Tilt unmanned aerial vehicle mode switching control method and system based on distributed decoupling

Abstract

The application discloses a kind of based on distributed decoupling's tilting unmanned aerial vehicle mode switching control method and system, the present application is built by multi-source data acquisition and preprocessing data base, rely on distributed pose-velocity-mode coupling aerodynamic feedforward dynamics algorithm constructs accurate adaptation multi-mode six-degree-of-freedom dynamics model, using distributed aerodynamic sensitive weighted null space decoupling technology realizes control subspace effective separation, combined with multi-constraint condition and three target collaborative optimization algorithm constructs optimization model, solves by improved lagrange multiplier method and dynamically feedback adjustment, this method effectively resolves control coupling problem in mode switching, improves flight stability and adaptability, optimizes energy consumption utilization, adapts to various complex operation scenarios, significantly enhances the reliability and practical value of unmanned aerial vehicle operation.The present application can be widely applied in the field of unmanned aerial vehicle control.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a tilt UAV mode switching control method and system based on distributed decoupling. Background Technology

[0002] As a novel type of aircraft that combines the vertical takeoff and landing capabilities of helicopters with the high-speed cruise advantages of fixed-wing aircraft, tiltrotor unmanned aerial vehicles (UAVs) have shown broad application prospects in many fields due to their multi-mode flight characteristics. The core technical challenge lies in achieving smooth switching between three modes: vertical takeoff and landing, transitional flight, and horizontal cruise. This process involves multiple technical challenges, including multi-source data interaction, complex aerodynamic coupling, and distributed control coordination. As UAV applications expand into more complex environments, the requirements for stability, response speed, and energy consumption control during mode switching are constantly increasing. Distributed structures, due to their advantages in redundancy, fault tolerance, and control flexibility, have gradually become the mainstream design direction for tiltrotor UAVs. However, distributed layouts also bring challenges to the coordinated control between rotor units. It is necessary to integrate multi-dimensional information such as hardware characteristics, flight status, and aerodynamic parameters to construct an accurate dynamic model and an efficient decoupled control framework in order to ensure the stability of flight attitude and position during mode switching and meet the flight requirements of different mission scenarios.

[0003] Traditional tiltrotor UAV mode switching control methods have many limitations. They are difficult to adapt to the complex characteristics of distributed structures and high flight requirements. In the decoupled control link, traditional solutions lack a dynamic allocation mechanism for multi-mode adaptation, making it difficult to effectively divide the position and attitude control subspaces. This can easily lead to interference between different control targets, affecting the smoothness of switching and limiting the application of UAVs in high-requirement scenarios. Summary of the Invention

[0004] In view of this, in order to solve the technical problem of difficulty in achieving effective separation of control subspaces in mode switching control methods, resulting in low switching smoothness, the present invention proposes a tilt-type UAV mode switching control method based on distributed decoupling, which includes the following steps: Multi-source data acquisition and preprocessing: Collect distributed hardware inherent parameters, multi-mode real-time flight status data, airspeed data and mode switching judgment parameters of distributed tilt-rotor UAVs, and perform filtering and noise reduction and cross-node synchronous calibration on the collected data.

[0005] Model adaptation dynamics model construction: Based on preprocessed multi-source data, a six-degree-of-freedom distributed dynamics model integrating multi-mode aerodynamic feedforward terms is constructed using a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm. Mode switching decoupling framework construction: Based on the distributed control input matrix output by the six-degree-of-freedom distributed dynamics model and the net control requirement parameters for mode switching, a multi-mode adaptive control input-target allocation matrix is ​​constructed. The allocation matrix is ​​processed by the distributed aerodynamic sensitive weighted zero-space decoupling algorithm to obtain the mode switching decoupling projection matrix, and the position control subspace and attitude control subspace under different modes are divided.

[0006] Establishment of mode switching optimization model: Based on the results of mode switching decoupling projection matrix and six-degree-of-freedom distributed dynamic model, combined with mode switching stability constraints and distributed rotor actuator physical constraints, a three-objective collaborative optimization algorithm of task-energy consumption-switching smoothness is applied to construct a distributed thrust-torque collaborative switching optimization allocation model.

[0007] Dynamic mode switching control execution: The optimal switching control input is obtained by solving the cooperative switching optimization allocation model using the improved Lagrange multiplier method. The optimal switching control input is then converted into distributed control commands and sent to each rotor execution node. The mode switching status data is collected in real time to dynamically adjust the control parameters. The optimal switching control input is fed back to the six-degree-of-freedom distributed dynamic model to update the initial parameters, thereby achieving smooth switching between different flight modes.

[0008] Secondly, based on the above-mentioned tilt-rotor UAV mode switching control method, this invention also proposes a tilt-rotor UAV mode switching control system based on distributed decoupling. The system includes an acquisition module, a dynamic model construction module, a model output module, a collaborative optimization model construction module, and a final command output module.

[0009] Based on the above scheme, the present invention provides a tilt-type UAV mode switching control method and system based on distributed decoupling, the technical effects of which include: 1. By acquiring and preprocessing multi-source data, integrating key information such as inherent hardware parameters and flight status data, and combining a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm, a six-degree-of-freedom distributed dynamic model integrating multi-mode aerodynamic feedforward terms is constructed. This model accurately captures the aerodynamic characteristics and dynamic laws under different flight modes. Based on this model, a multi-mode adaptive control input-target allocation matrix is ​​built. The distributed aerodynamic sensitive weighted zero-space decoupling algorithm is used to obtain the mode switching decoupling projection matrix, realizing a clear division and smooth transition between the position control subspace and the attitude control subspace. This design effectively resolves the control coupling problem caused by abrupt changes in aerodynamic parameters during different mode switching, improves the control adaptability of UAVs in different stages such as vertical take-off and landing, transitional flight, and horizontal cruise, reduces attitude fluctuations and position deviations during mode switching, ensures the stability and controllability of the flight process, and enhances the adaptability of UAVs to complex flight environments. 2. By constructing a distributed thrust-torque coordinated switching optimization allocation model based on multi-dimensional constraints, this model comprehensively considers both the smoothness of mode switching and the physical characteristics of the actuators. A three-objective coordinated optimization algorithm—task-energy consumption-switching smoothness—is employed to achieve multi-objective balance. An improved Lagrange multiplier method is used to efficiently solve for the optimal switching control input, ensuring the accuracy and timeliness of control commands. Simultaneously, control parameters are dynamically adjusted by real-time acquisition of mode switching state data, and this data is fed back to the dynamic model to update the initial calculation conditions, forming a complete dynamic control closed loop. This design optimizes the energy utilization efficiency of the UAV and enhances the coherence and accuracy of the mode switching process, avoiding flight risks caused by control command delays or parameter mismatches. This allows the UAV to efficiently complete operational requirements and maintain stable flight performance in diverse mission scenarios, significantly improving overall operational reliability and practical value. Attached Figure Description

[0010] Figure 1 This is a flowchart of the steps of a tilting UAV mode switching control method based on distributed decoupling according to the present invention. Detailed Implementation

[0011] In addition to the technical issues mentioned in the background, in terms of dynamic modeling, traditional methods often fail to fully integrate aerodynamic feedforward information from multiple modes and do not adequately consider the coupling relationship between attitude, airspeed, and flight mode, resulting in insufficient model accuracy and an inability to provide reliable theoretical support for mode switching. In terms of optimization target design, traditional methods often focus on a single performance index and fail to take into account the coordinated optimization of task completion, energy consumption control, and switching smoothness. Furthermore, they do not fully consider practical factors such as the physical constraints of distributed actuators and the coordination delay of control nodes. In addition, traditional solution algorithms are difficult to balance response speed and accuracy, and cannot quickly generate optimal control commands, which can easily lead to problems such as switching delay and attitude fluctuation in complex flight environments.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0014] It should be understood that the terms "system," "apparatus," "unit," and / or "module" used in this application are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] As indicated in this application, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0016] In the description of the embodiments of this application, "a plurality of" refers to two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0017] Furthermore, flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Additionally, other operations can be added to these processes, or one or more steps can be removed from them.

[0018] Reference Figure 1 This is a flowchart illustrating an optional example of the tilt-type UAV mode switching control method based on distributed decoupling proposed in this invention. This method can be applied to computer equipment, and the control method proposed in this embodiment may include, but is not limited to, the following steps: Step S1: Obtain the parameters and status data of the distributed tiltrotor UAV to obtain the collected data; Step S2: Filter and denoise the collected data and perform cross-node synchronization calibration. Step S3: Based on the collected data, construct a six-degree-of-freedom distributed dynamic model using a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm; Step S4: Based on the six-degree-of-freedom distributed dynamics model, generate the distributed control input matrix and the net control demand parameters for mode switching, and construct the control input-target allocation matrix; Step S5: Apply the distributed aerodynamically sensitive weighted null space decoupling algorithm to process the allocation matrix, generate the mode switching decoupling projection matrix, and divide the position control subspace and attitude control subspace under different modes. Step S6: Based on the mode switching decoupling projection matrix and the six-degree-of-freedom distributed dynamics model, and combined with the preset constraints, solve the cooperative optimization allocation model to generate the optimal switching control input.

[0019] In some feasible embodiments, the parameters of the distributed tiltrotor UAV in step S1 include distributed hardware inherent parameters and mode switching determination parameters, wherein: The inherent parameters of distributed hardware include distributed rotor layout parameters, mass and moment of inertia of each rotor unit, communication delay parameters of distributed control nodes, and rotor installation position matrix. Distributed rotor layout parameters were collected using a three-dimensional laser measuring instrument. The mass of each rotor unit was collected using a high-precision electronic scale, and the moment of inertia was collected using a three-line pendulum inertia test bench. The communication delay parameters of the distributed control node were calibrated using a synchronous clock. All parameters were collected three times independently and the average value was taken. The mode switching determination parameters include flight speed threshold, attitude angle threshold, and mission requirement trigger signal, which are used to trigger the mode switching determination logic.

[0020] In some feasible embodiments, the state data in step S1 includes multi-mode real-time flight state data and airspeed data, wherein: The multi-mode real-time flight status data includes position in the inertial coordinate system, Euler angles of attitude in each mode, airframe angular velocity, and distributed rotor unit attitude synchronization data, which are collected by the airborne distributed inertial measurement unit cluster at a frequency of 200Hz. Airspeed data is jointly collected by the nose pitot tube and auxiliary airspeed sensors of each distributed rotor unit, and cross-sensor calibration is performed in conjunction with atmospheric pressure and temperature data.

[0021] In some feasible embodiments, step S3 specifically includes: Based on the preprocessed distributed hardware inherent parameters and multi-mode real-time attitude data, the mode-dependent distributed inertia matrix, Coriolis-centrifugal force distributed matrix, gravity vector and distributed control input matrix are calculated. Combining calibrated airspeed data and real-time attitude Euler angles of multiple modes, the attitude-airspeed-mode coupled aerodynamic damping matrix, attitude-airspeed-mode coupled aerodynamic gain matrix, and distributed aerodynamic force vector under the current mode condition are calculated using a multi-mode aerodynamic model calibrated by wind tunnel test for vertical take-off and landing mode, transition flight mode, and horizontal cruise mode. Finally, the above matrices and aerodynamic parameters are substituted into the mathematical expression of the unified algorithm for distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics, and integrated to obtain a six-degree-of-freedom distributed dynamic model that incorporates multi-mode aerodynamic feedforward terms.

[0022] In some feasible embodiments, the mathematical expression of the distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm in step S3 is: in, To unify the state vector, The first derivative of the unified state vector, , The position vector in the inertial coordinate system. Let Euler angles be the attitude vectors. For the distributed rotor unit angular velocity synchronization vector, This represents the transpose operation. It is the first derivative of the attitude Euler angle vector. For the mode identification parameters, set it to 1 for vertical takeoff and landing mode, 2 for transitional flight mode, and 3 for horizontal cruise mode. For pattern-dependent distributed inertia matrix, For the Coriolis-centrifugal force distributed matrix, This is the attitude-airspeed-mode coupled aerodynamic damping matrix. The gravity vector For distributed control input matrix, For distributed control input vectors, This is the attitude-airspeed-mode coupled aerodynamic gain matrix. For Hadamard products, For distributed aerodynamic vectors, Indicates airspeed.

[0023] In some feasible embodiments, the control input-target allocation matrix for multi-mode adaptation in step S4 is a 6×(2N) dimensional matrix, where N is the number of distributed rotor units. The first three rows of the matrix correspond to the position control forces in the x, y, and z directions, and the last three rows correspond to the control torques in the roll, pitch, and yaw directions. The 2N columns correspond to the thrust input and tilt angle input of each of the N distributed rotor units. The matrix elements follow the mode identifier parameters. Dynamic adjustment.

[0024] In some feasible embodiments, the mathematical expression of the distributed aerodynamically sensitive weighted null space decoupling algorithm in step S5 is: in, Decouple the projection matrix for mode switching. This is a pattern-dependent distributed aerodynamic sensitivity weight matrix. Assign matrices to control inputs and targets for multi-mode adaptation. As a regularization factor, It is the identity matrix. This is a task priority weight matrix that depends on the mode. Vertical takeoff and landing mode prioritizes position control, horizontal cruise mode prioritizes attitude control, and transition mode balances priorities. This represents the transpose operation.

[0025] In some feasible embodiments, the mode switching decoupling projection matrix in step S5 is a (2N)×(2N) dimensional matrix. The matrix elements are obtained by calculating the correlation between the multi-mode adaptation allocation matrix, the distributed aerodynamic sensitivity weight matrix, the mode-dependent task priority weight matrix, and the regularization factor. Each element corresponds to the dynamic mapping coefficient between a single distributed control input and the decoupled control subspace under different modes, thereby achieving a smooth transition of the subspace during the mode switching process.

[0026] In some feasible embodiments, the constraints of step S6 include: Physical constraints of the distributed rotor actuator: the thrust output range of each distributed rotor is 1N to 10N, and the tilt angle range is -30° to 30°; Mode switching stability constraints: During mode switching, the rate of change of attitude angle shall not exceed 5° / s, and the position fluctuation amplitude shall not exceed 0.5m; Distributed coordination constraint: The delay difference of control commands from each distributed control node does not exceed 1ms; The constraints are embedded in the distributed thrust-torque coordinated switching optimization allocation model in the form of inequalities.

[0027] In some feasible embodiments, in step S6, the mathematical expression corresponding to the three-objective collaborative optimization algorithm of task-energy consumption-switching smoothness is: st in, This is a pattern-dependent dynamic task priority matrix. It is a distributed weighted matrix. Initial values ​​are input for distributed control. This is the energy consumption penalty coefficient. To switch the smoothness penalty coefficient, This is a pattern-dependent distributed aerodynamic energy consumption coupling matrix. The expected total control quantity for pattern-dependent operations. Let L be the square of the L2 norm of the vector. Decouple the projection matrix for mode switching. Assign matrices to control inputs and targets for multi-mode adaptation. This is the attitude-airspeed-mode coupled aerodynamic gain matrix. For distributed aerodynamic vectors, For Hadamard products, This represents the distributed control input difference vector between adjacent modes. Let Euler angles be the attitude vectors. Indicates airspeed. This is the pattern identifier parameter.

[0028] In some feasible embodiments, the solution process of step S6 specifically includes: A mode-switching Lagrangian function is constructed, which incorporates the three objective functions and constraints of the cooperative switching optimization allocation model into the function, and introduces a mode-switching compensation multiplier term. Calculate the first-order partial derivatives of the mode-switching Lagrange function with respect to the distributed control input, the Lagrange multiplier, and the mode-switching compensation multiplier, and set all partial derivative results to zero to obtain a system of linear equations containing the distributed control input variable, the Lagrange multiplier variable, and the mode-switching compensation multiplier. The distributed Gaussian elimination method is used to solve the linear equation system. The accuracy threshold is set to 1e-6. The number of iterations during mode switching does not exceed 30 to ensure the timeliness of switching response. The thrust and tilt angle values ​​of each distributed rotor unit are extracted from the solution results and used as the optimal switching control inputs. The optimal switching control input is converted into distributed control commands and sent to each rotor execution node. Real-time acquisition of mode switching status data dynamically adjusts control parameters. The optimal switching control input is fed back to the six-degree-of-freedom distributed dynamics model to update the initial parameters, thereby achieving smooth switching between different flight modes.

[0029] The mathematical expression for the mode switching Lagrange function is: in, For mode switching Lagrangian functions, For distributed control input vectors, To decouple the control objective constraint Lagrange multiplier vector corresponding to the projection matrix for mode switching. The three objective functions of the coordinated handover optimization allocation model are used. This is an inequality constraint vector composed of the physical constraints and distributed cooperative constraints of the distributed rotor actuator. The equation-based control target constraint vector corresponding to the decoupling projection matrix for mode switching. The input difference constraint vector is used for mode switching control. This represents the transpose operation. Let Lagrange multiplier vector be the vector corresponding to the inequality constraints. This is the Lagrange multiplier vector corresponding to the mode switching smoothness constraint.

[0030] Based on the above overall method, the present invention also provides relevant application scenarios. Scene 1: Example of urban low-altitude logistics delivery scenario.

[0031] S1, Multi-source data acquisition and preprocessing: In this scenario, the weight of the goods to be delivered is fixed, and the drone needs to complete the delivery task from the logistics station to the community between urban buildings, requiring frequent switching between vertical take-off and landing, transition flight, and horizontal cruise modes. First, the inherent parameters of the distributed hardware of the distributed tilt-rotor drone are collected. Distributed rotor layout parameters are collected using a 3D laser measuring instrument, the mass of each rotor unit is collected using a high-precision electronic scale, the moment of inertia is collected using a three-line pendulum inertia test bench, and the communication delay parameters of the distributed control nodes are calibrated using a synchronous clock. Each parameter is collected three times independently and the average value is taken to ensure data accuracy and lay a solid foundation for subsequent precise control. Real-time flight status data for multiple modes is collected by the onboard distributed inertial measurement unit cluster at a frequency of 200Hz, including the drone's position in the inertial coordinate system, the Euler angles of attitude in each of the vertical take-off / landing / transition / horizontal cruise modes, the body angular velocity, and the attitude of the distributed rotor units. Step data acquisition, high-frequency acquisition, can capture real-time changes in the drone's flight dynamics; airspeed data is jointly acquired by the nose pitot tube and auxiliary airspeed sensors of each distributed rotor unit, combined with urban low-altitude atmospheric pressure and temperature data for cross-sensor calibration, eliminating the influence of environmental differences in different regions on airspeed measurement and ensuring accurate flight parameters; mode switching judgment parameters are set as follows: flight speed threshold 0-20m / s (below 5m / s triggers vertical take-off and landing mode, 5-15m / s triggers transitional flight mode, above 15m / s triggers horizontal cruise mode), attitude angle threshold ±15°, and a fixed-point landing trigger signal in logistics delivery tasks, which can accurately trigger mode switching judgment logic according to flight status and mission progress. All the above-acquired data undergoes filtering and noise reduction processing to remove noise from urban electromagnetic interference and airflow disturbances, improving data purity. At the same time, cross-node synchronous calibration is performed to ensure the time consistency of data from each distributed node, allowing subsequent model calculations to be based on a unified time series benchmark, such as... Figure 1 As shown.

[0032] S2, Construction of the Mode-Adaptive Dynamics Model: Based on preprocessed multi-source data, a six-degree-of-freedom distributed dynamic model integrating multi-mode aerodynamic feedforward terms is constructed by applying a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm. First, using preprocessed distributed hardware intrinsic parameters and multi-mode real-time attitude data, the mode-dependent distributed inertial matrix, Coriolis-centrifugal force distributed matrix, gravity vector, and distributed control input matrix are calculated. These matrices accurately reflect the mechanical basis of the relationship between the UAV's hardware characteristics and attitude. Then, combining calibrated airspeed data and real-time attitude Euler angles for each mode, a multi-mode aerodynamic model calibrated through wind tunnel testing is used to calculate the attitude-airspeed-mode coupled aerodynamic damping matrix, attitude-airspeed-mode coupled aerodynamic gain matrix, and corresponding distributed aerodynamic force vector under the vertical take-off and landing mode (mode identifier parameter set to 1), as well as the same aerodynamic parameters under the transition flight mode (mode identifier parameter set to 2) and the horizontal cruise mode (mode identifier parameter set to 3). This fully adapts to the aerodynamic characteristics of different flight stages at low altitudes in urban areas, allowing the model to accurately map the UAV's dynamic behavior in each mode. Finally, all calculated matrices and aerodynamic parameters are substituted into a unified distributed attitude-speed-mode coupled aerodynamic feedforward dynamics algorithm to form a complete six-degree-of-freedom distributed dynamics model. This provides precise dynamic support for subsequent mode switching control, ensuring that the control strategy design conforms to the actual flight laws of the UAV.

[0033] S3, Mode Switching Decoupling Framework Setup: Based on the distributed control input matrix output by the six-degree-of-freedom distributed dynamics model and the net control requirement parameters for mode switching, a multi-mode adaptive control input-target allocation matrix is ​​constructed. This matrix is ​​6×(2N) dimensional (N is the number of distributed rotor units). The first three rows correspond to the position control forces in the x, y, and z directions, which can accurately adjust the spatial position of the UAV to meet the precise positioning requirements in logistics and delivery, ensuring that the goods can accurately reach the target location. The last three rows correspond to the control torques in the roll, pitch, and yaw directions, which can stabilize the flight attitude of the UAV and ensure the safety of flight between buildings. The 2N columns correspond to the thrust input and tilt angle input of each of the N distributed rotor units. The matrix elements are dynamically adjusted according to the mode identifier parameters to adapt to the control requirements of different modes. The allocation matrix was then processed using a distributed aerodynamic sensitivity weighted null space decoupling algorithm to obtain a mode switching decoupling projection matrix. This matrix is ​​(2N)×(2N) dimensional, and its elements are obtained by calculating the correlation between the multi-mode adaptation allocation matrix, the distributed aerodynamic sensitivity weight matrix, the mode-dependent task priority weight matrix, and the regularization factor. Each element corresponds to the dynamic mapping coefficient between a single distributed control input and the decoupled control subspace under different modes. The projection matrix is ​​used to divide the position control subspace and attitude control subspace under different modes. In the vertical take-off and landing mode, the position control subspace has a higher priority to ensure that the UAV can accurately land in the designated area when taking off from the station and landing in the cell. In the horizontal cruise mode, the attitude control subspace has a higher priority to ensure that the attitude does not fluctuate greatly due to airflow and other factors when flying horizontally between buildings. In the transition mode, the priorities of the two are balanced to reduce the sudden changes in flight state during mode switching and achieve a smooth transition.

[0034] S4, Mode Switching Optimization Model Establishment: Based on the decoupled projection matrix of mode switching and the results of the six-degree-of-freedom distributed dynamics model, and combined with the constraints in this scenario, a three-objective collaborative optimization algorithm of task-energy consumption-switching smoothness is applied to construct a distributed thrust-torque collaborative switching optimization allocation model. The constraints are set as follows: the physical constraints of the distributed rotor actuators are that the thrust output range of each distributed rotor is 1N to 10N, and the tilt angle range is -30° to 30°, which not only meets the power requirements of urban low-altitude flight but also prevents safety hazards caused by rotor overload or operation outside the range; the mode switching smoothness constraint is that the attitude angle change rate during mode switching does not exceed 5° / s, and the position fluctuation amplitude does not exceed 0.5m, which can avoid displacement or damage to the cargo due to violent shaking during the switching process and ensure the integrity of the delivered cargo; the distributed collaborative constraint is that the control command delay difference of each distributed control node does not exceed 1ms, which ensures that the actions of the multi-rotor units are coordinated and consistent, and improves the stability and control accuracy during flight. The above constraints are embedded in the optimization allocation model in the form of inequalities. Through the three-objective collaborative optimization algorithm of task-energy consumption-switching smoothness, the energy consumption of drones is reduced as much as possible to extend the single delivery mileage while ensuring the accurate completion of logistics and delivery tasks. At the same time, the smoothness of mode switching is improved to reduce flight impact, so that drones can better adapt to the complex flight environment of urban low-altitude and dense building distribution.

[0035] S5, Dynamic Mode Switching Control Execution: An improved Lagrange multiplier method is employed to solve the cooperative switching optimization allocation model. Specifically, a mode-switching Lagrange function is constructed, incorporating the three objective functions and constraints of the cooperative switching optimization allocation model. A mode-switching compensation multiplier term is introduced to improve constraint satisfaction. First-order partial derivatives of this function with respect to the distributed control input, Lagrange multipliers, and mode-switching compensation multipliers are calculated, and all partial derivatives are set to zero, resulting in a system of linear equations containing relevant variables. This system of equations is solved using distributed Gaussian elimination, with a precision threshold of 1e-6. The number of iterations during mode switching is limited to no more than 30 to ensure the switching response meets the efficiency requirements of urban logistics distribution and avoids delays in delivery due to excessive computation time. The thrust and tilt angle values ​​of each distributed rotor unit are extracted from the solution results and used as the optimal switching control input. These inputs are then converted into distributed control commands and sent to each rotor execution node, enabling each rotor to precisely execute control actions. During flight, real-time data on mode switching is collected, and control parameters are dynamically adjusted to cope with changes in the flight environment. At the same time, the optimal switching control input is fed back to the six-degree-of-freedom distributed dynamics model to update the initial parameters, forming a closed-loop control mechanism. This enables smooth switching between vertical take-off and landing mode (station take-off) → transitional flight mode (leaving the station area) → horizontal cruise mode (flying between buildings) → transitional flight mode (approaching the community area) → vertical take-off and landing mode (community landing), ensuring that cargo is delivered to its destination safely, accurately, and efficiently.

[0036] In summary, this embodiment addresses the frequent mode switching requirements of inter-building logistics delivery in cities by implementing a five-step tilt thrust vector allocation method. First, multi-source data acquisition and preprocessing establish a solid data foundation. Then, a precise dynamic model is constructed based on a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm. Subsequently, a mode switching decoupling framework is built to divide the control subspace. A three-objective optimization model is constructed in conjunction with scenario constraints. Finally, dynamic closed-loop control is achieved through an improved Lagrange multiplier method. This method adapts fully to the urban low-altitude environment and delivery needs, ensuring smooth mode switching and cargo safety while also considering delivery accuracy and energy consumption optimization, efficiently completing cargo delivery tasks from stations to communities.

[0037] Scene 2: Example of an emergency reconnaissance scenario in mountainous terrain.

[0038] S1, Multi-source data acquisition and preprocessing: In this scenario, the UAV needs to perform emergency reconnaissance missions in mountainous terrain, facing complex airflow environments and frequent terrain undulations. It must flexibly switch flight modes to adapt to the terrain and reconnaissance requirements. First, the inherent parameters of the distributed hardware of the distributed tiltrotor UAV are collected. Distributed rotor layout parameters are collected using a 3D laser measuring instrument, the mass of each rotor unit is collected using a high-precision electronic scale, and the moment of inertia is collected separately using a three-line pendulum inertia test bench. The communication delay parameters of the distributed control nodes are calibrated using a synchronous clock. Each parameter is collected three times independently and averaged to ensure data reliability and provide solid data support for flight control in complex mountainous environments. Real-time flight status data in multiple modes is collected by an onboard distributed inertial measurement unit cluster at a frequency of 200Hz. This includes the UAV's position in the inertial coordinate system, attitude Euler angles for vertical takeoff and landing / transition / horizontal cruise modes, airframe angular velocity, and distributed rotor unit attitude synchronization data. High-frequency acquisition can accurately capture mountainous terrain. The dynamic changes of the UAV during flight allow the control system to promptly grasp the UAV's real-time status. Airspeed data is jointly collected by the nose pitot tube and auxiliary airspeed sensors of each distributed rotor unit. Cross-sensor calibration is performed using atmospheric pressure and temperature data at different altitudes in mountainous terrain to eliminate the impact of altitude differences on airspeed measurements and ensure the accuracy of airspeed data at various altitudes. Mode switching parameters are set as follows: flight speed threshold 0-18 m / s (below 4 m / s triggers vertical takeoff and landing mode, 4-12 m / s triggers transitional flight mode, and above 12 m / s triggers horizontal cruise mode), attitude angle threshold ±20°, and target lock trigger signal in reconnaissance missions. This allows for timely mode switching based on terrain and mission requirements, enabling the UAV to quickly adapt to changes in the mountainous environment. The collected data undergoes filtering and noise reduction processing to remove noise from mountainous airflow turbulence and terrain reflections, improving data quality and ensuring the accuracy of control decisions. Simultaneously, cross-node synchronous calibration is performed to ensure data consistency across distributed nodes, facilitating smoother multi-node collaborative work.

[0039] S2, Construction of the Mode-Adaptive Dynamics Model: First, based on the preprocessed distributed hardware inherent parameters and multi-mode real-time attitude data, the mode-dependent distributed inertial matrix, Coriolis-centrifugal force distributed matrix, gravity vector, and distributed control input matrix are calculated. These matrices can fully reflect the mechanical characteristics of the UAV in different attitudes and modes. Then, combining the calibrated airspeed data and the real-time attitude Euler angles for each mode, the attitude-airspeed-mode calculations are performed under the following conditions using a multi-mode aerodynamic model calibrated by wind tunnel testing: vertical takeoff and landing mode (mode identifier parameter is set to 1), transitional flight mode (mode identifier parameter is set to 2), and horizontal cruise mode (mode identifier parameter is set to 3). The model incorporates coupled aerodynamic damping matrices, attitude-airspeed-mode coupled aerodynamic gain matrices, and distributed aerodynamic vectors under the current mode conditions. This fully adapts to the aerodynamic characteristics of different flight modes in complex mountainous airflow environments, enabling the model to accurately simulate the flight state of UAVs in mountainous environments. Finally, all the aforementioned matrices and aerodynamic parameters are substituted into a unified algorithm for distributed attitude-airspeed-mode coupled aerodynamic feedforward dynamics, integrating them to form a six-degree-of-freedom distributed dynamic model that incorporates multi-mode aerodynamic feedforward terms. This provides precise dynamic support for mode switching control in mountainous environments, ensuring that the control strategy conforms to the flight patterns of UAVs in complex mountainous environments.

[0040] S3, Mode Switching Decoupling Framework Setup: Based on the distributed control input matrix output by the six-degree-of-freedom distributed dynamics model and the net control requirement parameters for mode switching, a multi-mode adaptive control input-target allocation matrix is ​​constructed. This matrix is ​​6×(2N) dimensional (N is the number of distributed rotor units). The first three rows correspond to the position control forces in the x, y, and z directions, which can accurately regulate the UAV's spatial position to ensure that the UAV can accurately avoid obstacles in mountainous terrain, avoid collision risks, and ensure flight safety. The last three rows correspond to the control torques in the roll, pitch, and yaw directions, which can stabilize the UAV's flight attitude and ensure that the UAV's attitude is stable during reconnaissance, allowing the reconnaissance equipment to clearly capture target information and improve the reconnaissance effect. The 2N columns correspond to the thrust input and tilt angle input of each of the N distributed rotor units. The matrix elements are dynamically adjusted according to the mode identifier parameters to adapt to the control requirements of different modes. The distributed aerodynamic sensitivity weighted null space decoupling algorithm is applied to process the allocation matrix to obtain the mode switching decoupling projection matrix. This matrix is ​​(2N)×(2N) dimensional, and its elements are obtained by calculating the correlation between the multi-mode adaptation allocation matrix, the distributed aerodynamic sensitivity weight matrix, the mode-dependent task priority weight matrix, and the regularization factor. Each element corresponds to the dynamic mapping coefficient between a single distributed control input and the decoupled control subspace under different modes. The projection matrix is ​​used to divide the position control subspace and attitude control subspace under different modes. The vertical take-off and landing mode emphasizes position control priority to ensure the UAV's accurate take-off and landing in narrow mountainous areas and adapt to the limited take-off and landing sites in mountainous areas. The horizontal cruise mode emphasizes attitude control priority to ensure the stable flight of the UAV during reconnaissance and allow the reconnaissance mission to continue to advance efficiently. The transition mode balances the priorities of the two modes, reduces the fluctuation of flight status during mode switching, achieves a smooth transition, adapts to the undulating terrain of mountainous areas, and allows the UAV to fly smoothly in different terrain sections.

[0041] S4, Mode Switching Optimization Model Establishment: Based on the decoupled projection matrix of mode switching and the results of the six-degree-of-freedom distributed dynamics model, and combined with the constraints of the mountain emergency reconnaissance scenario, a three-objective collaborative optimization algorithm of task-energy consumption-switching smoothness is applied to construct a distributed thrust-torque collaborative switching optimization allocation model. The constraints are set as follows: the physical constraints of the distributed rotor actuators are that the thrust output range of each distributed rotor is 1N to 10N, and the tilt angle range is -30° to 30°, which not only meets the power requirements of mountain flight but also copes with the power output requirements of complex mountain terrain, ensuring flight safety; the mode switching smoothness constraint is that the attitude angle change rate during mode switching does not exceed 5° / s, and the position fluctuation amplitude does not exceed 0.5m, which can avoid the drastic switching caused by airflow disturbance and terrain changes, affecting the reconnaissance effect and ensuring the continuity and accuracy of reconnaissance data; the distributed collaborative constraint is that the control command delay difference of each distributed control node does not exceed 1ms, which ensures the synchronization of multi-rotor collaborative work and improves the flight stability of the UAV in complex airflow. The above constraints are embedded into the optimization allocation model in the form of inequalities. Through the three-objective collaborative optimization algorithm of task, energy consumption and switching smoothness, the energy consumption of UAVs is reduced and the endurance is extended to meet the needs of long-distance reconnaissance in mountainous areas, while ensuring the smooth completion of emergency reconnaissance missions. At the same time, the smoothness of mode switching is improved so that UAVs can better adapt to the complex flight environment in mountainous areas, ensuring the continuous and efficient advancement of reconnaissance missions.

[0042] S5, Dynamic Mode Switching Control Execution: An improved Lagrange multiplier method is employed to solve the cooperative switching optimization allocation model. Specifically, a mode-switching Lagrange function is constructed, incorporating the three objective functions and constraints of the cooperative switching optimization allocation model. A mode-switching compensation multiplier term is introduced to improve constraint satisfaction accuracy. First-order partial derivatives of this function with respect to the distributed control input, Lagrange multipliers, and mode-switching compensation multipliers are calculated, and all partial derivatives are set to zero, resulting in a system of linear equations containing relevant variables. This system of equations is solved using distributed Gaussian elimination, with an accuracy threshold of 1e-6. The number of iterations during mode switching is limited to no more than 30 to ensure the timely switching response meets the real-time requirements of emergency reconnaissance and allows for timely acquisition of target information. The thrust and tilt angle values ​​of each distributed rotor unit are extracted from the solution results and used as the optimal switching control input. These inputs are then converted into distributed control commands and sent to each rotor execution node, enabling each rotor to precisely execute control actions. During reconnaissance flights, real-time data on mode switching is collected, and control parameters are dynamically adjusted to cope with changes in mountain airflow and terrain. Simultaneously, the optimal switching control input is fed back to the six-degree-of-freedom distributed dynamics model to update the initial parameters, forming a closed-loop control mechanism. This enables smooth switching between different flight modes, such as vertical take-off and landing (mountain take-off and landing point) → transitional flight mode (over low hills) → horizontal cruise mode (plain area reconnaissance) → transitional flight mode (approaching valley area) → vertical take-off and landing mode (valley reconnaissance point). This ensures that the UAV can stably complete emergency reconnaissance missions in complex mountainous environments and provides reliable data support for emergency decision-making.

[0043] In summary, this embodiment is adapted to the emergency reconnaissance needs in complex mountainous terrain and airflow environments, strictly adhering to a five-step vector allocation method. The data acquisition phase specifically eliminates altitude and turbulence interference; the model construction fully matches the multi-mode aerodynamic characteristics of mountainous terrain; the decoupling framework balances obstacle avoidance and attitude stability requirements in different modes; the optimized model incorporates mountain flight constraints; and the dynamic execution phase rapidly responds to terrain and mission changes. The entire process achieves smooth switching between modes through closed-loop control, extending the UAV's endurance for long-range reconnaissance while ensuring flight stability and data continuity, thus enabling the UAV to efficiently complete emergency reconnaissance missions in mountainous environments.

[0044] A tilt-type unmanned aerial vehicle (UAV) mode switching control system based on distributed decoupling includes: The data acquisition module is used to execute step S1; The preprocessing module is used to perform step S2; The dynamic model building module is used to execute step S3; The model output module is used to execute step S4; The collaborative optimization model construction module is used to execute step S5; The final instruction output module is used to execute step S6.

[0045] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0046] A tilt-type UAV mode switching control device based on distributed decoupling: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the tilting UAV mode switching control method based on distributed decoupling as described above.

[0047] The content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0048] A storage medium storing processor-executable instructions, which, when executed by a processor, are used to implement the distributed decoupling-based tilt-type UAV mode switching control method described above.

[0049] The content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0050] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A tilt-type unmanned aerial vehicle (UAV) mode switching control method based on distributed decoupling, characterized in that, Includes the following steps: Acquire parameters and status data of the distributed tiltrotor UAV to obtain the collected data; Based on the collected data, a six-degree-of-freedom distributed dynamic model is constructed using a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm. Based on the aforementioned six-degree-of-freedom distributed dynamics model, a distributed control input matrix and net control demand parameters for mode switching are generated, and a control input-target allocation matrix is ​​constructed. The allocation matrix is ​​processed by a distributed aerodynamically sensitive weighted zero-space decoupling algorithm to generate a mode switching decoupling projection matrix and divide the position control subspace and attitude control subspace under different modes. Based on the mode switching decoupling projection matrix and the six-degree-of-freedom distributed dynamics model, and combined with preset constraints, the cooperative optimization allocation model is solved to generate the optimal switching control input.

2. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 1, characterized in that, Also includes: The collected data is then filtered for noise reduction and subjected to cross-node synchronization calibration.

3. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 1, characterized in that, The parameters of the distributed tiltrotor UAV include inherent parameters of the distributed hardware and mode switching determination parameters. The status data includes multi-mode real-time flight status data and airspeed data, wherein: The inherent parameters of the distributed hardware include distributed rotor layout parameters, mass and moment of inertia of each rotor unit, communication delay parameters of distributed control nodes, and rotor installation position matrix. The mode switching determination parameters include flight speed threshold, attitude angle threshold, and mission requirement trigger signal. The state data includes position in the inertial coordinate system, Euler angles of each mode attitude, airframe angular velocity, and attitude synchronization data of the distributed rotor unit.

4. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 3, characterized in that, The step of constructing a six-degree-of-freedom distributed dynamic model based on the acquired data and using a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm specifically includes: Based on the inherent parameters of the distributed hardware and the real-time flight status data of multiple modes, the distributed inertial matrix, Coriolis-centrifugal force distributed matrix, gravity vector and distributed control input matrix that depend on the mode are calculated. Combining the airspeed data and the Euler angles of each mode attitude, the attitude-airspeed-mode coupled aerodynamic damping matrix, attitude-airspeed-mode coupled aerodynamic gain matrix, and distributed aerodynamic force vector under the current mode condition are calculated using the multi-mode aerodynamic model calibrated by wind tunnel test for vertical take-off and landing mode, transition flight mode, and horizontal cruise mode, respectively. The distributed inertial matrix, the Coriolis-centrifugal force distributed matrix, the gravity vector, the distributed control input matrix, and the attitude-airspeed-mode coupled aerodynamic damping matrix, attitude-airspeed-mode coupled aerodynamic gain matrix, and the distributed aerodynamic force vector in the vertical takeoff and landing mode, transition flight mode, and horizontal cruise mode are substituted into the mathematical expression of the unified algorithm for distributed attitude-speed-mode coupled aerodynamic feedforward dynamics to obtain a six-degree-of-freedom distributed dynamic model.

5. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 4, characterized in that, The mathematical expression for the distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm is as follows: in, To unify the state vector, The first derivative of the unified state vector, , The position vector in the inertial coordinate system. Let Euler angles be the attitude vectors. For the distributed rotor unit angular velocity synchronization vector, This represents the transpose operation. It is the first derivative of the attitude Euler angle vector. For pattern identifier parameters, For pattern-dependent distributed inertia matrix, For the Coriolis-centrifugal force distributed matrix, This is the attitude-airspeed-mode coupled aerodynamic damping matrix. The gravity vector For distributed control input matrix, For distributed control input vectors, This is the attitude-airspeed-mode coupled aerodynamic gain matrix. For Hadamard products, For distributed aerodynamic vectors, Indicates airspeed.

6. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 1, characterized in that, The mathematical expression of the distributed aerodynamically sensitive weighted null space decoupling algorithm is: in, Decouple the projection matrix for mode switching. This is a pattern-dependent distributed aerodynamic sensitivity weight matrix. Assign matrices to control inputs and targets for multi-mode adaptation. As a regularization factor, It is the identity matrix. This is a task priority weight matrix that depends on the mode. Vertical takeoff and landing mode prioritizes position control, horizontal cruise mode prioritizes attitude control, and transition mode balances priorities. This represents the transpose operation.

7. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 6, characterized in that, The constraints include: Physical constraints of the distributed rotor actuator: the thrust output range of each distributed rotor is 1N to 10N, and the tilt angle range is -30° to 30°; Mode switching stability constraints: During mode switching, the rate of change of attitude angle shall not exceed 5° / s, and the position fluctuation amplitude shall not exceed 0.5m; Distributed collaborative constraint: The delay difference of control commands between each distributed control node does not exceed 1ms.

8. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 6, characterized in that, The mathematical expression of the collaborative optimization allocation model is: s.t. in, This is a pattern-dependent dynamic task priority matrix. It is a distributed weighted matrix. Initial values ​​are input for distributed control. This is the energy consumption penalty coefficient. To switch the smoothness penalty coefficient, This is a pattern-dependent distributed aerodynamic energy consumption coupling matrix. The expected total control quantity for pattern-dependent operations. Let L be the square of the L2 norm of the vector. Decouple the projection matrix for mode switching. Assign matrices to control inputs and targets for multi-mode adaptation. This is the attitude-airspeed-mode coupled aerodynamic gain matrix. For distributed aerodynamic vectors, For Hadamard products, This represents the distributed control input difference vector between adjacent modes. Let Euler angles be the attitude vectors. Indicates airspeed. This is the pattern identifier parameter.

9. The tilt-type UAV mode switching control method based on distributed decoupling according to claim 1, characterized in that, The step of solving the cooperative handover optimization allocation model to generate the optimal handover control input specifically includes: Construct a mode-switching Lagrangian function, integrate the objective function and constraints into the function, and introduce a mode-switching compensation multiplier term; The first-order partial derivatives of the mode-switching Lagrange function with respect to the distributed control input, the Lagrange multiplier, and the mode-switching compensation multiplier are calculated, and all partial derivative results are set to zero to obtain a system of linear equations containing the distributed control input variable, the Lagrange multiplier variable, and the mode-switching compensation multiplier. The system of linear equations was solved using the distributed Gaussian elimination method, and the solution results were obtained. The thrust and tilt angle values ​​of each distributed rotor unit are extracted from the solution results and used as the optimal switching control input.

10. A tilt-type unmanned aerial vehicle (UAV) mode switching control system based on distributed decoupling, characterized in that, include: The data acquisition module is used to acquire the parameters and status data of the distributed tiltrotor UAV, and obtain the acquired data. The dynamic model construction module, based on the acquired data, uses a distributed attitude-velocity-mode coupled aerodynamic feedforward dynamics unified algorithm to construct a six-degree-of-freedom distributed dynamic model; The model output module, based on the six-degree-of-freedom distributed dynamics model, generates a distributed control input matrix and mode-switching net control requirement parameters, and constructs a control input-target allocation matrix; it applies a distributed aerodynamically sensitive weighted null space decoupling algorithm to process the allocation matrix, generates a mode-switching decoupling projection matrix, and divides the position control subspace and attitude control subspace under different modes; The collaborative optimization model construction module constructs a collaborative switching optimization allocation model based on the mode switching decoupling projection matrix and the six-degree-of-freedom distributed dynamics model, combined with preset constraints and collaborative optimization methods. The final instruction output module is used to solve the cooperative handover optimization allocation model and generate the optimal handover control input.

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