the error exceeds the set requirement, an overdetermined linear equation system is constructed based on the iterative error estimation variables, and the pseudo-inverse algorithm is called to update the target values ​​of the internal coefficients of the digital twin system.

[0012] The digital twin system uses updated internal coefficient target values ​​and sliding surface to calculate sliding mode switching variables and iteration error estimation variables to predict the mechanical system;

[0013] If the error meets the set requirements, the digital twin system will predict the mechanical system based on the target values ​​of the internal coefficients of the digital twin system and the sliding surface, and calculate the sliding mode switching variables and the iteration error estimation variables.

[0014] Furthermore, the dynamic equations of the mechanical system are expressed as follows:

[0015]

[0016] in, Let be the state vector of the mechanical system. The state of the nth oscillator in the mechanical system can be the displacement, angle, angular velocity, etc. of the oscillator in the mechanical system. Those skilled in the art can select the state as the system state according to the configuration of the mechanical system. and These are the first and second derivatives of the mechanical system's state vector with respect to time, respectively. This indicates the influence of system parameters on the dynamic model of the mechanical system. for Internal coefficients; The effect of control input on the dynamic model of a mechanical system is expressed as: , This represents the effect of the m-th controller on the n-th oscillator in a mechanical system. for The internal coefficients, where N is the number of oscillators in the system. This refers to the number of system controllers. It serves as the control input for the mechanical system.

[0017] Furthermore, the dynamic model of the digital twin system is expressed as follows:

[0018]

[0019] in, The state vector predicted for the digital twin system; This represents the state of the Nth oscillator in a digital twin system; and These are the first and second derivatives of the state vector of the digital twin system with respect to time, respectively. The internal coefficients represent the influence of the parameters of the digital twin system on the dynamic model. ; The internal coefficients represent the influence of the control input of the digital twin system on the dynamic model. , represented as ,in This represents the effect of the m-th controller on the n-th oscillator in a digital twin system; For the control input of the mechanical system; To switch the variable vector for sliding mode, Let be the sliding mode switching variable vector for the Nth oscillator; For the error estimation variable vector, Let be the error estimation variable vector for the Nth oscillator.

[0020] Preferably, in this embodiment , This represents the parameter values ​​of the mechanical system. , The parameters represent the parameter values ​​of a mechanical system model constructed by a person skilled in the art based on mechanical theory. Due to uncertainties in the mechanical system, there may be deviations between the two sets of parameters. In this invention, the deviations between them are estimated based on the super-helical sliding mode observation algorithm, thereby restoring the parameter values ​​of the mechanical system and improving the accuracy of observation and prediction of the mechanical system.

[0021] Furthermore, the sliding surface is calculated based on the state of the digital twin system and the state vectors obtained from the mechanical system, and is expressed as:

[0022]

[0023] in, Let be the sliding surface vector, represented as , Represents the sliding surface vector The i-th element is the sliding surface corresponding to the i-th oscillator in the mechanical system; and The parameters of the sliding surface can be selected and adjusted by those skilled in the art based on experience, or determined and adjusted based on simulation or physical test results. This represents the actual state vector of the mechanical system. The predicted state vector for the digital twin system; for The first derivative; for The first derivative.

[0024] Furthermore, the process of calculating the sliding mode switching variables includes:

[0025]

[0026] in, This represents the sliding mode switching variable for the i-th oscillator in the mechanical system. It is an absolute value function. It is the hyperbolic tangent function. and The sliding mode switching variable parameters can be selected and adjusted by those skilled in the art based on experience, or determined and adjusted based on simulation or physical test results.

[0027] Furthermore, the process of calculating the iterative error estimation variables includes:

[0028]

[0029] in, This represents the iterative error estimation variable for the i-th oscillator in the mechanical system; and The parameters for error estimation variables can be selected and adjusted by those skilled in the art based on experience, or determined and adjusted based on simulation or physical test results.

[0030] Furthermore, after the motion flow of the digital twin system approximates the motion flow of the mechanical system, a time interval is selected. The overdetermined linear equation system is expressed as:

[0031]

[0032] in, At time t, the target value of the first coefficient within the digital twin system is... The influence of digital twin system parameters on the dynamic model under certain conditions; At time t, the target value of the second coefficient within the digital twin system is... The impact of control inputs on the dynamic model of a digital twin system under certain conditions; For the control input of the mechanical system; Representing time t, the first coefficient within the digital twin system is... The influence of digital twin system parameters on the dynamic model under certain conditions; Representing time t, the second coefficient within the digital twin system is... The impact of control inputs on the dynamic model of a digital twin system under certain conditions; This represents the iteration error estimation variable at time t.

[0033] Furthermore, in order to approximate the current internal coefficients of the mechanical system , The current internal coefficients of the digital twin system , Updated to , , , The process of obtaining includes:

[0034]

[0035]

[0036]

[0037] in, Let A be the inverse of matrix A, when matrix A is not invertible. It is the Moore-Penrose generalized inverse matrix.

[0038] This invention provides a self-updating system for mechanical system models based on digital twin technology, used to implement a self-updating method for mechanical system models based on digital twin technology. The system includes a digital twin system, an error criterion module, a superspiral sliding mode observation module, and a pseudo-inverse module, wherein:

[0039] Digital twin systems are used to track, observe, and predict mechanical systems, and to control the mechanical systems based on the state values ​​obtained from tracking, observation, and prediction.

[0040] The super-helical sliding mode observation module is used to calculate the sliding surface based on the real-time true state value of the mechanical system and the real-time predicted state value of the digital twin system, and to calculate the sliding mode switching variables and iteration error estimation variables based on the sliding surface;

[0041] The error criterion module is used to determine whether the parameters in the digital twin system need to be corrected based on the sliding surface.

[0042] The pseudo-inverse module is used to construct an overdetermined linear equation system based on the estimated variables of the iterative error when it is necessary to correct the parameters in the digital twin system. It then calls the pseudo-inverse algorithm to update the target values ​​of the internal coefficients of the digital twin system and uses the target values ​​to update the internal parameters of the digital twin system.

[0043] The self-updating algorithm for mechanical system models based on digital twin technology proposed in this invention has the following beneficial effects:

[0044] 1. The application of this invention is not limited to specific mechanical systems and has broad applicability. In the construction phase of a digital twin system, for functional... and Without specific requirements, this patented method can be applied to any mechanical system with this universal form. Therefore, this invention can be applied to various mechanical systems such as drones, robotic arms, logistics vehicles, and spacecraft, providing theoretical guidance for their control systems and assisting them in improving control performance.

[0045] 2. This invention fully considers the real-time performance of the enabling algorithm and is hardware feasible. For the super-helical sliding mode observation module, it can achieve finite-time convergence of the mechanical system state under given reasonable parameters. At the same time, the pseudo-inverse module is not updated based on iterative methods, but directly solves the internal coefficients of the digital twin system by constructing an overdetermined linear equation system, further ensuring the algorithm's real-time response capability to the mechanical system.

[0046] 3. This invention considers the time-varying characteristics and modeling uncertainties of mechanical systems, exhibiting a certain degree of robustness. Based on the theoretical framework of digital twin technology, this invention couples the superhelical sliding mode observation module with a pseudo-inverse module, such as using... Function to replace traditional superspiral sliding mode observation algorithm The sign function can make the error estimation variables smoother, which facilitates efficient solution by the pseudo-inverse algorithm. The super-helical sliding mode observation module can perform online real-time estimation of system uncertainty. The pseudo-inverse module can learn the numerical values ​​of the error estimation variables into the internal coefficients of the digital twin system, reduce system error and uncertainty, and further approximate the mechanical system. Attached Figure Description

[0047] Figure 1 This is a flowchart of a self-updating method for a mechanical system model based on digital twin technology according to the present invention;

[0048] Figure 2 This is a schematic diagram of a self-updating system structure for a mechanical system model based on digital twin technology according to the present invention. Detailed Implementation

[0049] 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, and 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.

[0050] This invention proposes a self-updating method for mechanical system models based on digital twin technology. A mechanical system is constructed, and considering system uncertainties, a digital twin system of this mechanical system is built. The digital twin system is used to predict the state changes of the mechanical system, and the mechanical system is tracked, observed, and predicted based on the predicted values. The mechanical system is then controlled based on the prediction results, synchronizing the mechanical system and the digital twin system. Figure 1 This includes the following steps:

[0051] Construct a mechanical system and, considering system uncertainties, build a digital twin system for that mechanical system;

[0052] Obtain the real state vector from the mechanical system in real time, and obtain the predicted state vector from the digital twin system in real time;

[0053] The sliding surface is calculated based on the state of the digital twin system and the state vectors obtained from the mechanical system, and the sliding mode switching variables and iteration error estimation variables are calculated based on the sliding surface;

[0054] Determine whether the error between the digital twin system and the mechanical system exceeds the set requirements based on the sliding surface;

[0055] If the error exceeds the set requirement, an overdetermined linear equation system is constructed based on the iterative error estimation variables, and the pseudo-inverse algorithm is called to update the target values ​​of the internal coefficients of the digital twin system.

[0056] The digital twin system uses updated internal coefficient target values ​​and sliding surface to calculate sliding mode switching variables and iteration error estimation variables to predict the mechanical system;

[0057] If the error meets the set requirements, the digital twin system will predict the mechanical system based on the target values ​​of the internal coefficients of the digital twin system and the sliding surface, and calculate the sliding mode switching variables and the iteration error estimation variables.

[0058] This embodiment also proposes a self-updating system for mechanical system models based on digital twin technology, used to implement the self-updating method for mechanical system models based on digital twin technology as described in claim 1, such as... Figure 2 The system includes a digital twin system, an error criterion module, a superspiral sliding mode observation module, and a pseudo-inverse module, among which:

[0059] Digital twin systems are used to track, observe, and predict mechanical systems, and to control the mechanical systems based on the state values ​​obtained from tracking, observation, and prediction.

[0060] The super-helical sliding mode observation module is used to calculate the sliding surface based on the real-time true state value of the mechanical system and the real-time predicted state value of the digital twin system, and to calculate the sliding mode switching variables and iteration error estimation variables based on the sliding surface;

[0061] The error criterion module is used to determine whether the parameters in the digital twin system need to be corrected based on the sliding surface.

[0062] The pseudo-inverse module is used to construct an overdetermined linear equation system based on the estimated variables of the iterative error when it is necessary to correct the parameters in the digital twin system. It then calls the pseudo-inverse algorithm to update the target values ​​of the internal coefficients of the digital twin system and uses the target values ​​to update the internal parameters of the digital twin system.

[0063] The present invention provides a detailed description of four modules: the digital twin system, the error criterion module, the superhelical sliding mode observation module, and the pseudo-inverse module, in the following embodiments.

[0064] (I) Digital Twin System

[0065] For mechanical systems such as drones, robotic arms, and spacecraft, their dynamic equations have the following unified paradigm:

[0066] (1)

[0067] in, Let be the state vector of the mechanical system. This represents the state of the nth oscillator in the mechanical system, where n = {1, 2, ..., N}; and These are the first and second derivatives of the mechanical system's state vector with respect to time, respectively. (abbreviated as f) represents the influence of system parameters on the dynamic model of the mechanical system, expressed as: f n The internal coefficients of f represent the influence of mechanical system parameters on the dynamic model of the nth oscillator of the mechanical system. ; (abbreviated as g) represents the influence of the control input on the dynamic model of the mechanical system, expressed as: , This represents the effect of the m-th controller on the n-th oscillator in a mechanical system, where m = {1, 2, ..., M}, and the internal coefficient of g is... ; For the control input of the mechanical system, it is represented as u mThis represents the control input of the m-th controller; N is the number of system oscillators; and M is the number of system controllers.

[0068] Based on digital twin technology, a digital twin system of the corresponding mechanical system is constructed, and its dynamic model is established as follows:

[0069] (2)

[0070] in, For the state vector of the digital twin system, This represents the state of the nth oscillator in a digital twin system; and These are the first and second derivatives of the state vector of the digital twin system with respect to time, respectively. (abbreviated as) The influence of the digital twin system parameters on the dynamic model is expressed as follows: , The internal coefficients represent the influence of the digital twin system parameters on the dynamic model of the nth oscillator. ; (abbreviated as) The influence of the control input of the digital twin system on the dynamic model is expressed as follows: , This represents the influence of the m-th controller on the n-th oscillator in a digital twin system, with internal coefficients being... ; To switch the variable vector for sliding mode, Let be the sliding mode switching variable vector for the Nth oscillator; For the error estimation variable vector, Let be the error estimation variable vector for the Nth oscillator.

[0071] Considering inaccurate modeling or time-varying parameters, the internal coefficients of the mechanical system and With the internal coefficients of the digital twin system and Errors exist between them, which negatively impact the observation, tracking, and prediction capabilities of digital twin systems.

[0072] (II) Superhelical Sliding Mode Observation Module

[0073] To make the digital twin system approximate the motion flow of the actual system and estimate the system's uncertainties, a super-helical sliding mode observation module is introduced to construct the sliding surface, including:

[0074] (3)

[0075] in, Let be the sliding surface vector, represented as , Represents the sliding surface vector The i-th element is the sliding surface corresponding to the i-th oscillator in the mechanical system; and This is the parameter vector for the sliding surface.

[0076] For the sliding mode switching variable, the aim is to make the motion flow of the digital twin system approximate the motion flow of the mechanical system within a finite time. The numerical calculation method is given as follows:

[0077] (4)

[0078] in, It is an absolute value function. It is the hyperbolic tangent function. and The variable parameters are used for sliding mode switching.

[0079] For the error estimation variable, we intend to estimate the uncertainty of the system. The numerical update method is given as follows:

[0080] (5)

[0081] in, and Parameters for error estimation variables. Notably, both formulas (5) and (6) use . The function replaces the traditional superspiral sliding mode observation algorithm. The sign function can make the error estimation variables smoother, which facilitates efficient solution by the pseudo-inverse module.

[0082] (III) Pseudo-inverse module

[0083] Once the motion flow of the digital twin system has approximated the motion flow of the mechanical system, a time interval is selected. The overdetermined linear equation system is as follows:

[0084] (6)

[0085] in, At time t, the target value of the first coefficient within the digital twin system is... The influence of digital twin system parameters on the dynamic model under certain conditions; At time t, the target value of the second coefficient within the digital twin system is... The impact of control inputs on the dynamic model of a digital twin system under certain conditions; For the control input of the mechanical system; Representing time t, the first coefficient within the digital twin system is... The influence of digital twin system parameters on the dynamic model under certain conditions; Representing time t, the second coefficient within the digital twin system is... The impact of control inputs on the dynamic model of a digital twin system under certain conditions; The variable representing the iterative error estimate at time t is denoted by t.

[0086] The overdetermined linear equations in equation (6) are transformed into the following form:

[0087] (7)

[0088] in and .

[0089] Target values ​​of internal coefficients in a digital twin system and The solution is as follows:

[0090] (8)

[0091] in, Let A be the inverse of matrix A, when matrix A is not invertible. It is the Moore-Penrose generalized inverse matrix.

[0092] (iv) Error Criterion Module

[0093] Based on the state input of the mechanical system, the digital twin system keeps tracking through the super-helical sliding mode observation module. When the error exceeds the given upper limit, it is considered that the mechanical system has changed. Considering the function of the sliding surface, the error criterion is constructed as shown in equation (9). When equation (9) is triggered, the pseudo-inverse module needs to be called to update the internal coefficients of the digital twin system.

[0094] (9)

[0095] in, For the Euclidean norm, This is the preset error limit.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A self-updating method for mechanical system models based on digital twin technology, characterized in that, Constructing a mechanical system and considering system uncertainties, a digital twin system for the mechanical system is built. The digital twin system is used to predict the state changes of the mechanical system, and the mechanical system is tracked, observed, and predicted based on the predicted values. The mechanical system is then controlled based on the prediction results. Synchronizing the mechanical system and the digital twin system specifically includes the following steps: Construct a mechanical system and, considering system uncertainties, build a digital twin system for that mechanical system; Obtain the real state vector from the mechanical system in real time, and obtain the predicted state vector from the digital twin system in real time; The sliding surface is calculated based on the state of the digital twin system and the state vectors obtained from the mechanical system, and the sliding mode switching variables and iteration error estimation variables are calculated based on the sliding surface; Determine whether the error between the digital twin system and the mechanical system exceeds the set requirements based on the sliding surface; If the error exceeds the set requirement, an overdetermined linear equation system is constructed based on the iterative error estimation variables, and the pseudo-inverse algorithm is called to update the target values ​​of the internal coefficients of the digital twin system. The digital twin system calculates sliding mode switching variables and iteration error estimation variables based on the updated target values ​​of internal coefficients and the sliding surface to predict the mechanical system; If the error meets the set requirements, the digital twin system will predict the mechanical system based on the target values ​​of the internal coefficients of the digital twin system and the sliding surface, and calculate the sliding mode switching variables and the iterative error estimation variables.

2. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, The dynamic equation of the mechanical system is expressed as: in, Let be the state vector of the mechanical system. This represents the state of the nth oscillator in the mechanical system, where n = {1, 2, ..., N}; and These are the first and second derivatives of the mechanical system's state vector with respect to time, respectively. This indicates the influence of system parameters on the dynamic model of the mechanical system. for Internal coefficients; The effect of control input on the dynamic model of a mechanical system is expressed as: , This represents the effect of the m-th controller on the n-th oscillator in a mechanical system, where m = {1, 2, ..., M}. for The internal coefficients are N, where N is the number of system oscillators and M is the number of system controllers; It serves as the control input for the mechanical system.

3. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, The dynamic model of a digital twin system is represented as follows: in, The state vector predicted for the digital twin system; This represents the state of the Nth oscillator in a digital twin system; and These are the first and second derivatives of the state vector of the digital twin system with respect to time, respectively. The internal coefficients represent the influence of the parameters of the digital twin system on the dynamic model. ; The internal coefficients represent the influence of the control input of the digital twin system on the dynamic model. , represented as , This represents the effect of the m-th controller on the n-th oscillator in a digital twin system; For the control input of the mechanical system; To switch the variable vector for sliding mode, Let be the sliding mode switching variable vector for the Nth oscillator; For the error estimation variable vector, Let M be the error estimation variable vector for the Nth oscillator; N is the number of oscillators in the system, and M is the number of system controllers.

4. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, The sliding surface is calculated based on the state of the digital twin system and the state vectors obtained from the mechanical system, and is expressed as: in, Let the sliding surface vector be the vector. and Here is the parameter vector for the sliding surface; This represents the actual state vector of the mechanical system. The predicted state vector for the digital twin system; for The first derivative; for The first derivative.

5. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, The process of calculating sliding mode switching variables includes: in, This represents the sliding mode switching variable for the i-th oscillator in the mechanical system. Represents the sliding surface vector The i-th element; It is an absolute value function. It is the hyperbolic tangent function. and The variable parameters are for sliding mode switching.

6. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, The process of calculating the iterative error estimation variables includes: in, This represents the iterative error estimation variable for the i-th oscillator in the mechanical system; Represents the sliding surface vector The i-th element; and For error estimation variables and parameters; It is the hyperbolic tangent function.

7. The self-updating method for a mechanical system model based on digital twin technology according to claim 1, characterized in that, Once the motion flow of the digital twin system has approximated the motion flow of the mechanical system, a time interval is selected. The overdetermined linear equation system is expressed as: in, At time t, the target value of the first coefficient within the digital twin system is... The influence of digital twin system parameters on the dynamic model under certain circumstances; At time t, the target value of the second coefficient within the digital twin system is... The impact of control inputs on the dynamic model of a digital twin system under certain conditions; For the control input of the mechanical system; Representing time t, the first coefficient within the digital twin system is The influence of digital twin system parameters on the dynamic model under certain circumstances; Representing time t, the second coefficient within the digital twin system is The impact of control inputs on the dynamic model of a digital twin system under certain conditions; The variable representing the iterative error estimate at time t is denoted by t.

8. The self-updating method for a mechanical system model based on digital twin technology according to claim 7, characterized in that, In order to approximate the current internal coefficients of the mechanical system , The current internal coefficients of the digital twin system , Updated to , , , The process of obtaining includes: in, Let A be the inverse of matrix A, when matrix A is not invertible. It is the Moore-Penrose generalized inverse matrix.

9. A self-updating system for mechanical system models based on digital twin technology, characterized in that, The method for self-updating a mechanical system model based on digital twin technology as described in claim 1 includes a digital twin system, an error criterion module, a superspiral sliding mode observation module, and a pseudo-inverse module, wherein: Digital twin systems are used to track, observe, and predict mechanical systems, and to control the mechanical systems based on the state values ​​obtained from tracking, observation, and prediction. The super-helical sliding mode observation module is used to calculate the sliding surface based on the real-time true state value of the mechanical system and the real-time predicted state value of the digital twin system, and to calculate the sliding mode switching variables and iteration error estimation variables based on the sliding surface; The error criterion module is used to determine whether the parameters in the digital twin system need to be corrected based on the sliding surface. The pseudo-inverse module is used to construct an overdetermined linear equation system based on the estimated variables of the iterative error when it is necessary to correct the parameters in the digital twin system. It then calls the pseudo-inverse algorithm to update the target values ​​of the internal coefficients of the digital twin system and uses the target values ​​to update the internal parameters of the digital twin system.

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