A flight control method, an aircraft, an electronic device and a storage medium

Abstract

The application relates to the field of flight control technology, in particular to a flight control method, an aircraft, an electronic device and a storage medium, the method comprising: acquiring observation data of a sensor on an aircraft at a current time; predicting an aircraft state at the current time based on state estimation and state covariance updated at a previous iteration time; performing state updating on the aircraft state by using the observation data to obtain target state estimation at the current time; and outputting the target state estimation to a flight control system of the aircraft to guide navigation or control of the aircraft. The application constructs and solves a state updating process as a second-order cone programming problem with constraints, improves state estimation accuracy and system robustness without increasing calculation complexity, and effectively avoids false triggering of a fault redundancy mechanism.

Description

Technical Field

[0001] This application relates to the field of flight control technology, and in particular to a flight control method, an aircraft, electronic equipment, and a storage medium. Background Technology

[0002] The Extended Kalman Filter (EKF) has become the mainstream algorithm for accurate state estimation in modern flight control systems due to its ability to effectively handle nonlinear systems. In particular, the third-generation algorithm (EKF3) is widely used to correct the predicted state vector using sensor observation data, including two main steps: time update and measurement update.

[0003] However, existing general EKF3 algorithm frameworks typically do not incorporate the inherent physical and model constraints of the aircraft system (such as normalization constraints of attitude quaternions, non-negativity constraints of altitude and mass) into the calculation process when performing state updates. Existing technologies mainly verify the calculated state vector after the state update step is completed and forcibly truncate or correct state variables that do not conform to preset constraints.

[0004] This post-hoc forced correction method has significant drawbacks: on the one hand, it makes the final output state estimate not a statistically optimal solution based on all available information, causing the estimated state to deviate from the true state and reducing the overall accuracy; on the other hand, the forced correction state value may produce unreasonable differences with the subsequent sensor observations, which may easily trigger the system's fault detection and isolation (FDI) mechanism, causing the system to misjudge and reduce or even eliminate the credibility of normal sensors, thereby causing flight safety hazards. Summary of the Invention

[0005] The purpose of this invention is to address at least one deficiency in the existing technology and provide a flight control method, an aircraft, an electronic device, and a storage medium. This invention constructs and solves the state update process as a constrained second-order cone programming problem, thereby simultaneously improving the accuracy of state estimation and the robustness of the system without increasing computational complexity, and effectively avoids the false triggering of the fault redundancy mechanism.

[0006] To achieve the above objectives, in a first aspect, the present invention provides an aircraft navigation and control method applied to a flight control system, comprising the following steps: Acquire observation data from the sensors on the aircraft at the current moment; Based on the state estimate and state covariance updated in the previous iteration, the current state of the aircraft is predicted. Using the observation data, the state of the aircraft is updated to obtain the target state estimate at the current moment; The target state estimate is output to the flight control system of the aircraft for controlling the aircraft's navigation.

[0007] Furthermore, the step of updating the aircraft's state using the observed data to obtain the target state estimate at the current moment includes: The state update process is constructed as a constrained optimization problem, wherein the constraints include at least one physical constraint on the state variables of the flight control system. The optimization problem is transformed into a second-order cone programming problem. By solving the second-order cone programming problem, the target state estimate at the current moment that satisfies at least one physical constraint is obtained.

[0008] Furthermore, the method also includes: Obtain the state covariance at the current moment, which is obtained based on the state estimate and state covariance prediction updated at the previous iteration moment; The state covariance at the current time is updated based on the target state estimate and used for state prediction at the next iteration time.

[0009] Furthermore, the at least one physical constraint includes equality constraints and / or inequality constraints, wherein the equality constraints include the unit norm constraint of the quaternion used to describe the attitude, and the inequality constraints include the Earth's magnetic field strength constraint based on prior geographic information.

[0010] Furthermore, the objective function of the optimization problem is a robust estimation function based on Huber loss. The objective function is configured such that: when the state estimation error is not greater than a set threshold, the objective function penalizes the error based on a quadratic form; when the state estimation error is greater than the set threshold, the objective function penalizes the error based on a linear form, and the penalty is lower than that of the mean squared error.

[0011] Furthermore, the state estimation error is the difference between the current state estimate and the predicted state estimate.

[0012] Furthermore, the step of outputting the target state estimate to the flight control system of the aircraft for controlling the navigation of the aircraft includes: The target state estimate is output to the navigation filter of the flight control system; Based on the target state estimation, the navigation state of the aircraft is updated, the navigation state including position and velocity. At least one of degree and attitude; wherein the updated navigation state is used for the flight path planning or guidance of the aircraft.

[0013] Secondly, the present invention also provides an aircraft, comprising: The sensor array is used to acquire observational data of the aircraft at the current moment; The processing unit is configured to: predict based on the updated state estimate and state covariance from the previous iteration. The aircraft state at the current moment is determined; using the observation data acquired by the sensor group, the aircraft state is updated to obtain the target state estimate at the current moment. The flight control module receives the target state estimate output by the processing unit and generates control commands based on it to guide the navigation or control of the aircraft.

[0014] Thirdly, the present invention also provides an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

[0015] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the first aspect.

[0016] This invention embeds the physical constraints of the aircraft into the optimization process of state estimation, ensuring that the final output target state estimate conforms to physical laws from the outset. This significantly improves the accuracy and realism of the state estimation and avoids deviations introduced by post-processing forced corrections. Secondly, the constraint-embedded estimation method fundamentally avoids the situation where sensor fault redundancy mechanisms are erroneously triggered due to state values ​​violating physical constraints, effectively enhancing the reliability and robustness of the flight control system. Furthermore, since second-order cone programming is a convex optimization problem with efficient and stable solution algorithms, this invention introduces the aforementioned beneficial effects without incurring additional computational complexity, ensuring the algorithm's feasibility in real-time embedded platforms such as flight control systems. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a constrained extended Kalman filter method based on second-order cone programming according to Embodiment 1 of the present invention; Figure 2 Here is a block diagram of a constrained extended Kalman filter system based on second-order cone programming, as described in Embodiment 2 of this aspect. Figure 3 This is a comparison chart of the state estimation performance of the conventional extended Kalman filter and the constrained extended Kalman filter in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, 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 merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. It should be understood that the various steps described in the method embodiments of the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0020] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0024] Example 1 Please see Figure 1 A preferred embodiment of the present invention provides an aircraft navigation control method applied to a flight control system, comprising the following steps: S1: Acquire the observation data from the sensors on the aircraft at the current moment; S2: Based on the state estimate and state covariance updated in the previous iteration, predict the state of the aircraft at the current moment; S3: Using the observation data, update the state of the aircraft to obtain the target state estimate at the current moment; S4: Output the target state estimate to the flight control system of the aircraft for controlling the navigation of the aircraft.

[0025] This embodiment embeds the physical constraints of the aircraft into the state estimation optimization process, ensuring that the final output target state estimate conforms to physical laws from the outset. This significantly improves the accuracy and realism of the state estimation and avoids deviations introduced by post-processing forced corrections. Secondly, the constraint-embedded estimation method fundamentally avoids the situation where sensor fault redundancy mechanisms are erroneously triggered due to state values ​​violating physical constraints, effectively enhancing the reliability and robustness of the flight control system. Furthermore, since second-order cone programming is a convex optimization problem with efficient and stable solution algorithms, this invention introduces the aforementioned beneficial effects without incurring additional computational complexity, ensuring the algorithm's feasibility in real-time embedded platforms such as flight control systems.

[0026] In an optional embodiment, in step S1, the flight control system typically includes a processing unit and a sensor array connected thereto. The sensor array is used to collect observation data of the aircraft at the current moment. This observation data includes, but is not limited to, one or more of the following: angular velocity and acceleration data from an inertial measurement unit (IMU), geomagnetic field data from a magnetometer, position and velocity data from a Global Navigation Satellite System (GNSS) receiver, and altitude data from a barometer. This observation data serves as input for state updates in the method of the present invention. Furthermore, in the flight control system, the current moment refers to a discrete time series point driven by the clock or timer of the flight control computer. The algorithm executes cyclically with a fixed sampling period (e.g., 100Hz for IMU, 1Hz for GPS). The moment at the start of processing in each cycle is the current moment k. It is a logical time point determined by the system hardware and software scheduling.

[0027] In an optional embodiment, in step S2, the method of the present invention is executed cyclically with a preset sampling period. Let the current cycle period be time k, then the previous cycle period is time k-1. It can be understood that the current time refers to the sampling time point corresponding to time k. This step updates the state estimate based on the previous iteration time k-1. State covariance The method predicts the aircraft state at the current time k, where the state estimate is a mathematical representation of the aircraft's navigation state, and its state vector includes at least position, velocity, and attitude. The state covariance is a matrix used to quantify the uncertainty of each component in the state estimate and their interrelationship. It should be noted that the method requires initialization upon first execution (k=0), i.e., determining the initial state estimate and initial state covariance based on the first valid sensor data or preset values ​​to initiate the recursive prediction and update process. This step is further explained below. This step is based on the standard extended Kalman filter prediction process. Specifically, it is assumed that the state model and observation model of the flight control system are as follows:

[0028] Where, vector express The state of the flight control system at all times, the state including at least speed and position, and optionally angular velocity and attitude. represent The inputs to the flight control system include controls for the roll aileron deflection angle, the elevator deflection angle for the pitch angle, and the rudder deflection angle for the yaw angle. Optionally, it may also include throttle commands for controlling thrust. (Right now Follow the mean variance is Gaussian noise) and Represent Noise in the time-state model and noise in the observation model. Input to the system; linearize the observation model and calculate the observation matrix:

[0029] Based on the posterior state estimation of the system at time k-1 State covariance estimation Prediction is made using the above state equations: Predict the system state: = ; Predicted state covariance: = +

[0030] in, Is the state function f in The Jacobian matrix at position , and the posterior state estimate at the previous time step. The calculation is obtained at that point, i.e. | , It is the process noise covariance matrix.

[0031] In an optional embodiment, in step S3, this step utilizes the sensor observations at the current time k. This is the process of updating the predicted system state. Unlike traditional EKF which directly calculates the Kalman gain, this step introduces physical constraints and transforms them into an optimization problem to be solved, including: 1. The state update process is constructed as a constrained optimization problem, wherein the constraints include at least one physical constraint on the state variables of the flight control system; 2. Transform the optimization problem into a second-order cone programming problem; 3. By solving the second-order cone programming problem, the target state estimate at the current moment that satisfies at least one physical constraint is obtained.

[0032] This embodiment constructs the state update process as a constrained optimization problem and transforms it into a second-order cone programming problem for solution, achieving embedded satisfaction of physical constraints. This ensures that the target state estimation fundamentally conforms to the physical laws of the system, improving estimation accuracy; at the same time, it avoids the false triggering of fault detection mechanisms due to state constraint violations, enhancing system reliability. Furthermore, second-order cone programming, as a convex optimization problem, has an efficient and stable solution algorithm, allowing this improvement to enhance performance without significantly increasing computational burden, making it suitable for real-time embedded systems such as flight control systems.

[0033] Furthermore, the physical constraints include equality constraints and / or inequality constraints. In this embodiment, the equality constraints include the unit norm constraint of the quaternion used to describe the attitude. The inequality constraints include the Earth's magnetic field strength constraint based on prior geographic information. Specifically, this embodiment uses two typical constraints as examples: 1. Hard Constraint (Equality Constraint): Identify that the quaternion states describing the aircraft's attitude must satisfy the unit norm constraint. This constraint can be expressed as:

[0034] Furthermore, this constraint can be expressed as a partial-state 2-norm constraint, i.e.:

[0035] in, From the state vector The matrix from which quaternion components are extracted. This constraint is a hard constraint that must be strictly followed.

[0036] 2. Soft constraints (inequality constraints): Identify the Earth's magnetic field state components (Mag) N Mag E Mag D The geomagnetic field strength constraint must be met. The total magnetic field strength at a known geographical location must be obtained from the World Geomagnetic Model (WMM). This constraint can be expressed as:

[0037] This soft constraint can be written as:

[0038] Furthermore, this constraint can be written as a constraint on state variables, that is:

[0039] This constraint is a soft constraint that allows for minor violations.

[0040] After obtaining the soft and hard constraints, the state update process of the flight control system is constructed as an optimization problem containing the aforementioned soft and hard constraints. The objective function of this optimization problem is consistent with the traditional EKF, which is to minimize the mean square error of the posterior estimate, and the expression is as follows:

[0041] Specifically, this can be achieved by finding an optimal gain matrix. To determine the mean squared error of the posterior estimate, the objective function is equivalent to minimizing the posterior covariance matrix. Before explaining the improved EKF algorithm proposed in this invention, the traditional EKF algorithm will first be described. The explanation of the traditional algorithm is based on the following two considerations: First, the traditional algorithm constitutes the technical foundation for the improvements made in this invention, and its steps and formulas are necessary prerequisites for understanding the starting point and technical contributions of this invention; second, by clarifying the specific methods used by the traditional algorithm in handling system state constraints (such as forced normalization and measurement rejection logic), its limitations can be clearly revealed, thus providing a clear technical problem background and innovative motivation for introducing a systematic constraint optimization method in this invention. The following section will specifically explain the process of the traditional EKF algorithm and its typical constraint handling methods.

[0042] 1. Initialization: Given the initial state estimate and covariance, as follows: ,

[0043] 2. Prediction (1) State prediction: = +

[0044] (2) Covariance prediction: = +

[0045] 3. Update (1) Innovation (Residual): = -

[0046] (2) Innovation Covariance: =

[0047] (3) Kalman gain: =

[0048] (4) Status update: =

[0049] (5) Covariance update: =(I- )

[0050] At this point, the state update steps of the traditional EKF algorithm are:

[0051] That is, the first Estimated state of the system at time t Not only with Predicted state of the system at time k It is also related to the system information. related.

[0052] As mentioned above, the objective function is equivalent to minimizing the posterior covariance matrix. The trace is represented as follows:

[0053] The traditional EKF algorithm, by neglecting both hard and soft constraints, yields a gain matrix that is less efficient in this case. as follows:

[0054] In the traditional EKF algorithm, constraints are ignored during the update step, and then operations such as forced normalization are used to handle hard constraints. For example, for the quaternion unit norm constraint describing the pose, the normalization operation is as follows:

[0055] Regarding the state of Earth's magnetic field, Mag N Mag E Mag D For the soft constraints between them, EKF uses a simple rejection logic, that is, if the difference between the sensor reading and the expected geomagnetic field strength is too large, the sensor reading will be rejected or downweighted.

[0056] Therefore, to avoid additional computational complexity and enhance the reliability and robustness of the flight control system, this embodiment improves the traditional EKF algorithm, specifically including the following steps: (1). Sensor data acquisition: The flight controller acquires the observation data of the IMU unit at the current time k through the sensor interface, and forms the observation vector. .

[0057] (2). State prediction step, based on the posterior state estimate updated at time k-1. State covariance estimation , Through the system's nonlinear state equations Make a prediction to obtain the prior state estimate at the current time k. and prior state covariance , where k .

[0058] (3). Constrained state update step. This step utilizes the sensor observations at the current time k. This is the process of updating the predicted system state. Its core lies in combining a robust objective function with the system's hard and soft constraints, and transforming it into a convex optimization problem for solution. Specifically, S3.1: This embodiment constructs a robust optimization problem that integrates soft and hard constraints, and defines a robust objective function, as follows:

[0059]

[0060] S3.2: The constrained optimization problem described above is transformed into a standard second-order cone programming (SOCP) problem. This transformation involves reparameterizing the norm constraints and the objective function to conform to the standard form of SOCP. Second-order cone programming (SOCP) is an important class of problems in the field of convex optimization. Its standard form contains a linear objective function and a set of second-order cone constraints. A second-order cone constraint requires that the Euclidean norm (i.e., the second norm) of the affine functions of some decision variables does not exceed the value of another affine function, which essentially defines a generalized feasible region of an "ice cream cone" shape. SOCP has two major advantages: First, as a convex optimization problem, it can guarantee the efficient finding of the global optimum, usually solvable in polynomial time using the interior-point method; second, its modeling capability is very powerful, naturally describing various constraints, such as Euclidean norm constraints, inequality constraints, and certain types of geometric mean constraints. This makes it widely used in fields such as financial engineering, signal processing, and state estimation of control systems in which this invention is based, ensuring both global optimum and algorithmic efficiency.

[0061] S3.3: The transformed SOCP problem is input into a numerical optimization solver for solving. The interior-point method is a commonly used and efficient algorithm for solving this type of problem. The solution output by the solver is the target state estimate at time k that simultaneously satisfies sensor observation updates and all physical constraints. Target state estimation refers to the optimal estimate of the aircraft's state (including at least position, velocity, and attitude) obtained at the current moment by fusing sensor observation data and system physical constraints and after state update, for use in subsequent navigation and control.

[0062] This embodiment ensures the mathematical rigor of the attitude estimation results by specifically defining the equality constraints as quaternion unit norm constraints, fundamentally avoiding the problem of attitude description distortion due to numerical errors and improving the mathematical rigor of state estimation. Simultaneously, by specifically defining inequality constraints, including constraints based on prior geographical information regarding the Earth's magnetic field strength, known physical laws are incorporated as soft constraints into the optimization, significantly enhancing the physical rationality and consistency of the state estimation. This effectively suppresses estimation biases caused by magnetometer interference or abnormal observations, thereby improving the overall robustness and reliability of the navigation system in complex electromagnetic environments.

[0063] Furthermore, in some embodiments, in order to suppress the negative impact of sensor outliers on target state estimation, the objective function of step S3.1 is optimized. Specifically, a weighted minimum mean square error loss function based on robust statistics is first defined as follows:

[0064] in, It is the error of the lag estimation. The Component (i.e., the first) The state estimation error (the difference between the current state estimate and the predicted state estimate) provides a clear and computable input benchmark for the Huber loss function. This definition directly connects the prediction and update steps, ensuring that the robust estimation function can accurately measure and handle the deviation between the prediction and the optimal estimate. This gives the entire state update process clear mathematical and physical meaning, laying a reliable foundation for the system to stably and consistently perform error penalty and state correction even when outliers exist. It represents the weights between the various state components and satisfies... , This is the Huber loss function:

[0065] When the above state estimation error When it is not greater than the set threshold: that is, when The function is At this point, the penalty for the error is based on a quadratic form, and its behavior is consistent with the mean squared error objective of the standard EKF. When the state estimation error... When it exceeds the set threshold: that is, when The function is At this point, the penalty for the error is based on linearity. Since the rate of linear growth is much lower than that of quadratic growth, the penalty of the Huber loss function for large errors is significantly lower than the penalty of the mean squared error.

[0066] This effectively suppresses the negative impact of sensor outliers on target state estimation. The final optimization problem is expressed as follows:

[0067]

[0068] After obtaining the final optimization problem, the processing steps are the same as those in S3.2 and S3.3. To avoid duplication, they will not be repeated here.

[0069] This embodiment introduces an adaptive error penalty mechanism into the state update process by employing a robust estimation function based on Huber loss as the objective function. When the state estimation error is small, a quadratic penalty is used, consistent with the optimization objective of classical Kalman filtering, thus maintaining high estimation accuracy under normal observation conditions. When the error exceeds a set threshold (usually caused by sensor anomalies or sudden interference), it automatically switches to a more slowly increasing linear penalty. This mechanism can significantly suppress the excessive influence of outliers or abnormal observations on the overall state estimation, improving the robustness of the system under complex conditions such as non-Gaussian noise or intermittent sensor failures. Simultaneously, the continuously differentiable nature of this function ensures good numerical properties of the optimization problem, ultimately effectively enhancing the anti-interference capability and reliability of the flight control system without excessively sacrificing accuracy.

[0070] In an optional embodiment, in step S4, the obtained target state estimate is... The navigation filter is output to the flight control system; based on the state estimation, the navigation state of the aircraft is updated, the navigation state including at least one of position, velocity, and attitude; wherein, the updated navigation state is used for the aircraft's trajectory planning or guidance. The state covariance is also updated, completing this filtering cycle.

[0071] This embodiment explicitly outputs the target state estimate to the navigation filter and directly updates the aircraft's navigation state, constructing a seamless link from high-precision, highly robust state estimation to the generation of actual flight control commands. This step ensures that the optimized state estimation results can be applied to the flight control loop in real time and completely, providing more accurate and reliable navigation state inputs (such as position, velocity, and attitude) for trajectory planning and guidance algorithms. This fundamentally improves the accuracy and response quality of the flight control system's decision-making and control based on the environment and mission, enabling the aircraft to perform more precise route tracking, more stable attitude maintenance, and safer obstacle avoidance maneuvers, ultimately translating the estimation advantages at the algorithm level into a systematic improvement in actual flight performance.

[0072] Furthermore, the method of the present invention also includes: S5: Obtain the state covariance at the current time, which is obtained based on the state estimate and state covariance prediction updated in the previous iteration. S6: Update the state covariance at the current time based on the target state estimate, for use in the state prediction at the next iteration time.

[0073] This embodiment ensures the continuity and consistency of the method as a complete closed-loop estimation framework by explicitly including a recursive prediction and update step for the state covariance. This step not only provides key weight information reflecting the prediction uncertainty for the constrained optimization state update at the current moment, but more importantly, it outputs the necessary posterior covariance for the state prediction at the next iteration. This guarantees that the entire estimation process can operate in real time, continuously, and self-consistently, enabling the system to dynamically evaluate and transmit the reliability of the estimation results. This provides a stable and reliable state estimation sequence for the flight control system, forming the foundation for achieving high-precision, robust navigation and control.

[0074] In addition, this embodiment conducted a test flight experiment using a small multi-rotor UAV to test the performance of different Kalman filter algorithms. Taking the roll angle as an example, the state estimation results are shown below. Figure 3 Through such Figure 3 The flight experiment comparison results clearly show that, under the same flight conditions, the roll angle curve estimated using the method of this invention (constrained EKF) is smoother than that of the traditional EKF, and outliers are significantly suppressed. This proves that the present invention, through embedded constraint solving, can effectively resist sensor noise and interference, effectively improve the accuracy and robustness of state estimation, and avoid the problem of falsely triggering the sensor fault detection mechanism due to state violation of physical constraints. Furthermore, in order to suppress the negative impact of sensor outliers on target state estimation, this embodiment is based on a robust statistical weighted minimum mean square error loss function. On the one hand, the "weighting" aims to distinguish the importance of different state variables. In flight control, the accuracy of attitude angles is usually much more important than the accuracy of magnetic field bias. On the other hand, the Huber loss function is introduced to solve the problem that the standard EKF is sensitive to sensor outliers.

[0075] Example 2 like Figure 2 As shown, an aircraft according to an embodiment of the present invention includes: The sensor array is used to acquire observational data of the aircraft at the current moment; The processing unit is configured to: predict based on the updated state estimate and state covariance from the previous iteration. The aircraft state at the current moment is determined; using the observation data acquired by the sensor group, the aircraft state is updated to obtain the target state estimate at the current moment. The flight control module receives the target state estimate output by the processing unit and generates control commands based on it to guide the navigation or control of the aircraft.

[0076] The aircraft proposed in this embodiment is based on a flight control method described in Embodiment 1. Therefore, the options proposed in Embodiment 1 are also applicable to this embodiment. To avoid repetition, they will not be described again here.

[0077] Example 3 Please see Figure 4 This is a schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0078] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0079] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0080] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above.

[0081] In some embodiments, the constrained extended Kalman filter method based on second-order cone programming can be implemented as a computer program, which is implicitly included in a computer program product. When executed by a processor, the computer program implements the constrained extended Kalman filter method based on second-order cone programming of the present invention. The computer program product can be understood as a software product that primarily implements its solution through a computer program. The computer program used to implement the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0082] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0083] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0084] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0085] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0086] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0087] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An aircraft navigation and control method, applied to a flight control system, characterized in that, Includes the following steps: Acquire observation data from the sensors on the aircraft at the current moment; Based on the state estimate and state covariance updated in the previous iteration, the current state of the aircraft is predicted. Using the observation data, the state of the aircraft is updated to obtain the target state estimate at the current moment; The target state estimate is output to the flight control system of the aircraft for controlling the aircraft's navigation.

2. The method according to claim 1, characterized in that, The step of updating the aircraft's state using the observed data to obtain the target state estimate at the current moment includes: The state update process is constructed as a constrained optimization problem, wherein the constraints include at least one physical constraint on the state variables of the flight control system. The optimization problem is transformed into a second-order cone programming problem. By solving the second-order cone programming problem, the target state estimate at the current moment that satisfies at least one physical constraint is obtained.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the state covariance at the current moment, which is obtained based on the state estimate and state covariance prediction updated at the previous iteration moment; The state covariance at the current time is updated based on the target state estimate and used for state prediction at the next iteration time.

4. The method according to claim 2, characterized in that, The at least one physical constraint includes equality constraints and / or inequality constraints, wherein the equality constraints include the unit norm constraint of the quaternion used to describe the attitude, and the inequality constraints include the Earth's magnetic field strength constraint based on prior geographic information.

5. The method according to claim 4, characterized in that, The objective function of the optimization problem is a robust estimation function based on Huber loss. The objective function is configured such that: when the state estimation error is not greater than a set threshold, the objective function penalizes the error based on a quadratic form; when the state estimation error is greater than the set threshold, the objective function penalizes the error based on a linear form, and the penalty is lower than that of the mean squared error.

6. The method according to claim 5, characterized in that, The state estimation error is the difference between the current state estimate and the predicted state estimate.

7. The method according to claim 1, characterized in that, The step of outputting the target state estimate to the flight control system of the aircraft for controlling the navigation of the aircraft includes: The target state estimate is output to the navigation filter of the flight control system; Based on the target state estimation, the navigation state of the aircraft is updated, the navigation state including position and velocity. At least one of degree and attitude; wherein the updated navigation state is used for the flight path planning or guidance of the aircraft.

8. An aircraft, characterized in that, include: The sensor array is used to acquire observational data of the aircraft at the current moment; The processing unit is configured to: predict based on the updated state estimate and state covariance from the previous iteration. The current state of the aircraft; Using the observation data acquired by the sensor group, the state of the aircraft is updated to obtain the target state estimate at the current moment; The flight control module receives the target state estimate output by the processing unit and generates control commands based on it to guide the navigation or control of the aircraft.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

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

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