An online parameter self-tuning method and system of a variable-inertia servo system

Abstract

The application discloses an online parameter self-tuning method and system of a variable-inertia servo system, and relates to the technical field of servo control. The method comprises the following steps: setting a speed loop expected performance index and injecting a test instruction; based on system feedback, performing a steady-state observation and a dynamic estimation algorithm in parallel, and obtaining a high-precision equivalent rotational inertia estimation value in real time through adaptive fusion; according to the estimation value and the set performance index, automatically calculating the proportional and integral gain parameters of the speed loop controller by using a preset mapping relationship; and finally, updating the parameters to the controller in real time to realize the self-adaptation to the load inertia change. The application considers the steady-state precision and dynamic rapidity of identification through composite inertia identification, and makes the tuning process intuitive and the system dynamic performance consistent through performance index mapping, thereby effectively solving the oscillation problem of the servo system under the variable-inertia working condition in a wide range.

Description

Technical Field

[0001] This invention relates to the field of servo control technology, specifically to an online parameter self-tuning method and system for a variable inertia servo system. Background Technology

[0002] In the fields of industrial automation and high-end equipment manufacturing, high-precision servo systems are the core drive units for key equipment such as robots, CNC machine tools, and semiconductor equipment. As application scenarios become increasingly complex, servo systems face increasingly demanding operating conditions. Their load moment of inertia often changes drastically during operation, while simultaneously requiring extremely wide speed control. These extreme operating conditions with variable inertia and wide speed range pose a severe challenge to the robustness and adaptability of servo controllers.

[0003] Traditional servo control systems mostly employ fixed-parameter PID controllers, which can meet basic performance requirements when the load inertia is fixed or does not change significantly. However, when the load inertia fluctuates significantly, the fixed-parameter controller cannot adaptively adjust, easily leading to overshoot, oscillation, or even instability, severely impacting the machining accuracy, operational stability, and reliability of the equipment. To address this issue, existing technologies include model reference adaptive control, active disturbance rejection control, and online self-tuning. However, these methods either require continuous high-frequency excitation signals that could affect normal operation, have complex algorithms that are difficult to implement in engineering, or have limited adaptive capabilities and insufficient robustness under large-scale parameter perturbations, failing to effectively suppress oscillations across the entire operating range while maintaining high dynamic response.

[0004] Therefore, there is an urgent need for a new online parameter self-tuning method that can quickly and accurately sense changes in system inertia and automatically and intelligently adjust controller parameters accordingly, thereby ensuring the smooth, efficient, and oscillation-free operation of the servo system under wide speed range and large load fluctuations. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an online parameter self-tuning method and system for a variable inertia servo system, which addresses the shortcomings of the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for online parameter self-tuning of a variable inertia servo system includes the following steps:

[0008] Step S1: Set the desired dynamic performance index of the servo system speed loop and inject periodic speed test commands into the system;

[0009] Step S2: Based on the system feedback signal under the speed test command, execute the steady-state observation algorithm and the dynamic estimation algorithm in parallel, and obtain the estimated value of the equivalent rotational inertia of the servo motor and the load in real time through an adaptive fusion strategy.

[0010] Step S3: Based on the dynamic performance index set in step S1 and the estimated equivalent moment of inertia obtained in step S2, automatically calculate the proportional gain parameter and integral gain parameter of the speed loop controller through a preset mapping relationship.

[0011] Step S4: Update the proportional gain parameter and integral gain parameter calculated in step S3 to the speed loop controller of the servo drive in real time to adapt to changes in load inertia.

[0012] Furthermore, in step S1, the dynamic performance indicators include at least the desired system response bandwidth and the desired closed-loop damping characteristics; the periodic speed test command is a square wave signal, and the amplitude and period of the square wave signal are set according to the rated speed and allowable speed adjustment range of the servo system.

[0013] Furthermore, step S2 specifically includes the following steps:

[0014] Step S2.1: Construct a state observer based on the motor motion equation, collect the motor speed and electromagnetic torque in real time, and calculate the first inertia estimate through the state observer;

[0015] Step S2.2: When the system goes through the start-up, braking or commutation phase, select a time window, integrate the net acceleration torque within the time window and compare it with the speed increment within the window to calculate the estimated value of the second inertia.

[0016] Step S2.3: Construct a smooth switching function with the absolute value of the system's real-time acceleration as the input variable and generate fusion coefficients. Use the fusion coefficients to perform a weighted summation of the first inertia estimate and the second inertia estimate, and output the final equivalent rotational inertia estimate.

[0017] Furthermore, in step S2.3, the smooth switching function is an S-shaped function. When the absolute value of the real-time acceleration of the system is lower than a preset first threshold, the fusion coefficient makes the output result tend towards the first inertia estimate; when the absolute value of the real-time acceleration is higher than a preset second threshold, the fusion coefficient makes the output result tend towards the second inertia estimate.

[0018] Furthermore, in step S3, the preset mapping relationship is configured as follows: the desired system response bandwidth and the desired closed-loop damping characteristics are jointly converted into constraints on the poles of the velocity loop closed-loop system; based on the pole placement principle, the direct calculation relationship between the proportional gain and integral gain of the velocity loop controller and the estimated equivalent moment of inertia is derived.

[0019] Furthermore, the desired closed-loop damping characteristics are selected by the user according to the requirements for overshoot, and the desired system response bandwidth allows for online switching during system operation according to different process stages to achieve different dynamic performance requirements at different stages.

[0020] An online parameter self-tuning system for a variable inertia servo system, used to implement any one of the online parameter self-tuning methods for a variable inertia servo system, comprising:

[0021] The signal acquisition and processing module is used to acquire signals from the servo motor in real time.

[0022] The instruction and performance management module is used to receive and set speed instructions and dynamic performance indicators.

[0023] The composite inertia identification module has a steady-state observation unit and a dynamic estimation unit set in parallel inside, and is connected to an adaptive fusion unit to output the estimated value of the total equivalent rotational inertia in real time.

[0024] The parameter self-tuning calculation module has a built-in performance-parameter mapper, which is used to calculate the optimal speed loop controller parameters in real time based on the current performance indicators and inertia estimates.

[0025] The dynamic parameter update interface is used to update the calculated speed loop controller parameters to the running speed loop controller.

[0026] Furthermore, the steady-state observation unit in the composite inertia identification module is an enhanced disturbance observer. Based on the traditional disturbance observer structure, a feedforward compensation channel for electromagnetic torque commands is added to accelerate the observation response speed to changes in load torque and system parameters.

[0027] Furthermore, the performance-parameter mapper in the parameter self-tuning calculation module allows for receiving external commands to switch between high-rigidity mode and vibration damping mode.

[0028] Furthermore, the system also includes a diagnostic and protection module; the diagnostic and protection module monitors the rate of change of the inertia estimate output by the composite inertia identification module in real time. When it detects that the inertia estimate has changed beyond a preset safety threshold within a unit time, the diagnostic and protection module will generate an alarm signal and restore the system to the default controller parameters to ensure system safety.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] 1. This invention enables the system to track load inertia changes in real time and automatically maintain optimal control performance within a wide speed range by using online identification of composite inertia and performance-oriented parameter mapping. This solves the oscillation problem that is prone to occur in the system under variable inertia conditions and ensures stable operation.

[0031] 2. The composite identification strategy of steady-state observation and dynamic estimation proposed in this invention combines the high precision advantage of the disturbance observer in steady state with the speed of the direct calculation method in dynamic process, realizing the rapid and accurate capture of the equivalent rotational inertia of the system in full working state, and providing a reliable basis for parameter self-tuning.

[0032] 3. This invention adopts a method of directly mapping user-understandable performance indicators to controller parameters, which makes the tuning objectives clear, the process intuitive, and ensures that the dynamic performance of the system remains highly consistent when the inertia changes, greatly improving the ease of engineering use.

[0033] 4. The built-in diagnostic and protection module of this invention can monitor the rationality of the inertia identification results and parameter tuning process in real time. It can promptly alarm and switch to safe mode in case of abnormal jumps or over-limit situations, effectively preventing system loss of control due to misidentification or extreme working conditions, and enhancing the reliability and safety of the entire solution. Attached Figure Description

[0034] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0035] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0036] Figure 2 This is a system schematic diagram according to an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram illustrating the working principle of the composite inertia identification module in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] like Figure 1 As shown, an online parameter self-tuning method for a variable inertia servo system includes the following steps:

[0040] Step S1: Set the desired dynamic performance index of the servo system speed loop and inject periodic speed test commands into the system;

[0041] Step S2: Based on the system feedback signal under the speed test command, execute the steady-state observation algorithm and the dynamic estimation algorithm in parallel, and obtain the estimated value of the equivalent rotational inertia of the servo motor and the load in real time through an adaptive fusion strategy.

[0042] Step S3: Based on the dynamic performance index set in step S1 and the estimated equivalent moment of inertia obtained in step S2, automatically calculate the proportional gain parameter and integral gain parameter of the speed loop controller through a preset mapping relationship.

[0043] Step S4: Update the proportional gain parameter and integral gain parameter calculated in step S3 to the speed loop controller of the servo drive in real time to adapt to changes in load inertia.

[0044] In step S1, the dynamic performance indicators include at least the expected system response bandwidth and the expected closed-loop damping characteristics; the periodic speed test command is a square wave signal, and the amplitude and period of the square wave signal are set according to the rated speed and allowable speed adjustment range of the servo system.

[0045] The desired dynamic performance parameters of the speed loop are set via the host computer or driver panel, including the desired system response bandwidth and the desired closed-loop damping ratio. Simultaneously, the system injects a periodic speed test command into the speed loop. This command is typically a square wave signal, with its amplitude A and period T set according to the servo system's rated speed and allowable speed range to ensure effective system excitation without affecting normal operation.

[0046] Step S2 specifically includes the following steps:

[0047] Step S2.1: Construct a state observer based on the motor motion equation, collect the motor speed and electromagnetic torque in real time, and calculate the first inertia estimate through the state observer. Specifically, this includes: establishing the motion equation of the servo motor:

[0048]

[0049] in, This represents the total equivalent moment of inertia of the motor and the load, expressed in kg·m. 2 , This represents the mechanical angular velocity of the electric motor, measured in rad / s. Represents electromagnetic torque, in Nm. This represents the total disturbance torque, in Nm.

[0050] The specific formula for designing a state observer is as follows:

[0051]

[0052] in, Generalized momentum The estimated value, in kg·m 2 ·rad / s, This represents an estimated value of the total disturbance torque, in Nm. This indicates the rated inertia of the motor, measured in kg·m. 2 Used as the initial reference for the observer. This represents the feedforward compensation gain, used to improve the observer's response speed to torque commands; it is typically taken as 0.2 to 0.5. , The observer gain is represented by the observer poles and is determined by configuring them. express The derivative with respect to time;

[0053] The specific formula for the observer gain is:

[0054]

[0055] in, This represents the desired pole of the observer, and is usually set to 3-5 times the desired pole of the velocity loop.

[0056] Depend on and measurements The first inertia estimate can be calculated:

[0057]

[0058] in, This represents the estimated first moment of inertia, in kg·m. 2 , A constant representing the prevention of division by zero error, such as 1 × 10⁻⁶. -6 ;

[0059] Step S2.2: During the system's start-up, braking, or commutation phases, a time window is selected. The net acceleration torque within the time window is integrated and compared with the velocity increment within the window to calculate the estimated value of the second inertia. Specifically, this includes:

[0060] When the system undergoes a large acceleration process (such as starting, braking, or commutation), a short time window, such as 10ms, is selected, and the net acceleration torque within this window is integrated and compared with the speed increment:

[0061]

[0062] in, This represents the estimated value of the second moment of inertia, in kg·m. 2 , Indicates the start time of the time window. Indicates the length of the time window. This represents the model-based fixed disturbance feedforward value in Nm, obtained through offline calibrated friction curves to improve estimation accuracy. The numerator is the integral of the accelerating torque over the time window, and the denominator is the velocity increment over the time window.

[0063] Step S2.3: Construct a smooth switching function with the absolute value of the system's real-time acceleration as the input variable and generate fusion coefficients. Use the fusion coefficients to perform a weighted summation of the first inertia estimate and the second inertia estimate, and output the final equivalent rotational inertia estimate. Specifically, this includes:

[0064] To balance accuracy and dynamic speed, a smooth switching function is constructed to generate fusion coefficients:

[0065]

[0066] in, This represents the fusion coefficient, with a value range of [0, 1]. Represents the absolute value of the motor's acceleration, in rad / s. 2 It is obtained from speed difference or acceleration observation. Represents the shape factor, in units of s. 2 / rad controls the steepness of the switching. This indicates the preset switching threshold, in rad / s. 2 ;

[0067] The switching threshold is typically set to 10% to 20% of the servo system's rated acceleration. When the absolute value of the acceleration is below this threshold, the system is considered to be in a quasi-steady state, and steady-state observations are preferred. The shape factor is used to control the smoothness of the fusion transition region, and its value is typically between 5 and 20 s. 2 Between / rad, a larger k makes the switching faster, while a smaller k makes the switching smoother, which can prevent the fusion coefficient jitter caused by acceleration measurement noise.

[0068] When the acceleration is very small, the fusion coefficient is approximately 1, and the steady-state observations are trusted. When the acceleration is very large, the fusion coefficient is approximately 0, and the dynamic estimation results are trusted. Finally, the estimated value of the composite moment of inertia is:

[0069]

[0070] in, This represents the final estimated equivalent moment of inertia.

[0071] In step S2.3, the smooth switching function is an S-shaped function. When the absolute value of the real-time acceleration of the system is lower than a preset first threshold, the fusion coefficient makes the output result tend to the first inertia estimate; when the absolute value of the real-time acceleration is higher than a preset second threshold, the fusion coefficient makes the output result tend to the second inertia estimate.

[0072] In step S3, the preset mapping relationship is configured as follows: the desired system response bandwidth and the desired closed-loop damping characteristics are jointly converted into constraints on the poles of the velocity loop closed-loop system; based on the pole placement principle, the direct calculation relationship between the proportional gain and integral gain of the velocity loop controller and the estimated equivalent moment of inertia is derived.

[0073] Based on the performance indicators set in step S1, calculate the expected closed-loop natural frequency:

[0074]

[0075] in, This represents the desired closed-loop natural frequency. This represents a correction factor, typically ranging from 1.2 to 1.5, used to compensate for the differences between actual high-order systems and ideal second-order models. This represents the expected system response bandwidth;

[0076] Since the current loop employs a high-bandwidth design, within the frequency band of interest to the velocity loop, its closed-loop transfer function can be approximated as a first-order inertial element with a gain of 1 and minimal phase lag. To simplify tuning calculations, it is further equivalent to an ideal proportional element, ensuring the effectiveness and practicality of the pole placement method in engineering applications. The controlled object of the velocity loop is:

[0077]

[0078] in, This represents the transfer function of the controlled object in the velocity loop. Indicates the motor torque coefficient. Represents the Laplace operator;

[0079] The speed loop uses a PI controller:

[0080]

[0081] in, This represents the PI controller transfer function. Indicates proportional gain. Indicates integral gain;

[0082] The closed-loop transfer function is then:

[0083]

[0084] The desired second-order dominant pole is determined by the desired closed-loop natural frequency and the desired closed-loop damping ratio, and its characteristic polynomial is:

[0085]

[0086] in, This represents the desired closed-loop damping ratio;

[0087] By setting the coefficients of the characteristic equation of the actual system to be equal to the expected polynomial, we obtain the self-tuning formula:

[0088]

[0089] The obtained estimated value of equivalent moment of inertia and the calculated value , Substituting these values, we can obtain the adjusted proportional gain and integral gain.

[0090] The desired closed-loop damping characteristics are selected by the user according to the requirements for overshoot. The desired system response bandwidth allows for online switching during system operation according to different process stages, so as to achieve different dynamic performance requirements at different stages.

[0091] The calculated proportional gain and integral gain are written to the speed loop PI controller via the dynamic parameter update interface. To prevent oscillations caused by sudden parameter changes, a first-order low-pass filter is used for smooth transition. The system continues to run, returning to step S2 to form a closed-loop self-tuning.

[0092] like Figure 2 As shown, an online parameter self-tuning system for a variable inertia servo system is used to implement any of the online parameter self-tuning methods for a variable inertia servo system, comprising:

[0093] The signal acquisition and processing module is used to acquire signals from the servo motor in real time.

[0094] The instruction and performance management module is used to receive and set speed instructions and dynamic performance indicators.

[0095] The composite inertia identification module has a steady-state observation unit and a dynamic estimation unit set in parallel inside, and is connected to an adaptive fusion unit to output the estimated value of the total equivalent rotational inertia in real time.

[0096] The parameter self-tuning calculation module has a built-in performance-parameter mapper, which is used to calculate the optimal speed loop controller parameters in real time based on the current performance indicators and inertia estimates.

[0097] The dynamic parameter update interface is used to update the calculated speed loop controller parameters to the running speed loop controller.

[0098] The steady-state observation unit in the composite inertia identification module is an enhanced disturbance observer. Based on the traditional disturbance observer structure, it adds a feedforward compensation channel for electromagnetic torque commands to accelerate the observation response speed to changes in load torque and system parameters.

[0099] The performance-parameter mapper in the parameter self-tuning calculation module allows the reception of external commands to switch between high-rigidity mode and vibration damping mode.

[0100] The high-rigidity mode corresponds to a higher response bandwidth setting, while the vibration suppression mode corresponds to a higher damping ratio setting, in order to meet the needs of different application scenarios.

[0101] The system also includes a diagnostic and protection module; the diagnostic and protection module monitors the rate of change of the inertia estimate output by the composite inertia identification module in real time. When it detects that the inertia estimate has changed beyond the preset safety threshold within a unit time, the diagnostic and protection module will generate an alarm signal and restore the system to the default controller parameters to ensure system safety.

[0102] like Figure 3 As shown, the module receives real-time motor speed and electromagnetic torque from the signal processing module. Internally, two algorithm units run in parallel: a steady-state observation unit, based on a state observer, provides a high-precision first inertia estimate during stable system operation; during system acceleration / deceleration, the dynamic estimation unit quickly outputs a second inertia estimate by directly calculating the net torque integral and speed increment within a short time window; simultaneously, it calculates the system acceleration in real time, dynamically generates a fusion coefficient through an S-shaped smoothing switching function, and performs a weighted summation of the first and second inertia estimates, ultimately outputting an equivalent rotational inertia estimate that combines high precision and fast response. This design ensures reliable inertia identification results under any dynamic operating conditions.

[0103] The specific workflow of the system of this invention includes:

[0104] 1. Power-on initialization: Load default parameters, and allow users to set performance indicators such as the desired system response bandwidth and the desired closed-loop damping ratio;

[0105] 2. Entering the self-tuning loop: The signal acquisition module acquires real-time signals; the composite inertia identification module executes the steady-state observation algorithm and the dynamic estimation algorithm in parallel, and then fuses the output. The parameter self-tuning calculation module calculates the proportional gain and integral gain using a mapping formula; the dynamic parameter update interface smoothly updates the controller parameters.

[0106] 3. Continuous Monitoring and Adjustment: The system continuously runs the above cycle. When the load inertia changes, As the system changes, it automatically adjusts the controller parameters to keep the dynamic performance of the system constant.

[0107] 4. Diagnosis and protection, real-time monitoring If the rate of change exceeds the safety threshold, such as if the inertia changes by more than 5 times within 0.1 seconds, it is judged as abnormal, the controller parameters are immediately locked and an alarm is triggered.

[0108] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0109] The examples described herein are merely preferred embodiments of the invention and are not intended to limit the concept and scope of the invention. Any modifications and improvements made by those skilled in the art to the technical solutions of the invention without departing from the design concept of the invention should fall within the protection scope of the invention.

Claims

1. A method for online parameter self-tuning of a variable inertia servo system, characterized in that, Includes the following steps: Step S1: Set the desired dynamic performance index of the servo system speed loop and inject periodic speed test commands into the system; Step S2: Based on the system feedback signal under the speed test command, obtain the estimated equivalent rotational inertia of the servo motor and the load in real time, specifically including the following steps: Step S2.1: Construct a state observer based on the motor motion equation, collect the motor speed and electromagnetic torque in real time, and calculate the first inertia estimate through the state observer; Step S2.2: When the system goes through the start-up, braking or commutation phase, select a time window, integrate the net acceleration torque within the time window and compare it with the speed increment within the window to calculate the estimated value of the second inertia. Step S2.3: Construct a smooth switching function with the absolute value of the system's real-time acceleration as the input variable and generate fusion coefficients. Use the fusion coefficients to perform a weighted summation of the first inertia estimate and the second inertia estimate, and output the final equivalent rotational inertia estimate. Step S3: Based on the dynamic performance index set in step S1 and the estimated equivalent moment of inertia obtained in step S2, automatically calculate the proportional gain parameter and integral gain parameter of the speed loop controller through a preset mapping relationship. Step S4: Update the proportional gain parameter and integral gain parameter calculated in step S3 to the speed loop controller of the servo drive in real time to adapt to changes in load inertia.

2. The method according to claim 1, characterized in that, In step S1, the dynamic performance indicators include at least the expected system response bandwidth and the expected closed-loop damping characteristics; the periodic speed test command is a square wave signal, and the amplitude and period of the square wave signal are set according to the rated speed and allowable speed adjustment range of the servo system.

3. The method according to claim 2, characterized in that, In step S2.3, the smooth switching function is an S-shaped function. When the absolute value of the real-time acceleration of the system is lower than a preset first threshold, the fusion coefficient makes the output result tend to the first inertia estimate; when the absolute value of the real-time acceleration is higher than a preset second threshold, the fusion coefficient makes the output result tend to the second inertia estimate.

4. The method according to claim 3, characterized in that, In step S3, the preset mapping relationship is configured as follows: the desired system response bandwidth and the desired closed-loop damping characteristics are jointly converted into constraints on the poles of the velocity loop closed-loop system; based on the pole placement principle, the direct calculation relationship between the proportional gain and integral gain of the velocity loop controller and the estimated equivalent moment of inertia is derived.

5. The method according to claim 4, characterized in that, The desired closed-loop damping characteristics are selected by the user according to the requirements for overshoot. The desired system response bandwidth allows for online switching during system operation according to different process stages, so as to achieve different dynamic performance requirements at different stages.

6. An online parameter self-tuning system for a variable inertia servo system, used to implement the online parameter self-tuning method for a variable inertia servo system as described in any one of claims 1-5, characterized in that, include: The signal acquisition and processing module is used to acquire signals from the servo motor in real time. The instruction and performance management module is used to receive and set speed instructions and dynamic performance indicators. The composite inertia identification module has a steady-state observation unit and a dynamic estimation unit set in parallel inside, and is connected to an adaptive fusion unit to output the estimated value of the total equivalent rotational inertia in real time. The parameter self-tuning calculation module has a built-in performance-parameter mapper, which is used to calculate the optimal speed loop controller parameters in real time based on the current performance indicators and inertia estimates. The dynamic parameter update interface is used to update the calculated speed loop controller parameters to the running speed loop controller.

7. The system according to claim 6, characterized in that, The steady-state observation unit in the composite inertia identification module is an enhanced disturbance observer. Based on the traditional disturbance observer structure, it adds a feedforward compensation channel for electromagnetic torque commands to accelerate the observation response speed to changes in load torque and system parameters.

8. The system according to claim 7, characterized in that, The performance-parameter mapper in the parameter self-tuning calculation module allows the reception of external commands to switch between high-rigidity mode and vibration damping mode.

9. The system according to claim 8, characterized in that, The system also includes a diagnostic and protection module; the diagnostic and protection module monitors the rate of change of the inertia estimate output by the composite inertia identification module in real time. When it detects that the inertia estimate has changed beyond the preset safety threshold within a unit time, the diagnostic and protection module will generate an alarm signal and restore the system to the default controller parameters to ensure system safety.

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