Error compensation control method for self-stabilizing holder under multi-branch redundancy cooperation

Abstract

The invention relates to the technical field of multi-degree-of-freedom motion control and self-stabilization platforms, in particular to an error compensation control method for a self-stabilization holder under multi-branch-chain redundancy collaboration, which comprises the following steps of: acquiring a pose and a disturbance signal based on branch chain physical topology, constructing a disturbance propagation path, and identifying a main error source branch chain and a secondary error branch chain; performing orthogonal decomposition on the error component of the main error source branch chain, and mapping the error of each secondary error branch chain to a unified error coordinate system; extracting time domain features of the direction and amplitude error, and generating a corresponding compensation signal; distributing a branch chain weight according to the disturbance intensity ratio, and constructing a redundant branch chain cooperative control instruction; synchronously driving each branch chain to execute compensation, and verifying whether the attitude error is converged or not; and if not, correcting the error identification parameter and the disturbance model, and feeding back to the control cycle. According to the invention, multi-branch chain high-dynamic coordination and error self-learning compensation can be realized, and the attitude stability and anti-disturbance capability of the holder are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-degree-of-freedom motion control and self-stabilizing platforms, and in particular to an error compensation control method for a self-stabilizing platform under multi-branch redundant coordination. Background Art

[0002] With the widespread application of multi-degree-of-freedom execution platforms in drone pods, ground-based stabilization systems, and ocean observation equipment, self-stabilizing gimbals with multi-branch redundant drive capabilities have gradually become a key structural form for high-precision stable control. This type of system is usually composed of multiple spatially distributed drive branches, each with independent attitude adjustment capabilities and feedback paths. In the face of external disturbances or structural coupling uncertainties, the overall attitude can be quickly adjusted and stabilized through coordinated control between redundant branches. To meet the high robustness requirements in complex application scenarios, self-stabilizing gimbal control systems need to have comprehensive capabilities such as high-precision error identification, multi-branch coordinated compensation, and dynamic feedback correction.

[0003] However, existing multi-branch self-stabilizing gimbal error compensation methods still face several key technical difficulties. On the one hand, most traditional solutions adopt fixed topology assumptions or static branch determination rules, lacking the ability to dynamically model the actual propagation path of disturbances between multiple branches, which can easily lead to problems such as inaccurate error source identification and compensation direction offset. On the other hand, existing methods are mostly based on single-branch error closed-loop control, making it difficult to achieve dynamic fusion and weight coordination of compensation signals between different branches, resulting in insufficient overall gimbal compensation capabilities in strongly coupled disturbance scenarios. In addition, existing systems generally lack feedback learning and disturbance model self-update mechanisms when errors do not converge, making it impossible to achieve continuous optimization and stable iteration under complex disturbance or multi-source error conditions. Summary of the Invention

[0004] Based on the above objectives, the present invention provides an error compensation control method for a self-stabilizing gimbal under multi-branch redundant collaboration, and provides a multi-branch redundant collaborative control method that integrates disturbance path identification, error mapping fusion, weight distribution and feedback learning mechanism to achieve error compensation control goals with high precision, high responsiveness and strong self-stability.

[0005] A method for error compensation control of a self-stabilizing pan / tilt platform under multi-branch redundant coordination comprises the following steps: S1: Based on the physical connection topology of the gimbal branches, the pose signals and disturbance signals of each branch are collected in real time, the dynamic disturbance propagation path is identified, and the main error source branch and secondary error branch are determined; S2: Orthogonalize and decompose the error components of the main error source branch chain to obtain the direction error component and the amplitude error component, and map the error components of the secondary error branch chain to the error coordinate system of the main error source branch chain; S3: generating a direction compensation signal and an amplitude compensation signal according to the time domain characteristics of the direction error component and the amplitude error component, wherein the direction compensation signal is used to adjust the driving direction of the branch chain, and the amplitude compensation signal is used to correct the driving output; S4: Based on the disturbance intensity ratio of the main error source branch and the secondary error branch, the coordinated compensation weight of each branch is dynamically allocated to generate the redundant branch coordinated control instructions; S5: Synchronously drive each branch chain to perform compensation actions according to the redundant branch chain collaborative control instructions, and verify in real time whether the gimbal attitude error after compensation converges to the preset threshold; S6: If the attitude error has not converged, return to S1 to update the error source identification result until the error meets the stability condition.

[0006] Optionally, the S1 includes: S11: Based on the physical connection topology of the gimbal branches, an adjacency matrix of the motion coupling relationship between each branch is constructed; S12: Sensors deployed at the joints of each branch chain are used to synchronously collect the posture signals and disturbance signals of all branches in real time; S13: Inputting the posture signal and the disturbance signal into the pre-trained disturbance propagation model, combining the adjacency matrix of the physical connection topology, and calculating the transmission gain of the disturbance between branches; S14: constructing a dynamic disturbance propagation path according to the transfer gain, where the path represents the diffusion direction and intensity of the disturbance from the source branch to the associated branches; S15: Based on the transfer gain extreme value in the dynamic disturbance propagation path, the initial disturbance source is identified as the main error source branch, and the associated branches affected by its propagation are marked as secondary error branches.

[0007] Optionally, the S2 includes: S21: extracting the error component of the main error source branch chain, and performing orthogonal decomposition based on the driving coordinate system of the branch chain; S22: In the driving coordinate system, the decomposed tangential projection is defined as a direction error component, the normal projection is defined as an amplitude error component, and an error coordinate system of the main error source branch is established; S23: Obtain the error components of all secondary error branches, and analyze the posture transformation relationship between the error coordinate system of each secondary error branch and the main error source branch; S24: Based on the posture transformation relationship, the error components of each secondary error branch are mapped to the error coordinate system of the main error source branch through a homogeneous transformation matrix to generate a mapped error component with a unified reference.

[0008] Optionally, the S3 includes: S31: performing time-frequency analysis on the direction error component and the amplitude error component respectively to extract time domain features, wherein the time domain features include a phase offset of the direction error component and an envelope fluctuation of the amplitude error component; S32: Calculating an angle compensation amount of the driving shaft according to the phase offset of the direction error component, and generating a direction compensation signal having a negative feedback relationship with the phase offset; S33: Calculating an amplitude correction amount of the driving force according to the envelope fluctuation amount of the amplitude error component, and generating an amplitude compensation signal that is in inverse proportion to the envelope fluctuation amount.

[0009] Optionally, the S4 includes: S41: Calculate the disturbance energy integral value of the main error source branch and each secondary error branch respectively, wherein the disturbance energy integral value is the square integral of the disturbance signal in the time domain; S42: Divide the disturbance energy integral value of each secondary error branch by the disturbance energy integral value of the main error source branch to obtain a disturbance intensity ratio of the main error source branch to each secondary error branch.

[0010] Optionally, the S4 further includes: S43: According to the disturbance intensity ratio, the synergistic compensation weights of each branch are allocated in an inverse proportional relationship, wherein the weight of the main error source branch is , the weight of the secondary error branch is , is the perturbation intensity ratio of the i-th secondary branch; S44: The direction compensation signal, amplitude compensation signal and corresponding collaborative compensation weight of each branch are integrated to generate a redundant branch collaborative control instruction.

[0011] Optionally, the S5 includes: S51: parsing the redundant branch chain coordinated control instruction, extracting the direction compensation and amplitude compensation of each branch chain, and generating a corresponding motor drive pulse signal; S52: Synchronously sending the motor drive pulse signal to the execution motors of all branches to drive each branch to synchronously execute the compensation action.

[0012] Optionally, the S5 further includes: S53: The compensated gimbal attitude data is collected in real time through the attitude sensors of each branch chain, and the current gimbal attitude error is calculated; S54: Compare the current gimbal posture error with a preset threshold value to determine whether the posture error converges to a preset threshold range.

[0013] Optionally, the S6 includes: S61: If the attitude error does not converge to a preset threshold range, the unconverged attitude error of the current gimbal and its corresponding branch motion state are recorded; S62: Based on the unconverged posture error, correct the identification parameters of the main error source branch and the secondary error branch, and update the disturbance propagation model; S63: Feedback the updated disturbance propagation model to S1 and restart the error compensation control loop.

[0014] Beneficial effects of the present invention: The present invention, by introducing a dynamic disturbance path identification mechanism based on the adjacency matrix and the disturbance propagation model in S1, can extract the transfer gain from the real-time collected posture signal and disturbance signal, construct a disturbance diffusion path diagram, and accurately identify the main error source branch and the secondary error branch; compared with the traditional empirical method or the fixed model method, the present method has stronger environmental adaptability and dynamic identification ability, provides a precise branch division basis for subsequent compensation strategies, and effectively avoids the problems of incorrect compensation or system oscillation caused by misjudgment.

[0015] The present invention constructs an error coordinate system and performs orthogonal decomposition of error components in a multi-branch collaborative control scenario. At the same time, a homogeneous transformation matrix is ​​introduced to map the error components of the secondary error branch to the error coordinate system of the main error source branch, thereby realizing fusion analysis of error components of different branches under a unified benchmark; the phase offset of the directional error component and the envelope fluctuation of the amplitude error component are extracted by combining the time-frequency feature extraction algorithm, and dynamic compensation signals of the direction and amplitude are constructed, which significantly enhances the sensitivity and control accuracy of the compensation strategy to different types of errors.

[0016] The present invention constructs a collaborative compensation weight distribution model driven by the disturbance intensity ratio to ensure that the main error source branch and the secondary error branch participate in compensation according to their contribution, and generates redundant branch collaborative control instructions through weighted fusion of multi-branch direction compensation signals and amplitude compensation signals to achieve system-level compensation execution; in the scenario where the error has not converged, the dynamic correction of the main / secondary error branch identification parameters and the disturbance propagation model is achieved through the associated feedback of the posture error and the branch motion state, and a feedback closed-loop control process is formed to improve the system's posture convergence speed and self-stabilization robustness in strong disturbance and complex coupling environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1Schematic diagram of a method flow in an embodiment of the present invention; Figure 2 This is a schematic diagram of the S5 process of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] like Figure 1-Figure 2 As shown, a method for error compensation control of a self-stabilized gimbal under multi-branch redundant coordination includes the following steps: S1: Based on the physical connection topology of the gimbal branches, the pose signals and disturbance signals of each branch are collected in real time, the dynamic disturbance propagation path is identified, and the main error source branch and secondary error branch are determined; S2: Orthogonalize and decompose the error components of the main error source branch chain to obtain the direction error component and the amplitude error component, and map the error components of the secondary error branch chain to the error coordinate system of the main error source branch chain; S3: Generate a direction compensation signal and an amplitude compensation signal according to the time domain characteristics of the direction error component and the amplitude error component. The direction compensation signal is used to adjust the driving direction of the branch chain, and the amplitude compensation signal is used to correct the driving output; S4: Based on the disturbance intensity ratio of the main error source branch and the secondary error branch, the coordinated compensation weight of each branch is dynamically allocated to generate the redundant branch coordinated control instructions; S5: Synchronously drive each branch chain to perform compensation actions according to the redundant branch chain collaborative control instructions, and verify in real time whether the gimbal attitude error after compensation converges to the preset threshold; S6: If the attitude error has not converged, return to S1 to update the error source identification result until the error meets the stability condition.

[0021] S1 includes: S11, adjacency matrix construction: First, based on the physical connection topology of the gimbal branches, the adjacency matrix representing the motion coupling relationship between the branches is constructed, denoted as , where n is the number of branches, and the matrix elements represents the direct coupling relationship between branch i and branch j. If there is a rigid connection or cooperative control mechanism between the two branches, then: ; For example, if in a typical three-branch redundant PTZ, branches 1 and 2, and branches 1 and 3 have a collaborative driving relationship, but branches 2 and 3 have no direct connection, then the adjacency matrix is ​​as follows: ; S12, posture and disturbance signal acquisition: Deploy high-precision sensors at the key joints of each branch to synchronously collect the posture signals of each branch and disturbance signal ,in: Posture signal represents the spatial position and posture of branch i; disturbance signal They represent the disturbance force, joint torque, and angular velocity on the branch chain, respectively. All data will be synchronously input into the subsequent processing module to ensure the timing consistency between multiple branches.

[0022] S13, disturbance propagation gain calculation: take the above collected signal as input, combine it with the adjacency matrix A, and input the pre-trained disturbance propagation model , calculate the transfer gain matrix of disturbance between branches , where the elements It represents the intensity of the disturbance transmitted from branch i to branch j at time t. Its basic expression is: ; in, Represents the set of all branch perturbation signals. The model is built using a graph convolutional neural network (GCN) or a perturbation energy decay law, and has the ability to model the dynamic characteristics of cross-branch conduction.

[0023] S14, dynamic disturbance propagation path construction: based on the calculated transfer gain matrix , construct a dynamic disturbance propagation path map , where the path direction is given by The path strength is determined by the corresponding gain value. The propagation path graph has a directed weighted graph structure, which truly reflects the diffusion path of the disturbance from the source branch to the affected branch.

[0024] For example, if 、 , it means that the disturbance of branch 1 has a high intensity impact on branch 2, and branch 2 further affects branch 3, but the degree of impact decays step by step.

[0025] S15, Error source identification and branch classification: Finally, the starting point of the path with the largest transfer gain value is selected from the dynamic disturbance propagation path as the main error source branch , which is calculated as follows: ; That is, the cumulative disturbance influence intensity of a branch chain on all other branches is the largest one, which is the initial disturbance source. (in is the propagation strength threshold) are marked as secondary error branches .

[0026] For example, in a three-branch redundant structure, if the disturbance transfer gain matrix is: ; The total output disturbance of branch 1 is 0.88+0.73=1.61, which is higher than that of branches 2 and 3. Branch 1 is identified as the main error source branch, and the other two are secondary error branches.

[0027] S2 includes: S21, Error component extraction and orthogonal decomposition: First, extract the instantaneous error component of the main error source branch, which is defined as: ; in, is the desired posture, is the actual posture; The pose consists of a three-dimensional position vector and an Euler angle or quaternion attitude vector.

[0028] The error component Projected to the driving coordinate system of the main error source branch, the coordinate system is recorded as , using its basis vectors Represent the driving direction (tangential) and the amplitude direction (normal), respectively. Then the orthogonal decomposition is expressed as: ; in, represents the direction error component, Represents the amplitude error component, remember to keep Ensure orthogonality.

[0029] S22, error coordinate system establishment: in the drive coordinate system On the graph, a two-dimensional error subspace is constructed with the tangential direction as the X-axis and the normal direction as the Y-axis. This serves as the error coordinate system for the subsequent unified analysis of multi-branch errors. In this coordinate system, the error of the main error source branch can be expressed as a two-dimensional vector: ; This coordinate system serves as the target reference, and the error components of all secondary error branches will be mapped to this coordinate system through transformation.

[0030] S23, secondary error branch pose transformation analysis: For each secondary error branch , extract its error component , and based on the main error source branch chain and branch chain The spatial geometric relationship between them is calculated to calculate the pose transformation relationship between them. This transformation relationship can be expressed as a quaternion rotation With translation vector , or construct a homogeneous transformation matrix: ; in, is the rotation matrix between the main error source branch and the secondary branch, is the translation vector.

[0031] S24, error homogeneous mapping: The transformation relationship is obtained through offline calibration or real-time estimation, which reflects the spatial mapping of the two coordinate systems after the current mechanism structure is deformed. The error component Perform coordinate mapping and unify it to the error coordinate system of the main error source branch. The process is as follows: Expand the error vector to homogeneous coordinates: ; Mapping via a homogeneous transformation matrix: ; Extract the first 3 dimensions as the transformed post-mapping error components: ; Therefore, the errors of all secondary error branches are in the error coordinate system of the main error source branch. It is expressed in , forming a unified measurement benchmark, which is convenient for vector synthesis and weighting in subsequent collaborative compensation strategies.

[0032] Example: Assume that the error vectors of the main error source branch and the secondary error branch are: ; The unit vector of the driving direction of the main error source branch chain is , the normal direction is , the rotation matrix is , if the translation is zero, the error component after mapping is still the original error vector, the direction error is 1.5, and the amplitude error is 0.5.

[0033] S3 includes: S31, time domain feature extraction of direction error component and amplitude error component: First, the direction error component obtained in the unified error coordinate system is extracted. and amplitude error components Perform time-frequency analysis to extract characteristic parameters for compensation calculation.

[0034] right Using Hilbert transform Construct the parsing signal: ; The phase offset is calculated from its instantaneous phase: ; right Perform envelope extraction to obtain envelope fluctuation : ; Then set the analysis window Calculate separately: Phase offset of the direction error component ; Envelope fluctuation of the amplitude error component , where std represents the standard deviation.

[0035] It should be noted that, in the present invention, the "direction error component" and the "amplitude error component" can represent the error vector decomposition result at a certain moment, or can represent its continuous change sequence within a certain time window. Specifically: Directional Error Component It represents the projection component of the error vector of the main error source branch in the driving direction at a fixed moment, which is a scalar static quantity; “Directional error component versus time curve” It represents the changing trajectory of the directional error component in the time dimension during continuous sampling, and is used to characterize the dynamic evolution behavior of the error. Similarly, the “amplitude error component” Its time series form Represents the instantaneous and dynamic error information in the normal direction.

[0036] Therefore, the time-frequency analysis of the direction error component and the amplitude error component in S31 actually refers to the analysis of the direction error component and the amplitude error component within the defined time window. and The continuous sampling data is processed to extract dynamic features such as phase offset and envelope fluctuation, rather than static error value analysis at a single moment.

[0037] S32, direction compensation signal generation: based on the extracted phase offset , construct a direction compensation mechanism to generate a direction compensation signal that has a negative feedback relationship with the phase shift , the compensation signal is used to correct the deflection angle of the branch chain driving direction.

[0038] The compensation angle is calculated as: ; in, is the direction compensation gain coefficient, and its value range is set according to the actual control system sensitivity.

[0039] The direction compensation signal is input into the support drive controller in digital form to directly adjust the actuator attitude angle output.

[0040] Example: If the current phase offset is rad, the compensation gain coefficient is set to , then the direction compensation angle is: ; S33, amplitude compensation signal generation: based on the envelope fluctuation , construct the amplitude adjustment mechanism of the driving force output, and generate an amplitude compensation signal that is inversely proportional to the envelope fluctuation , used to adjust the output thrust of the driver. Its calculation method is:

[0041] in, is the rated drive amplitude, To adjust the sensitivity coefficient.

[0042] Example: If =100N, =5, the current envelope fluctuation is ,but: ; The amplitude compensation signal is input into the driver control module to adjust its thrust output in real time and suppress force fluctuations caused by disturbance coupling.

[0043] S4 includes: S41, calculation of the integrated value of disturbance energy: perform square integration of the disturbance signals of the main error source branch and all secondary error branches during the observation period, and extract the total disturbance energy as an indicator to quantify its disturbance capability. Assume: The disturbance signal of the main error source branch; The disturbance signal of the i-th secondary error branch; T is the duration of the integration window; The corresponding perturbation energy integral values ​​are: ; ; in, represents the Euclidean norm, that is, the modulus of the three-dimensional disturbance signal.

[0044] Example: If the sampling time window is 2 seconds, the disturbance signal is a three-dimensional force / torque vector, and it is sampled at 100 Hz, then the integration can be achieved using numerical approximation (such as the trapezoidal method).

[0045] S42, calculation of disturbance intensity ratio: To measure the degree of disturbance influence of the secondary error branch relative to the main error source branch, the disturbance energy integral value of each secondary error branch is divided by the disturbance energy integral value of the main error source branch to obtain the corresponding disturbance intensity ratio. : ; Among them, if If it approaches 0, it means that the secondary branch chain is less affected by the main error source disturbance; if , it indicates that the disturbance energy of the secondary branch is relatively strong and may constitute a significant synergistic compensation factor.

[0046] S43, collaborative compensation weight allocation: according to the above disturbance intensity ratio , the collaborative compensation weight of each branch is distributed in an inversely proportional manner, so that the secondary branch with greater disturbance intensity obtains a higher degree of compensation participation. The specific weight distribution formula is as follows: Compensation weights of the main error source branches: ; The compensation weight of the i-th secondary error branch: ; Where N is the total number of secondary error branches.

[0047] All weights meet the normalization conditions: ; Example: Assume that a system contains two secondary error branches, and the perturbation intensity ratios are , , the weight is calculated as follows: ; ; ; S44, redundant branch coordinated control instruction generation: the direction compensation signal calculated by each branch is and amplitude compensation signal Combined with the corresponding collaborative compensation weights, a weighted synthesis strategy is used to generate redundant branch collaborative control instructions at the system level. , its structure is as follows: ; The control instruction consists of two components: Weighted direction compensation synthesis (used to adjust the branch chain attitude output); Weighted amplitude compensation synthesis (used to adjust branch chain thrust or driving force output).

[0048] Control instructions are synchronously sent to each branch execution unit through the control bus or drive interface to ensure that the collaborative compensation strategy is globally consistent and locally responsive.

[0049] It should be noted that the direction compensation signal in S33 and amplitude compensation signal It is used to describe the error response of the main error source branch in the time dimension. In S44, in order to realize the coordinated fusion control of multiple branches, the compensation signals of the main error source branch and all secondary error branches need to be identified as 、 and 、 (i is the secondary branch number), thus constructing a unified weighted fusion model. The above symbols express the semantic extension at different control levels, aiming to distinguish the processing objectives of the two stages: single-branch compensation calculation and multi-branch compensation synthesis.

[0050] S5 includes: S51, control instruction analysis and pulse signal generation: the redundant branch chain constructed in S4 above is coordinated with the control instruction The decomposition is done into the direction compensation and amplitude compensation of each branch, and based on this, a motor drive pulse signal that can be used for the actuator is generated. The specific process is as follows: Assume that the compensation amount of the i-th branch is: Direction compensation angle: ; Driving force output amplitude: ; The pulse width modulation (PWM) parameters of the drive motor are set as follows: Angle controlled PWM duty cycle: ; Drive amplitude PWM duty cycle: ; in, are the pulse width modulation gain factors for angle and thrust compensation respectively, : Control the output of the motor position loop and force loop respectively.

[0051] Example description: If the compensation angle of a branch chain direction is rad, the driving amplitude is N, the gain factor is 、 ,but: ; Convert it into PWM control parameters for microcontroller driver module setting.

[0052] S52, Synchronous Drive Execution: The motor drive pulse signals corresponding to each branch are uniformly loaded into their respective servo drive controllers. Through a bus scheduling mechanism or high-speed synchronous trigger interrupt mechanism, all branches execute compensation actions synchronously. Control commands are coordinated with execution timing using a unified timestamp or synchronous trigger signal to ensure consistency in posture adjustment actions within the redundant structure.

[0053] S53, attitude error acquisition and calculation: Through the posture sensors (such as inertial measurement units, rotary encoders, and gyroscopes) deployed at each branch joint or end, the overall attitude data of the gimbal after compensation is obtained in real time, which is recorded as: ; And the expected posture value set by the system: ; Perform interpolation operation to obtain the current gimbal attitude error: ; The error can be converted into an attitude error scalar after normalization according to the Euclidean norm: ; S54, attitude error convergence judgment: the currently calculated attitude error The error threshold is set in the system Compare and determine whether the attitude error convergence conditions are met: ; If the conditions are met (i.e., the gimbal attitude error has converged), this round of collaborative compensation is completed and the system can enter a stable state or maintain current control. If the convergence conditions are not met, the system proceeds to step S6 to restart the error source identification and compensation iterative process to ensure the system's dynamic self-stabilizing closed-loop characteristics.

[0054] Example description: If the current attitude error is =0.018rad, the system sets the threshold value as =0.02rad, it is considered that the compensation has reached the stable condition and the system enters the holding mode.

[0055] S6 includes: S61, non-convergence error record and state association: when the current gimbal attitude error calculated in S5 The convergence condition is not met (i.e. ), the system will automatically record the key status parameters at that moment, including: The current non-converged attitude error vector ; The actual motion state of each branch at that moment, including the posture signal drive output (such as angular velocity, driving force, compensation instruction history); The recorded data will be stored in the state cache queue to form error-drive-response mapping sample pairs under abnormal working conditions for subsequent model correction processing; S62, disturbance propagation model correction: based on the unconverged attitude error recorded above , the identification parameters of the main error source branch and the secondary error branch are adjusted, and the key function structure used to calculate the disturbance intensity ratio and transfer gain in the disturbance propagation model is corrected. The correction method includes two aspects: Identification parameter correction: The determination of the main error source branch in the original model is based on the maximum cumulative value of the disturbance gain: ; When the error does not converge, it is necessary to combine the current The error weighting term is introduced to modify the identification strategy based on the difference between the perturbation response of the corresponding branch chain and the original one: ; in, is the error response difference between branches, is the error feedback weight coefficient (experienced setting); Perturbation propagation model update: The perturbation propagation model is usually expressed in the form of a graph neural network or an energy recursion function, which can be expressed as: ; in, is the disturbance transfer gain matrix at the current moment, is a gain correction term based on historical error deviation, in the form of: ; are the step size coefficient and feedback sensitivity factor respectively, is the error gain mapping vector.

[0056] S63, feedback to S1 to start a new round of error compensation control cycle: the corrected disturbance propagation model and the new error source identification results 、 Feedback to S1, re-execute adjacency matrix update, dynamic perturbation path reconstruction, and primary / secondary error branch division; This initiates a new round of error compensation control loop. This mechanism has the ability of self-learning and adaptive parameter adjustment, and is particularly suitable for uncertain control scenarios with multi-source disturbances and multi-path propagation under strongly coupled redundant structures.

[0057] Supplementary note: The system can iteratively update the perturbation model each time the error fails to converge. However, to prevent overfitting or oscillation, an upper limit on the number of iterations or a convergence enhancement coefficient threshold can be set as a termination condition.

[0058] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0059] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for error compensation control of a self-stabilized pan / tilt platform under multi-branch redundant coordination, characterized in that: The following steps are involved: S1: Based on the physical connection topology of the gimbal branches, the pose signals and disturbance signals of each branch are collected in real time, the dynamic disturbance propagation path is identified, and the main error source branch and secondary error branch are determined; S2: Orthogonalize and decompose the error components of the main error source branch chain to obtain the direction error component and the amplitude error component, and map the error components of the secondary error branch chain to the error coordinate system of the main error source branch chain; S3: generating a direction compensation signal and an amplitude compensation signal according to the time domain characteristics of the direction error component and the amplitude error component, wherein the direction compensation signal is used to adjust the driving direction of the branch chain, and the amplitude compensation signal is used to correct the driving output; S4: Based on the disturbance intensity ratio of the main error source branch and the secondary error branch, the coordinated compensation weight of each branch is dynamically allocated to generate the redundant branch coordinated control instructions; S5: Synchronously drive each branch chain to perform compensation actions according to the redundant branch chain collaborative control instructions, and verify in real time whether the gimbal attitude error after compensation converges to the preset threshold; S6: If the attitude error has not converged, return to S1 to update the error source identification result until the error meets the stability condition.

2. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 1, characterized in that: Said S1 comprises: S11: Based on the physical connection topology of the gimbal branches, an adjacency matrix of the motion coupling relationship between each branch is constructed; S12: Sensors deployed at the joints of each branch chain are used to synchronously collect the posture signals and disturbance signals of all branches in real time; S13: Inputting the posture signal and the disturbance signal into the pre-trained disturbance propagation model, combining the adjacency matrix of the physical connection topology, and calculating the transmission gain of the disturbance between branches; S14: constructing a dynamic disturbance propagation path according to the transfer gain, where the path represents the diffusion direction and intensity of the disturbance from the source branch to the associated branches; S15: Based on the transfer gain extreme value in the dynamic disturbance propagation path, the initial disturbance source is identified as the main error source branch, and the associated branches affected by its propagation are marked as secondary error branches.

3. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 2, characterized in that: The S2 includes: S21: extracting the error component of the main error source branch chain, and performing orthogonal decomposition based on the driving coordinate system of the branch chain; S22: In the driving coordinate system, the decomposed tangential projection is defined as a direction error component, the normal projection is defined as an amplitude error component, and an error coordinate system of the main error source branch is established; S23: Obtain the error components of all secondary error branches, and analyze the posture transformation relationship between the error coordinate system of each secondary error branch and the main error source branch; S24: Based on the posture transformation relationship, the error components of each secondary error branch are mapped to the error coordinate system of the main error source branch through a homogeneous transformation matrix to generate a mapped error component with a unified reference.

4. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 3, characterized in that: The S3 includes: S31: performing time-frequency analysis on the direction error component and the amplitude error component respectively to extract time domain features, wherein the time domain features include a phase offset of the direction error component and an envelope fluctuation of the amplitude error component; S32: Calculating an angle compensation amount of the driving shaft according to the phase offset of the direction error component, and generating a direction compensation signal having a negative feedback relationship with the phase offset; S33: Calculating an amplitude correction amount of the driving force according to the envelope fluctuation amount of the amplitude error component, and generating an amplitude compensation signal that is in inverse proportion to the envelope fluctuation amount.

5. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 4, characterized in that: The S4 includes: S41: Calculate the disturbance energy integral value of the main error source branch and each secondary error branch respectively, wherein the disturbance energy integral value is the square integral of the disturbance signal in the time domain; S42: Divide the disturbance energy integral value of each secondary error branch by the disturbance energy integral value of the main error source branch to obtain a disturbance intensity ratio of the main error source branch to each secondary error branch.

6. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 5, characterized in that: Said S4 further comprises: S43: According to the disturbance intensity ratio, the synergistic compensation weights of each branch are allocated in an inverse proportional relationship, wherein the weight of the main error source branch is , the weight of the secondary error branch is , is the perturbation intensity ratio of the i-th secondary branch; S44: The direction compensation signal, amplitude compensation signal and corresponding collaborative compensation weight of each branch are integrated to generate a redundant branch collaborative control instruction.

7. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 6, characterized in that: The S5 includes: S51: parsing the redundant branch chain coordinated control instruction, extracting the direction compensation and amplitude compensation of each branch chain, and generating a corresponding motor drive pulse signal; S52: Synchronously sending the motor drive pulse signal to the execution motors of all branches to drive each branch to synchronously execute the compensation action.

8. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 7, characterized in that: The S5 further includes: S53: The compensated gimbal attitude data is collected in real time through the attitude sensors of each branch chain, and the current gimbal attitude error is calculated; S54: Compare the current gimbal posture error with a preset threshold value to determine whether the posture error converges to a preset threshold range.

9. The error compensation control method for a self-stabilized pan / tilt platform under multi-branch redundant coordination according to claim 8, characterized in that: The S6 includes: S61: If the attitude error does not converge to a preset threshold range, the unconverged attitude error of the current gimbal and its corresponding branch motion state are recorded; S62: Based on the unconverged posture error, correct the identification parameters of the main error source branch and the secondary error branch, and update the disturbance propagation model; S63: Feedback the updated disturbance propagation model to S1 and restart the error compensation control loop.

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