A dual-spin guidance method, system, medium, and device

Abstract

This application discloses a dual-rotor guidance method, system, medium, and device, relating to the field of guidance and control technology. First, based on a dynamic model, the application calculates the predicted state and control commands of the previous cycle. The modeling information of the dynamic model includes information describing the angular momentum coupling characteristics of the front and rear compartments of the dual-rotor, avoiding treating the entire missile as a single rotating rigid body. The output predicted state meets the accuracy requirements of high-precision guidance for attitude and position. Second, navigation source information is asynchronously fused with the model to achieve continuous and stable global optimal state estimation. Based on this, basic control commands are generated through hierarchical decision-making. Hierarchical decision-making can dynamically match the response requirements of different flight stages, achieving a full-trajectory balance between response speed and accuracy. Finally, vector rotation correction is applied to the basic control commands to offset systematic directional deviations, generating the final high-precision target control commands for guidance control, thus improving the guidance effect on dual-rotor configurations.

Description

Technical Field

[0001] This application relates to the field of guidance and control technology, specifically to a dual-spin guidance method, system, medium, and device. Background Technology

[0002] Existing rolling unmanned aerial vehicles (UAVs) and artificial fire extinguishing projectiles (hereinafter referred to as projectiles) require a low-cost precision guidance kit (PGK). The ballistic calculation and guidance law design of these products are based on a six-degree-of-freedom rigid body model. This model treats the entire projectile as a single rotating rigid body and is only applicable to a single-rotational structure where the projectile body and guidance compartment are fixed together. It completely fails in the dual-rotational configuration of the precision guidance kit. Furthermore, the existing single control strategy is difficult to take into account the dynamic requirements of different flight stages, resulting in a global suboptimal problem. Summary of the Invention

[0003] In summary, the main objective of this application is to provide a dual-spin guidance method, system, medium, and device, aiming to solve the problem of poor guidance effect for dual-spin configurations in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In a first aspect, embodiments of this application provide a dual-spin guidance method, comprising the following steps:

[0006] The predicted state and control commands of the target twin-spinning body in the previous cycle are calculated based on the dynamic model to obtain the current predicted state data of the target twin-spinning body; the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target twin-spinning body.

[0007] Asynchronous fusion of the current predicted state data of the target double helix and navigation source information is performed to obtain the globally optimal state estimation vector.

[0008] Hierarchical decision-making is performed based on the global optimal state estimation vector to obtain basic control commands;

[0009] Vector rotation correction is performed based on the basic control commands to obtain the target control commands.

[0010] In one possible implementation of the first aspect, the globally optimal state estimation vector is obtained by asynchronously fusing the current predicted state data of the target double-spinning body and the navigation source information, including:

[0011] Based on a federated Kalman filter architecture, a main fusion unit and local processors are configured.

[0012] Based on the asynchronous update of navigation source information by the local processor, the covariance matrix of the local processor is obtained;

[0013] Based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data and navigation source information of the target double-spinning body to obtain the globally optimal state estimation vector.

[0014] In one possible implementation of the first aspect, based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data and navigation source information of the target double-spinning body to obtain the globally optimal state estimation vector, including:

[0015] Based on the covariance matrix, the information matrix of the local processor is obtained;

[0016] Weights are assigned according to the information matrix to obtain the information allocation coefficients of the local processor;

[0017] Based on the information allocation coefficient and the information matrix, a global information matrix is ​​obtained to enable the periodic fusion of the current predicted state data and navigation source information of the target double helix in the main fusion unit.

[0018] Based on the global information matrix, information allocation coefficients, and the information state vector of the local processor, the globally optimal state estimation vector is obtained.

[0019] In one possible implementation of the first aspect, hierarchical decision-making is performed based on the globally optimal state estimation vector to obtain basic control commands, including:

[0020] Based on the global optimal state estimation vector, deviation prediction, filtering, and sliding mode control are performed to make hierarchical decisions and obtain the nacelle roll angle command of each control output.

[0021] The command weight is obtained based on the weight of the control cabin roll angle adjustment command during the flight phase.

[0022] Based on the command weight and the nacelle roll angle command, the basic control commands are obtained.

[0023] In one possible implementation of the first aspect, vector rotation correction is performed based on the basic control commands to obtain the target control commands, including:

[0024] Based on real-time flight status parameters, the deviation angle is obtained by querying the structured deviation database; the structured deviation database stores the control force direction deviation under different operating conditions.

[0025] Based on the deviation angle, rotate to the rudder control coordinate system according to the current roll angle, perform vector rotation correction on the basic control commands, and obtain the target control commands.

[0026] In one possible implementation of the first aspect, before calculating the predicted state and control commands of the target double helix in the previous cycle based on the dynamic model to obtain the current predicted state data of the target double helix, the method further includes:

[0027] Define the state space represented by multidimensional vectors;

[0028] An angular momentum conservation constraint is embedded in the state space to describe the angular momentum coupling characteristics of the fore and aft compartments of the target double-rotor body. Aerodynamic coupling corrections are performed and solutions are obtained to establish a dynamic model.

[0029] In one possible implementation of the first aspect, after obtaining the target control command by performing vector rotation correction based on the basic control command, the method further includes:

[0030] If, during the process of controlling the actuator to perform actions according to the target control command, the navigation source information is interrupted or the actuator does not respond, a degradation strategy is executed.

[0031] Secondly, embodiments of this application provide a dual-spin guidance system, comprising:

[0032] The calculation module is used to calculate the predicted state and control commands of the target double-spinning body in the previous cycle based on the dynamic model, and obtain the current predicted state data of the target double-spinning body; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target double-spinning body;

[0033] The fusion module is used to asynchronously fuse the current predicted state data and navigation source information of the target double helix body to obtain the globally optimal state estimation vector.

[0034] The decision module is used to make hierarchical decisions based on the global optimal state estimation vector to obtain basic control commands;

[0035] The correction module is used to perform vector rotation correction based on the basic control commands to obtain the target control commands.

[0036] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the dual-spin guidance method provided in any of the first aspects above.

[0037] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein,

[0038] Memory is used to store computer programs;

[0039] The processor is used to load and execute computer programs to cause the electronic device to perform the dual-spin guidance method provided in any of the first aspects above.

[0040] Compared with the prior art, the beneficial effects of this application are:

[0041] This application proposes a dual-rotor guidance method, system, medium, and device. The method includes: calculating the predicted state and control commands of the target dual-rotor in the previous cycle based on a dynamic model to obtain the current predicted state data of the target dual-rotor; wherein, the dynamic model includes information describing the angular momentum coupling characteristics of the fore and aft compartments of the target dual-rotor; asynchronously fusing the current predicted state data of the target dual-rotor and navigation source information to obtain a globally optimal state estimation vector; performing hierarchical decision-making based on the globally optimal state estimation vector to obtain basic control commands; and performing vector rotation correction based on the basic control commands to obtain target control commands. This application uses real-time data acquisition for state prediction and guidance control. For the current state of the dual-rotor configuration, it first calculates the predicted state and control commands of the previous cycle based on a dynamic model. The modeling information of the dynamic model includes information describing the angular momentum coupling characteristics of the front and rear compartments of the dual-rotor, avoiding treating the entire missile as a single rotating rigid body. The output predicted state meets the accuracy requirements of attitude and position for high-precision guidance. Secondly, it asynchronously fuses navigation source information with the predicted state, overcoming the difficulties of asynchronous sensor sampling frequencies and signal interruptions, achieving continuous and stable global optimal state estimation. Then, based on this, it generates basic control commands through hierarchical decision-making. Hierarchical decision-making can dynamically match the response requirements of different flight stages, achieving a full-trajectory balance between response speed and accuracy. Finally, it performs vector rotation correction on the basic control commands to offset systematic directional deviations, generating the final high-precision target control commands for guidance control, thus improving the guidance effect on the dual-rotor configuration. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application;

[0043] Figure 2 A schematic flowchart illustrating the dual-spin guidance method provided in this application embodiment;

[0044] Figure 3 This is a schematic diagram of the angular momentum coupling mechanism of a dual-spin body in the dual-spin body guidance method provided in the embodiments of this application;

[0045] Figure 4 A schematic diagram illustrating the timing relationship of multi-source asynchronous fusion in the dual-spin guidance method provided in this application embodiment;

[0046] Figure 5 A schematic diagram of hierarchical decision weight allocation in one implementation of the dual-spin guidance method provided in this application embodiment;

[0047] Figure 6 A schematic diagram of a fault detection and degradation control strategy in the dual-spin guidance method provided in the embodiments of this application;

[0048] Figure 7This is a schematic diagram of the modules of the dual-spin guidance system provided in the embodiments of this application;

[0049] Figure 8 A schematic diagram of a module in one embodiment of the dual-spin guidance system provided in this application;

[0050] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0052] See attached document Figure 1 , attached Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0053] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0054] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a dual-spin guidance system.

[0055] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device, and the electronic device calls the dual-spin guidance system stored in the memory 105 through the processor 101 and executes the dual-spin guidance method provided in the embodiment of this application.

[0056] The existing technology has the following drawbacks:

[0057] 1. Theoretical Blind Spots and Engineering Consequences of Dual-Helix Dynamics Modeling: Existing ballistic calculations and guidance law designs are based on a six-degree-of-freedom rigid body model. This model treats the entire missile as a single rotating rigid body and is only suitable for single-helix structures where the missile body and guidance bay are fixed together. It completely fails in the dual-helix configuration of the Precision Guidance Kit (PGK). The PGK achieves physical isolation between the front guidance bay and the rear missile body through a roll bearing. During flight, the rear missile body maintains a high rotational speed, while the front guidance bay is relatively stable by rolling at a certain low rotational speed. The two constitute a seven-degree-of-freedom system. The traditional six-degree-of-freedom model cannot characterize three key coupling effects: first, the angular momentum transfer effect, which leads to large errors in gyro torque calculation under high-rotation conditions and large errors in angle-of-attack prediction; second, the control force direction distortion effect, which causes the command force to deviate from the actual correction direction by 8-10°; and third, the model error accumulation effect, as existing improvement schemes have not yet established independent differential equations, and the model error increases nonlinearly with the roll rate after long-term flight.

[0058] 2. The contradiction between the lack of information dimensions and reliability of measurement systems in high dynamic environments; PGK requires high-frequency and high-precision observation of position, velocity, roll angle and other states, but due to constraints of small size, low cost and high transmission overload, existing measurement schemes have structural contradictions: single satellite navigation cannot observe roll angle, is prone to loss of lock and velocity calculation delay under high speed and high rotation; single geomagnetic measurement can only provide attitude information, has weak anti-electromagnetic interference capability, and has obvious phase lag under high rotation; in satellite-inertial integrated navigation, IMU cost is too high, low-cost MEMS-IMU has large cumulative error, and signal aliasing requires complex compensation, none of which can simultaneously meet the requirements of high dynamic response and low cost coupling.

[0059] 3. The traditional control architecture faces a conflict between real-time performance and robustness under highly dynamic time-varying disturbances; during PGK flight, it faces aerodynamic parameter perturbations and gust interference, and the traditional control architecture is difficult to take into account multiple targets: single perturbation guidance does not filter noise, the landing point deviation is amplified when aerodynamic parameters fluctuate, and the utilization rate of correction force is insufficient; single sliding mode control is prone to high-frequency chattering, and the pure feedback characteristics lead to correction lag and accuracy deterioration; existing solutions often separate prediction, filtering, and control modules, and fail to achieve collaborative optimization, resulting in suboptimal global performance.

[0060] 4. The mechanism of control force direction deviation is unclear and compensation is lacking; the relative roll motion of the two-rotor system causes a dynamic mismatch between the control surface correction force direction and the desired correction direction, with a large deviation under high-rotation conditions. Existing fixed compensation schemes do not consider the nonlinear effects of roll velocity, projectile velocity, and angle of attack, and are far from meeting the compensation accuracy required for high-precision guidance. Furthermore, this systemic error cannot be eliminated by statistical filtering.

[0061] To address this, this application provides a solution: Real-time data acquisition is used for state prediction and guidance control. For the current state of the dual-rotor configuration, the predicted state and control commands of the previous cycle are first calculated based on a dynamic model. The dynamic model includes information describing the angular momentum coupling characteristics of the front and rear compartments of the dual-rotor, avoiding the entire missile being treated as a single rotating rigid body. The output predicted state meets the accuracy requirements of high-precision guidance for attitude and position. Secondly, navigation source information is asynchronously fused with this information to overcome the difficulties of asynchronous sensor sampling frequencies and signal interruptions, achieving continuous and stable global optimal state estimation. Then, based on this, basic control commands are generated through hierarchical decision-making. Hierarchical decision-making can dynamically match the response requirements of different flight stages, achieving a full-trajectory balance between response speed and accuracy. Finally, vector rotation correction is applied to the basic control commands to offset systematic directional deviations, generating the final high-precision target control commands for guidance control, thus improving the guidance effect on the dual-rotor configuration.

[0062] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide a dual-spin guidance method, including the following steps:

[0063] S10: Based on the dynamic model, the predicted state and control commands of the target double-spinning body in the previous cycle are calculated to obtain the current predicted state data of the target double-spinning body; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target double-spinning body.

[0064] In practical implementation, the structure of the double-spinning body refers to the configuration where the projectile body and the guidance compartment are physically isolated in the roll direction and can rotate independently. First, the periodic scheduling framework and data interaction mechanism need to be clearly defined, such as using a fixed processing cycle of 10ms, triggered by a high-precision timer of the onboard processor, with the timing allocation of each stage strictly meeting the engineering real-time requirements. The predicted state data and control commands from the previous cycle are input for calculation to obtain the current predicted state data of the target double-spinning body. To ensure the dynamic model fits the double-spinning body structure, the angular momentum coupling characteristics of its front and rear compartments are introduced during modeling. That is: before obtaining the current predicted state data of the target double-spinning body based on the predicted state and control commands from the previous cycle using the dynamic model, the method also includes:

[0065] Define the state space represented by multidimensional vectors;

[0066] An angular momentum conservation constraint is embedded in the state space to describe the angular momentum coupling characteristics of the fore and aft compartments of the target double-rotor body. Aerodynamic coupling corrections are performed and solutions are obtained to establish a dynamic model.

[0067] In the specific implementation process, a twelve-dimensional vector is adopted. This provides an independent mathematical description of angular momentum interactions, where each letter represents a single dimension, the number in parentheses indicates the number of dimensions, and P represents the geocentric fixed connection location, including P... x P y P z Three terms, V represents speed, including V x V y V z The three terms represent velocities in the three axial directions. These represent the missile's pitch angle and yaw angle, respectively. Represents the projectile's angular velocity, with components along the three axes. The relative roll angle of the control module is added as a seventh degree of freedom compared to the traditional six-degree-of-freedom model. The aft missile body and guidance module are considered as a combined roll system, and angular momentum conservation constraints are explicitly introduced. The angular acceleration of the aft missile body is calculated using constraint derivatives to accurately characterize the inverse coupling response between the two, suppressing constraint drift accumulation. The angular momentum coupling mechanism of the dual-rotor system is shown in the appendix. Figure 3 As shown, The roll rate of the forward guidance module relative to the rear missile body. Let be the angular velocity of the rear projectile around its axis. A dynamic torque distribution coefficient, updated online with fuel consumption, can be introduced to ensure that the torque distribution error is ≤2% throughout the entire process. Finally, the translation equation adopts the fourth-order Runge-Kutta method, and the rotation equation adopts the implicit Euler method. Combined with the Baumgarte stabilization method, the constraints are corrected, and the high-frequency attitude reference and state parameters are output.

[0068] S20: Asynchronously fuse the current predicted state data of the target double helix body and the navigation source information to obtain the globally optimal state estimation vector.

[0069] In the specific implementation process, asynchronous fusion of multiple data sources provides a unified state input for decision-making. Navigation source information, such as raw data from GPS and geomagnetic sensors, is received and fused with the aforementioned predicted state data to output a globally optimal state estimation vector. Asynchronous fusion is divided into asynchronous update and periodic fusion, which can be performed using a federated Kalman filter architecture. That is, based on the current predicted state data of the target double helix and navigation source information, asynchronous fusion is performed to obtain the globally optimal state estimation vector, including:

[0070] Based on a federated Kalman filter architecture, a main fusion unit and local processors are configured.

[0071] Based on the asynchronous update of navigation source information by the local processor, the covariance matrix of the local processor is obtained;

[0072] Based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data and navigation source information of the target double-spinning body to obtain the globally optimal state estimation vector.

[0073] In its implementation, federated Kalman filtering is a hierarchical multi-sensor data fusion architecture consisting of a master filter (master fusion unit) and multiple sub-filters (local processors). It employs a divide-and-conquer fusion strategy to process data from different sensors. Each sub-filter independently processes its local sensor data, performing local Kalman filtering calculations to generate local state estimates and their error covariance. The temporal relationship of its asynchronous fusion is shown in the appendix. Figure 4 As shown, specifically, this application embodiment is divided into two local processors. One is the position-velocity channel, whose triggering and data preprocessing include: GPS data frames (including latitude, longitude, altitude, velocity, and satellite number n) sat The position precision factor (PDOP) is stored in a circular buffer via a serial port interrupt, triggering an asynchronous update.

[0074] Adaptive noise adjustment: Dynamic correction of observation noise covariance, as follows:

[0075]

[0076] In the formula: R GPS R is the GPS observation noise covariance matrix; GPS，nominal This is the GPS nominal observation noise covariance matrix; and For example, the empirical adjustment coefficient. =0.3、 =0.5. When the satellite geometry deteriorates (n sat <6 or PDOP>2), R GPS Automatic zoom-in reduces the weight of GPS observations;

[0077] Degradation logic: If there is no data for 5000ms, the system is considered to be in failure, the information allocation coefficient β1=0, and the position-velocity is extrapolated from the seven-degree-of-freedom model in an open-loop manner.

[0078] The second is the attitude-angular velocity channel, triggering and noise suppression: the geomagnetic sensor stores data via DMA interrupt and performs pre-flight ellipsoidal calibration.

[0079]

[0080] In the formula: The calibrated geomagnetic intensity; This is the sensor sensitivity matrix; is the original geomagnetic measurement value; b is the sensor's zero bias vector.

[0081] By combining 8-point moving average filtering, the noise standard deviation can be reduced from 0.5° to 0.3°.

[0082] Multiple solutions elimination: Introducing predicted state data of , As auxiliary observations, these are the predicted projectile pitch and yaw angles, respectively. The observation equations are constructed using the magnetic field vector and converged to the correct roll angle through an extended Kalman filter. To avoid 180° blur.

[0083] Degradation logic: A sliding window performs 10 samples (80ms). If the roll angle estimation variance > 10°, magnetic interference is detected, and the system switches to angular velocity integration. The information allocation coefficient β2 = 0, and the attitude is determined by: Integral extrapolation (Δt is the integration step size, (where ω is the angular velocity component of the rear projectile around its axis), and the covariance amplification rate is 3° / s.

[0084] The main filter integrates these local estimates through the information allocation principle to generate the globally optimal trajectory. Specifically: based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data of the target double-spinning body and the navigation source information to obtain the globally optimal state estimation vector, including:

[0085] Based on the covariance matrix, the information matrix of the local processor is obtained;

[0086] Weights are assigned according to the information matrix to obtain the information allocation coefficients of the local processor;

[0087] Based on the information allocation coefficient and the information matrix, a global information matrix is ​​obtained to enable the periodic fusion of the current predicted state data and navigation source information of the target double helix in the main fusion unit.

[0088] Based on the global information matrix, information allocation coefficients, and the information state vector of the local processor, the globally optimal state estimation vector is obtained.

[0089] In practice, information allocation is based on the information content of the state equation and the information content of the observation equation. The main filter weights and fuses the sub-filter outputs according to information weights (such as the inverse covariance matrix) to achieve global state estimation. Specifically, adaptive weight fusion allocates weights according to the trace of the information matrix.

[0090]

[0091] In the formula: β1 and β2 are the information allocation coefficients of the two local processors, that is, β1 is the information allocation coefficient of the position-velocity channel and β2 is the information allocation coefficient of the attitude-angular velocity channel.

[0092] The condition β1 + β2 = 1 is satisfied; Let P be the information matrix of the i-th local processor. i tr( is the covariance matrix;) The trace operation is performed on the matrix, reflecting the estimated confidence level. Global state calculation:

[0093]

[0094]

[0095] In the formula: I global This is the global information matrix; This is the globally optimal estimated state vector; Let be the information state vector of the i-th local processor. Output and Reset: Write to the global shared area. The global covariance matrix is ​​fed back to the two local processors as the starting value for the next cycle. Specifically, It is the inverse of the global information matrix. The global covariance matrix is ​​equal to the inverse of the global information matrix. ), used to quantify the uncertainty of the global state estimate and feed it back to the local processor as the initial covariance for the next cycle.

[0096] S30: Perform hierarchical decision-making based on the global optimal state estimation vector to obtain basic control commands.

[0097] In practice, the decision-making layer processes the fused state data, combines it with the weights of each stage of dynamic scheduling during flight, and outputs basic control commands. Specifically, hierarchical decision-making is performed based on the globally optimal state estimation vector to obtain basic control commands, including:

[0098] Based on the global optimal state estimation vector, deviation prediction, filtering, and sliding mode control are performed to make hierarchical decisions and obtain the nacelle roll angle command of each control output.

[0099] The command weight is obtained based on the weight of the control cabin roll angle adjustment command during the flight phase.

[0100] Based on the command weight and the nacelle roll angle command, the basic control commands are obtained.

[0101] In the specific implementation process, the weighting coefficients of each layer of control output are determined based on the flight phase to synthesize basic control commands. Based on the fused state, a three-stage serial processing is executed—disturbance deviation generation, unscented Kalman smoothing filtering, and adaptive sliding mode tracking control—to balance response speed and trajectory smoothness. Specifically:

[0102] Disturbance bias generation: Firstly, by Position P and velocity V are used to query a pre-built standard ballistic library and obtain reference ballistic points. , call The associated sensitivity coefficient matrix G, execute In the formula This represents the original landing point deviation.

[0103] Unscented Kalman Smoothing Filter: State and Prediction Step: 4-Dimensional State (ΔY and ΔZ are the components of the deviation in the Y and Z directions, respectively) , (For deviation rate), the state transition matrix Φ contains a gravity compensation term, and the process noise covariance Q is configured according to stages; Unscented transformation: generate 9 sigma points (2n+1=9, where n is the state dimension), and calculate the weighted mean and covariance after propagation through the observation function, avoiding Jacobian solution (computational complexity...). Next operation); Update step: with For observation, the output smoothed bias is generated after updating the state. , and differential signal, where This is the original bias observation vector. These are the deviation components in the Y and Z directions after filtering.

[0104] Adaptive sliding mode tracking control: Improved sliding surface: The switching term only applies to the area outside the boundary layer of s. Where: s is the sliding mode surface vector; The deviation rate vector; For integral gain, for example To prevent integral saturation. Adaptive gain and boundary layer: switching gain. In the formula: Reference gain; These are adaptive coefficients; This refers to the stage coefficient. Boundary layer: In the formula: As the reference boundary layer thickness, To adjust the coefficients, the boundary layer is expanded to reduce the switching frequency when there is a large deviation, and tightened to improve accuracy when there is a small deviation. Saturation function optimization: A 21-point saturation function table is pre-stored (resolution 0.01). If the value is outside the range, a sign function is used. The table lookup time is <0.02ms. During runtime, the table lookup replaces the sign function sign(s) to eliminate chattering.

[0105] During flight, the process is divided into three phases based on flight parameters: initial flight, mid-flight, and final flight, where t represents flight time. The judgment result must meet the Guard anti-shake conditions. Command synthesis:

[0106]

[0107] In the formula: Basic control commands; , , The roll angle commands output by each stage are the control cabin roll angle commands. These are the corresponding weighting coefficients, and their sum is 1.

[0108] For example, the weight allocation for hierarchical decision-making in a certain component is shown in the appendix. Figure 5 As shown: Flight weight configuration for 0-10s: prediction weight 0.2, filtering weight 0.3, control weight 0.5; Flight weight configuration for 10-35s: prediction weight 0.1, filtering weight 0.5, control weight 0.4; Flight weight configuration for >35s: prediction weight 0.05, filtering weight 0.05, control weight 0.9.

[0109] S40: Perform vector rotation correction based on basic control commands to obtain target control commands.

[0110] In practical implementation, based on real-time flight status queries and an offline-built control force direction deviation database, the current deviation angle is obtained through trilinear interpolation. This deviation angle is then corrected by vector rotation using a feedforward method to ultimately generate the nacelle roll angle command, i.e., the target control command, which is then sent to the actuators. This command, in turn, serves as the input for the next cycle, forming a closed-loop feedback. Specifically: based on the basic control command, vector rotation correction is performed to obtain the target control command, including:

[0111] Based on real-time flight status parameters, the deviation angle is obtained by querying the structured deviation database; the structured deviation database stores the control force direction deviation under different operating conditions.

[0112] Based on the deviation angle, rotate to the rudder control coordinate system according to the current roll angle, perform vector rotation correction on the basic control commands, and obtain the target control commands.

[0113] In the specific implementation process, a systematic directional deviation is offset through offline database construction, online query, and feedforward injection logic. First, deviation mechanism modeling and offline database construction are performed. Based on a seven-degree-of-freedom dynamic model, the directional deviation of the control force under different operating conditions is accurately calculated. Multiple sets of operating conditions are sampled using a gridded method through orthogonal experimental design to generate a structured deviation database. Specifically, before obtaining the deviation angle by querying the structured deviation database based on real-time state parameters during flight, the method also includes:

[0114] Multiple working conditions were sampled using a gridded experimental design based on orthogonal experiments, and the deviation of the control force direction under different working conditions was calculated based on the dynamic model to generate a structured deviation database.

[0115] Then, online table lookup and vector rotation compensation are performed. Real-time state parameters are extracted during flight, and the deviation angle is quickly looked up using separated-dimensional linear interpolation. The coordinates are then rotated to the control center command coordinate system according to the current roll angle, and the basic control commands are fed forward to correct, thus canceling systematic deviations without delay. Specifically:

[0116] Index calculation: Calculate the projectile's roll angular velocity ω after input. b Given the projectile velocity V and angle of attack α, calculate the three-dimensional index and interpolation coefficients:

[0117]

[0118] In the formula: , , For the integer index of the three-dimensional deviation table; , , These are the minimum values ​​of the corresponding parameters, namely the minimum values ​​of the rear roll angular velocity, projectile velocity, and angle of attack. , , This represents the sampling step size for the corresponding parameter; , , These are the interpolation coefficients.

[0119] Trilinear interpolation: Pre-fabricated deviation table based on orthogonal experimental design Perform layered interpolation to obtain the ballistic system deviation. This refers to the deviation in the direction of the control force under a ballistic system.

[0120] Vector rotation and command synthesis:

[0121] First, perform a coordinate system transformation: ballistic system deviation. Transformed to the rudder nacelle system via a rotation matrix:

[0122]

[0123] In the formula: For the rear roll angle of the projectile, , These are the components of the ballistic system deviation in the y and z directions; , This represents the components of the nacelle system deviation in the y and z directions obtained after rotation matrix transformation; trigonometric functions are implemented through pre-stored lookup tables to avoid real-time calculation. Final Instructions In the formula: This is the final control cabin roll angle command, i.e., the target control command. The basic control instructions are encapsulated as 24-bit fixed-point numbers and issued to the execution mechanism.

[0124] In one embodiment, after obtaining the target control command by performing vector rotation correction based on the basic control command, the method further includes:

[0125] If, during the process of controlling the actuator to perform actions according to the target control command, the navigation source information is interrupted or the actuator does not respond, a degradation strategy is executed.

[0126] In practical implementation, during flight, a fault degradation strategy is provided, defining three types of fault modes to achieve fault-safety assurance, as shown in the appendix. Figure 6 As shown in Table 1, the fault detection and degradation control strategies are as follows:

[0127] Table 1 - Fault Detection and Degradation Control Strategies

[0128]

[0129] In this embodiment, state prediction and guidance control are performed by real-time data acquisition. For the current state of the twin-rotor, the predicted state and control commands of the previous cycle are first calculated based on the dynamic model. The modeling information of the dynamic model includes information describing the angular momentum coupling characteristics of the front and rear compartments of the twin-rotor, avoiding treating the entire missile as a single rotating rigid body. The output predicted state meets the accuracy requirements of attitude and position for high-precision guidance. Secondly, the navigation source information is asynchronously fused with it to overcome the difficulties of asynchronous sensor sampling frequencies and signal interruption, and to achieve continuous and stable global optimal state estimation. Then, based on this, basic control commands are generated through hierarchical decision-making. Hierarchical decision-making can dynamically match the response requirements of different flight stages and achieve a full-trajectory balance between response speed and accuracy. Finally, vector rotation correction is performed on the basic control commands to offset systematic directional deviations and generate the final high-precision target control commands for guidance control, thereby improving the guidance effect on the twin-rotor configuration.

[0130] See attached document Figure 7 Based on the same inventive concept as in the foregoing embodiments, this application also provides a dual-spin guidance system, comprising:

[0131] The calculation module is used to calculate the predicted state and control commands of the target double-spinning body in the previous cycle based on the dynamic model, and obtain the current predicted state data of the target double-spinning body; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target double-spinning body;

[0132] The fusion module is used to asynchronously fuse the current predicted state data and navigation source information of the target double helix body to obtain the globally optimal state estimation vector.

[0133] The decision module is used to make hierarchical decisions based on the global optimal state estimation vector to obtain basic control commands;

[0134] The correction module is used to perform vector rotation correction based on the basic control commands to obtain the target control commands.

[0135] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In practical applications, they can be fully or partially integrated onto one or more actual carriers. These modules can be implemented entirely in software via processing unit calls, entirely in hardware, or a combination of software and hardware. In one embodiment, the dual-rotor guidance system can also adopt the method shown in the attached figure. Figure 8 The module settings shown are as follows:

[0136] The seven-degree-of-freedom solution unit receives the input predicted state and raw sensor data, and outputs attitude prediction, torque distribution coefficients and state parameters. The predicted state is sent to the multi-source asynchronous fusion unit, which combines GNSS position / velocity and geomagnetic vectors for asynchronous updates and periodic fusion, and outputs the fused global optimal estimated state and the confidence weights of each information source. The hierarchical adaptive decision unit executes prediction-filtering-control in sequence, and outputs basic control commands and the current flight phase identifier. Finally, the online feedforward compensation unit combines the real-time state to query deviations and calculate compensation, and outputs the compensated final control commands.

[0137] It should be noted that each module in the dual-spin guidance system in this embodiment corresponds one-to-one with each step in the dual-spin guidance method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned dual-spin guidance method, and will not be repeated here.

[0138] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the dual-spin guidance method provided in the embodiments of this application.

[0139] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein,

[0140] Memory is used to store computer programs;

[0141] The processor is used to load and execute computer programs to cause the electronic device to perform the dual-spin guidance method provided in the embodiments of this application.

[0142] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0143] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0144] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0145] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0146] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0147] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0148] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0149] In summary, this application provides a dual-rotor guidance method, system, medium, and device. The method includes: calculating the predicted state and control commands of the target dual-rotor in the previous cycle based on a dynamic model to obtain the current predicted state data of the target dual-rotor; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target dual-rotor; asynchronously fusing the current predicted state data of the target dual-rotor and navigation source information to obtain a globally optimal state estimation vector; performing hierarchical decision-making based on the globally optimal state estimation vector to obtain basic control commands; and performing vector rotation correction based on the basic control commands to obtain target control commands. This application uses real-time data acquisition for state prediction and guidance control. For the current state of the dual-rotor configuration, it first calculates the predicted state and control commands of the previous cycle based on a dynamic model. The modeling information of the dynamic model includes information describing the angular momentum coupling characteristics of the front and rear compartments of the dual-rotor, avoiding treating the entire missile as a single rotating rigid body. The output predicted state meets the accuracy requirements of attitude and position for high-precision guidance. Secondly, it asynchronously fuses navigation source information with the predicted state, overcoming the difficulties of asynchronous sensor sampling frequencies and signal interruptions, achieving continuous and stable global optimal state estimation. Then, based on this, it generates basic control commands through hierarchical decision-making. Hierarchical decision-making can dynamically match the response requirements of different flight stages, achieving a full-trajectory balance between response speed and accuracy. Finally, it performs vector rotation correction on the basic control commands to offset systematic directional deviations, generating the final high-precision target control commands for guidance control, thus improving the guidance effect on the dual-rotor configuration.

[0150] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A dual-rotor guidance method, characterized by, Includes the following steps: The predicted state and control commands of the target twin-spinning body in the previous cycle are calculated based on the dynamic model to obtain the current predicted state data of the target twin-spinning body; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target twin-spinning body; Asynchronous fusion is performed based on the current predicted state data of the target double helix and the navigation source information to obtain the globally optimal state estimation vector. Based on the global optimal state estimation vector, hierarchical decision-making is performed to obtain basic control commands; Based on the basic control commands, vector rotation correction is performed to obtain the target control commands; The hierarchical decision-making based on the globally optimal state estimation vector to obtain basic control commands includes: Based on the global optimal state estimation vector, deviation prediction, filtering, and sliding mode control are performed to make hierarchical decisions and obtain the nacelle roll angle command of each layer of control output. The weight of the control cabin roll angle command is obtained by adjusting the weight of the control cabin roll angle command during the flight phase. Based on the command weight and the nacelle roll angle command, basic control commands are obtained.

2. The dual-rotor guidance method of claim 1, wherein, The asynchronous fusion of the current predicted state data and navigation source information of the target double helix to obtain the globally optimal state estimation vector includes: Based on a federated Kalman filter architecture, a main fusion unit and local processors are configured. Based on the asynchronous update of navigation source information by the local processor, the covariance matrix of the local processor is obtained; Based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data of the target double helix with the navigation source information to obtain the globally optimal state estimation vector.

3. The dual-rotor guidance method of claim 2, wherein, Based on the covariance matrix, the main fusion unit periodically fuses the current predicted state data of the target double-spiral body and the navigation source information to obtain the globally optimal state estimation vector, including: Based on the covariance matrix, the information matrix of the local processor is obtained; The information allocation coefficients of the local processor are obtained by assigning weights according to the information matrix. Based on the information allocation coefficient and the information matrix, a global information matrix is ​​obtained to enable the periodic fusion of the current predicted state data of the target double helix and the navigation source information in the main fusion unit. Based on the global information matrix, the information allocation coefficients, and the information state vector of the local processor, a globally optimal state estimation vector is obtained.

4. The dual-rotor guidance method of claim 1, wherein, The process of performing vector rotation correction based on the basic control commands to obtain the target control commands includes: Based on real-time flight status parameters, the deviation angle is obtained by querying the structured deviation database; wherein, the structured deviation database stores the control force direction deviation under different operating conditions. Based on the deviation angle, rotate to the rudder control coordinate system according to the current roll angle, and perform vector rotation correction on the basic control command to obtain the target control command.

5. The dual-rotor guidance method of claim 1, wherein, Before calculating the predicted state and control commands of the target double helix in the previous cycle based on the dynamic model to obtain the current predicted state data of the target double helix, the method further includes: Define the state space represented by multidimensional vectors; An angular momentum conservation constraint is embedded in the state space to describe the angular momentum coupling characteristics of the front and rear compartments of the target twin-spinning body. Aerodynamic coupling corrections are performed and solutions are obtained to establish the dynamic model.

6. The dual-rotor guidance method of claim 1, wherein, After obtaining the target control command by performing vector rotation correction based on the basic control command, the method further includes: If, during the process of controlling the actuator to perform actions according to the target control command, the navigation source information is detected to be interrupted or the actuator is unresponsive, a degradation strategy is executed.

7. A dual-spin guidance system, characterized in that, include: The calculation module is used to calculate the predicted state and control commands of the target twin-spinning body in the previous cycle based on the dynamic model, and obtain the current predicted state data of the target twin-spinning body; wherein, the dynamic model contains information describing the angular momentum coupling characteristics of the fore and aft compartments of the target twin-spinning body; The fusion module is used to asynchronously fuse the current predicted state data and navigation source information of the target double helix body to obtain the globally optimal state estimation vector. The decision-making module is used to perform hierarchical decision-making based on the globally optimal state estimation vector to obtain basic control commands; the hierarchical decision-making based on the globally optimal state estimation vector to obtain basic control commands includes: Based on the global optimal state estimation vector, deviation prediction, filtering, and sliding mode control are performed to make hierarchical decisions and obtain the nacelle roll angle command of each layer of control output. The weight of the control cabin roll angle command is obtained by adjusting the weight of the control cabin roll angle command during the flight phase. Based on the command weight and the nacelle roll angle command, the basic control command is obtained; The correction module is used to perform vector rotation correction based on the basic control command to obtain the target control command.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the dual-spin guidance method as described in any one of claims 1-6.

9. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the dual-spin guidance method as described in any one of claims 1-6.

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