High-precision stability control method and system for servo turntable with integrated compensation of multi-source disturbances

Abstract

The application discloses a high-precision stability control method and system for a servo turntable with integrated compensation of multi-source disturbances. The method comprises: collecting real-time operation state data of the servo turntable; extracting and separating multi-source disturbance characteristics from the operation state data to generate multi-source disturbance estimation values including external load disturbance, internal parameter fluctuation disturbance and nonlinear friction disturbance; based on the multi-source disturbance estimation values and a preset reference position instruction, using an integrated compensation control strategy to generate a feedforward compensation signal and a feedback correction signal; fusing and superimposing the feedforward compensation signal, the feedback correction signal and a basic control signal to generate an integrated compensation control signal and outputting the integrated compensation control signal to a driver. The application can accurately separate and compensate multi-source disturbances, realize the collaborative fusion of multi-channel control signals, avoid control conflicts and saturation, and significantly improve the steady-state accuracy, dynamic response performance and robustness of the servo turntable under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of servo control technology, and in particular to a high-precision stabilization control method and system for a servo turntable with integrated compensation for multi-source disturbances. Background Technology

[0002] Servo rotary tables, as indispensable core electromechanical equipment in modern industry, defense equipment, and aerospace, are widely used in photoelectric tracking, radar array scanning, precision machine tools, and flight simulation. With the increasing demands of modern equipment for dynamic response speed and static positioning accuracy, the challenge of high-precision and stable control of servo rotary tables under complex working conditions is becoming increasingly prominent.

[0003] In actual operation, servo turntables are inevitably affected by a variety of complex disturbances. These disturbance sources are usually multi-source, heterogeneous, and mutually coupled, and can be mainly classified into three categories: First, external load disturbances, such as wind load disturbances, kinematic coupling disturbances caused by the swaying of the carrier base, and random impact loads; second, internal parameter fluctuation disturbances, mainly manifested in the perturbation of key electromechanical parameters such as moment of inertia and damping coefficient caused by factors such as temperature changes and structural deformation during system operation; and third, nonlinear friction disturbances, especially in low-speed operation or commutation stages, where the nonlinear abrupt changes in static friction, Coulomb friction, and viscous friction in the mechanical shaft system of the servo turntable can easily cause the system's crawling phenomenon or steady-state error.

[0004] Traditional servo turntable control schemes often employ proportional-integral-derivative (PID) control strategies based on nominal models. However, PID is essentially an error-driven, lag-type feedback control. While it effectively suppresses known or measurable disturbances, it often struggles to balance speed and accuracy when dealing with multi-source, unknown disturbances. To improve disturbance rejection performance, various improvement schemes have emerged in existing technologies. For example, some schemes use a single extended state observer to centrally observe and compensate for the total system disturbance. Although this method is structurally simple, treating all disturbances as lumped disturbances makes it difficult to accurately characterize the physical characteristics of different disturbance sources. Under conditions of strong nonlinear friction or variable loads, the observer is prone to phase lag, resulting in large compensation residuals. Another scheme uses a decoupled compensation method based on multiple independent observers, designing compensators separately for friction and load. However, this approach often neglects the control allocation and logical coordination between disturbance channels, and often employs a simple linear superposition method when generating the final control command. Once the system is subjected to severe disturbances beyond the expected range, the multiple independently generated compensation signals and the basic feedback signals are very likely to cancel each other out or conflict with the control logic, resulting in deep saturation at the driver input, which in turn leads to system instability or a sharp deterioration in dynamic performance.

[0005] Therefore, there is an urgent need for a high-precision stabilization control method and system for servo turntables that can accurately separate and specifically compensate for multi-source disturbances, has rigorous control logic, and can achieve collaborative fusion of multi-channel signals, in order to solve the current technical bottlenecks of insufficient anti-disturbance capability of servo turntables under complex working conditions and easy conflict and saturation of multi-channel control signals. Summary of the Invention

[0006] This application provides a high-precision stabilization control method and system for servo turntables with integrated compensation for multi-source disturbances, in order to solve the problem of insufficient stability control accuracy and dynamic response performance of servo turntables under complex working conditions in the prior art.

[0007] To solve the above-mentioned technical problems, this application adopts the following technical solution: a high-precision stabilization control method for a servo turntable with integrated compensation for multi-source disturbances. The method includes: Step S1: Real-time acquisition of the operating status data of the servo turntable, including at least the turntable position, motor speed, motor current, and turntable frame angular velocity; Step S2: Extract and separate multi-source disturbance features from the operating status data to generate multi-source disturbance estimates including external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. Step S3: Based on the estimated value of the multi-source disturbance and the preset reference position command, an integrated compensation control strategy is used to generate a feedforward compensation signal and a feedback correction signal. Step S4: The feedforward compensation signal, the feedback correction signal, and the basic control signal are fused and superimposed to generate an integrated compensation control signal, which is then output to the driver of the servo turntable to achieve high-precision and stable control.

[0008] In some embodiments, step S2, generating the external load disturbance, includes: Construct an external load observation model based on an extended state observer; The preprocessed turntable position data, motor speed data, and motor current data are input into the external load observation model. The unknown external torque acting on the servo turntable axis is estimated in real time by the external load observation model and output as the external load disturbance.

[0009] In some embodiments, step S2, generating the internal parameter fluctuation disturbance, includes: Establish a discrete-time parameter identification model for the servo turntable; Using the recursive least squares method, with motor current and motor speed as inputs, the rotational inertia and damping coefficient of the servo turntable are identified online. Calculate the deviation between the currently identified moment of inertia and damping coefficient and the nominal parameters, and use this deviation as the fluctuation disturbance of the internal parameters.

[0010] In some embodiments, step S2, generating the nonlinear frictional disturbance includes: A nonlinear friction feedforward compensator is established based on the Stribek friction model. The direction sign function is calculated using the preprocessed angular velocity data of the turntable frame, and combined with the Stribek friction model, the static friction, Coulomb friction and viscous friction components are separated and synthesized to obtain the nonlinear friction disturbance.

[0011] In some embodiments, step S3, which involves generating a feedforward compensation signal and a feedback correction signal using an integrated compensation control strategy, includes: The external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance that constitute the multi-source disturbance estimate are normalized according to their respective control channels. The normalized nonlinear friction disturbance and the internal parameter fluctuation disturbance are superimposed to form a feedforward compensation signal targeting the internal mechanism of the system. The normalized external load disturbance is input to the robust feedback controller, and a feedback correction signal is generated to suppress external dynamic disturbances by combining the deviation between the reference position command and the turntable position.

[0012] In some embodiments, the robust feedback controller is a sliding mode controller. When constructing the control law, the sliding mode controller introduces the external load disturbance as a feedforward compensation term and uses an exponential approach law to generate the feedback correction signal, thereby reducing the switching gain of the sliding mode controller and weakening the chattering phenomenon.

[0013] In some embodiments, in step S4, generating the integrated compensation control signal includes: Calculate the tracking error between the reference position command and the turntable position; The basic control signal is generated based on the tracking error using a proportional-integral-differential algorithm. The basic control signal, the feedforward compensation signal, and the feedback correction signal are weighted and superimposed to form the initial control law; The initial control law is subjected to amplitude limiting and anti-saturation processing to generate the final integrated compensation control signal.

[0014] In some embodiments, after step S4, an adaptive weight adjustment step is further included: The root mean square value of the tracking error of the servo turntable is calculated in real time. When the root mean square value of the tracking error exceeds a preset threshold, the weighting coefficients of the feedforward compensation signal and the feedback correction signal are dynamically adjusted when they are superimposed to enable the system to converge to a high-precision stable control state.

[0015] In some embodiments, dynamically adjusting the weighting coefficients of the feedforward compensation signal and the feedback correction signal during weighted superposition includes: The perturbation suppression effect of the system is judged by combining the rate of change of the root mean square value of the tracking error between the current time and the previous time. When the rate of change of the root mean square value of the tracking error shows a divergent trend, the weighting coefficient of the feedback correction signal is increased and the weighting coefficient of the feedforward compensation signal is decreased. When the rate of change of the root mean square value of the tracking error shows a converging trend, the weighting coefficient of the feedforward compensation signal is increased and the weighting coefficient of the feedback correction signal is decreased.

[0016] To solve the above-mentioned technical problems, another technical solution adopted in this application is: to provide a high-precision stabilization and control system for a servo turntable with integrated compensation for multi-source disturbances, applied to implement the method described above, wherein the system includes: The data acquisition module is used to collect the operating status data of the servo turntable in real time. The operating status data includes at least the turntable position, motor speed, motor current and turntable frame angular velocity. The disturbance estimation module is used to extract and separate multi-source disturbance features from the operating status data, and generate multi-source disturbance estimates including external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. The control strategy module is used to generate feedforward compensation signals and feedback correction signals based on the multi-source disturbance estimates and preset reference position commands, using an integrated compensation control strategy. The drive execution module is used to fuse and superimpose the feedforward compensation signal, the feedback correction signal and the basic control signal to generate an integrated compensation control signal, and output it to the driver of the servo turntable to achieve high-precision and stable control.

[0017] The beneficial effects of this application are as follows: Unlike the prior art, this application discloses a high-precision stabilization control method and system for servo turntable with integrated compensation for multi-source disturbances. This application embodiment extracts and separates multi-source disturbance features from the collected operating status data, accurately decoupling the complex total system disturbance into external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. This allows for targeted estimation of disturbances with different mechanisms, avoiding phase lag and compensation residuals caused by traditional lumped observation. Subsequently, feedforward compensation signals and feedback correction signals are generated based on the multi-source disturbance estimates. Feedforward direct cancellation is used for disturbances of internal system mechanisms, while feedback robust suppression is used for external dynamic disturbances, achieving reasonable allocation and integrated coordination of control strategies. Finally, by fusing and superimposing the feedforward compensation signal, feedback correction signal, and basic control signal, an integrated compensation control signal is generated, achieving comprehensive and accurate compensation for multi-source disturbances. This scheme not only improves the accurate compensation capability for multi-source disturbances through separation and decoupling but also avoids control conflicts and saturation problems caused by simple superposition of multi-channel signals through an integrated signal fusion mechanism, significantly improving the steady-state accuracy, dynamic response capability, and system robustness of the servo turntable under complex operating conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart illustrating an embodiment of the high-precision stabilization method for servo turntable with integrated compensation for multi-source disturbances provided in this application. Figure 2 This is a schematic diagram of an embodiment of the high-precision stabilization and control system for a servo turntable with integrated compensation for multi-source disturbances provided in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] To address the problems in existing technologies where servo turntables suffer from decreased control accuracy due to intertwined multi-source disturbances under complex operating conditions, and where single compensation strategies are prone to phase lag and control signal conflicts, this application provides a high-precision stabilization and control method for servo turntables with integrated compensation for multi-source disturbances. This method achieves comprehensive suppression of internal and external disturbances in the servo turntable system through precise separation of multi-source disturbance characteristics and organic integration of an integrated compensation strategy.

[0023] Specifically, this application provides a high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances, see reference. Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the high-precision stabilization method for a servo turntable with integrated compensation for multi-source disturbances provided in this application. The method includes: Step S1: Collect the operating status data of the servo turntable in real time. The operating status data includes at least the turntable position, motor speed, motor current, and turntable frame angular velocity.

[0024] During the operation of a servo turntable, accurate and real-time acquisition of system operating status data in various dimensions is a fundamental prerequisite for achieving high-precision and stable control. In this embodiment, a sensor network distributed across key nodes of the servo turntable is used to achieve synchronous acquisition of multi-source data.

[0025] Specifically, turntable position data is typically acquired using a high-precision absolute photoelectric encoder or magnetoelectric encoder mounted on the end of the servo motor or the turntable spindle. This turntable position data reflects the current actual spatial angular displacement of the servo turntable and is a core parameter for evaluating the system's tracking accuracy and generating basic control signals. Motor speed data can be obtained through a tachogenerator or by differential calculation using a post-processing circuit of the encoder signal. It reflects the instantaneous operating speed of the system and plays an important role in analyzing the system's dynamic response and observing disturbances. Motor current data is acquired through a Hall current sensor or a high-precision sampling resistor connected in series in the motor driver winding circuit. Since the magnitude of the motor current in a permanent magnet synchronous motor or DC servo motor is strictly proportional to the electromagnetic torque output by the motor, the motor current data can directly reflect the output state of the system's active control force and is a key input for identifying internal parameters and observing external loads. Turntable frame angular velocity data is mainly acquired through a high-precision microelectromechanical gyroscope or fiber optic gyroscope mounted on the turntable base or load end. When the turntable base is subjected to external motion disturbances or wind load impacts, the gyroscope can directly measure the absolute angular velocity of the turntable frame, providing direct inertial space motion feedback for the system.

[0026] In some embodiments, in order to ensure strict alignment of multi-source data in the time dimension and avoid control phase errors caused by sensor sampling delay or asynchronous communication, a multi-sensor fusion module is used to synchronously collect turntable position data, motor speed data, motor current data, and turntable frame angular velocity data, and the collected data is preprocessed with timestamp alignment and low-pass filtering.

[0027] Specifically, a multi-sensor fusion module typically includes a high-speed field-programmable gate array (FPGA) or digital signal processor (DSP), which integrates a hardware synchronization trigger circuit. At the start of each control cycle, the synchronization trigger circuit sends synchronization sampling pulses to the position encoder, current sensor, and gyroscope, ensuring that all sensors complete data sampling at the same physical moment. Subsequently, the sensor data is transmitted to the DSP via a high-speed serial peripheral interface or controller area network (CLAN) bus. The DSP appends a uniform, high-precision timestamp to the received data to complete timestamp alignment.

[0028] Furthermore, due to the unavoidable presence of high-frequency electromagnetic interference and sensor quantization noise in industrial environments, the raw operating status data often contains superimposed high-frequency noise components. If this high-frequency noise is directly input into subsequent disturbance observation and control algorithms, it can easily lead to observer divergence or severe jitter in the control signal. Therefore, low-pass filtering preprocessing of the acquired data is an essential step. In this embodiment, low-pass filtering preprocessing is implemented using a finite impulse response (FIR) digital filter or an infinite impulse response (IOR) digital filter. Taking the FIR as an example, its design process uses the window function method to determine the filter coefficients, setting the cutoff frequency to five to ten times the system control bandwidth to ensure effective filtering of high-frequency noise above the system's dynamic frequency band without introducing significant phase delay. Through low-pass filtering preprocessing, the output operating status data is smoother and more realistic, laying a solid data foundation for subsequent multi-source disturbance feature extraction and separation.

[0029] In some embodiments, considering that sensors may fail under extreme operating conditions or transient pulse interference may cause abnormal data jumps, directly using abnormal data for disturbance estimation would lead to system malfunction. Therefore, before performing multi-source disturbance feature extraction and separation on the operating status data in step S2, data verification of the operating status data is also included.

[0030] Specifically, the system determines whether the collected operational status data at the current moment exceeds the corresponding physical range threshold. For example, for turntable position data, it checks whether it exceeds the physical limit range of the turntable's mechanical structure; for motor current data, it checks whether it exceeds the maximum allowable peak current of the driver. If so, the corresponding sensor is determined to be faulty or severely interfered with, and historical data from the previous valid moment is used to replace and fill in the current moment's data. This data retention mechanism ensures the continuity of multi-source disturbance feature extraction and separation, greatly improving the fault tolerance of the control system in harsh environments.

[0031] Step S2: Extract and separate multi-source disturbance features from the operating status data to generate multi-source disturbance estimates, including external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance.

[0032] The total disturbance experienced by a servo turntable system during operation is a complex coupled entity. If a traditional single-observer centralized observation method is used, the observer struggles to simultaneously achieve both rapid response and accurate estimation of all disturbances due to the significant differences in the frequency band characteristics and physical mechanisms of different disturbance sources. For example, external load disturbances typically manifest as large, low-frequency changes; nonlinear friction disturbances exhibit high-frequency, nonlinear abrupt changes at zero velocity crossings; and internal parameter fluctuations exhibit slowly varying drift characteristics. Therefore, this application employs a feature extraction and separation strategy to decouple the total disturbance into three types of physically meaningful disturbances.

[0033] Specifically, the process of generating external load disturbance includes: constructing an external load observation model based on an extended state observer; inputting preprocessed turntable position data, motor speed data, and motor current data into the external load observation model; estimating the unknown external torque acting on the servo turntable axis system in real time through the external load observation model, and outputting it as the external load disturbance.

[0034] Extended state observer is a robust state observation technique that does not rely on a precise mathematical model of the system. Its core idea is to treat all unknown external disturbances and unmodeled dynamics acting on the system as an extended state of the system, and to reconstruct this extended state in real time through the system's inputs and outputs. Inputs include motor current or electromagnetic torque converted from current, and outputs include turntable position or motor speed.

[0035] In this embodiment, the mechanical motion equations of the servo turntable are reconstructed into an extended state observation model. Specifically, the turntable's acceleration is considered as the derivative of its velocity, and its velocity as the derivative of its position. All unknown external torques, except for the motor's electromagnetic torque, are defined as unknown extended state variables. The external load observation model uses preprocessed motor current data (representing the active control force), turntable position data, and motor speed data as known inputs. It iteratively calculates these using a series of error-feedback-based nonlinear state equations, such as error correction terms constructed using nonlinear power functions. In each control cycle, the external load observation model predicts the theoretical position and velocity of the turntable based on the current input data and compares these theoretical predictions with the actual measured turntable position and motor speed by the sensors to obtain the observation error. Subsequently, this observation error is used to correct the internal state estimate and the extended state estimate in real time according to a preset observer gain matrix. After several cycles of iterative convergence, the extended state observer can accurately track the actual state of the system and output stable estimates of unknown external torques, which serve as the external load disturbance. Because the extended state observer has excellent tracking ability for low-frequency slowly varying disturbances, the external load disturbance extracted by this method can truly reflect the external environmental impact on the turntable.

[0036] The process of generating internal parameter fluctuation disturbance includes: establishing a discrete-time parameter identification model of the servo turntable; using the recursive least squares method, with motor current and motor speed as inputs, identifying the turntable's moment of inertia and damping coefficient online; calculating the deviation between the currently identified moment of inertia and damping coefficient and the nominal parameters, and using this deviation as the internal parameter fluctuation disturbance.

[0037] When a servo turntable operates for a long time or under changing conditions, its mechanical structure may experience perturbations in key dynamic parameters due to temperature gradients, load eccentricity, or mechanical wear. Among these, moment of inertia and damping coefficient are the most critical parameters affecting the system's dynamic response. To accurately obtain the real-time changes of these parameters, this embodiment employs the recursive least squares method for online parameter identification.

[0038] Specifically, a discrete-time parameter identification model for the servo turntable is first established. The continuous-time mechanical motion equations are transformed into discrete-time difference equations using the Euler discretization method. These difference equations are then mathematically transformed so that the rate of change of the turntable's angular velocity is expressed as a linear combination of the motor current and the motor speed. The reciprocal of the moment of inertia and the ratio of the damping coefficient to the moment of inertia are then organized into a parameter vector to be identified.

[0039] In each control cycle, the current and historical motor current and speed data are used as a known input matrix, and the calculated rate of change of angular velocity is used as the target output vector. The recursive least squares algorithm updates the parameter vector to be identified in real time by minimizing the sum of squared errors between the model output and the actual output. To avoid excessive lag in the identification of current parameters due to older data, a forgetting factor is introduced during the recursive process. This forgetting factor assigns greater weight to recent data, enabling the identification algorithm to quickly track the time-varying characteristics of the parameters. Through recursive calculation, the system outputs the identified values ​​of the moment of inertia and damping coefficient online at the current moment.

[0040] Furthermore, the system memory pre-stores the nominal moment of inertia and nominal damping coefficient measured by the servo turntable during factory testing or cold start. The deviations in moment of inertia and damping coefficient are calculated by subtracting the currently identified online parameter values ​​from the nominal parameters. Since these deviations directly reflect the degree of perturbation of the system's internal physical characteristics, failure to compensate for them will result in insufficient or excessive control torque from the controller designed based on the nominal model. Therefore, this parameter deviation is converted into an equivalent torque disturbance form, which is output as an internal parameter fluctuation disturbance. This internal parameter fluctuation disturbance accurately characterizes the slowly varying drift characteristics of the system's internal mechanism.

[0041] The process of generating nonlinear frictional disturbance includes: establishing a nonlinear frictional feedforward compensator based on the Stribek friction model; calculating the direction sign function using the preprocessed turntable frame angular velocity data; and combining the Stribek friction model to separate the static friction, Coulomb friction, and viscous friction components, and synthesizing the nonlinear frictional disturbance.

[0042] Mechanical friction is the most significant nonlinear factor affecting the low-speed stability and reversing accuracy of servo turntables. The relationship between frictional torque and relative motion speed is highly nonlinear, especially with a strong Stribek effect near zero speed.

[0043] In this embodiment, a nonlinear friction feedforward compensator is established using the classic Stribeck friction model. The Stribeck friction model decomposes the total friction torque into static friction, Coulomb friction, and viscous friction components. The static friction component exists when the system is at extremely low speed or stationary and about to move; its direction is opposite to the motion trend, and its magnitude does not exceed the maximum static friction torque. The Coulomb friction component is a friction torque with a constant magnitude and opposite direction to the velocity. The viscous friction component is proportional to the velocity, reflecting the damping effect of the fluid.

[0044] In specific calculations, the nonlinear friction feedforward compensator reads the pre-processed angular velocity data of the turntable frame in real time. First, it uses this angular velocity data to calculate a direction sign function, which is used to extract the direction information of the current motion. When the absolute value of the angular velocity is greater than a very small preset dead zone threshold, the system determines that it is in motion. At this time, according to the Stribeck friction model, combined with the preset Coulomb friction coefficient, viscous damping coefficient, and Stribeck characteristic negative slope coefficient, the corresponding Coulomb friction component and viscous friction component are calculated respectively, and the static friction component after smooth transition due to the non-zero velocity is superimposed. When the absolute value of the angular velocity is less than the preset dead zone threshold, the system determines that it is in a stationary or critical commutation state. At this time, the effect of the static friction component is mainly considered, and its magnitude is dynamically determined according to the difference between the driving torque generated by the motor current and the maximum static friction torque.

[0045] Through a clear mathematical analysis of the aforementioned physical processes, the nonlinear friction feedforward compensator can separate the three components—static friction, Coulomb friction, and viscous friction—in real time and synthesize them into the total nonlinear friction disturbance at the current moment according to the principle of physical superposition. Since the Stribek friction model is calculated directly based on the absolute motion variable of the turntable frame angular velocity, it can accurately reflect the relative friction characteristics between shafts, thus providing an extremely accurate nonlinear feedforward quantity for subsequent feedforward compensation.

[0046] Through feature extraction and separation across these three dimensions, the system successfully decouples the complex total disturbance into external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance, which together constitute the multi-source disturbance estimate. Since each disturbance is estimated independently based on a clear physical mechanism, mutual coupling interference is avoided, greatly improving the accuracy and resolution of the disturbance estimation.

[0047] After generating multi-source disturbance estimates, the extended state observer, when estimating unknown external torques, may experience high-frequency jumps due to the introduction of high-frequency measurement noise. Simultaneously, the recursive least squares method, during online parameter identification, often incorporates high-frequency computational noise in the identification results due to differential calculations. Directly introducing these estimates containing high-frequency noise into the control loop can easily excite high-frequency unmodeled dynamics in the system, leading to harsh whistling in the mechanical structure or current oscillations in the actuator.

[0048] Therefore, in some embodiments, in step S3, before generating the feedforward compensation signal and the feedback correction signal, the multi-source disturbance estimate is also subjected to low-pass filtering to remove high-frequency measurement noise. Specifically, corresponding low-pass filters are designed for external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. Considering that these three types of disturbances have different frequency band characteristics, external load disturbance is mainly low-frequency, so its filter cutoff frequency can be set relatively low, for example, below 50 Hz; while nonlinear friction disturbance changes more drastically when the velocity crosses zero, its filter cutoff frequency needs to be appropriately relaxed, for example, 200 Hz, to ensure the real-time performance of disturbance compensation. Through this channel-specific customized low-pass filtering, high-frequency noise superimposed on the estimate is effectively removed, while the useful frequency band of the real disturbance signal is preserved to the greatest extent, laying the foundation for subsequent high-quality compensation.

[0049] Step S3: Based on the multi-source disturbance estimate and the preset reference position command, an integrated compensation control strategy is adopted to generate feedforward compensation signal and feedback correction signal.

[0050] Existing technologies, when dealing with multi-source disturbances, often simply superimpose the disturbance estimates from each observation channel and send them uniformly to the feedback controller. This approach fails to fully utilize the physical characteristics of different disturbances. This application deeply understands that: internal parameter fluctuations and nonlinear friction are self-consistent disturbances determined by the system's internal mechanisms and can be accurately characterized by models or empirical laws, making them suitable for direct cancellation at the control law generation end using a feedforward approach; while external load disturbances are greatly affected by random environmental influences and have strong uncertainty, making them suitable for suppression using robust feedback control. Based on this, this embodiment adopts an integrated compensation control strategy.

[0051] Specifically, the process of generating feedforward compensation signals and feedback correction signals using an integrated compensation control strategy includes: First, the external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance that constitute the multi-source disturbance estimate are normalized according to their corresponding control channels. Since these three types of disturbances differ in their physical dimensions and dynamic ranges—for example, external load disturbances may reach the Newton-meter level, while nonlinear friction disturbances may only be on the millinewton level—direct superposition would cause smaller disturbances to be submerged. Normalization maps each disturbance to a unified dimensionless interval of zero to one by dividing it by its corresponding maximum physical range or nominal reference value. This not only ensures the numerical comparability of the signals from each channel but also provides a standard numerical interface for subsequent weighted fusion.

[0052] Subsequently, the normalized nonlinear frictional disturbance and internal parameter fluctuation disturbance are superimposed to form a feedforward compensation signal targeting the system's internal mechanism. Since both the nonlinear frictional disturbance and the internal parameter fluctuation disturbance have been precisely separated and extracted, they represent known or observable internal deterministic disturbances within the system. The feedforward compensation signal formed by superimposing these two can be equivalently converted into a compensation electromagnetic torque command that the motor needs to output additionally. This feedforward compensation signal acts directly on the system's forward path, proactively changing the motor's output torque to counteract the effects of these internal disturbances before the system experiences positional deviations due to friction or parameter perturbations. This preventative feedforward compensation mechanism significantly reduces the burden on the feedback controller, enabling the system to maintain extremely high smoothness and tracking accuracy even during low-speed commutation or gradual changes in operating conditions.

[0053] Furthermore, the normalized external load disturbance is input to the robust feedback controller. Combined with the deviation between the reference position command and the turntable position, a feedback correction signal is generated to suppress external dynamic disturbances. Because external load disturbances are highly random and unpredictable, a simple feedforward model cannot completely cover all external shocks; therefore, a feedback mechanism based on closed-loop error must be introduced. The robust feedback controller uses the deviation between the reference position command and the turntable position as the main error input, while simultaneously receiving the external load disturbance as an auxiliary input. Through an internal robust control algorithm, such as a sliding mode control algorithm or an active disturbance rejection control algorithm with a nonlinear state error feedback law, it dynamically calculates the required feedback correction signal to quickly eliminate the position deviation caused by external disturbances.

[0054] In some more specific embodiments, the robust feedback controller is a sliding mode controller. Sliding mode controllers are widely used in servo control due to their simple structure and strong robustness to system parameter perturbations and external disturbances. However, traditional sliding mode controllers inevitably produce high-frequency chattering when suppressing disturbances due to the existence of discontinuous switching control terms. This chattering not only increases mechanical wear but may also excite unmodeled high-frequency dynamics of the system.

[0055] In this embodiment, the sliding mode controller introduces the external load disturbance as a feedforward compensation term when constructing the control law, and uses an exponential approach law to generate the feedback correction signal, so as to reduce the switching gain of the sliding mode controller and thus weaken the chattering phenomenon.

[0056] Specifically, the core of a sliding mode controller lies in designing a sliding surface that can reflect the system's state deviation. In this embodiment, a linear sliding surface is designed based on the turntable position error and its derivative. When constructing the sliding mode control law, the external load disturbance output by the aforementioned extended state observer is directly introduced into the equivalent control part of the control law. Since traditional sliding mode control relies on a large switching gain to overcome unknown external disturbances, the upper bound of the unknown disturbance that the sliding mode controller needs to overcome is significantly reduced after introducing the external load disturbance as a feedforward compensation term; it is only the estimated residual of the external load disturbance.

[0057] Therefore, the system can adaptively reduce the switching gain of the sliding mode controller. Simultaneously, an exponential reaching law is employed when generating the feedback correction signal. The exponential reaching law consists of two parts: an exponential decay term, ensuring that the system state can move towards the sliding surface at a relatively fast speed when moving away from it, shortening the settling time; and a constant-velocity reaching term, ensuring that the system state still has a certain speed when approaching the sliding surface, preventing the system from remaining near the origin and unable to reach the sliding surface due to excessively rapid exponential decay.

[0058] By introducing dual optimizations of external load disturbance feedforward cancellation and exponential reaching law, the sliding mode controller can reduce chattering to an engineering-acceptable range while ensuring strong robustness, outputting a smooth and accurate feedback correction signal, and effectively resisting the impact of external random loads.

[0059] Step S4: The feedforward compensation signal, feedback correction signal and basic control signal are fused and superimposed to generate an integrated compensation control signal, which is then output to the servo turntable driver to achieve high-precision and stable control.

[0060] Traditional servo controllers often only output a single error-driven control signal, while this application, based on separate compensation for feedforward and feedback, requires the scientific fusion of multiple signals.

[0061] Specifically, the process of generating the integrated compensation control signal includes: First, calculate the tracking error between the reference position command and the turntable position. This tracking error is the basis for adjusting the drive system.

[0062] Next, a basic control signal is generated based on the tracking error using the proportional-integral-derivative (PID) algorithm. As a classic algorithm in industrial control, the PID algorithm uses a proportional term to respond quickly to the current error, an integral term to eliminate the system's steady-state error, and a derivative term to predict the error's trend and provide a damping effect. This basic control signal constitutes the base control force for the stable operation of the servo turntable, ensuring the system has stable tracking capability within the most basic closed-loop framework.

[0063] Subsequently, the basic control signal, feedforward compensation signal, and feedback correction signal are weighted and superimposed to form the initial control law. This weighted superposition is not a simple arithmetic addition, but rather a dynamic allocation based on the dominant control position of each signal in different frequency bands of the system. In the low-frequency band, the basic control signal and feedforward compensation signal dominate, ensuring the steady-state accuracy of the system and proactively suppressing internal disturbances. In the mid-to-high-frequency band, the weight of the feedback correction signal gradually increases to cope with rapidly changing external disturbances. By setting a reasonable initial weight matrix, these three signals are fused into the initial control law, which contains all the torque command information required for the system to maintain precise operation.

[0064] Finally, the initial control law is subjected to amplitude limiting and anti-saturation processing to generate the final integrated compensation control signal. In practical engineering, the servo motor driver has a maximum allowable current limit. If the torque command calculated by the initial control law exceeds the physical output limit of the driver, the control signal will experience deep saturation. Once saturation occurs, the integrator of the closed-loop system will continuously accumulate errors, leading to severe overshoot or even instability after the system escapes saturation. Amplitude limiting and anti-saturation processing uses conditional integration or inverse calculation anti-saturation mechanisms. When the initial control law is detected to exceed the threshold, not only is the output signal hard-cut off, but the integrator's continued accumulation is also limited, or the gain of the feedforward channel is dynamically reduced. This ensures that the final integrated compensation control signal output to the driver is always within a safe and controllable linear operating range, achieving high-precision and stable control.

[0065] In some more demanding operating conditions, the disturbance environment faced by the servo turntable is not static but exhibits strong time-varying characteristics. For example, when the turntable base is suddenly subjected to a strong impact, or when a rapid change in the external ambient temperature causes deformation of the mechanical structure, the aforementioned initial control law based on fixed weights and weighted superposition may not guarantee that the system is always in the optimal control state. If the internal model on which the feedforward compensation signal relies (such as the friction model or parameter identification model) deviates significantly due to a sudden change in operating conditions, but still occupies a large weight in the weighted superposition, it will cause the control command to generate an incorrect pre-bias, thereby triggering system overshoot or oscillation.

[0066] To address this lack of dynamic adaptability, in some embodiments, an adaptive weight adjustment step is included after step S4.

[0067] Specifically, the root mean square (RMS) value of the tracking error of the servo turntable is calculated in real time. The RMS value comprehensively reflects the overall level and fluctuation energy of the system's tracking error over a period of time. Compared to the instantaneous error at a single point, it is more statistically significant and less susceptible to high-frequency noise interference. In specific implementations, a fixed-length sliding window is set within the system, for example, containing error data from the past fifty control cycles. In each control cycle, the system adds the squared latest tracking error to the window and removes the oldest squared error value from the window. Then, the average and square root of all squared error values ​​within the window are calculated to obtain the current RMS value of the tracking error. This RMS value characterizes the system's current disturbance suppression effect and closed-loop stability in real time.

[0068] Furthermore, when the root mean square (RMS) value of the tracking error exceeds a preset threshold, the weighting coefficients of the feedforward compensation signal and the feedback correction signal are dynamically adjusted during weighted superposition to bring the system to a high-precision stable control state. The preset threshold is usually set based on the extreme tracking accuracy of the servo turntable under ideal no-load conditions. Once the RMS value exceeds this threshold, it means that the disturbance level of the current system has exceeded the digestion capacity of the conventional compensation strategy, and the fixed weight allocation of the feedforward and feedback channels is no longer applicable, requiring the triggering of an adaptive reconfiguration mechanism.

[0069] Specifically, the process of dynamically adjusting the weighting coefficients includes: The perturbation suppression effect of the system is judged by combining the rate of change of the root mean square value of the tracking error between the current time and the previous time. This rate of change is obtained by calculating the difference between the root mean square value at the current time and the root mean square value at the previous time, and its sign and magnitude directly reflect the evolution trend of the system error.

[0070] When the rate of change of the root mean square (RMS) value of the tracking error exhibits a divergent trend—that is, the rate of change remains positive and the RMS value continues to increase—it indicates that the disturbance currently affecting the system is intensifying, and the feedforward compensation signal generated based on the internal mechanism model may have suffered severe mismatch, even amplifying it in the opposite direction. At this point, the system controller determines that the feedforward channel is unreliable and executes the first adjustment strategy: rapidly increasing the weighting coefficient of the feedback correction signal while simultaneously decreasing the weighting coefficient of the feedforward compensation signal proportionally or gradient-wise. By enhancing the dominance of the feedback correction signal and fully utilizing the strong suppression capability of robust feedback algorithms such as sliding mode controllers against uncertain disturbances, the system is forced to reconverge, preventing instability.

[0071] Conversely, when the rate of change of the root mean square (RMS) value of the tracking error shows a converging trend—that is, the rate of change turns from positive to negative, and the RMS value begins to decrease and approach the threshold—it indicates that the strong external disturbance has passed or the system is recovering to stability. At this point, the feedback controller has essentially eliminated a large range of errors. However, excessive reliance on feedback control over a long period can lead to bandwidth limitations under high-frequency, small disturbances due to the inherent hysteresis of feedback control. Therefore, the system controller implements a second adjustment strategy: gradually increasing the weighting coefficients of the feedforward compensation signal and decreasing the weighting coefficients of the feedback correction signal. By reinforcing the feedforward compensation based on the accurate model, internal friction and parameter fluctuations are offset in advance, thereby relieving the pressure on the feedback controller and further improving the steady-state accuracy and dynamic response smoothness of the system.

[0072] Through the aforementioned adaptive weight adjustment mechanism, the system can dynamically and smoothly switch between model-driven feedforward compensation and error-driven feedback correction based on real-time operational health, completely avoiding the performance degradation of fixed parameter controllers under complex operating conditions and ensuring that the servo turntable can converge to a high-precision stable control state throughout the entire operating cycle.

[0073] Based on the above-mentioned high-precision stabilization and control method for servo turntables with integrated multi-source disturbance compensation, this application also provides a high-precision stabilization and control system 100 for servo turntables with integrated multi-source disturbance compensation. (See reference...) Figure 2 , Figure 2 This is a schematic diagram of an embodiment of the high-precision stabilization and control system for a servo turntable with integrated compensation for multi-source disturbances provided in this application. The system strictly corresponds to the method embodiment in terms of hardware or software architecture, and includes a data acquisition module 10, a disturbance estimation module 20, a control strategy module 30, and a drive execution module 40.

[0074] The data acquisition module 10 is used to acquire the operating status data of the servo turntable in real time. At the hardware level, the data acquisition module 10 includes a high-precision absolute photoelectric encoder mounted on the end of the servo motor shaft, a Hall current sensor connected in series with the motor winding circuit, and a high-precision microelectromechanical gyroscope or fiber optic gyroscope mounted on the turntable base or load end.

[0075] To ensure the time consistency of multi-source data, the data acquisition module 10 also includes a time synchronization unit and a filtering unit. The time synchronization unit integrates a field-programmable gate array, which generates strictly synchronized hardware sampling pulses to control each sensor to complete data sampling at the same physical moment and attach a unified timestamp. The filtering unit uses a finite impulse response digital filter to perform low-pass filtering and noise reduction on the sampled signal, and outputs clean data on turntable position, motor speed, motor current, and turntable frame angular velocity.

[0076] The disturbance estimation module 20 is used to extract and separate multi-source disturbance features from the operating status data. This disturbance estimation module 20 is the core computing unit of the entire system, and it is further subdivided into three parallel sub-units: The external load observation subunit incorporates an external load observation model based on an extended state observer. This subunit takes filtered motor current, turntable position, and motor speed as inputs, and iteratively calculates the extended state variables in real time through a nonlinear state error feedback mechanism, outputting the external load disturbance representing the impact of unknown external environments.

[0077] The internal parameter identification subunit incorporates an online identification algorithm based on recursive least squares. This subunit constructs a regression matrix using motor current and motor speed, identifies the turntable's moment of inertia and damping coefficient in real time, and calculates the difference between these and pre-stored nominal parameters to output the internal parameter fluctuation disturbance reflecting the drift of the system's internal physical characteristics.

[0078] The friction estimation subunit incorporates a nonlinear friction feedforward compensator based on the Stribek friction model. This subunit directly reads the angular velocity data of the turntable frame, analyzes the static friction, Coulomb friction, and viscous friction components, and outputs a highly nonlinear friction disturbance.

[0079] The three sub-units operate in parallel without interfering with each other, and their respective outputs are ultimately combined into a multi-source perturbation estimate. This modular, decoupled design greatly reduces the coupling complexity of the algorithm and improves computational efficiency.

[0080] The control strategy module 30 is used to generate feedforward compensation signals and feedback correction signals based on multi-source disturbance estimates and preset reference position commands, employing an integrated compensation control strategy. This control strategy module 30 corresponds to the allocation and generation of control logic, and internally includes a feedforward generation unit and a robust feedback unit.

[0081] The feedforward generation unit is responsible for normalizing and superimposing the internal parameter fluctuation disturbance and the nonlinear friction disturbance to generate an advanced feedforward compensation signal for the deterministic disturbance inside the system, which is directly used to offset the foreseeable internal disturbance torque.

[0082] The robust feedback unit incorporates a sliding mode controller, which receives external load disturbances and the deviation between the reference position command and the turntable position. When constructing the sliding mode control law, the robust feedback unit introduces the external load disturbance as a feedforward compensation term into the equivalent control part, thereby significantly reducing the switching gain. It also uses an exponential reaching law to generate a smooth feedback correction signal, thus robustly suppressing external random disturbances.

[0083] The drive execution module 40 is used to fuse and superimpose the feedforward compensation signal, the feedback correction signal and the basic control signal to generate an integrated compensation control signal, and output it to the driver of the servo turntable to achieve high-precision and stable control.

[0084] The drive execution module 40 includes a signal fusion unit and a limiting and anti-saturation unit. The signal fusion unit contains an adaptive weight adjustment algorithm, which dynamically allocates weighting coefficients for the feedforward compensation signal and the feedback correction signal based on the real-time calculated root mean square value and rate of change of the tracking error. These two coefficients are then weighted and superimposed with the basic control signal generated based on a proportional-integral-derivative algorithm to form the initial control law. The limiting and anti-saturation unit imposes physical boundary constraints on the initial control law, employing conditional integration or inverse calculation mechanisms to prevent the integrated compensation control signal output to the servo driver from exceeding the maximum allowable current or voltage limit of the motor driver. This effectively avoids system overshoot and instability caused by deep saturation, ensuring the absolute safety of the turntable's mechanical structure and electrical system.

[0085] Unlike existing technologies, this application discloses a high-precision stabilization control method and system for a servo turntable with integrated compensation for multi-source disturbances. Through multi-source data acquisition and feature separation, it precisely decouples the complex total system disturbance into three types of disturbances with clear physical meaning: external load, internal parameter fluctuations, and nonlinear friction. Furthermore, it adopts an integrated collaborative control strategy of "internal feedforward cancellation and external feedback suppression" for disturbances with different mechanisms. Simultaneously, it introduces an adaptive weight adjustment mechanism based on the root mean square error value, achieving dynamic optimal allocation of feedforward and feedback channels under complex time-varying conditions. This scheme completely overcomes the bottlenecks of traditional servo control systems under multi-source disturbances, such as phase lag, large compensation residuals, and control signal conflict saturation. It significantly improves the steady-state tracking accuracy, dynamic response speed, and robustness of the servo turntable under extreme conditions, possessing extremely high industrial application value.

[0086] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances, characterized in that, include: S1: Real-time acquisition of the operating status data of the servo turntable, the operating status data including at least the turntable position, motor speed, motor current and turntable frame angular velocity; S2: Extract and separate multi-source disturbance features from the operating status data to generate multi-source disturbance estimates including external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. S3: Based on the estimated value of the multi-source disturbance and the preset reference position command, an integrated compensation control strategy is adopted to generate a feedforward compensation signal and a feedback correction signal; S4: The feedforward compensation signal, the feedback correction signal and the basic control signal are fused and superimposed to generate an integrated compensation control signal, which is then output to the driver of the servo turntable to achieve high-precision and stable control.

2. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 1, characterized in that, In step S2, generating the external load disturbance includes: Construct an external load observation model based on an extended state observer; The preprocessed turntable position data, motor speed data, and motor current data are input into the external load observation model. The unknown external torque acting on the servo turntable axis is estimated in real time by the external load observation model and output as the external load disturbance.

3. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 1, characterized in that, In step S2, generating the internal parameter fluctuation disturbance includes: Establish a discrete-time parameter identification model for the servo turntable; Using the recursive least squares method, with motor current and motor speed as inputs, the rotational inertia and damping coefficient of the servo turntable are identified online. Calculate the deviation between the currently identified moment of inertia and damping coefficient and the nominal parameters, and use this deviation as the fluctuation disturbance of the internal parameters.

4. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 1, characterized in that, In step S2, generating the nonlinear frictional disturbance includes: A nonlinear friction feedforward compensator is established based on the Stribek friction model. The direction sign function is calculated using the preprocessed angular velocity data of the turntable frame, and combined with the Stribek friction model, the static friction, Coulomb friction and viscous friction components are separated and synthesized to obtain the nonlinear friction disturbance.

5. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 1, characterized in that, In step S3, the generation of feedforward compensation signal and feedback correction signal using an integrated compensation control strategy includes: The external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance that constitute the multi-source disturbance estimate are normalized according to their respective control channels. The normalized nonlinear friction disturbance and the internal parameter fluctuation disturbance are superimposed to form a feedforward compensation signal targeting the internal mechanism of the system. The normalized external load disturbance is input to the robust feedback controller, and a feedback correction signal is generated to suppress external dynamic disturbances by combining the deviation between the reference position command and the turntable position.

6. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 5, characterized in that, The robust feedback controller is a sliding mode controller. When constructing the control law, the sliding mode controller introduces the external load disturbance as a feedforward compensation term and uses an exponential approach law to generate the feedback correction signal, so as to reduce the switching gain of the sliding mode controller and thus weaken the chattering phenomenon.

7. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 1, characterized in that, In step S4, generating the integrated compensation control signal includes: Calculate the tracking error between the reference position command and the turntable position; The basic control signal is generated based on the tracking error using a proportional-integral-differential algorithm. The basic control signal, the feedforward compensation signal, and the feedback correction signal are weighted and superimposed to form the initial control law; The initial control law is subjected to amplitude limiting and anti-saturation processing to generate the final integrated compensation control signal.

8. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 7, characterized in that, Following step S4, an adaptive weight adjustment step is also included: The root mean square value of the tracking error of the servo turntable is calculated in real time. When the root mean square value of the tracking error exceeds a preset threshold, the weighting coefficients of the feedforward compensation signal and the feedback correction signal are dynamically adjusted when they are superimposed to enable the system to converge to a high-precision stable control state.

9. The high-precision stabilization and control method for a servo turntable with integrated compensation for multi-source disturbances according to claim 8, characterized in that, The dynamic adjustment of the weighting coefficients when the feedforward compensation signal and the feedback correction signal are weighted and superimposed includes: The perturbation suppression effect of the system is judged by combining the rate of change of the root mean square value of the tracking error between the current time and the previous time. When the rate of change of the root mean square value of the tracking error shows a divergent trend, the weighting coefficient of the feedback correction signal is increased and the weighting coefficient of the feedforward compensation signal is decreased. When the rate of change of the root mean square value of the tracking error shows a converging trend, the weighting coefficient of the feedforward compensation signal is increased and the weighting coefficient of the feedback correction signal is decreased.

10. A high-precision stabilization and control system for a servo turntable with integrated compensation for multi-source disturbances, characterized in that, The system is adapted to implement the method as described in any one of claims 1 to 9, the system comprising: The data acquisition module is used to collect the operating status data of the servo turntable in real time. The operating status data includes at least the turntable position, motor speed, motor current and turntable frame angular velocity. The disturbance estimation module is used to extract and separate multi-source disturbance features from the operating status data, and generate multi-source disturbance estimates including external load disturbance, internal parameter fluctuation disturbance, and nonlinear friction disturbance. The control strategy module is used to generate feedforward compensation signals and feedback correction signals based on the multi-source disturbance estimates and preset reference position commands, using an integrated compensation control strategy. The drive execution module is used to fuse and superimpose the feedforward compensation signal, the feedback correction signal and the basic control signal to generate an integrated compensation control signal, and output it to the driver of the servo turntable to achieve high-precision and stable control.

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