A control method of a continuous and rapid delivery system of unmanned transport vehicles

Abstract

The application provides a control method of a continuous and rapid delivery system of an unmanned transport machine, relates to the unmanned transport machine delivery control technical field, and solves the problem that the continuous and rapid delivery system is difficult to control the unmanned transport machine. In the method, aircraft control model parameters of the unmanned transport machine and loading scheme data of the delivery system are acquired, a preset center of gravity safety limit range is combined, a continuous and rapid delivery timing strategy is determined, and the continuous and rapid delivery timing strategy includes unlocking sequences and time parameters of each delivery system; according to the continuous and rapid delivery timing strategy, control instructions are output to unlocking mechanisms of corresponding delivery systems in a cargo compartment of the unmanned transport machine, and unlocking is sequentially performed according to a preset timing; a delivery system cabin separation state is monitored in real time, a cabin separation signal is acquired, and flight control parameters of the unmanned transport machine are dynamically updated. The application improves flight stability and anti-interference ability of the unmanned transport machine in air drop operations, and realizes the unity of air drop efficiency and flight safety.

Description

Technical Field

[0001] This invention relates to the field of unmanned transport aircraft delivery control technology, specifically to a control method for a continuous and rapid delivery system of an unmanned transport aircraft. Background Technology

[0002] In applications requiring stringent timeliness and reliability, such as emergency relief and long-distance material delivery, medium and large-sized unmanned transport aircraft have become crucial technological platforms for airdrop missions due to their outstanding payload capacity, wide operating range, and strong environmental adaptability. To meet comprehensive economic demands such as high payload capacity, high space utilization, controllable costs, and simplified system structure, current mainstream platforms generally adopt a delivery system configuration of centralized loading within the cabin and unified delivery from the tail. This configuration effectively integrates internal space resources, simplifies the design of the delivery mechanism, reduces manufacturing and maintenance complexity, and provides fundamental support for the efficient transfer of large-scale materials.

[0003] The choice of airdrop strategy directly impacts mission efficiency and platform operational safety. While single-item sequential delivery can control the disturbance to the center of gravity within a small range, helping to maintain short-term flight stability, it significantly extends the operation cycle and reduces delivery efficiency per unit time. In real-world environments with tight emergency response windows, this not only easily leads to delays in the delivery of critical materials but also increases the platform's exposure time in complex airspace, causing hidden risks and overall costs to rise simultaneously, thus limiting its practical application value. Although continuous rapid delivery strategies can significantly improve the delivery rate of materials and shorten the overall mission time, the continuous movement and release of multiple heavy delivery units along the longitudinal direction of the fuselage from front to back within a very short period of time causes a sharp and continuous shift in the total mass and center of gravity of the unmanned transport aircraft, severely testing the dynamic adjustment capabilities of the flight control system.

[0004] Current unmanned transport aircraft platforms still lag behind manned aircraft in terms of control precision, sensor feedback timeliness, and actuation response sensitivity. Faced with sudden center-of-gravity disturbances induced by continuous deployments, their flight control systems are prone to issues such as command generation delays, insufficient correction amplitude, or oscillations during adjustment, making it difficult to effectively suppress abnormal fluctuations in attitude parameters in a timely manner. If the disturbance exceeds the system's compensation capability limits, it may cause critical flight states such as pitch and roll to diverge continuously, seriously threatening the platform's structural safety and mission continuity. Existing technological approaches often focus on optimizing single aspects, either sacrificing efficiency for safety or prioritizing speed over stability, failing to establish a dynamic coordination mechanism between deployment actions and flight control.

[0005] Therefore, exploring a technical approach that balances continuous and rapid delivery efficiency with flight safety within the inherent control capabilities of unmanned transport aircraft has become a core issue restricting its practical application. The industry urgently needs to break through the limitations of traditional control logic and develop new methods that can adapt to dynamic changes in the center of gravity and enhance system robustness, providing crucial support for the reliable application of unmanned transport aircraft in demanding airdrop missions. Summary of the Invention

[0006] The purpose of this invention is to address the significant challenges in controlling continuous rapid delivery systems for unmanned transport aircraft. Therefore, a control method for such systems is proposed. This invention employs strategies such as pre-setting an aircraft control model, a center of gravity limitation range, and a delivery system loading scheme. It intelligently calculates the continuous rapid delivery sequence of each delivery system, and precisely and safely controls the unlocking mechanism of the delivery systems within the cargo hold based on the calculated sequence. Finally, sensors in the tail section of the unmanned transport aircraft detect the delivery system exiting the cargo hold, sending an exit signal to the intelligent control computer. The computer then adjusts the attitude control strategy based on changes in the aircraft's weight and center of gravity, achieving the advantages of a continuous rapid delivery system.

[0007] The present invention employs the following technical solutions to achieve its objective: A control method for a continuous and rapid delivery system of an unmanned transport aircraft includes the following steps: S1. Obtain the aircraft control model parameters of the unmanned transport aircraft, which are used to characterize the flight dynamics and control characteristics of the unmanned transport aircraft; simultaneously obtain the loading scheme data of the delivery system in this mission. S2. Based on the aircraft control model parameters, the loading scheme data, and the preset center of gravity safety limit range, determine the continuous rapid delivery timing strategy; the continuous rapid delivery timing strategy includes the unlocking sequence and time parameters of each delivery system; S3. Based on the continuous rapid delivery timing strategy, output control commands to the unlocking mechanisms of the corresponding delivery systems in the cargo hold of the unmanned transport aircraft, and control each unlocking mechanism to perform unlocking actions in sequence according to the preset timing. S4. After the unlocking action is performed, the corresponding delivery system leaves the cabin; the departure status of the delivery system is monitored in real time and the departure signal is obtained, and the flight control parameters of the unmanned transport aircraft are dynamically updated based on the departure signal.

[0008] Specifically, in step S1, the aircraft control model parameters include physical aerodynamic parameters, inner loop control parameters, and outer loop control parameters; The physical aerodynamic parameters include at least one of the following: stability derivative, control effectiveness derivative, moment of inertia, mass, wingspan, mean aerodynamic chord length, and engine thrust curve. The inner loop control parameters include at least one of proportional gain, integral gain, derivative gain, and attitude loop gain; The outer loop control parameters include at least one of the following: guidance parameters, position gain, and airspeed gain.

[0009] Specifically, in step S1, obtaining the aircraft control model parameters of the unmanned transport aircraft includes: loading the aircraft control model parameters into the onboard intelligent control computer of the unmanned transport aircraft through hardware preloading; and simultaneously obtaining the loading scheme data of the delivery system in this mission includes: uploading the loading scheme data corresponding to this mission to the onboard intelligent control computer through ground station command and control software.

[0010] Specifically, in step S1, the loading scheme data of the loading system includes the type, dimensions, weight, and distribution of the loading system in each cargo compartment within the cargo hold. The type includes standard loading systems and non-standard loading systems.

[0011] Preferably, in step S2, the process of determining the continuous rapid delivery timing strategy specifically includes: calculating and generating multiple candidate delivery timing strategies with different safety margins based on the aircraft control model parameters, the load planning data, and the center of gravity safety limit range; transmitting the multiple candidate delivery timing strategies to the ground station in real time via an airborne data link; and receiving a confirmation instruction returned by the ground station, wherein the confirmation instruction designates one of the candidate delivery timing strategies as the continuous rapid delivery timing strategy.

[0012] Preferably, in step S2, the multiple candidate delivery timing strategies with different safety margins each have a corresponding delivery efficiency level. The configuration range of the delivery efficiency level is 50% to 100%, and the delivery efficiency level is used to characterize the density of delivery actions completed per unit time.

[0013] Specifically, in step S3, the unlocking mechanism is an electromagnet-type unlocking mechanism; when outputting control commands to the unlocking mechanism, the control commands are DC signals directly output by the onboard intelligent control computer of the unmanned transport aircraft, and the DC signals are connected to the drive end of the electromagnet-type unlocking mechanism via a dedicated control line.

[0014] Specifically, in step S4, the real-time monitoring of the cargo delivery system's departure status is performed using a diffuse reflection infrared sensor located in the exit area of ​​the tail section of the unmanned transport aircraft; the departure signal is a discrete electrical signal in the ground / open state, which is processed by the signal detection circuit and then transmitted to the onboard intelligent control computer of the unmanned transport aircraft.

[0015] Specifically, in step S4, the flight control parameters of the unmanned transport aircraft are dynamically updated based on the departure signal, including: identifying the number and location of the delivery systems that have left the aircraft based on the departure signal; calculating the current total weight and center of gravity coordinates of the unmanned transport aircraft in real time based on the identification results; and inputting the calculation results into the flight control rate to adjust the control parameters in the attitude control loop of the unmanned transport aircraft.

[0016] Preferably, before or during step S1 of acquiring the aircraft control model parameters, the method further includes: presetting the center of gravity safety limit range in the onboard intelligent control computer of the unmanned transport aircraft; the center of gravity safety limit range is determined comprehensively based on the flight envelope, structural strength constraints and aerodynamic stability boundaries of the unmanned transport aircraft, and the presetting process is configured and updated online through a ground station.

[0017] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: The method of this invention constructs a collaborative calculation method for the aircraft control model and the loading scheme, and combines it with real-time feedback of the off-board status to form dynamic adjustment. This effectively suppresses attitude disturbances caused by continuous shifts in the center of gravity during continuous and rapid deployment, significantly improves the flight stability and anti-interference capability of the unmanned transport aircraft in airdrop operations, and provides reliable safety assurance for the entire airdrop process.

[0018] In the method of this invention, the generation of a continuous and rapid delivery timing strategy with multiple safety margins is combined with the confirmation process by ground operators. This allows the delivery rhythm to be flexibly optimized according to the actual needs of the mission. Under the premise of adhering to the center of gravity safety boundary, the delivery potential of the unmanned transport aircraft platform is fully released. This avoids the extended operation cycle caused by single-item delivery and avoids the control risks caused by disordered continuous delivery, thus achieving a balance between airdrop efficiency and flight safety.

[0019] The method of this invention has good overall task adaptability and environmental robustness, and can be compatible with the loading requirements of delivery systems of different specifications and types. It also enhances the flexibility of strategy deployment through parameter preloading and online configuration mechanisms. The various control links are closely connected and respond in a timely manner, which significantly improves the operational reliability and task completion quality under complex weather and emergency scenarios, and establishes a reliable foundation for the large-scale application of unmanned transport aircraft in high-time-efficiency tasks such as disaster relief and long-distance resupply. Attached Figure Description

[0020] The present invention is described in detail with reference to the following figures, which specifically include one figure as follows: Figure 1 This is a schematic diagram illustrating the overall flow of the control method of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The parts of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] Example 1 A control method for a continuous and rapid delivery system for unmanned transport aircraft. Figure 1 The overall process of this method is briefly described below and can be viewed concurrently; the key steps of this method can be summarized as follows: S1. Obtain the aircraft control model parameters of the unmanned transport aircraft. The aircraft control model parameters are used to characterize the flight dynamics and control characteristics of the unmanned transport aircraft. Simultaneously, obtain the loading scheme data of the delivery system in this mission. S2. Based on the aircraft control model parameters, loading scheme data, and preset center of gravity safety limits, determine the continuous rapid delivery sequence strategy; the continuous rapid delivery sequence strategy includes the unlocking sequence and time parameters of each delivery system; S3. Based on the continuous and rapid delivery sequence strategy, output control commands to the unlocking mechanisms of the corresponding delivery systems in the cargo hold of the unmanned transport aircraft, and control each unlocking mechanism to perform unlocking actions in sequence according to the preset sequence. S4. After the unlocking action is performed, the corresponding delivery system leaves the cabin; the departure status of the delivery system is monitored in real time and the departure signal is obtained, and the flight control parameters of the unmanned transport aircraft are dynamically updated based on the departure signal.

[0024] In this embodiment, it is preferable to preload preset aircraft control model parameters into the onboard intelligent control computer based on the technical status of the unmanned transport aircraft through hardware preloading. These parameters mainly include physical aerodynamic parameters, inner-loop control parameters, and outer-loop control parameters. Subsequently, ground station mission operators load the loading scheme data of the delivery system into the unmanned transport aircraft through command and control software. This data mainly includes the type, dimensions, weight, and distribution of the delivery system within the cargo hold, as well as the location of each cargo slot within the cargo hold.

[0025] The onboard intelligent control computer of the unmanned transport aircraft calculates multiple continuous rapid delivery timing strategies with different margins, based on aircraft control model parameters, loading scheme data, and preset center of gravity safety limits, ensuring safe airdrop of the unmanned transport aircraft. The calculation results are transmitted in real-time to the ground station via the onboard data link for mission operators to select and confirm. After the ground station mission operators confirm the selection of a suitable continuous rapid delivery timing strategy, the continuous rapid delivery process is initiated once the unmanned transport aircraft reaches the airdrop target area.

[0026] The onboard intelligent control computer of the unmanned transport aircraft schedules the unlocking mechanisms of each delivery system in the cargo hold to unlock in sequence according to the selected continuous rapid delivery timing strategy. The tail section of the unmanned transport aircraft adopts a method of detecting the delivery system leaving the cargo hold through sensors. When the delivery system leaves the cargo hold, the sensor reports the departure signal to the onboard intelligent control computer, which can identify the changes in the weight and center of gravity of the unmanned transport aircraft, and then adjust the attitude control strategy in a synchronous manner to achieve safe control of the continuous rapid delivery system.

[0027] Example 2 Based on Example 1, this example provides a detailed optimization of each step in the control method of the unmanned transport aircraft's continuous and rapid delivery system.

[0028] In step S1 of this embodiment, the acquired aircraft control model parameters are a digital representation of the flight dynamics characteristics and control system behavior of the unmanned transport aircraft. In this embodiment, the unmanned transport aircraft uses a tail-drop method to construct the model. Such unmanned transport aircraft have strict requirements for aerodynamic stability and control response during airdrop operations. Therefore, the model parameters are pre-loaded into the onboard intelligent control computer to ensure parameter reliability and real-time access, providing a foundation for subsequent dynamic analysis of the center of gravity and generation of the drop strategy. In this embodiment, the aircraft control model parameters include physical aerodynamic parameters, inner-loop control parameters, and outer-loop control parameters.

[0029] Physical aerodynamic parameters are the inherent behavioral laws of unmanned transport aircraft in aerodynamic environments. In this embodiment, they include at least one of the following: stability derivative, control effectiveness derivative, moment of inertia, mass, wingspan, mean aerodynamic chord length, and engine thrust curve. The stability derivative describes the aircraft's inherent ability to recover equilibrium after being disturbed, and can include three sub-terms: lift curve slope, reflecting the sensitivity of lift to changes in angle of attack; pitch damping, reflecting the characteristics of the damping moment caused by pitch angular velocity; and directional static stability, characterizing the aerodynamic recovery tendency in the yaw direction. The control effectiveness derivative quantifies the actual effect of each control surface deflection on flight attitude, and can include two sub-terms: aileron roll effectiveness, relating aileron deflection to roll angular acceleration; and elevator pitch effectiveness, defining the contribution of elevator input to the pitch moment. Among the remaining parameters, moment of inertia characterizes the distribution of inertial drag when the airframe rotates around its longitudinal, transverse, and vertical axes; mass parameters determine the basis of gravity loads and inertial response; wingspan and mean aerodynamic chord, as key geometric features of the wing, directly affect lift distribution, aerodynamic center position, and longitudinal stability; and the engine thrust curve fully describes the thrust output characteristics of the engine under different throttle commands and flight conditions, providing a basis for dynamic response modeling. To optimize the aircraft control model parameters, all items in the physical aerodynamic parameters are used to jointly determine the aircraft's dynamic response prediction capability under sudden load changes.

[0030] The inner-loop control parameters act on the underlying attitude stabilization loop of the flight control system, directly determining the response of the control surface actuators to attitude commands. The inner-loop control parameters include at least one of the following: proportional gain, integral gain, derivative gain, and attitude loop gain. The proportional gain adjusts the linearity between the attitude error and the control output; the integral gain continuously eliminates steady-state deviations; the derivative gain suppresses overshoot and high-frequency oscillations during attitude changes; and the attitude loop gain comprehensively optimizes the response speed and damping characteristics of the roll, pitch, and yaw channels. In this embodiment, the inner-loop control parameters, calibrated through ground simulation and flight testing, ensure that the corresponding unmanned transport aircraft can quickly and smoothly maintain its target attitude when encountering gusts of wind or instantaneous changes in internal loads, thus providing flight safety during continuous deployment.

[0031] The outer-loop control parameters, located at the navigation and guidance level, are responsible for translating mission trajectory commands into executable attitude guidance signals for the inner loop. The outer-loop control parameters include at least one of the following: guidance parameters, position gain, and airspeed gain. Guidance parameters embody the core coefficients of a specific guidance algorithm; for example, in L1 nonlinear guidance, they correlate the look-ahead distance and convergence characteristics of trajectory tracking, while in the overall energy control system, they coordinate the energy allocation strategy for altitude and airspeed. Position gain adjusts the aircraft's sensitivity to horizontal position deviation corrections; airspeed gain affects the response strength and stability of airspeed command tracking. The outer-loop control parameters enable the unmanned transport aircraft to follow a preset route and dynamically adjust its flight trajectory based on real-time changes in the center of gravity during deployment, maintaining trajectory accuracy and energy status.

[0032] As a preferred embodiment, the acquisition of aircraft control model parameters employs a hardware pre-loading mechanism. This mechanism, during the factory commissioning or periodic maintenance phase of the unmanned transport aircraft, uses a dedicated programming interface to permanently store the verified, complete model parameters into the non-volatile storage unit of the onboard intelligent control computer. This method ensures that the parameters are strictly matched to the aircraft's physical characteristics, possessing reliable features such as anti-interference and prevention of accidental modification, and avoiding parameter loss or distortion due to communication link fluctuations or software anomalies during flight. Once written, the parameters become the inherent reference of the flight control system and are invoked at the start of each mission, significantly reducing the load on onboard computing resources during parameter initialization.

[0033] In step S1 of this embodiment, the acquisition of loading scheme data relies on ground station command and control software to achieve mission configuration. During the mission planning phase, the mission operator completes the identification of the type, weight entry, external dimension annotation, and slot coordinate setting of each delivery system in the cargo hold in the command and control software interface according to the actual delivery requirements. The software automatically generates a structured loading data package. Therefore, in this embodiment, the loading scheme data of the delivery system includes the type, external dimensions, weight, and distribution position of the delivery system in each cargo slot in the cargo hold. The types include standard delivery systems and non-standard delivery systems.

[0034] After integrity verification and encryption, the data packets generated by the command and control software are uploaded in real time to the dedicated task cache area of ​​the onboard intelligent control computer during the standby phase of the unmanned transport aircraft via the onboard data link, and are confirmed in conjunction with the pre-flight self-check process. In this embodiment, at the initialization moment of the continuous rapid deployment control process, the dual state verification and logical association of the pre-stored model parameters and the current mission payload data are automatically completed to ensure that the two are strictly aligned in terms of time and data logic.

[0035] In this embodiment, before the unmanned transport aircraft performs a continuous and rapid deployment mission, the onboard intelligent control computer needs to pre-configure the center of gravity safety limit range. This configuration operation can be completed independently before acquiring the aircraft control model parameters, or it can be implemented synchronously with the model parameter loading process to optimize the timing efficiency of the mission preparation process.

[0036] The center of gravity safety limit is a core constraint boundary for ensuring flight safety. Its definition integrates the flight envelope characteristics, structural strength limits, and aerodynamic stability requirements of the unmanned transport aircraft. The flight envelope clarifies the safe operating range of the aircraft under various speeds, altitudes, and maneuvers. Shifts in the center of gravity position directly affect the effective boundary of the envelope and the control margin. Structural strength constraints ensure that under instantaneous aerodynamic loads caused by sudden load changes, the stress levels of key structural components such as wings and cargo hold hardpoints remain within the allowable range of the materials. The aerodynamic stability boundary considers the decisive role of the center of gravity position in the aircraft's static stability margin, avoiding pitch control lag due to excessive forward shift of the center of gravity or pitch divergence due to excessive rearward shift. Similarly, after verification through flight mechanics simulation, structural load analysis, and flight test data, this limit comprehensively determines the three-dimensional permissible area of ​​the center of gravity in the aircraft's coordinate system, expressed as a dynamic range in the longitudinal and lateral coordinates, providing a rigid safety threshold for the subsequent deployment process.

[0037] In a preferred embodiment, in step S2, the airborne intelligent control computer intelligently generates a continuous rapid delivery sequence strategy based on the loaded aircraft control model parameters, the payload configuration data for this mission, and the preset center of gravity safety limit range. This process first analyzes the aircraft control model to deduce the attitude control capability boundary and dynamic response characteristics of the unmanned transport aircraft under the current payload configuration. It then combines the influence of the weight distribution of each delivery system, slot coordinates, and departure sequence on the center of gravity trajectory in the payload configuration, and comprehensively solves for the constraints within the center of gravity safety limit range. Under the premise of ensuring flight safety, the calculation process automatically generates a set of candidate delivery sequence strategies with differentiated safety margins. Each strategy corresponds to a specific delivery efficiency level, with a configuration range covering 50% to 100%.

[0038] In this embodiment, the delivery efficiency level essentially represents the density of unlocking and unloading actions completed per unit time. The higher the level value, the shorter the unlocking time interval between adjacent delivery systems, and the more compact and efficient the overall delivery rhythm. However, at the same time, the requirements for the instantaneous compensation capability of the flight control system are also increased, and the safety control margin is dynamically reduced, forming a quantitative trade-off between efficiency and safety.

[0039] The multi-level candidate strategies generated by the airborne intelligent control computer are transmitted encrypted via airborne data link to the ground station command and control terminal, where the expected center of gravity change curve, attitude disturbance amplitude, and safety margin assessment results for each strategy are presented in a visual format. Ground station mission operators make a comprehensive judgment based on real-time airspace environment, weather conditions, and mission urgency, and select the strategy level most suitable for the current flight status through the command and control software and issue a confirmation command.

[0040] Upon receiving the confirmation command, the onboard intelligent control computer locks the strategy as the continuous rapid delivery sequence strategy for this mission. This embodiment combines automated calculation with human decision-making, fully leveraging the onboard model's precise ability to analyze physical constraints while granting operators flexible discretion based on the situation on-site. By presetting multiple levels and supporting online strategy confirmation, it effectively avoids the limitations of a single fixed sequence in complex mission scenarios. While ensuring the unmanned transport aircraft remains within its center of gravity safety envelope throughout the entire mission, it achieves a dynamic optimal match between delivery efficiency and flight safety, significantly improving the reliability and mission flexibility of continuous airdrop operations.

[0041] In step S3 of this embodiment, the unlocking mechanism of each delivery system in the cargo hold adopts an electromagnet-type structure in the form of an electromagnetic hook. Its core working principle is as follows: when the drive end receives a DC signal, the electromagnetic coil is instantaneously energized to generate a strong magnetic field, which drives the armature to disengage the mechanical hook, thereby achieving a zero-delay separation between the delivery system and the cargo hold mounting point, and then ejecting from the tail section of the unmanned transport aircraft. Due to its simplified moving parts and lack of complex transmission links, this structure exhibits excellent reliability and long lifespan characteristics in aviation operating environments such as vibration and temperature changes. At the same time, it has a high response speed and low manufacturing and maintenance costs, meeting the dual requirements of high determinism of the actuator for continuous and rapid delivery and high economy of the UAV.

[0042] In this embodiment, to maximize control timeliness and signal fidelity, the airborne intelligent control computer integrates a dedicated drive circuit to directly generate and output a DC signal adapted to the operating voltage of the unlocking mechanism. This signal is then directly connected to the drive end of each electromagnetic hook via an independently laid shielded control line. This direct-drive architecture avoids the command delay, voltage drop, or signal distortion problems that may be caused by relaying through the airborne main power distribution module, ensuring that timing commands are accurately transmitted to the target mechanism. The dedicated line can employ twisted-pair shielding and redundant deployment of key nodes to effectively suppress the impact of electromagnetic interference and mechanical vibration during flight on signal integrity, providing a stable and reliable hardware path for high-density continuous unlocking.

[0043] After entering the continuous rapid delivery process, the onboard intelligent control computer, based on the confirmed timing strategy, uses a built-in timer and multi-channel output unit to drive the corresponding unlocking mechanisms sequentially according to the preset time intervals and slot order. The issuance time and duration of each command can be pre-rehearsed and verified to ensure that the unloading actions of adjacent delivery systems are closely linked in the time domain and do not cause control conflicts. The entire unlocking control process in this embodiment is executed autonomously by the onboard system without external intervention. This achieves efficient and intensive execution of delivery actions per unit time, and the rapid response and precise timing of the hardware ensure the coordination between the delivery actions and the dynamic adjustment of the flight control system. This serves as another foundation for the attitude stability and trajectory controllability of the unmanned transport aircraft during continuous payload release.

[0044] As a preferred embodiment, in step S4, a diffuse reflection infrared sensor is integrated into the exit area of ​​the tail section of the unmanned transport aircraft. Its working principle is based on the real-time comparison of the intensity of the infrared beam emission and the reflection intensity from the object's surface. When the delivery system passes through the detection area, the reflected signal undergoes a step change, and the sensor immediately outputs a discrete electrical signal in either grounded or on state. This signal is the departure signal, which is filtered, shaped, and level-adapted by a mature and highly interference-resistant dedicated signal detection circuit, forming a clear departure event identifier and transmitting it to the onboard intelligent control computer. In the application scenario of unmanned transport aircraft, this sensing method is lightweight, has a fast response, and simple output logic. Its discrete signal characteristics greatly simplify subsequent interpretation logic. Combined with shielded transmission lines, it effectively avoids electromagnetic disturbances in the flight environment, perfectly meeting the engineering requirements of unmanned transport aircraft for weight sensitivity and long-term stable operation with its high reliability and low system load characteristics.

[0045] Upon receiving the unloading signal, the onboard intelligent control computer immediately correlates the weight attributes and slot coordinates of each delivery system in the pre-stored loading plan to determine the quantity, type, and spatial location of the unloaded objects. It then reconstructs and calculates the current total weight and three-dimensional center of gravity coordinates of the unmanned transport aircraft in real time. This calculation result serves as a key state parameter, which is immediately injected into the flight control law core module to drive the dynamic adaptive adjustment of the attitude control strategy.

[0046] In this embodiment, a typical multi-dimensional collaborative optimization dynamic adjustment is preferably implemented as follows: For overall weight reduction, the elevator trim reference and engine thrust feedforward command are simultaneously corrected to ensure instantaneous balance between lift and gravity; for center of gravity coordinate shift, the parameters of the inner loop attitude control loop are finely adjusted, for example, when the center of gravity shifts longitudinally backward, the pitch channel ratio and differential gain are moderately increased to enhance damping and suppress oscillation tendency, while when the center of gravity shifts laterally, the coordinated gain distribution of the ailerons and rudder is optimized to maintain roll stability; simultaneously, the outer loop guidance layer also undergoes linkage correction, recalibrating the airspeed control loop gain and energy management coefficients in the guidance algorithm based on the updated mass characteristics to ensure smooth transition of trajectory tracking accuracy and flight energy state. Parameter adjustments can be implemented using a smooth, gradual algorithm in real-time to avoid abrupt changes in control commands, resulting in a continuous, gentle, yet timely transition in aircraft attitude response.

[0047] Example 3 Based on the above embodiments, this embodiment demonstrates the practical application of the control method for the continuous and rapid delivery system of unmanned transport aircraft through specific scenario examples.

[0048] In a high-altitude emergency supplies delivery mission, a twin-engine piston-powered unmanned transport aircraft with a maximum takeoff weight of 4950 kg was tasked with the continuous airdrop of four standard medium-sized cargo delivery systems. During the mission preparation phase, technicians used a ground maintenance interface to embed the flight-verified aircraft control model parameters into the onboard intelligent control computer. Simultaneously, based on the aircraft's flight envelope, structural strength, and aerodynamic stability boundaries, they configured the center of gravity safety limits in the ground station's control software and uploaded the data with encryption. Mission operators simultaneously entered the loading plan: four cargo delivery systems, each with dimensions of 600mm x 600mm x 950mm and a weight of 300 kg, were to be arranged in a straight line along the 4-meter-long cargo hold in four controllable cargo slots, with a total airdrop weight of 1200 kg. The data was verified and loaded into the onboard system in real time.

[0049] The airborne intelligent control computer integrates aircraft control model parameters, load plan data, and center of gravity safety limits to instantly generate three candidate delivery timing strategies, with a delivery effectiveness range of 50% to 100%. Specifically, the unlock times (corresponding to delivery timings) for the 100% effectiveness strategy are 0s, 0.2s, 0.5s, and 0.8s; for the 75% effectiveness strategy, they are 0s, 0.3s, 0.7s, and 1.2s; and for the 50% effectiveness strategy, they are 0s, 0.4s, 0.9s, and 1.6s. Each strategy includes center of gravity trajectory prediction and safety margin assessment results, which are transmitted to the ground station via data link. Mission operators, considering the impact of thin air on aerodynamic characteristics at high altitudes and mission timeliness requirements, select the 75% effectiveness strategy. This strategy ensures sufficient control margin while meeting rapid delivery requirements, and a confirmation command is issued. The airborne intelligent control computer then locks onto this plan and awaits entry into the target airspace.

[0050] When the unmanned transport aircraft smoothly arrived at the target area at an altitude of 3,500 meters, maintaining an airspeed of 120 km / h, the continuous delivery process officially began. At 0:00, the onboard intelligent control computer output a DC drive signal to the electromagnet unlocking mechanism corresponding to the first delivery system. The mechanism can complete the energization and unlocking within 0.05 seconds. The mechanical structure with a locking force greater than 500N ensures reliable ejection action during unlocking. At 0.3, 0.7, and 1.2 seconds, the subsequent three items unlocked sequentially, and the delivery system slid along the guide rail from the forward direction to the backward direction.

[0051] A diffuse infrared sensor is positioned 250mm inside the tailgate exit to capture real-time release dynamics. At the moment the first piece slides out, the sensor outputs a discrete signal to the onboard intelligent control computer, which immediately identifies the release status, updates the aircraft's total weight from 4950kg to 4650kg, and calculates the longitudinal rearward shift of the center of gravity. The flight control rate is then smoothly adjusted: the elevator trim reference is changed to compensate for lift changes, the pitch channel proportional gain is moderately increased to enhance damping and suppress oscillation tendencies, and the airspeed control loop parameters are simultaneously corrected to maintain energy balance. Each subsequent release triggers a similar response: the second piece weighs 4350kg, the third 4050kg, and the fourth 3750kg, with control parameters continuously and dynamically optimized. Throughout the flight, the aircraft's pitch angle fluctuation is controlled within ±0.4 degrees, the altitude deviation remains less than 2 meters, and the attitude transition is smooth and stable.

[0052] All four delivery systems safely departed within 1.2 seconds, with their parachutes deploying in an orderly manner. The unmanned transport aircraft, with adaptive parameter adjustments, quickly returned to its preset trajectory and continued the return-to-home procedure. Ground telemetry data confirmed that the center-of-gravity trajectory throughout the delivery process remained within the preset safety envelope, with an unlocking timing error of less than 10ms, and timely and reliable sensor responses. This embodiment integrates model-driven strategy generation, direct hardware execution, and dynamic control based on sensor feedback. While ensuring the flight safety of large unmanned transport aircraft, it efficiently achieves high-density continuous airdrop operations, providing engineering-ready technical support for accurate logistics support in complex environments.

Claims

1. A control method for a continuous and rapid delivery system for unmanned transport aircraft, characterized in that, Includes the following steps: S1. Obtain the aircraft control model parameters of the unmanned transport aircraft, which are used to characterize the flight dynamics and control characteristics of the unmanned transport aircraft; simultaneously obtain the loading scheme data of the delivery system in this mission. S2. Based on the aircraft control model parameters, the loading scheme data, and the preset center of gravity safety limit range, determine the continuous rapid delivery timing strategy; the continuous rapid delivery timing strategy includes the unlocking sequence and time parameters of each delivery system; S3. Based on the continuous rapid delivery timing strategy, output control commands to the unlocking mechanisms of the corresponding delivery systems in the cargo hold of the unmanned transport aircraft, and control each unlocking mechanism to perform unlocking actions in sequence according to the preset timing. S4. After the unlocking action is performed, the corresponding delivery system leaves the cabin; the departure status of the delivery system is monitored in real time and the departure signal is obtained, and the flight control parameters of the unmanned transport aircraft are dynamically updated based on the departure signal.

2. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 1, characterized in that: In step S1, the aircraft control model parameters include physical aerodynamic parameters, inner loop control parameters, and outer loop control parameters; The physical aerodynamic parameters include at least one of the following: stability derivative, control effectiveness derivative, moment of inertia, mass, wingspan, mean aerodynamic chord length, and engine thrust curve. The inner loop control parameters include at least one of proportional gain, integral gain, derivative gain, and attitude loop gain; The outer loop control parameters include at least one of the following: guidance parameters, position gain, and airspeed gain.

3. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 2, characterized in that, In step S1, the aircraft control model parameters of the unmanned transport aircraft are obtained, including: loading the aircraft control model parameters into the onboard intelligent control computer of the unmanned transport aircraft through hardware preloading; and the loading scheme data of the delivery system in this mission is obtained synchronously, including: uploading the loading scheme data corresponding to this mission to the onboard intelligent control computer through ground station command and control software.

4. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 1, characterized in that: In step S1, the loading scheme data of the loading system includes the type, dimensions, weight, and distribution of the loading system in each cargo compartment within the cargo hold. The type includes standard loading systems and non-standard loading systems.

5. The control method for the unmanned transport aircraft continuous and rapid delivery system according to claim 1, characterized in that, Step S2, the process of determining the continuous rapid delivery timing strategy, specifically includes: calculating and generating multiple candidate delivery timing strategies with different safety margins based on the aircraft control model parameters, the load planning data, and the center of gravity safety limit range; transmitting the multiple candidate delivery timing strategies to the ground station in real time via an airborne data link; receiving a confirmation instruction returned by the ground station, wherein the confirmation instruction designates one of the candidate delivery timing strategies as the continuous rapid delivery timing strategy.

6. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 5, characterized in that: In step S2, the candidate delivery timing strategies with different safety margins each have a corresponding delivery efficiency level. The configuration range of the delivery efficiency level is 50% to 100%, and the delivery efficiency level is used to characterize the density of delivery actions completed per unit time.

7. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 1, characterized in that: In step S3, the unlocking mechanism is an electromagnet-type unlocking mechanism; when outputting control commands to the unlocking mechanism, the control commands are DC signals directly output by the onboard intelligent control computer of the unmanned transport aircraft, and the DC signals are connected to the drive end of the electromagnet-type unlocking mechanism via a dedicated control line.

8. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 1, characterized in that: In step S4, the real-time monitoring of the cargo delivery system's departure status is performed using a diffuse reflection infrared sensor located in the exit area of ​​the tail section of the unmanned transport aircraft. The departure signal is a discrete electrical signal in a ground / open state, which is processed by the signal detection circuit and then transmitted to the onboard intelligent control computer of the unmanned transport aircraft.

9. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 8, characterized in that, In step S4, the flight control parameters of the unmanned transport aircraft are dynamically updated based on the departure signal, including: identifying the number and location of the delivery systems that have left the aircraft based on the departure signal; calculating the current total weight and center of gravity coordinates of the unmanned transport aircraft in real time based on the identification results; and inputting the calculation results into the flight control rate to adjust the control parameters in the attitude control loop of the unmanned transport aircraft.

10. The control method for the unmanned transport aircraft continuous rapid delivery system according to claim 1, characterized in that, Before or during step S1 of acquiring the aircraft control model parameters, the method further includes: presetting the center of gravity safety limit range in the onboard intelligent control computer of the unmanned transport aircraft; the center of gravity safety limit range is determined comprehensively based on the flight envelope, structural strength constraints and aerodynamic stability boundaries of the unmanned transport aircraft, and the presetting process is configured and updated online through a ground station.

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