Method and system for adaptively adjusting vibration compensation of unmanned aerial vehicle based on multi-band IMU (Inertial Measurement Unit)

Abstract

The invention relates to a method and a system for adaptively adjusting vibration compensation of an unmanned aerial vehicle based on a multi-band IMU (Inertial Measurement Unit), and the method comprises the steps: W1, in the process that the unmanned aerial vehicle executes a flight task, cooperatively collecting data information of vibration signals of an unmanned aerial vehicle structure based on a plurality of airborne vibration detection IMU arrays, and constructing an unmanned aerial vehicle vibration spectrum analysis model, and calculating the body dominant vibration frequency of the unmanned aerial vehicle to obtain the data information of the body dominant vibration frequency of the unmanned aerial vehicle, and optimizing the acceleration and angular velocity of the unmanned aerial vehicle by adopting an improved weighted average optimization algorithm based on dynamic fusion weight to obtain the optimized data information of the acceleration and angular velocity of the unmanned aerial vehicle. According to the method, the problem of measurement distortion of a single IMU in a resonance frequency band is effectively avoided, it is ensured that high-fidelity inertial data can be output under all working conditions such as low-frequency maneuvering, intermediate-frequency cruising and high-frequency disturbance, and the method can be deployed on a mainstream unmanned aerial vehicle platform and has good popularization value.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method and system for adaptively adjusting UAV vibration compensation based on a multi-band IMU. Background Technology

[0002] Modern unmanned aerial vehicles (UAVs) are subject to a variety of factors during flight, including aerodynamic disturbances, motor vibrations, propeller imbalances, and landing gear impacts, resulting in broadband structural vibrations. These vibrations not only affect flight stability and mission payload performance (such as aerial imaging quality and lidar accuracy), but may also lead to measurement distortion of airborne sensors, accumulation of structural fatigue, and even the risk of misjudgment or loss of control by the flight control system.

[0003] Currently, drones generally use a single type of IMU for attitude estimation and motion state perception. However, traditional IMUs are usually optimized for a specific frequency band, with fixed mechanical structure or built-in filter parameters and an unadjustable natural frequency. When the actual vibration frequency of the drone approaches or exceeds the natural frequency of the IMU, it can easily cause mechanical resonance or signal phase distortion, leading to a significant increase in acceleration and angular velocity measurement errors, which in turn affects navigation and control accuracy.

[0004] In existing technologies, although some studies have obtained the structural vibration characteristics of UAVs through vibration testing, these offline test data have not been effectively used for the dynamic configuration of the IMU or the real-time adjustment of data fusion strategies during flight.

[0005] In the prior art, Chinese patent (application number: 202320639440.6, publication number: CN 219545089U) discloses an IMU shock absorption device and a drone, including a support plate, a shock-absorbing sponge shell, a shock-absorbing base, a shock-absorbing ball, a lower shell and an upper shell. The shock-absorbing sponge shell is fixed on the support plate and is formed by a bottom plate and an upwardly extending sponge fixing plate. The shock-absorbing sponge is fixed inside the sponge fixing plate. The shock-absorbing base is fixed on the bottom plate through a first fixing hole. The shock-absorbing base is provided with a first inclined plate extending horizontally upward or downward. The lower shell is provided with a second inclined plate extending horizontally upward or downward. The first inclined plate and the second inclined plate are connected by a shock-absorbing ball. The lower shell and the upper shell are provided with a receiving cavity. The IMU is fixed in the receiving cavity. The lower shell is provided with a slot. However, the damping characteristics of the device (such as the stiffness of the damping ball, the hardness of the sponge, and the tilt angle) are fixed after manufacturing. They cannot be dynamically adjusted according to the changes in the vibration spectrum generated by the UAV under different flight states (such as takeoff, high-speed cruise, and strong wind disturbance), making it difficult to maintain the optimal damping effect under all working conditions.

[0006] In the prior art, Chinese patent (application number: 202010757460.4, publication number: CN 112046783A) discloses a flight control method and system with triple IMU redundancy technology, including a self-testing CPU unit, a triple IMU redundancy unit, a triple redundancy voting output unit, and a flight action control unit. The self-testing CPU unit receives data transmitted in real time from the triple IMU redundancy unit and parses the received data packets. The triple redundancy voting output unit receives the data processed by the self-testing CPU unit and flight parameters and votes on them. The flight action control unit receives the flight parameters transmitted from the triple redundancy voting unit, converts the flight parameters into electrical signals, and then executes the action. However, the three IMUs have the same mechanical structure and natural frequency. When the UAV encounters strong vibrations in a specific frequency band, all IMUs may resonate simultaneously or experience signal distortion, causing the redundancy mechanism to fail and unable to provide effective backup. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the present invention provides a method and system for adaptive adjustment of UAV vibration compensation based on multi-band IMU. It not only effectively avoids the measurement distortion problem of a single IMU in the resonant frequency band, ensuring that high-fidelity inertial data can be output under all operating conditions such as low-frequency maneuvering, mid-frequency cruise, and high-frequency disturbance, but also can be deployed on mainstream UAV platforms and has good promotional value.

[0008] To achieve the above and other related objectives, the present invention provides the following technical solution: A method for adaptively adjusting UAV vibration compensation based on multi-band IMU, the method comprising: W1. During the flight mission, the UAV collects structural vibration signal data based on multiple airborne vibration detection IMU arrays, constructs a UAV vibration spectrum analysis model, calculates the dominant vibration frequency of the UAV body, and obtains the data information of the dominant vibration frequency of the UAV body. W2. Based on multiple airborne flight control multi-band heterogeneous IMU arrays, the raw acceleration and angular velocity data of the UAV are acquired in real time; W3. Based on the data information of the dominant vibration frequency of the UAV and the original acceleration and angular velocity data of the UAV, combined with the inherent frequencies of multiple airborne flight control multi-band heterogeneous IMUs, an improved weighted average optimization algorithm based on dynamic fusion weights is used to optimize the acceleration and angular velocity of the UAV, and the optimized acceleration and angular velocity data of the UAV are obtained. W4. Input the optimized acceleration and angular velocity data of the UAV into the flight control execution unit, and combine it with the preset flight control to generate motor PWM control signals to perform compensation control and adjustment of the UAV.

[0009] Furthermore, in step W1, the construction of the UAV vibration spectrum analysis model and the calculation of the dominant vibration frequencies of the UAV body include: W11. Based on the data information of the UAV structural vibration signal, noise reduction processing is performed to obtain the data information of the UAV structural vibration signal after noise reduction processing; W12. Based on the data information of the UAV structural vibration signal after noise reduction processing, establish the dominant vibration frequency function R_m of the UAV body. , Where, x i x represents the data information of the denoised UAV structural vibration signal at time i. i+1 This refers to the data information of the denoised UAV structural vibration signal at time i+1, where N is a positive integer, and α i It can be any constant parameter between 0 and 1; W13. Based on the dominant vibration frequency function R_m of the UAV, the dominant vibration frequency of the UAV is calculated to obtain the data information of the dominant vibration frequency of the UAV.

[0010] Furthermore, the constant parameter α i The constraint is that the sum of their squares is 1, and they are not equal to each other.

[0011] Furthermore, in step W3, the optimization of the UAV's acceleration and angular velocity using an improved weighted average optimization algorithm based on dynamic fusion weights includes: W31. Based on the data information of the dominant vibration frequency of the UAV and the natural frequencies of the multiple multi-band heterogeneous IMUs used for flight control, establish the deviation function G between the natural frequency and the dominant frequency of each IMU used for flight control. k , , Where, r k Given the inherent frequency of the k-th multi-band heterogeneous IMU used for flight control, the deviation between the inherent frequency and the dominant frequency of each IMU used for flight control is calculated to obtain data information on the deviation between the inherent frequency and the dominant frequency of each IMU used for flight control. W32. Based on the data information of the deviation between the inherent frequency and the dominant frequency of each IMU used in flight control, dynamically allocate the fusion weight Q of each IMU. k Q k =1 / (G k +1), to obtain the fusion weight data information of the multi-band heterogeneous IMU for flight control; W33. Based on the fusion weight data of the multi-band heterogeneous IMU used for flight control and the original acceleration and angular velocity data of the UAV, a target optimization function H is constructed. , Among them, z ak For the raw acceleration data of the UAV acquired by the k-th flight control multi-band IMU, z ωk For the raw angular velocity data of the UAV acquired by the k-th flight control multi-band IMU, Q k Using the fusion weight data of the k-th multi-band heterogeneous IMU for flight control, the acceleration and angular velocity of the UAV are optimized to obtain the optimized acceleration and angular velocity data of the UAV.

[0012] Furthermore, the data information Q of the fusion weights of the k-th flight control multi-band heterogeneous IMU k The constraint condition is Q k The value of is between 0 and 1, and Q1 + Q2 + ... + Q n =1.

[0013] Furthermore, the method also includes: W5. Based on the acceleration and angular velocity data of the optimized UAV, set preset acceleration and angular velocity. If the acceleration of the optimized UAV exceeds the preset acceleration or the angular velocity of the optimized UAV exceeds the preset acceleration, the UAV will experience abnormal vibration and issue a warning signal. If the acceleration of the optimized UAV is less than the preset acceleration and the angular velocity of the optimized UAV is less than the preset angular velocity, the UAV will operate normally and perform flight missions.

[0014] To achieve the above and other related objectives, the present invention also provides a multi-band IMU-based adaptive adjustment UAV vibration compensation system for implementing the aforementioned multi-band IMU-based adaptive adjustment UAV vibration compensation method. The system includes: The UAV's dominant vibration frequency data acquisition module is used to collect data information of UAV structural vibration signals based on the collaborative acquisition of multiple airborne vibration detection IMU arrays, and to construct a UAV vibration spectrum analysis model to calculate the UAV's dominant vibration frequency and obtain the data information of the UAV's dominant vibration frequency. The raw acceleration and angular velocity data acquisition module of the UAV is used to acquire the raw acceleration and angular velocity data of the UAV in real time based on multiple airborne flight control multi-band heterogeneous IMU arrays; The acceleration and angular velocity optimization module of the UAV is connected to the UAV's dominant vibration frequency data acquisition module and the UAV's original acceleration and angular velocity data acquisition module, respectively. It is used to combine the inherent frequencies of multiple airborne flight control multi-band heterogeneous IMUs and use an improved weighted average optimization algorithm based on dynamic fusion weights to optimize the acceleration and angular velocity of the UAV, so as to obtain the optimized acceleration and angular velocity data information of the UAV. The acceleration and angular velocity adjustment module of the UAV is connected to the acceleration and angular velocity optimization module of the UAV. It is used to input the optimized acceleration and angular velocity data of the UAV into the flight control execution unit, and combine it with the preset flight control to generate motor PWM control signals to perform compensation control and adjustment of the UAV.

[0015] Furthermore, the system includes an early warning module for the UAV, which is connected to the acceleration and angular velocity adjustment module of the UAV. It is used to set preset acceleration and angular velocity. If the acceleration of the optimized UAV exceeds the preset acceleration or the angular velocity of the optimized UAV exceeds the preset acceleration, the UAV will experience abnormal vibration and issue an early warning signal.

[0016] Furthermore, if the acceleration of the optimized drone is less than the preset acceleration and the angular velocity of the optimized drone is less than the preset angular velocity, then the drone is operating normally and performing a flight mission.

[0017] Furthermore, the system also includes a human-computer interaction module connected to the acceleration and angular velocity adjustment module of the drone, used to set the upper and lower limits of acceleration and angular velocity.

[0018] The present invention has the following positive effects: 1. This invention significantly improves attitude perception accuracy under wide-band vibration environments. By deploying a multi-band IMU array with differentiated natural frequencies and combining it with vibration spectrum for adaptive fusion, it effectively avoids the measurement distortion problem of a single IMU in the resonant frequency band, ensuring that high-fidelity inertial data can be output under all operating conditions such as low-frequency maneuvering, mid-frequency cruise, and high-frequency disturbance.

[0019] 2. This invention enhances the robustness and safety of the system under resonance conditions. Traditional isomorphic redundant IMUs are prone to common-mode failure at specific vibration frequencies. However, this invention achieves frequency band complementarity through heterogeneous design. Even if the performance of one IMU deteriorates due to proximity to resonance, the other IMUs can still provide reliable data, avoiding the risk of flight control misjudgment or loss of control.

[0020] 3. This invention achieves closed-loop fusion of vibration prior knowledge and flight perception, integrating vibration spectrum information obtained from ground modal tests or online estimation into the flight control perception link, enabling the system to have "perception-matching-optimization" capabilities, fully tapping the potential of sensor resources, and improving the level of intelligence.

[0021] 5. This invention supports structural health monitoring and mission reliability assessment. By continuously analyzing the relationship between vibration spectrum and IMU response, it can indirectly identify machine abnormalities (such as motor imbalance and arm loosening), providing a basis for predictive maintenance and mission termination decisions.

[0022] 6. This invention has strong compatibility and is easy to implement in engineering. It does not require disruptive modifications to the existing flight control architecture. It only requires adding a heterogeneous IMU at the hardware layer and embedding an adaptive fusion algorithm at the software layer. It can be deployed on mainstream UAV platforms and has good promotional value. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the process for constructing the vibration spectrum analysis model of an unmanned aerial vehicle (UAV) according to the present invention; Figure 3 This is a flowchart illustrating the improved weighted average optimization algorithm based on dynamic fusion weights of the present invention. Figure 4 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0024] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0025] Example 1: As Figure 1 As shown, a method for adaptively adjusting the vibration compensation of a UAV based on a multi-band IMU is described, the method comprising: W1. During the flight mission, the UAV collects structural vibration signal data based on multiple airborne vibration detection IMU arrays, constructs a UAV vibration spectrum analysis model, calculates the dominant vibration frequency of the UAV body, and obtains the data information of the dominant vibration frequency of the UAV body. W2. Based on multiple airborne flight control multi-band heterogeneous IMU arrays, the raw acceleration and angular velocity data of the UAV are acquired in real time; W3. Based on the data information of the dominant vibration frequency of the UAV and the original acceleration and angular velocity data of the UAV, combined with the inherent frequencies of multiple airborne flight control multi-band heterogeneous IMUs, an improved weighted average optimization algorithm based on dynamic fusion weights is used to optimize the acceleration and angular velocity of the UAV, and the optimized acceleration and angular velocity data of the UAV are obtained. W4. Input the optimized acceleration and angular velocity data of the UAV into the flight control execution unit, and combine it with the preset flight control to generate motor PWM control signals to perform compensation control and adjustment of the UAV.

[0026] In this embodiment, as Figure 2 As shown, in step W1, the construction of the UAV vibration spectrum analysis model and the calculation of the dominant vibration frequencies of the UAV body include: W11. Based on the data information of the UAV structural vibration signal, noise reduction processing is performed to obtain the data information of the UAV structural vibration signal after noise reduction processing; W12. Based on the data information of the UAV structural vibration signal after noise reduction processing, establish the dominant vibration frequency function R_m of the UAV body. , Where, x i x represents the data information of the denoised UAV structural vibration signal at time i. i+1 This refers to the data information of the denoised UAV structural vibration signal at time i+1, where N is a positive integer, and α i It can be any constant parameter between 0 and 1; W13. Based on the dominant vibration frequency function R_m of the UAV, the dominant vibration frequency of the UAV is calculated to obtain the data information of the dominant vibration frequency of the UAV.

[0027] In this embodiment, the constant parameter α i The constraint is that the sum of their squares is 1, and they are not equal to each other.

[0028] In this embodiment, as Figure 3 As shown, in step W3, the optimization of the UAV's acceleration and angular velocity using an improved weighted average optimization algorithm based on dynamic fusion weights includes: W31. Based on the data information of the dominant vibration frequency of the UAV and the natural frequencies of the multiple multi-band heterogeneous IMUs used for flight control, establish the deviation function G between the natural frequency and the dominant frequency of each IMU used for flight control. k , , Where, rk Given the inherent frequency of the k-th multi-band heterogeneous IMU used for flight control, the deviation between the inherent frequency and the dominant frequency of each IMU used for flight control is calculated to obtain data information on the deviation between the inherent frequency and the dominant frequency of each IMU used for flight control. W32. Based on the data information of the deviation between the inherent frequency and the dominant frequency of each IMU used in flight control, dynamically allocate the fusion weight Q of each IMU. k Q k =1 / (G k +1), to obtain the fusion weight data information of the multi-band heterogeneous IMU for flight control; W33. Based on the fusion weight data of the multi-band heterogeneous IMU used for flight control and the original acceleration and angular velocity data of the UAV, a target optimization function H is constructed. , Among them, z ak For the raw acceleration data of the UAV acquired by the k-th flight control multi-band IMU, z ωk For the raw angular velocity data of the UAV acquired by the k-th flight control multi-band IMU, Q k Using the fusion weight data of the k-th multi-band heterogeneous IMU for flight control, the acceleration and angular velocity of the UAV are optimized to obtain the optimized acceleration and angular velocity data of the UAV.

[0029] In this embodiment, the data information Q of the fusion weight of the k-th flight control multi-band heterogeneous IMU k The constraint condition is Q k The value of is between 0 and 1, and Q1 + Q2 + ... + Q n =1.

[0030] In this embodiment, the UAV sequentially performs typical tasks such as hovering, constant forward flight (5 m / s), accelerated climb (1.5 m / s²), and lateral maneuvering, with each phase lasting 60 seconds. All IMU data is synchronously sampled at 1 kHz and uploaded to the ground station via the CAN bus for post-processing analysis. The dominant vibration frequency is updated every 2 seconds, and the fusion algorithm runs at a frequency of 500 Hz on the Pixhawk 6C flight control board.

[0031] Table 1 shows the identification results of the dominant vibration frequencies and their corresponding optimal IMU weight allocations under different flight phases: Table 1

[0032] As can be seen, the dominant vibration frequency shifts significantly with changes in flight status, and the system can accurately track this trend and adjust the contribution ratio of the two heterogeneous IMUs accordingly. For example, during hovering, low-frequency vibrations dominate, and Xsens takes the lead due to its high resolution; while during acceleration and climb, high-frequency impacts increase, and VN-310 is given a higher weight due to its stronger vibration resistance.

[0033] In addition, to quantitatively evaluate system performance, three key metrics are defined: 1. Root mean square error of angular velocity (RMSE_ω): a reference value relative to a high-precision GNSS / INS integrated navigation system; 2. Attitude angle jitter standard deviation (σ_att): reflects the degree of short-term fluctuation in roll and pitch angles; 3. Control command fluctuation rate (CV_PWM): measures the stability of the motor's PWM output.

[0034] Table 2 Performance comparison experiment data:

[0035] The data in the table above shows that the present invention achieves the best performance in all indicators, especially in terms of angular velocity accuracy, which is nearly four times higher than that of traditional methods.

[0036] In this embodiment, the method further includes: W5. Based on the acceleration and angular velocity data of the optimized UAV, set preset acceleration and angular velocity. If the acceleration of the optimized UAV exceeds the preset acceleration or the angular velocity of the optimized UAV exceeds the preset acceleration, the UAV will experience abnormal vibration and issue a warning signal. If the acceleration of the optimized UAV is less than the preset acceleration and the angular velocity of the optimized UAV is less than the preset angular velocity, the UAV will operate normally and perform flight missions.

[0037] Example 2: Based on the method for adaptive adjustment of UAV vibration compensation based on multi-band IMU in Example 1, the present invention will be further described below.

[0038] like Figure 1 As shown, a method for adaptively adjusting the vibration compensation of a UAV based on a multi-band IMU is described, the method comprising: W1. During the flight mission, the UAV collects structural vibration signal data based on multiple airborne vibration detection IMU arrays, constructs a UAV vibration spectrum analysis model, calculates the dominant vibration frequency of the UAV body, and obtains the data information of the dominant vibration frequency of the UAV body. W2. Based on multiple airborne flight control multi-band heterogeneous IMU arrays, the raw acceleration and angular velocity data of the UAV are acquired in real time; W3. Based on the data information of the dominant vibration frequency of the UAV and the original acceleration and angular velocity data of the UAV, combined with the inherent frequencies of multiple airborne flight control multi-band heterogeneous IMUs, an improved weighted average optimization algorithm based on dynamic fusion weights is used to optimize the acceleration and angular velocity of the UAV, and the optimized acceleration and angular velocity data of the UAV are obtained. W4. Input the optimized acceleration and angular velocity data of the UAV into the flight control execution unit, and combine it with the preset flight control to generate motor PWM control signals to perform compensation control and adjustment of the UAV.

[0039] In this embodiment, as Figure 4 As shown, the present invention also provides a multi-band IMU-based adaptive adjustment UAV vibration compensation system for implementing the aforementioned multi-band IMU-based adaptive adjustment UAV vibration compensation method. The system includes: The UAV's dominant vibration frequency data acquisition module is used to collect data information of UAV structural vibration signals based on the collaborative acquisition of multiple airborne vibration detection IMU arrays, and to construct a UAV vibration spectrum analysis model to calculate the UAV's dominant vibration frequency and obtain the data information of the UAV's dominant vibration frequency. The raw acceleration and angular velocity data acquisition module of the UAV is used to acquire the raw acceleration and angular velocity data of the UAV in real time based on multiple airborne flight control multi-band heterogeneous IMU arrays; The acceleration and angular velocity optimization module of the UAV is connected to the UAV's dominant vibration frequency data acquisition module and the UAV's original acceleration and angular velocity data acquisition module, respectively. It is used to combine the inherent frequencies of multiple airborne flight control multi-band heterogeneous IMUs and use an improved weighted average optimization algorithm based on dynamic fusion weights to optimize the acceleration and angular velocity of the UAV, so as to obtain the optimized acceleration and angular velocity data information of the UAV. The acceleration and angular velocity adjustment module of the UAV is connected to the acceleration and angular velocity optimization module of the UAV. It is used to input the optimized acceleration and angular velocity data of the UAV into the flight control execution unit, and combine it with the preset flight control to generate motor PWM control signals to perform compensation control and adjustment of the UAV.

[0040] In this embodiment, the system includes an early warning module for the UAV, which is connected to the acceleration and angular velocity adjustment module of the UAV. The module is used to set preset acceleration and angular velocity. If the acceleration of the optimized UAV exceeds the preset acceleration or the angular velocity of the optimized UAV exceeds the preset acceleration, the UAV will experience abnormal vibration and issue an early warning signal.

[0041] In this embodiment, if the acceleration of the optimized drone is less than the preset acceleration and the angular velocity of the optimized drone is less than the preset angular velocity, the drone operates normally and performs a flight mission.

[0042] In this embodiment, the system further includes a human-computer interaction module connected to the acceleration and angular velocity adjustment module of the UAV, used to set the upper and lower limits of acceleration and angular velocity.

[0043] This invention has been successfully applied to a certain type of high-altitude material delivery UAV system. When performing missions at altitudes above 4500 meters, the thin air causes a decrease in propeller efficiency, requiring the motors to operate under high load for extended periods, leading to strong airframe resonance. After adopting this vibration compensation method, the flight control system's attitude estimation stability was significantly improved, the flight path tracking error was reduced from ±3.2 m to ±0.9 m, and the mission success rate increased to 98.6%. User feedback shows that the UAV can maintain stable flight even in strong winds and complex terrain conditions, and the camera footage is clear and shake-free, greatly improving operational reliability.

[0044] Effect evaluation and novelty assessment This invention constructs a multi-band IMU collaborative sensing system, enabling online modeling and adaptive compensation of UAV vibration characteristics. This effectively solves the attitude drift and control oscillation problems faced by traditional flight control systems under complex conditions. Experimental data confirms that this method can reduce angular velocity measurement errors to below 0.3° / s and attitude jitter by more than 60%, significantly improving flight quality and mission execution capabilities.

[0045] From the perspective of technological innovation, this invention possesses outstanding novelty and inventiveness: First, at the system architecture level, a dual-path design of "vibration sensing array + flight control heterogeneous IMU" is proposed, which breaks through the limitations of traditional single IMU or homogeneous array and achieves wide-bandwidth, multi-level state perception coverage.

[0046] Secondly, at the algorithm level, the proposed dynamic weighted fusion mechanism takes into account both frequency matching degree and real-time signal-to-noise ratio, and is more flexible and robust than static weighting or simple switching strategies, which is an important improvement on the weighted average algorithm.

[0047] Furthermore, the entire method achieves deep coupling between vibration analysis and flight control execution, forming a closed-loop adaptive control flow. This differs from previous approaches that only intervened in the signal preprocessing stage, demonstrating a higher level of system integration thinking.

[0048] In summary, this invention not only represents a substantial advancement in technical principles, but has also demonstrated significant benefits in practical applications, possessing promising prospects for industrialization and valuable patent protection.

[0049] The present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform the described method for adaptive adjustment of UAV vibration compensation based on multi-band IMU.

[0050] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0051] In summary, this invention not only effectively avoids the measurement distortion problem of a single IMU in the resonant frequency band, ensuring the output of high-fidelity inertial data under all operating conditions such as low-frequency maneuvering, mid-frequency cruise, and high-frequency disturbance, but also can be deployed on mainstream UAV platforms, making it highly valuable for widespread application.

[0052] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for adaptive adjustment of unmanned aerial vehicle vibration compensation based on multi-band IMU, characterized in that, The method comprises: W1. During the flight task of the unmanned aerial vehicle, the data information of the structural vibration signal of the unmanned aerial vehicle is collected based on the multiple vibration detection IMU arrays on board, a vibration frequency spectrum analysis model of the unmanned aerial vehicle is constructed, the dominant vibration frequency of the body of the unmanned aerial vehicle is calculated, and the data information of the dominant vibration frequency of the body of the unmanned aerial vehicle is obtained; W2. The original acceleration and angular velocity data information of the unmanned aerial vehicle are obtained in real time based on the multiple multi-frequency heterogeneous IMU arrays for flight control on board; W3. Based on the data information of the dominant vibration frequency of the body of the unmanned aerial vehicle and the data information of the original acceleration and angular velocity of the unmanned aerial vehicle, and in combination with the natural frequency of the multiple multi-frequency heterogeneous IMUs for flight control on board, the acceleration and angular velocity of the unmanned aerial vehicle are optimized by using an improved weighted average optimization algorithm based on dynamic fusion weight, and the data information of the optimized acceleration and angular velocity of the unmanned aerial vehicle is obtained; W4. The data information of the optimized acceleration and angular velocity of the unmanned aerial vehicle is input into a flight control execution unit, and in combination with a preset flight control, a motor PWM control signal is generated to perform compensation control and adjustment on the unmanned aerial vehicle.

2. The multi-band IMU based adaptive adjustment of UAV vibration compensation method of claim 1, wherein, In step W1, the construction of the vibration frequency spectrum analysis model of the unmanned aerial vehicle and the calculation of the dominant vibration frequency of the body of the unmanned aerial vehicle comprise: W11. Based on the data information of the structural vibration signal of the unmanned aerial vehicle, noise reduction processing is performed to obtain the data information of the structural vibration signal of the unmanned aerial vehicle after noise reduction processing; W12. Based on the data information of the structural vibration signal of the unmanned aerial vehicle after noise reduction processing, a dominant vibration frequency function R_m of the body of the unmanned aerial vehicle is established, ， Wherein, x i is the data information of the de-noised unmanned aerial vehicle structure vibration signal at the i th moment, x i+1 is the data information of the de-noised unmanned aerial vehicle structure vibration signal at the i+1 th moment, N is a positive integer, and ɑ i is an arbitrary constant parameter between 0 and 1. W13. Based on the dominant vibration frequency function R_m of the body of the unmanned aerial vehicle, the dominant vibration frequency of the body of the unmanned aerial vehicle is calculated, and the data information of the dominant vibration frequency of the body of the unmanned aerial vehicle is obtained. 3.The multi-band IMU based adaptive adjustment of UAV vibration compensation method of claim 2, wherein: The constant parameter a i The constraint condition is that the sum of their squares is 1, and they are not equal to each other.

4. The multi-band IMU based adaptive adjustment UAV vibration compensation method of claim 2, wherein, In step W3, the optimization of the acceleration and angular velocity of the unmanned aerial vehicle by using the improved weighted average optimization algorithm based on dynamic fusion weight comprises: W31. based on the data information of the main dominant vibration frequency of the body of the unmanned aerial vehicle and the inherent frequency of the multi-frequency heterogeneous IMU for flight control, a deviation function G of the inherent frequency of each IMU for flight control and the dominant frequency is established k , , wherein r k is the natural frequency of the kth IMU for flight control, the deviation degree of the natural frequency of each IMU for flight control from the dominant frequency is calculated to obtain data information of the deviation degree of the natural frequency of each IMU for flight control from the dominant frequency; W32. dynamically allocating the fusion weight Q of each IMU based on the data information of the deviation degree of the inherent frequency of each IMU from the dominant frequency for the flight control k , Q k =1 / (G k +1), obtaining the data information of the fusion weight of the multi-band heterogeneous IMU for the flight control W33. Based on the data information of the fusion weight of the multi-frequency heterogeneous IMU for flight control and the data information of the original acceleration and angular velocity of the unmanned aerial vehicle, a target optimization function H is constructed, ， wherein z ak is the data information of the original acceleration of the UAV obtained by the multi-band mechanism IMU for the kth flight control, z ωk is the data information of the original angular velocity of the UAV obtained by the multi-band mechanism IMU for the kth flight control, Q k is the data information of the fusion weight of the multi-band heterogeneous IMU for the kth flight control, and the acceleration and angular velocity of the UAV are optimized to obtain the data information of the optimized acceleration and angular velocity of the UAV.

5. The multi-band IMU based adaptive adjustment UAV vibration compensation method of claim 4, wherein: The data information Q of the fusion weight of the multi-band heterogeneous IMU of the kth flight control k The constraint condition of Q is k The value range of Q is between 0 and 1, and Q1+Q2+...+Q n =1.

6. The multi-band IMU based adaptive adjustment UAV vibration compensation method of claim 1, wherein, The method further comprises: W5. Based on the data information of the optimized acceleration and angular velocity of the unmanned aerial vehicle, a preset acceleration and angular velocity are set, if the optimized acceleration of the unmanned aerial vehicle exceeds the preset acceleration or the optimized angular velocity of the unmanned aerial vehicle exceeds the preset acceleration, the unmanned aerial vehicle abnormally vibrates, a warning signal is sent, if the optimized acceleration of the unmanned aerial vehicle is less than the preset acceleration and the optimized angular velocity of the unmanned aerial vehicle is less than the preset angular velocity, the unmanned aerial vehicle operates normally, and the flight task is performed.

7. A multi-band IMU-based adaptive adjustment unmanned aerial vehicle vibration compensation system, characterized in that, The system for implementing the unmanned aerial vehicle vibration compensation method based on multi-frequency IMU adaptive adjustment according to any one of claims 1-6, the system comprises: A body dominant vibration frequency data acquisition module of an unmanned aerial vehicle, configured to collect data information of a structural vibration signal of the unmanned aerial vehicle based on multiple vibration detection IMU arrays on board, construct a vibration frequency spectrum analysis model of the unmanned aerial vehicle, calculate the dominant vibration frequency of the body of the unmanned aerial vehicle, and obtain the data information of the dominant vibration frequency of the body of the unmanned aerial vehicle; The data acquisition module of the original acceleration and angular velocity of the unmanned aerial vehicle is used to acquire the data information of the original acceleration and angular velocity of the unmanned aerial vehicle based on the multi-band heterogeneous IMU array for multiple flight controls on board. The acceleration and angular velocity optimization module of the unmanned aerial vehicle is connected with the body main vibration frequency data acquisition module of the unmanned aerial vehicle and the data acquisition module of the original acceleration and angular velocity of the unmanned aerial vehicle, respectively, and is used to combine the natural frequency of the multi-band heterogeneous IMU for multiple flight controls on board, adopt the improved weighted average optimization algorithm based on dynamic fusion weight, optimize the acceleration and angular velocity of the unmanned aerial vehicle, and obtain the data information of the optimized acceleration and angular velocity of the unmanned aerial vehicle. The acceleration and angular velocity adjustment module of the unmanned aerial vehicle is connected with the acceleration and angular velocity optimization module of the unmanned aerial vehicle, is used to input the data information of the optimized acceleration and angular velocity of the unmanned aerial vehicle into the flight control execution unit, combine the preset flight control to generate the motor PWM control signal, and perform compensation control and adjustment on the unmanned aerial vehicle.

8. The multi-band IMU based adaptive adjustment UAV vibration compensation system of claim 7, wherein, The system includes the early warning module of the unmanned aerial vehicle connected with the acceleration and angular velocity adjustment module of the unmanned aerial vehicle, is used to set the preset acceleration and angular velocity, if the optimized acceleration of the unmanned aerial vehicle exceeds the preset acceleration or the optimized angular velocity of the unmanned aerial vehicle exceeds the preset acceleration, the unmanned aerial vehicle abnormally vibrates, and an early warning signal is sent.

9. The multi-band IMU based adaptive adjustment UAV vibration compensation system of claim 8, wherein: If the optimized acceleration of the unmanned aerial vehicle is less than the preset acceleration and the optimized angular velocity of the unmanned aerial vehicle is less than the preset angular velocity, the unmanned aerial vehicle operates normally and performs a flight task.

10. The multi-band IMU based adaptive adjustment UAV vibration compensation system of claim 7, wherein: The system further includes the human-computer interaction module connected with the acceleration and angular velocity adjustment module of the unmanned aerial vehicle, is used to set the upper limit value and the lower limit value of the acceleration and the angular velocity.

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