Intelligent attitude control system with multi-servo-node cooperative response

Abstract

The invention discloses a multi-servo-node cooperative response intelligent attitude control system, and the system comprises a master control node which is used for scanning and recognizing servo nodes, carrying out the time sequence synchronization of the servo nodes, and distributing an action instruction to each servo node; and the plurality of servo nodes are connected with the master control node through a communication bus, and are used for receiving the time sequence synchronization instruction and the action instruction issued by the master control node, and responding and executing according to the unified action schedule in the action instruction. According to the method, synchronous linkage control and real-time state management of the server are realized through unified time sequence scheduling and distributed servo node identification technologies, and the method is suitable for an aircraft simulation platform, a display model, a bionic system and tactical demonstration equipment.

Description

Technical Field

[0001] This invention relates to the field of servo control and attitude management technology, and in particular to an intelligent attitude control system with multi-servo node collaborative response. Background Technology

[0002] With the rapid development of aircraft model simulation, simulation platforms, and industrial-grade multi-degree-of-freedom control systems (such as robots and precision simulation platforms), near-extreme demands have been placed on the realism, synchronization, and reliability of motion control. The core of these systems often relies on the coordinated work of a large number of servo actuators (such as servo motors and motors) to accurately reproduce or realize complex spatial attitudes and motion trajectories.

[0003] Currently, the traditional multi-node control systems widely used in the industry, while possessing certain advantages in modularity and scalability in their architecture, still suffer from the following problems when dealing with high-level application scenarios: real-time bottlenecks and synchronization misalignment. Traditional systems have inherent delays in communication and processing between different layers (such as the planning layer, control layer, and execution layer). When the system faces high dynamic loads or needs to handle different control cycles (such as attitude calculation cycles and servo drive cycles), synchronization misalignment is highly likely to occur. This "staggered pace" leads to delayed responses and discrete actions from each servo node, severely disrupting the overall motion continuity and accuracy, directly manifesting as attitude distortion and stiff movements on aircraft models and simulation platforms. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent attitude control system with multi-servo node collaborative response.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A first aspect of the present invention provides an intelligent attitude control system with multi-servo node cooperative response, comprising:

[0007] The master node is used to scan and identify servo nodes, synchronize the timing of servo nodes, and distribute action commands to each servo node.

[0008] Multiple servo nodes are connected to the master control node via a communication bus to receive timing synchronization instructions and action instructions issued by the master control node, and to respond and execute according to the unified action schedule in the action instructions.

[0009] Furthermore, the servo node includes a master-level computing unit and an execution-level computing unit;

[0010] The master control level computing unit is used to identify different servo nodes, and also to receive action instructions distributed by the master control node and perform task planning to form the first level control instructions; the execution level computing unit executes high-frequency current loop and / or speed loop control algorithms according to the first level control instructions to generate the second level control instructions to directly drive external power devices.

[0011] Furthermore, the servo node also includes a shared memory area configured as a circular buffer;

[0012] The master-level computing unit writes the first-level control instructions into the shared memory area, and the execution-level computing unit directly reads the latest first-level control instructions from the shared memory area via DMA.

[0013] Furthermore, the servo node also includes a heartbeat detection and switching circuit, and the execution-level computing unit includes two synchronously running processing cores;

[0014] The heartbeat detection and switching circuit continuously monitors the key output signals of the two synchronous processing cores. Once an abnormal output or loss of "heartbeat" is detected in either core, the heartbeat detection and switching circuit will automatically and seamlessly switch the final control output of the system to the normal processing core within microseconds.

[0015] Furthermore, the servo node is also used to report status data to the master node; the master node is also used to complete intelligent health monitoring and predictive maintenance based on the status data reported by the servo node.

[0016] Furthermore, the intelligent health monitoring and predictive maintenance includes:

[0017] The master node constructs a node fault mathematical model based on indicators including servo node logic resource utilization, chip junction temperature, and communication bit error rate. The fault prediction probability value is calculated by the above indicators through the node fault mathematical model.

[0018] Predictive maintenance is achieved when the fault prediction probability value is greater than a threshold.

[0019] Furthermore, for the servo node, the step of responding and executing according to the unified action schedule in the action instruction includes:

[0020] During execution, the servo node calculates the intermediate state angle using a time base obtained through timing synchronization;

[0021] Based on the timestamp, calculate the current position in the target trajectory, generate intermediate angles according to the corresponding interpolation algorithm, and balance the transition between adjacent target points.

[0022] Furthermore, generating the intermediate angle according to the corresponding interpolation algorithm includes:

[0023] Select the appropriate interpolation algorithm to generate the intermediate angle based on the system load.

[0024] Furthermore, the step of selecting the appropriate interpolation algorithm to generate the intermediate angle based on the system load includes:

[0025] When the system load L>70%, switch to linear interpolation to reduce the computational load and calculate the intermediate angle of the linear interpolation.

[0026] When the system load L satisfies 30%≤L≤70%, cubic spline interpolation is used to calculate the intermediate angle.

[0027] When the system load L < 30%, the intermediate angle is calculated using quintic spline interpolation to improve smoothness and second derivative continuity.

[0028] Furthermore, after generating the intermediate angle, the interpolation parameters are dynamically adjusted based on real-time error feedback.

[0029] The beneficial effects of this invention are:

[0030] In an exemplary embodiment of the present invention, an intelligent attitude control system with multi-servo node collaborative response is provided. Through unified timing scheduling and distributed servo node identification technology, synchronous linkage control and real-time status management of servos are realized. It is applicable to aircraft simulation platforms, display models, bionic systems and tactical demonstration equipment. Attached Figure Description

[0031] Figure 1 A schematic diagram of an intelligent attitude control system with multi-servo node cooperative response provided as an exemplary embodiment of the present invention;

[0032] Figure 2 The core logic flowchart of an intelligent attitude control system with multi-servo node collaborative response provided as an exemplary embodiment of the present invention is shown. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0034] See Figure 1 , Figure 1 A schematic diagram of an intelligent attitude control system with multi-servo node cooperative response provided by an exemplary embodiment of the present invention is shown, including:

[0035] The master node is used to scan and identify servo nodes, synchronize the timing of servo nodes, and distribute action commands to each servo node.

[0036] Multiple servo nodes are connected to the master control node via a communication bus to receive timing synchronization instructions and action instructions issued by the master control node, and to respond and execute according to the unified action schedule in the action instructions.

[0037] Specifically, in this exemplary embodiment, an intelligent attitude control system with multi-servo node collaborative response is provided. Through unified timing scheduling and distributed servo node identification technology, synchronous linkage control and real-time status management of servos are realized. It is applicable to aircraft simulation platforms, display models, bionic systems and tactical demonstration equipment.

[0038] The following will describe various preferred exemplary embodiments of the intelligent attitude control system:

[0039] More preferably, in an exemplary embodiment, the servo node includes a master-level computing unit and an execution-level computing unit;

[0040] The master control level computing unit is used to identify different servo nodes, and also to receive action instructions distributed by the master control node and perform task planning to form the first level control instructions; the execution level computing unit executes high-frequency current loop and / or speed loop control algorithms according to the first level control instructions to generate the second level control instructions to directly drive external power devices.

[0041] Specifically, in this exemplary embodiment, a hybrid heterogeneous computing and control architecture is adopted: for multiple servo nodes, each node contains two computing units, namely a master-level computing unit and an execution-level computing unit.

[0042] (1) The main control computing unit uses a multi-core general-purpose processor (ARM) as its core and runs a non-real-time operating system (Linux). This unit has an internal ID identification module to identify different nodes; it is also responsible for receiving tasks from the main control node and performing complex tasks such as task planning, kinematic calculation, status monitoring, and human-computer interaction, which are non-real-time or soft real-time tasks. Its working cycle is the first cycle T1, with a value of 10ms. More preferably, each servo node has two ARM processors, i.e., a hardware dual-redundancy arrangement.

[0043] (2) The execution-level computing unit uses a field-programmable gate array (FPGA) as its core, running a real-time operating system (RTOS) or directly performing bare-metal programming. This unit communicates with the main control-level computing unit via a high-speed bus, and is responsible for receiving the planned first-level control instructions. It also utilizes the hardware parallel capabilities of the FPGA to execute high-frequency current loop and speed loop control algorithms, generating second-level control instructions to directly drive the power devices. Its working cycle is the second cycle T2, which takes a value of 1ms, and T2 is less than T1.

[0044] More preferably, in an exemplary embodiment, the servo node further includes a shared memory area configured as a circular buffer;

[0045] The master-level computing unit writes the first-level control instructions into the shared memory area, and the execution-level computing unit directly reads the latest first-level control instructions from the shared memory area via DMA.

[0046] Specifically, this exemplary embodiment provides an efficient data interaction mechanism. To ensure efficient data flow between the two-level computing units, this exemplary embodiment allocates a shared memory area in physical memory and configures it as a ring buffer. The master-level computing unit (ARM) writes the planned instructions and parameters into the shared area, and the execution-level computing unit (FPGA) can directly read the latest data from the shared area via DMA without waiting or ARM intervention. This "zero-copy" communication mechanism completely eliminates memory copying overhead and minimizes communication latency.

[0047] More preferably, in an exemplary embodiment, the servo node further includes a heartbeat detection and switching circuit, and the execution-level computing unit includes two processing cores running synchronously;

[0048] The heartbeat detection and switching circuit continuously monitors the key output signals of the two synchronous processing cores. Once an abnormal output or loss of "heartbeat" is detected in either core, the heartbeat detection and switching circuit will automatically and seamlessly switch the final control output of the system to the normal processing core within microseconds.

[0049] Specifically, in this exemplary embodiment, the solution integrates a hardware dual-core redundancy module within the execution-level computing unit (FPGA), which is the core of achieving high reliability.

[0050] (1) Dual-core synchronous operation

[0051] Within the execution-level computing unit (FPGA), two completely symmetrical, synchronously operating processing cores (CoreA and CroeB) are constructed. They receive the same input data and execute completely identical control algorithms.

[0052] (2) Heartbeat detection and lossless switching

[0053] Design a dedicated heartbeat detection and switching circuit (implemented by hardware logic, not software program). This circuit continuously compares the key output signals (PWM wave, fault flag, etc.) of the dual cores with nanosecond-level precision. Once an abnormal output or loss of "heartbeat" is detected in either core, the switching circuit will automatically and seamlessly switch the system's final control output to the normal processing core within microseconds. This process is imperceptible to the external load, achieving true "fault concealment."

[0054] More preferably, in an exemplary embodiment, the servo node is further configured to report status data to the master node; the master node is further configured to perform intelligent health monitoring and predictive maintenance based on the status data reported by the servo node.

[0055] More preferably, in an exemplary embodiment, the intelligent health monitoring and predictive maintenance includes:

[0056] The master node constructs a node fault mathematical model based on indicators including servo node logic resource utilization, chip junction temperature, and communication bit error rate. The fault prediction probability value is calculated by the above indicators through the node fault mathematical model.

[0057] Predictive maintenance is achieved when the fault prediction probability value is greater than a threshold.

[0058] Specifically, existing technologies suffer from weak fault handling mechanisms and poor system robustness: they lack efficient and intelligent fault detection and self-healing capabilities. When one or more servo nodes in the system fail (such as freezing, drifting, or communication interruption), traditional methods struggle to achieve rapid and accurate fault isolation and system reconfiguration. The result is often that the fault is simply amplified, or the entire system crashes, failing to maintain degraded operation while ensuring safety, severely damaging system reliability and task continuity. In this exemplary embodiment, intelligent health monitoring and predictive maintenance functions are implemented. Fault detection and prevention are achieved by constructing a fault function to calculate the node failure probability in real time. This exemplary embodiment provides an example of this function.

[0059] Nodes possess multiple health metrics, such as ARM / FPGA logic resource utilization, chip junction temperature (corresponding to servo nodes), and communication bit error rate. Based on these metrics, a node fault mathematical model is constructed. This mathematical model synthesizes the aforementioned basic metrics into a fault probability value, resulting in the following mathematical model:

[0060] ;

[0061] in: The probability of failure is based on logical resource utilization. This represents the failure probability based on the chip junction temperature. Let be the failure probability based on the communication bit error rate. Then the node failure probability is:

[0062] ;

[0063] ;

[0064] ;

[0065] Where: L represents the logical resource utilization rate. The probability of failure when resource utilization reaches 100%; T is the chip junction temperature. To achieve the chip junction temperature The probability of failure at that time; BER is the bit error rate.

[0066] The system calculates the fault prediction probability value of nodes by periodically collecting L, T, and BER values. A threshold is set for notification; when the calculated fault prediction probability value exceeds the threshold, the system can issue an early warning or initiate a preventative switch between the primary and backup control nodes, transforming passive response into proactive prevention.

[0067] More preferably, in an exemplary embodiment, a blockchain-based audit log is used to generate hash values ​​for all key switching events and health indicator data and store them in the blockchain network.

[0068] Specifically, in this exemplary embodiment, the immutability and traceability of blockchain are utilized to provide absolutely reliable data for post-fault analysis, greatly shortening the time for troubleshooting root causes of faults.

[0069] More preferably, in an exemplary embodiment, for a servo node, the step of responding according to the unified action schedule in the action instruction includes:

[0070] During execution, the servo node calculates the intermediate state angle using a time base obtained through timing synchronization;

[0071] Based on the timestamp, calculate the current position in the target trajectory, generate intermediate angles according to the corresponding interpolation algorithm, and balance the transition between adjacent target points.

[0072] Specifically, in this exemplary embodiment, compared with traditional distributed systems, this control system adopts an interpolation compensation mechanism to improve control accuracy and achieve high-precision control. That is, during the execution of the servo node, the system calculates the intermediate state angle through a time base to smoothly transition. Based on the timestamp, the position of the current moment in the target trajectory is calculated. (Preferably, according to the system load, a suitable interpolation algorithm is selected to generate the intermediate angle to balance the transition between adjacent target points. (Preferably, combined with real-time error feedback, the interpolation parameters are dynamically adjusted.)

[0073] More preferably, in an exemplary embodiment, generating the intermediate angle according to the corresponding interpolation algorithm includes:

[0074] Select the appropriate interpolation algorithm to generate the intermediate angle based on the system load.

[0075] More preferably, in an exemplary embodiment, the step of selecting the corresponding interpolation algorithm to generate the intermediate angle based on the system load includes:

[0076] When the system load is high, switch to linear interpolation to reduce the computational load and calculate the intermediate angle of the linear interpolation.

[0077] When the system load is normal, cubic spline interpolation is used to calculate the intermediate angle.

[0078] When the system load is low, fifth-order spline interpolation is used to calculate the intermediate angle to improve smoothness and second-derivative continuity.

[0079] Specifically, the global clock of the master control node is used as the system time base. Real-time monitoring of time deviation and local time of servo nodes Deviation from reference time:

[0080] ;

[0081] when When this occurs, the clock synchronization mechanism is triggered; This is the deviation threshold.

[0082] Given the target angle sequence { } and its corresponding timestamp { }, find the current time The time interval [ ] Calculate the time scaling factor s, representing Relative position within the interval:

[0083] (0≤s≤1);

[0084] (1) When the system load is high (L>70%), switch to linear interpolation to reduce the computational load and calculate the intermediate angle of the linear interpolation:

[0085] ;

[0086] Will As an intermediate angle Perform error compensation calculations.

[0087] (2) When the load is normal (30%≤L≤70%), cubic spline interpolation is used, as follows:

[0088] Cubic spline interpolation is used to ensure the continuity of the angular transition and the continuity of the first derivative. For the interval [ Construct a cubic polynomial from the angular trajectory within the ] area:

[0089] ;

[0090] in , , , Let the spline coefficients satisfy:

[0091] ;

[0092] ;

[0093] The first derivative at the endpoints is continuous:

[0094] ;

[0095] ;

[0096] V represents the target speed.

[0097] Substituting the time scaling factor 's' into the polynomial, calculate the intermediate angle:

[0098]

[0099] in .

[0100] (3) When the load factor is low (L<30%), quintic spline interpolation is used to improve smoothness and second derivative continuity for the interval [ Construct a fifth-degree polynomial from the angular trajectory within the range:

[0101] ;

[0102] in Let the spline coefficients satisfy:

[0103] ;

[0104] ;

[0105] The first derivative at the endpoints is continuous:

[0106] ;

[0107] ;

[0108] The endpoints are continuous second derivatives:

[0109] ;

[0110] ;

[0111] 'a' represents the target acceleration.

[0112] Substituting the time scaling factor 's' into the polynomial, calculate the intermediate angle:

[0113] ;

[0114] in .

[0115] More preferably, in an exemplary embodiment, after generating the intermediate angle, the interpolation parameters are dynamically adjusted in conjunction with real-time error feedback.

[0116] Specifically, existing technologies present a contradiction between control precision and coordination: while pursuing high-precision control of individual nodes, it is difficult to coordinate the overall collaborative response. The lack of effective synchronization compensation mechanisms and dynamic state feedback optimization makes it impossible for the system to compensate for dynamic errors between nodes (such as mechanical transmission backlash and response speed differences) in real time, resulting in a "pull" phenomenon when multiple nodes are linked, and the overall control precision actually decreases.

[0117] In this exemplary embodiment: the deviation between the actual angle and the interpolated angle is calculated:

[0118] ;

[0119] Define speed compensation coefficient and acceleration compensation coefficient Adjust dynamically based on error:

[0120] ;

[0121] ;

[0122] in , This is the error gain parameter.

[0123] Based on error compensation, the final control angle is generated:

[0124] .

[0125] More preferably, in one exemplary embodiment, an efficient communication protocol suitable for Linux and RTOS systems is also proposed.

[0126] This approach addresses the efficiency and reliability issues of data interaction between heterogeneous systems through zero-copy data transmission and multi-channel redundancy mechanisms. Specifically, it includes: a zero-copy shared storage architecture, a multi-channel redundant communication mechanism, and cross-system data adaptation technology. Among these:

[0127] Zero-copy shared memory architecture builds a shared memory region between two systems, divides it into multiple circular buffers, and uses lock-free atomic operations to achieve direct data reading and writing, avoiding system calls and memory copy overhead, and significantly reducing transmission latency.

[0128] The multi-channel redundant communication mechanism integrates multiple communication channels such as Ethernet and CAN bus, and achieves automatic fault switching based on heartbeat detection and priority strategy to ensure the continuity of data transmission and anti-interference capability.

[0129] Cross-system data adaptation technology defines a unified data format standard, solves the problem of differences in byte order and data type, and supports dynamic load distribution to optimize communication efficiency.

[0130] The above methods can effectively improve data transmission efficiency between heterogeneous systems by more than 80%, ensuring product communication reliability.

[0131] More preferably, in an exemplary embodiment, based on such Figure 1 The intelligent attitude control system shown is a multi-servo node collaborative response system. Figure 2 The core logic flow of the system is shown:

[0132] (1) System initialization phase: The master node comes online and scans the number of servo nodes in the network. Each servo node will automatically report "Who am I (ID)", "What actions do I support", and "Current status". The master node will create an "Action Control Table" based on the feedback information to record the information of all nodes. Ensure that each servo is registered in the system and that the master node has the relevant information.

[0133] (2) Preload action phase: The master node issues action instructions to each servo in advance. For example, the servo number 3 executes the angle from 0° to 90° and the execution time is from 100ms to 500ms. These instructions will be temporarily cached in each servo node, but the servo node will not start moving yet, so that everyone can be ready and execute together, so that the actions will not be out of sync.

[0134] (3) Synchronous execution phase: After all actions are ready, the master node issues a "start command", such as: "At 100ms, everyone starts to execute". After all the servo nodes receive it, they use the target angle and time in the cache to smooth the action according to the interpolation algorithm and calculate how many angles to move at the current time. For example: "A total of 90 degrees is turned, which takes 400ms to complete, so every 10ms, it needs to turn 2.25°", avoiding sudden changes in action, but smooth and synchronous execution of the whole system, making the visuals more natural.

[0135] (4) Status detection and reporting phase: During execution, each servo node reports its status to the master node every tens of milliseconds, such as the current angle, current load (to determine if the rotor is stalled), and whether the action has been completed. If a servo node gets stuck, malfunctions, or has an excessively high temperature, the master node will issue a "stop action" or "limit adjustment" command to allow other servo nodes to also perform "visual buffering" to avoid a single action error from lowering the overall effect.

[0136] In summary, the above exemplary embodiments construct the structure of an adaptive multi-level collaborative control system based on a hybrid architecture, innovatively adopting a hybrid architecture mode of "distributed nodes + dual backup control". The system deploys a master control node at the top level, with distributed servo nodes below. Each servo node meticulously constructs a multi-level heterogeneous computing architecture while ensuring dual backup control. This architecture design organically integrates the scalability advantages of distributed systems with the high-precision characteristics of servo control systems, enabling the system to flexibly adapt to the needs of industrial production of different scales while achieving precise control. It implements a three-in-one mechanism of multi-servo node registration + interpolation execution + action caching control; introduces a unified clock + action synchronization execution chain to achieve sub-millisecond-level linkage action response; and provides real-time servo health status feedback capability through the construction of fault functions, suitable for dynamic demonstrations or practical platform simulation environments. The system has a highly modular design, supporting the expansion control of 3-20 servo nodes.

[0137] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An intelligent posture control system with multi-servo node coordinated response, characterized in that: The application relates to a servo system, comprising: a master node for scanning and identifying servo nodes, timing synchronization of the servo nodes, and distribution of action instructions to the servo nodes; a plurality of servo nodes connected with the master node through a communication bus, for receiving timing synchronization instructions and action instructions distributed by the master node, and responding to execution according to a uniform action schedule in the action instructions.

2. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 1, characterized in that: The servo node comprises a master-level computing unit and an execution-level computing unit; The master-level computing unit is used for identifying different servo nodes, receiving action instructions distributed by the master node, and planning tasks to form first-level control instructions; the execution-level computing unit executes high-frequency current loop and / or speed loop control algorithms according to the first-level control instructions to generate second-level control instructions for directly driving external power devices.

3. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 2, characterized in that: The servo node further comprises a shared memory area configured as a ring buffer; The master-level computing unit writes the first-level control instructions into the shared memory area, and the execution-level computing unit directly reads the latest first-level control instructions from the shared memory area through a DMA mode.

4. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 2, characterized in that: The servo node further comprises a heartbeat detection and switching circuit, and the execution-level computing unit comprises two synchronous processing cores; The heartbeat detection and switching circuit continuously monitors the key output signals of the two synchronous processing cores, and once detects that the output of any core is abnormal or the heartbeat is lost, the heartbeat detection and switching circuit automatically switches the final control output of the system to the normal processing core within a microsecond level time.

5. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 1 or 2, characterized in that: The servo node is further used for reporting state data to the master node; and the master node is further used for completing intelligent health monitoring and predictive maintenance according to the state data reported by the servo node.

6. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 5, characterized in that: The intelligent health monitoring and predictive maintenance comprises: The master node constructs a node fault mathematical model based on indexes such as logical resource utilization, chip junction temperature and communication error rate of the servo node, and calculates a fault prediction probability value through the node fault mathematical model; When the fault prediction probability value is greater than a threshold value, predictive maintenance is realized.

7. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 1 or 2, characterized in that: For the servo node, the responding to execution according to the uniform action schedule in the action instructions comprises: During execution, the servo node calculates an intermediate state angle through a time reference obtained through timing synchronization; Based on a timestamp, the servo node calculates a position in a target track at a current time, generates an intermediate angle according to a corresponding interpolation algorithm, and balances adjacent target points in transition.

8. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 7, characterized in that: The generating an intermediate angle according to a corresponding interpolation algorithm comprises: According to system load conditions, a corresponding interpolation algorithm is selected to generate an intermediate angle.

9. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 8, characterized in that: The generating an intermediate angle according to a corresponding interpolation algorithm according to system load conditions comprises: When system load L>70%, linear interpolation is switched to reduce the calculation amount, and a linear interpolation intermediate angle is calculated; When system load L satisfies 30%≤L≤70%, a cubic spline interpolation is adopted to calculate an intermediate angle; When system load L<30%, a quintic spline interpolation with improved smoothness and second-order derivative continuity is adopted to calculate an intermediate angle.

10. The intelligent attitude control system with coordinated response of multiple servo nodes according to claim 7, characterized in that: After the intermediate angle is generated, interpolation parameters are dynamically adjusted in combination with real-time error feedback.

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