A UAV path tracking control method based on chaos encryption and disturbance observation

Abstract

This application provides a method for unmanned aerial vehicle (UAV) path tracking control based on chaotic encryption and disturbance observation, belonging to the fields of UAV control, data encryption, and anti-interference technology. The method includes: encrypting the aircraft's measurement data using a key stream, which is obtained through logistic map chaotic nested encryption, switching chaotic systems, and randomly distributing key stream coefficients; transmitting the encrypted measurement data to the aircraft controller for decryption via a communication interface; generating control instructions based on the decrypted measurement data; and combining a chaotic encryption algorithm with a disturbance observer control strategy to address the dual requirements of secure measurement data transmission and anti-interference control for VTOL-FW in dynamic flight scenarios.

Description

Technical Field

[0001] The present application belongs to the field of UAV control, data encryption and anti-interference technology, and specifically relates to a UAV path tracking control method based on chaotic encryption and disturbance observation. Background Art

[0002] Vertical take-off and landing fixed-wing aircraft (VTOL-FW) offer broad application prospects in military reconnaissance, logistics, disaster monitoring, and other fields, owing to their combination of vertical take-off and landing flexibility and fixed-wing high-speed cruising capabilities. However, operating in complex and dynamic environments (such as low-altitude urban areas, weather interference, and communication jamming) presents the following core challenges:

[0003] Aircraft rely on high-precision sensors (attitude angle, acceleration, position, etc.) to provide real-time feedback of status information. However, traditional communication protocols (such as encryption algorithms based on AES-256) have high computational latency (usually >50ms) and static key distribution that is easily reverse engineered. This allows attackers to induce actuator saturation by tampering with or forging measurement data, causing flight loss or even crashes.

[0004] During the mode switching phase, the aerodynamic characteristics of the aircraft change dramatically, and the coupling effect between external disturbances (crosswind, sensor noise) and the physical limits of the actuator (rudder deflection angle, thrust vector adjustment) is significant. Existing control algorithms (such as PID control) are difficult to balance robustness and real-time performance, resulting in increased path tracking errors and even system instability.

[0005] Existing technologies often design the encryption module and the control module independently, without considering the correlation between the dynamic state of the aircraft (such as communication frequency hopping and topology reorganization) and the key distribution and control law adjustment, resulting in a disconnect between the security architecture and control performance. Summary of the Invention

[0006] In order to solve at least one technical problem existing in the background technology, the present application provides a UAV path tracking control method based on chaos encryption and disturbance observation.

[0007] The technical solutions adopted in this application are:

[0008] The first embodiment of the present application provides a UAV path tracking control method based on chaotic encryption and disturbance observation, comprising:

[0009] The aircraft's measurement data is encrypted using a key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and random distribution of key stream coefficients.

[0010] Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface;

[0011] Generate control instructions based on the decrypted measurement data.

[0012] According to one embodiment of the present application, the encryption of the aircraft's measurement data by the key stream is specifically as follows:

[0013] Input the measurement data into the Logistic Map chaotic system to generate preliminary encrypted data;

[0014] According to the preset switching strategy, the Lorenz chaotic system or the Chen chaotic system is selected to generate the key stream, and the primary encrypted data is re-encrypted with the randomly assigned key stream coefficient to generate ciphertext measurement data;

[0015] Generate key stream coefficients through a seed random number generator, and distribute the key stream coefficients and chaotic system parameters to the decryption end;

[0016] During the aircraft mode switching phase, the switching strategy and key stream coefficient of the chaotic system are dynamically adjusted according to the flight status.

[0017] According to one embodiment of the present application, the transmitting of the encrypted measurement data to the aircraft controller for decryption based on the communication interface is specifically as follows:

[0018] Synchronize the switching strategy and Logistic Map parameters of the chaotic system according to the decryption key;

[0019] The plaintext measurement data is recovered by subtracting the key stream.

[0020] According to one embodiment of the present application, the seed random number generator generates key stream coefficients based on real-time status parameters of the aircraft to ensure that the key is bound to the flight phase.

[0021] According to one embodiment of the present application, the switching strategy dynamically adjusts the switching period of the switching chaotic system based on the communication frequency hopping and topology reorganization of the aircraft.

[0022] According to one embodiment of the present application, the generating of the control instruction according to the decrypted measurement data is specifically as follows:

[0023] Estimate external disturbances and introduce them into the control law;

[0024] Design the anti-saturation state feedback control law for the aircraft controller;

[0025] The proportional and integral terms are used to reduce steady-state errors and suppress disturbances.

[0026] According to one embodiment of the present application, estimating the external disturbance and introducing the external disturbance into the control law is specifically as follows:

[0027] Construct an extended system dynamic model with disturbances as unknown inputs;

[0028] Design the disturbance observer gain matrix so that the observation error converges to zero;

[0029] The disturbance estimate is output and fed back to the control law.

[0030] According to one embodiment of the present application, the key stream coefficient and the disturbance estimate value share the same seed random number generator;

[0031] The controller adjusts the sampling period of the disturbance observer according to the dynamic update frequency of the encryption end.

[0032] A second aspect of the present application provides a computer-readable storage medium having a program stored thereon, which implements the steps in the method when executed by a processor.

[0033] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the method when executing the program.

[0034] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows:

[0035] This application performs preliminary chaotic encryption on plaintext measurement data, leveraging the initial value sensitivity of the Logistic Map to break up data features, significantly increasing the difficulty of cracking. By dynamically switching between the Lorenz / Chen / Rossler chaotic systems (based on a preset strategy) and randomly distributing key stream coefficients (generated dynamically by a seeded random number generator), the private key length is extended and the key space complexity is increased, making it difficult for attackers to crack the key through static analysis or exhaustive analysis.

[0036] This application is based on the dynamic update of key stream coefficients and chaotic system parameters to ensure that encrypted data can still maintain low-latency transmission (<50ms) during the aircraft mode switching phase (such as hovering to level flight), avoiding attitude instability due to encryption delay.

[0037] This application expands the system dynamic model to incorporate estimates of external disturbances (such as crosswind and sensor noise) into the control law, dynamically adjusting control commands to reduce path tracking error. The nonlinear characteristics of the hyperbolic tangent (tanh) function are exploited to design an actuator anti-saturation state feedback control law. This limits the output range of control commands and prevents the actuator from entering a physical limit state due to erroneous measurement data (such as tampered attitude angles). Combining the proportional term (for rapid error response) with the integral term (for steady-state error elimination) further improves control accuracy and suppresses disturbance accumulation.

[0038] This application synchronizes the encryption module's key stream coefficients with the control module's disturbance observer sampling period through a seeded random number generator, ensuring dynamic adaptation of the encryption algorithm and control strategy when the aircraft's state changes (such as communication frequency hopping). The fully closed-loop design, from measurement data encryption to ciphertext transmission to decryption to control command generation, enables the aircraft to resist malicious attacks (such as replay attacks and data forgery) while maintaining stable flight performance in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 A flow chart of a UAV path tracking control method based on chaotic encryption and disturbance observation provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0042] Reference numerals:

[0043] 810 , processor; 820 , communication interface; 830 , memory; 840 , communication bus. DETAILED DESCRIPTION

[0044] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0045] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0046] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0047] Example 1

[0048] like Figure 1 As shown, the first embodiment of the present application provides a UAV path tracking control method based on chaos encryption and disturbance observation, comprising:

[0049] S100, encrypting the measurement data of the aircraft through a key stream, where the key stream is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and randomly distributing key stream coefficients.

[0050] As mentioned above, the aircraft's measurement data (such as attitude angle, velocity, acceleration, etc.) is protected through a complex encryption process. First, the plaintext measurement data is input into a Logistic Map chaotic system for initial encryption. Due to its high sensitivity to initial conditions, the Logistic Map can effectively break up the original data structure, providing a foundation for subsequent encryption. Next, according to a preset strategy, a Lorenz chaotic system or a Chen chaotic system is selected to generate a key stream. The initially encrypted data is then re-encrypted using randomly assigned key stream coefficients. This multi-level encryption mechanism not only increases the difficulty of cracking but also enhances encryption security.

[0051] For example, suppose an aircraft is currently in level flight, and its sensors collect a series of data on attitude angles. This data, as plaintext, first enters the Logistic Map module, where it is initially encrypted based on specific parameter settings, such as r = 3.99 (close to the chaotic region of the Logistic Map). Then, depending on the flight status (for example, whether a mode switch is in progress), the Lorenz or Chen chaotic system is dynamically selected for further encryption. If the Lorenz system is selected, the final encryption keystream is generated based on pre-set parameters σ, ρ, β and randomly generated keystream coefficients. This way, even if an attacker obtains part of the encrypted information, they cannot easily decrypt the original measurement data due to lack of knowledge of the specific chaotic system parameters and keystream coefficients.

[0052] It should be noted that in specific implementation scenarios, in addition to the Lorenz and Chen chaotic systems, the Rossler chaotic system or other new chaotic models can be introduced on the basis of the above scheme to enhance the complexity and randomness of the key stream and further improve the security of the encryption algorithm.

[0053] In a specific implementation scenario, based on the above solution, the generation rules of the key stream coefficients can be dynamically adjusted according to the real-time working environment of the aircraft (such as communication frequency hopping, flight status changes, etc.), so that the key stream is more in line with the needs of actual application scenarios, thereby improving the flexibility and security of the entire encryption system.

[0054] In specific implementation scenarios, based on the above scheme, it is also possible to consider combining this encryption method with other existing security measures, such as digital signatures, hash functions, etc., to form a multi-level security protection system to ensure the integrity and non-repudiation of aircraft measurement data during transmission.

[0055] In specific implementation scenarios, based on the above scheme, the strength of the encryption algorithm can be automatically adjusted according to the importance of the flight mission or the level of threat faced (such as changing the parameter r value of the Logistic Map or the parameters of the chaotic system) to achieve effective resource utilization and maximize security.

[0056] S200: Transmit the encrypted measurement data to the aircraft controller for decryption based on the communication interface.

[0057] As mentioned above, the aircraft's measurement data first undergoes an encryption process to generate ciphertext. This encrypted measurement data is then securely transmitted to the aircraft's controller via a communication interface module for decryption. The design of the communication interface ensures secure and real-time data transmission. It not only supports different communication protocols (such as Wi-Fi, Bluetooth, or dedicated short-range communications), but also dynamically adjusts communication parameters based on the aircraft's operating status to accommodate varying flight environments and mission requirements. Once the encrypted measurement data arrives at the controller, the decryption module reverse-processes the ciphertext based on pre-assigned keystream coefficients and chaotic system parameters to recover the original measurement data for use in the subsequent path-following control algorithm.

[0058] For example, suppose an aircraft is conducting a low-altitude urban patrol mission. During this process, its sensors collect data on the aircraft's attitude angles. This data is encrypted into ciphertext and transmitted to the aircraft's main controller via a pre-defined communication interface (such as an encrypted Wi-Fi signal). When the ciphertext measurement data arrives at the controller, the decryption module begins its operation. First, the decryption module synchronizes the chaotic system's switching strategy and logistic map parameters based on the received key stream coefficients. It then uses this information to accurately reverse the encryption process, ultimately recovering the original attitude angle data. This prevents attackers from easily obtaining or tampering with the aircraft's real measurement data, even in the presence of potential threats in the network environment.

[0059] It should be noted that in specific implementation scenarios, in addition to basic encrypted transmission, additional security measures can be introduced on the basis of the above solution, such as adding data integrity verification mechanisms (such as hash verification) and identity authentication technologies (such as digital certificates) at the communication interface level, thereby further improving the security of data transmission.

[0060] In specific implementation scenarios, based on the above solution, it is also possible to develop a technology that can automatically select the best communication method according to the environment in which the aircraft is located. For example, in high-interference areas, communication protocols with strong anti-interference capabilities are given priority; or according to the current network load, intelligently switch to a more effective communication channel to ensure the stability and efficiency of data transmission.

[0061] In specific implementation scenarios, based on the above solution, during the measurement data processing stage after decryption, it is possible to consider combining information from other sources (such as instructions from ground stations, satellite positioning data, etc.) to improve the aircraft's state perception capability and decision-making accuracy through advanced data fusion algorithms.

[0062] In a specific implementation scenario, a real-time monitoring system can be established based on the above solution to monitor the quality of the communication link and any abnormalities in the decryption process, and to respond quickly (such as automatically retransmitting lost data packets or adjusting communication parameters) to ensure the reliability of the entire data transmission and decryption process.

[0063] S300: Generate a control instruction according to the decrypted measurement data.

[0064] As mentioned above, the aircraft's measurement data is encrypted, transmitted, and decrypted before being restored to its original state. This data includes, but is not limited to, key parameters such as attitude angle, velocity, and acceleration. The controller receives and processes this data, calculating the required control commands using a pre-designed algorithm (such as a disturbance observer combined with a hyperbolic tangent function for anti-saturation path tracking control). This process first estimates external disturbances (such as crosswind or sensor noise) and then uses this information to adjust the control law to compensate for the effects of the disturbance and prevent actuator saturation. The resulting control commands are used to adjust the aircraft's attitude, thrust, and other parameters to ensure stable flight along the intended path.

[0065] For example, suppose an aircraft is transitioning from hover to level flight. During this transition, sensors collect data on the current attitude angle and velocity. This data is encrypted and securely transmitted to the aircraft controller via a communication interface. After receiving the decrypted data, the controller runs a disturbance observer to estimate the crosswind effects in the current environment. Based on this estimate and the aircraft's current state information (such as attitude angle and velocity), the controller uses a PI controller with proportional and integral terms to calculate the necessary correction commands. Furthermore, to prevent actuator saturation due to excessive control commands, a hyperbolic tangent function is used to limit the control commands. This allows the aircraft to smoothly transition to level flight without loss of control, even in the presence of large external disturbances.

[0066] It should be noted that, in specific implementation scenarios, the above solution could be further developed to dynamically adjust control parameters based on the aircraft's real-time status (e.g., battery charge level, payload weight changes, etc.). For example, when the aircraft approaches its maximum payload, the system could automatically reduce control gains to reduce actuator workload and extend equipment life.

[0067] In specific implementation scenarios, the above approach can be combined with a multi-objective optimization algorithm to ensure that control instructions not only ensure the aircraft follows the intended path but also meet other performance criteria, such as minimizing energy consumption or maximizing flight stability. This approach is particularly suitable for missions requiring long cruising periods.

[0068] In specific implementation scenarios, based on the above scheme, for multi-aircraft formation flight missions, it is possible to consider developing a collaborative control mechanism so that each aircraft not only relies on its own measurement data to generate control instructions, but also refers to information from neighboring aircraft to make collaborative decisions, thereby improving the coordination and robustness of the entire formation.

[0069] In specific implementation scenarios, the aforementioned solutions can be combined with historical data and machine learning to predict the health of aircraft components and issue early warnings for maintenance. This can be achieved by analyzing trends in control commands. For example, if a particular actuator frequently approaches its physical limit, it may indicate the need for inspection or replacement.

[0070] In some embodiments of the present application, the encryption of the aircraft's measurement data by the key stream is specifically as follows:

[0071] Input the measurement data into the Logistic Map chaotic system to generate preliminary encrypted data;

[0072] According to the preset switching strategy, the Lorenz chaotic system or the Chen chaotic system is selected to generate the key stream, and the primary encrypted data is re-encrypted with the randomly assigned key stream coefficient to generate ciphertext measurement data;

[0073] Generate key stream coefficients through a seed random number generator, and distribute the key stream coefficients and chaotic system parameters to the decryption end;

[0074] During the aircraft mode switching phase, the switching strategy and key stream coefficient of the chaotic system are dynamically adjusted according to the flight status.

[0075] As mentioned above, the measurement data generated by the aircraft (such as attitude angle and velocity) is input into a Logistic Map chaotic system. The Logistic Map is a simple yet effective chaotic system characterized by its high dependence on changes in initial conditions. By adjusting the parameters in the Logistic Map (such as the r value), a sequence that appears random but is actually determined by the initial conditions can be generated. This sequence is used to initially encrypt the original measurement data, thereby breaking up the original data structure and increasing the difficulty of cracking.

[0076] Based on the initial encryption, a Lorenz chaotic system or a Chen chaotic system is selected to generate the key stream according to a preset switching strategy. These two chaotic systems each have unique dynamic characteristics, capable of generating complex chaotic sequences. Combined with randomly assigned key stream coefficients, these chaotic sequences are used to further encrypt the initially encrypted data, generating the final ciphertext measurement data. This multi-level encryption mechanism not only increases encryption strength but also enhances system security.

[0077] To ensure the decryption end can accurately recover the original data, the keystream coefficients and chaotic system parameters must be securely distributed to the decryption end. Here, a seeded random number generator is used to generate the keystream coefficients and transmit them along with the chaotic system parameters. This approach not only extends the private key length but also increases the complexity of the key space, thereby enhancing the security of the entire encryption system. Furthermore, by dynamically updating the keystream coefficients, it can adapt to varying flight states, ensuring that the key is bound to the flight phase.

[0078] During the aircraft mode transition phase (for example, from hover to level flight or vice versa), the aircraft's state changes significantly. To adapt to these changes and maintain encryption effectiveness, the system dynamically adjusts the chaotic system switching strategy and keystream coefficients based on the current flight state. This means that different chaotic system combinations and keystream coefficients may be used in different flight phases to address various potential security threats. This flexibility ensures that the encryption scheme remains efficient and reliable even in highly dynamic environments.

[0079] In some embodiments of the present application, the transmitting of the encrypted measurement data to the aircraft controller for decryption based on the communication interface is specifically as follows:

[0080] Synchronize the switching strategy and Logistic Map parameters of the chaotic system according to the decryption key;

[0081] The plaintext measurement data is recovered by subtracting the key stream.

[0082] As mentioned above, to correctly decrypt the received ciphertext measurement data, the aircraft controller must first recover the chaotic system switching strategy used in the encryption process and the initial parameters of the Logistic Map based on the pre-agreed decryption key information. Specifically, the controller parses the key stream coefficients and chaotic system parameters distributed from the encryption end and, based on these, reconstructs the Lorenz or Chen chaotic system structure used to generate the key stream. It also sets the initial conditions of the Logistic Map (such as the initial value and the r parameter). Only when the encryption and decryption ends are fully synchronized can the subsequent decryption process be accurately guaranteed.

[0083] Once the chaotic system's switching strategy and logistic map parameters are synchronized, the controller regenerates the corresponding keystream sequence using the same process as the encryption end. This keystream sequence is then used to perform reverse operations on the transmitted ciphertext measurement data. Specifically, by subtracting the keystream from the ciphertext data, the original plaintext measurement data is gradually restored. This process is the inverse of the encryption process and requires the decryption end to have the same time synchronization mechanism and parameter consistency as the encryption end to avoid decryption failures due to minor deviations.

[0084] In some embodiments of the present application, the seed random number generator generates key stream coefficients based on real-time state parameters of the aircraft to ensure that the key is bound to the flight phase.

[0085] As mentioned above, during flight, an aircraft continuously generates various real-time state parameters, including but not limited to attitude angle, velocity, acceleration, and thrust vector direction. These state parameters reflect the aircraft's current dynamic characteristics and the flight environment (e.g., the presence of crosswind interference). The seeded random number generator first needs to obtain these real-time state parameters from the aircraft's sensor system.

[0086] The seeded random number generator uses the real-time state parameters collected above as input and generates keystream coefficients using a specific algorithm. This specific algorithm can be a pseudo-random number generator (PRNG), which dynamically adjusts the output random sequence based on the input state parameters. Because the aircraft's state parameters can vary significantly between different flight phases (for example, the attitude angle and speed during hovering differ significantly from those during level flight), the generated keystream coefficients will also change accordingly, thus achieving a close binding between the keystream coefficients and the flight phase.

[0087] The generated keystream coefficients are then used in the keystream generation step of the encryption process. Specifically, based on the initially encrypted data, the keystream coefficients are combined with a selected chaotic system (such as a Lorenz or Chen chaotic system) to generate the final keystream used to encrypt the plaintext measurement data. Because the keystream coefficients are dynamically updated as the aircraft's state changes, the encryption scheme is highly flexible and adaptable, maintaining high security even during different phases of the same flight mission.

[0088] To ensure the decryption end can correctly restore the original data, the generated keystream coefficients must be securely distributed to the decryption end. This is typically accomplished through a pre-established secure communication channel, ensuring that the keystream coefficients are synchronized between the encryption and decryption ends. Furthermore, given that aircraft state parameters may frequently change during different flight phases, the keystream coefficients must be dynamically updated and promptly synchronized to the decryption end to maintain consistency and reliability throughout the encryption-decryption process.

[0089] In some embodiments of the present application, the switching strategy dynamically adjusts the switching period of the switching chaotic system based on the communication frequency hopping and topology reorganization of the aircraft.

[0090] As mentioned above, during flight, aircraft may experience varying communication environments or network conditions, which can cause changes in their communication frequencies (so-called frequency hopping). Furthermore, when aircraft participate in multi-aircraft collaborative missions, the fleet's topology may also restructure due to changes in the relative positions of individual aircraft. To adapt to these changes and maintain secure and stable data transmission, the system needs to monitor these factors in real time.

[0091] When a change in communication frequency or a topology reshuffle is detected, the system evaluates the impact of these changes on the current encryption scheme. For example, an increase in communication frequency may require higher encryption processing speeds to avoid latency, while a reshuffled topology may lead to changes in security requirements between certain nodes. Based on these evaluation results, the system can determine whether key parameters in the encryption algorithm, such as the switching period of the chaotic system, need to be adjusted.

[0092] Based on this evaluation, the system dynamically adjusts the switching strategy for the chaotic system used to generate the key stream. Specifically, this involves choosing when to switch from one chaotic system (such as the Lorenz system) to another (such as the Chen system) and how to adjust the switching interval. This ensures the effectiveness and security of the encryption algorithm even in highly dynamic environments. For example, during high-frequency communication, the switching cycle of the chaotic system can be shortened to allow for more frequent key stream updates, thereby enhancing resistance to attacks. Furthermore, when the topology changes, the network layout can be reconfigured to determine which aircraft should use stronger encryption measures.

[0093] To ensure the decryption side can correctly interpret the received data, any changes to the switching strategy must be synchronized with the decryption side. This means that whenever the encryption side makes adjustments based on frequency fluctuations or topology reconfigurations, it sends the new switching strategy and corresponding keystream coefficients to the decryption side via a secure channel. This allows the decryption side to perform decryption operations based on the latest parameter settings, ensuring consistency and reliability throughout the encryption-decryption process.

[0094] In some embodiments of the present application, the control instruction is generated according to the decrypted measurement data, specifically:

[0095] Estimate external disturbances and introduce them into the control law;

[0096] Design the anti-saturation state feedback control law for the aircraft controller;

[0097] The proportional and integral terms are used to reduce steady-state errors and suppress disturbances.

[0098] As mentioned above, after obtaining the decrypted measurement data, the aircraft's control system first needs to estimate possible external disturbances. These disturbances can include environmental factors such as crosswinds and airflow changes, or they may arise from sensor noise or other non-ideal conditions. To achieve this, the system uses a disturbance observer, which estimates the current external disturbance in real time based on the measurement data and the system's dynamic model. Once the disturbance is accurately estimated, this information is incorporated into the control law so that the impact of these disturbances can be taken into account when generating control commands, thereby improving the robustness of the system.

[0099] To prevent actuator saturation (i.e., actuator output reaching its physical limits) due to excessive control commands, a state-feedback control law is designed to combat saturation. This control law typically incorporates a hyperbolic tangent function (tanh) to limit the size of control commands. For example, after calculating a preliminary control command, the hyperbolic tangent function is applied to constrain it to a reasonable range. This ensures that even in extreme situations, the control command will not exceed the actuator's capabilities, thus avoiding system instability or loss of control due to actuator saturation.

[0100] The core of the control system is a proportional-integral (PI) controller. This controller uses the proportional term to quickly respond to the current error and the integral term to gradually eliminate any remaining steady-state error. Specifically, the proportional term acts directly on the current error value, providing an immediate correction signal that enables the aircraft to quickly respond to deviations; while the integral term accumulates all past errors, gradually reducing or even completely eliminating long-standing deviations. This combination not only facilitates rapid adjustments to the aircraft's attitude or speed to track the intended path, but also effectively suppresses the impact of various disturbances on system performance, ensuring stable operation of the aircraft in complex environments.

[0101] In some embodiments of the present application, the estimating of the external disturbance and the introduction of the external disturbance into the control law are specifically as follows:

[0102] Construct an extended system dynamic model with disturbances as unknown inputs;

[0103] Design the disturbance observer gain matrix so that the observation error converges to zero;

[0104] The disturbance estimate is output and fed back to the control law.

[0105] As mentioned above, the first step is to build an extended system dynamics model for the aircraft. This model not only includes the aircraft's own dynamic equations (such as attitude angle and velocity), but also incorporates external disturbances as unknown inputs. For example, when describing the aircraft's motion, a state equation typically represents the aircraft's velocity, position, and their rate of change. By introducing additional terms in these equations to account for the effects of external disturbances (such as crosswind or airflow variations), the model can more accurately reflect the dynamic characteristics under actual flight conditions.

[0106] To estimate these external disturbances from the measured data, a disturbance observer needs to be designed. The core of this observer is a gain matrix, whose purpose is to make the observation error (i.e., the difference between the actual disturbance and the estimated disturbance) converge to zero as quickly as possible. Specifically, by analyzing the extended system dynamic model, appropriate gain values ​​are selected to adjust the behavior of the observer. The ideal gain matrix should be able to quickly and accurately track changes in the disturbance without affecting the stability of the system. This usually involves mathematical tools and techniques such as pole placement or linear matrix inequality (LMI) methods to optimize the design of the gain matrix.

[0107] When the disturbance observer successfully runs, it outputs real-time estimates of external disturbances. These estimates are then fed back into the control law to modify the original control instructions. For example, if a significant crosswind is detected, the control system can appropriately adjust the aircraft's attitude angle or thrust vector based on the disturbance estimate to compensate for the deviation caused by the crosswind. In this way, the aircraft can maintain a stable flight path even in the presence of significant external disturbances, reducing the risk of performance degradation or loss of control due to uncompensated disturbances.

[0108] In some embodiments of the present application, the key stream coefficients and the disturbance estimate value share the same seed random number generator;

[0109] The controller adjusts the sampling period of the disturbance observer according to the dynamic update frequency of the encryption end.

[0110] As mentioned above, to ensure consistency and synchronization between the encryption and control modules, a single seeded random number generator is used to generate the key parameters required for the keystream coefficients and disturbance estimates. This seeded random number generator produces a series of pseudorandom numbers based on the vehicle's real-time state parameters (such as attitude angle and velocity). These pseudorandom numbers are used not only to generate the keystream coefficients during encryption but also for certain computational steps in the disturbance observer, such as initializing or adjusting the gain matrix.

[0111] By using the same seeded random number generator, the keystream coefficients between the encryption and decryption ends can be dynamically updated and synchronized. Simultaneously, on the control side, using the same seeded random number generator as part of the disturbance observer input improves the accuracy and robustness of disturbance estimation. This design not only simplifies the overall system architecture but also enhances data transmission security and the consistency of control decisions.

[0112] The encryption module must dynamically update keystream coefficients based on the aircraft's state to maintain high security. Therefore, the encryption side has a specific dynamic update frequency, which is typically determined by the aircraft's current operating environment and the rate of change it experiences (such as communication frequency hopping and topology reconfiguration). The controller needs to monitor this update frequency in real time and adjust the operating parameters of its internal components accordingly.

[0113] After receiving dynamic updates from the encryption side, the controller adjusts the disturbance observer's sampling period accordingly. Specifically, when the encryption side's update frequency increases (indicating a more dynamic or complex environment), the controller also increases the disturbance observer's sampling frequency to more frequently acquire the latest measurement data and estimate disturbances. Conversely, if the encryption side's update frequency is lower, the disturbance observer's sampling frequency can be appropriately reduced to conserve computing resources.

[0114] This method of adjusting the disturbance observer sampling period based on the encryption end's update frequency enables the control system to more flexibly respond to varying flight conditions. It not only improves the system's responsiveness to rapidly changing environments, but also ensures that resources are not overconsumed under relatively stable conditions. Furthermore, through this collaborative mechanism, the entire system strikes a fine balance between safety and performance, ensuring stable operation of the aircraft in both high- and low-dynamic environments.

[0115] The second aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the cigarette box image recognition method of any of the first aspect embodiments is implemented.

[0116] Figure 2 An example of a physical structure diagram of an electronic device is shown below. Figure 2 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the method in any embodiment of the first aspect above, the method including:

[0117] The aircraft's measurement data is encrypted using a key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and random distribution of key stream coefficients.

[0118] Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface;

[0119] Generate control instructions based on the decrypted measurement data.

[0120] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as standalone products, stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (such as a personal computer, server, or network device) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories, random access memories, magnetic disks, or optical disks.

[0121] On the other hand, the present invention further provides a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the method provided by each of the above methods, including:

[0122] The aircraft's measurement data is encrypted using a key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and random distribution of key stream coefficients.

[0123] Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface;

[0124] Generate control instructions based on the decrypted measurement data.

[0125] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the method provided by the above methods, the method comprising:

[0126] The aircraft's measurement data is encrypted using a key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and random distribution of key stream coefficients.

[0127] Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface;

[0128] Generate control instructions based on the decrypted measurement data.

[0129] Finally, the present invention also provides a non-volatile computer storage medium having computer executable instructions stored thereon. When the computer program is executed by a processor, the method provided by the above methods is implemented, and the method includes:

[0130] The aircraft's measurement data is encrypted using a key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems, and random distribution of key stream coefficients.

[0131] Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface;

[0132] Generate control instructions based on the decrypted measurement data.

[0133] Example 2

[0134] The encrypted and decrypted model of the vertical take-off and landing fixed-wing aircraft:

[0135] A vertical take-off and landing fixed-wing aircraft is an innovative aircraft that combines the vertical take-off and landing capabilities of a multi-rotor with the long endurance and high-speed flight characteristics of a fixed wing. Its core design achieves vertical take-off and landing through a multi-rotor or tilt-rotor power system, and then switches to fixed-wing mode for efficient cruising. For example, the VOYAGER drone uses 16 vertical motors and 4 cruise motors. It can take off and land without a runway, and can fly continuously for more than 1 hour at a speed of 200 kilometers per hour. Its endurance far exceeds that of traditional multi-rotor drones. This type of aircraft is widely used in traffic supervision, oil field inspections, large-area mapping and other fields. Its load capacity and wind resistance further broaden the application scenarios. The state equation of a classic vertical take-off and landing fixed-wing aircraft is as follows:

[0136]

[0137] in, It's quality, is the acceleration due to gravity, Represent the angular velocity of the three axes respectively, Represents the translational velocity of the three axes, and the inertia matrix of the body is:

[0138]

[0139] Each element in the inertia matrix, such as , represents the moment of inertia in the corresponding direction;

[0140] : represents the moment of inertia of an object when it rotates about the x-axis. It measures the object's ability to resist rotation about the x-axis.

[0141] : The moment of inertia of an object when it rotates about the y-axis. It measures the object's ability to resist rotation about the y-axis.

[0142] : Represents the moment of inertia of an object when it rotates about the z-axis. It measures the object's ability to resist rotation about the z-axis.

[0143] Off-diagonal elements (products of inertia):

[0144] = : Represents the moment of inertia of an object between the x and y axes. It reflects the asymmetry of the object's mass distribution about the x and y axes.

[0145] = : represents the moment of inertia of an object between the x and z axes. It reflects the asymmetry of the object's mass distribution about the x and z axes.

[0146] = : represents the moment of inertia of an object between the y and z axes. It reflects the asymmetry of the object's mass distribution about the y and z axes.

[0147] Represents the combined force in three directions; Represents the resultant moment in three directions; , , Represent the rotor forces , , The components in three directions, , , Represents the fixed wing forces , , The components in three directions, , , They represent the torque on the rotor respectively. , , The components in three directions, , , They represent the moments acting on the fixed wing in , , Components in three directions.

[0148] Typically, a vertical take-off and landing fixed-wing aircraft is characterized by 12 states to characterize its dynamic and kinematic models, namely , representing the positions in three directions respectively Speed ​​in three directions , attitude angles in three directions and three angular velocities . Because the states of vertical take-off and landing fixed-wing aircraft are coupled with each other. The dynamic system of vertical take-off and landing fixed-wing aircraft has strong nonlinear characteristics and is affected by factors such as aerodynamic changes, large-scale attitude adjustments and coupling of propulsion systems. Designing a controller directly for a complete nonlinear model usually faces problems such as complex modeling, difficult analysis and difficult control laws to solve. In order to simplify the controller design, it is possible to choose to linearize the system near a specific operating point related to the flight mission. Through operating point linearization, a local linear approximate model can be obtained, which enables the application of traditional linear control theory (such as pole configuration, optimal control, robust control, etc.), thereby greatly reducing the complexity of controller design and stability analysis, while facilitating efficient engineering deployment and performance verification. Therefore, after the operating point linearization, the linear system description is as follows:

[0149]

[0150] in, is the system status, is the derivative of the state, is the control input,

[0151] is the system state matrix,

[0152] is the system input matrix, It's quality, is the acceleration due to gravity.

[0153] The output equation of the system is:

[0154]

[0155] in, is the system output matrix, is the system output. Note that It is the output before encryption.

[0156] This scheme defines the encryption function as , R is the set of real numbers, 6 is the dimension of the space, that is,

[0157]

[0158] and the decryption function ,Right now

[0159]

[0160] in, is the ciphertext output, is the decrypted output.

[0161] This scheme assumes that the decryption error is 0, and considers that the vertical take-off and landing fixed-wing aircraft actuator needs to be limited. In addition, the problem studied in this scheme considers bounded external interference Therefore, it is necessary to study the actuator saturation problem and the anti-disturbance controller design problem. In this problem, the linearization equation of the vertical take-off and landing fixed-wing aircraft is changed to:

[0162]

[0163] The saturation function is expressed as follows:

[0164]

[0165] in, is the upper bound of the saturated input, is a sign function.

[0166] This solution uses the hyperbolic tangent function to limit the amplitude of the state control input. This solution defines a function with smooth characteristics:

[0167]

[0168] Then, the saturation function can be expressed as:

[0169]

[0170] is a saturated function, the approximation error Is a bounded unknown function. Assume that the upper bound is ,but The linearized equation of the vertical take-off and landing fixed-wing aircraft is transformed into:

[0171]

[0172] For the convenience of description, this scheme defines bounded uncertainty as:

[0173]

[0174] Then the linearized equation of the vertical take-off and landing fixed-wing aircraft is transformed into:

[0175]

[0176] because Is a vector, where the hyperbolic tangent operation is performed on each dimension of the input element.

[0177] Saturation control algorithm based on hyperbolic tangent of disturbance observer:

[0178] This solution considers the problem of aircraft path tracking and defines the tracking path as , then the tracking error is defined as:

[0179]

[0180] in, is the tracking error;

[0181] Introducing integration error:

[0182]

[0183] in, is the integral error, τ is the integral variable, and the goal is to design a saturation tracking controller Make and We define the extended state variable . Its dynamic equation is:

[0184]

[0185] in For the tracking error, we can derive both sides at the same time:

[0186]

[0187] Therefore, the extended system dynamics is expressed as:

[0188]

[0189] Simplified to:

[0190]

[0191] in, , , .

[0192] In view of the extended system dynamics, this scheme designs the anti-disturbance control law as follows:

[0193]

[0194] in, is the integral gain matrix, is the proportional gain matrix, is a feedforward term, and the feedforward term is designed as:

[0195]

[0196] According to the aircraft model, the input matrix is full column rank, so the input matrix can be Calculate the left inverse, that is .

[0197] Therefore, Substituting into the expanded system we get:

[0198]

[0199] After expansion, we get:

[0200]

[0201] because and yes Therefore, the above formula can be changed to:

[0202]

[0203] Design the compensator as:

[0204]

[0205] Then the expanded variable equation is:

[0206]

[0207] in ,and is an estimate of uncertainty.

[0208] The uncertainty observer designed in this scheme is as follows:

[0209]

[0210] in, The state of the observer system, is the derivative of the state, and the observation error dynamic equation is:

[0211]

[0212] in Is a constant greater than 0. The above formula is simplified to:

[0213]

[0214] Then the saturated disturbance rejection tracking controller is designed as:

[0215]

[0216]

[0217]

[0218] The symbol arcth = arctanh. Then we get the tracking error converges asymptotically and is robust to disturbances. In the process of adjusting the control system parameters, as long as the control gain is selected Make The real part of the eigenvalue of is less than 0. Stability proves that the tracking error converges asymptotically, and increasing the integral gain can effectively reduce the steady-state error.

[0219] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0220] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0221] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present application.

Claims

1. A UAV path tracking control method based on chaos encryption and disturbance observation, characterized in that: include: The measurement data of the aircraft is encrypted through the key stream, which is obtained through Logistic map chaotic nested encryption, switching chaotic systems and random distribution of key stream coefficients. Specifically: Input the measurement data into the Logistic Map chaotic system to generate preliminary encrypted data; According to the preset switching strategy, the Lorenz chaotic system or the Chen chaotic system is selected to generate the key stream, and the primary encrypted data is re-encrypted with the randomly assigned key stream coefficient to generate ciphertext measurement data; Generate key stream coefficients through a seed random number generator, and distribute the key stream coefficients and chaotic system parameters to the decryption end; During the aircraft mode switching phase, the switching strategy and key stream coefficient of the chaotic system are dynamically adjusted according to the flight status; The key stream coefficient and the disturbance estimate value share the same seed random number generator; The controller adjusts the sampling period of the disturbance observer according to the dynamic update frequency of the encryption end; Transmitting the encrypted measurement data to the aircraft controller for decryption based on the communication interface; Generate control instructions based on the decrypted measurement data.

2. The method according to claim 1, characterized in that The encrypted measurement data is transmitted to the aircraft controller for decryption based on the communication interface, specifically: Synchronize the switching strategy and Logistic Map parameters of the chaotic system according to the decryption key; The plaintext measurement data is recovered by subtracting the key stream.

3. The method according to claim 1, characterized in that The seed random number generator generates key stream coefficients based on the real-time state parameters of the aircraft to ensure that the key is bound to the flight phase.

4. The method according to claim 1, wherein The switching strategy dynamically adjusts the switching period of the switching chaotic system based on the communication frequency hopping and topology reorganization of the aircraft.

5. The method according to claim 1, wherein The control instructions are generated according to the decrypted measurement data, specifically: Estimate external disturbances and introduce them into the control law; Design the anti-saturation state feedback control law for the aircraft controller; The proportional and integral terms are used to reduce steady-state errors and suppress disturbances.

6. The method according to claim 5, characterized in that The external disturbance is estimated and introduced into the control law, specifically: Construct an extended system dynamic model with disturbances as unknown inputs; Design the disturbance observer gain matrix so that the observation error converges to zero; The disturbance estimate is output and fed back to the control law.

7. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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