A method and device for altitude control of unmanned aerial vehicles

Abstract

This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically relating to a method and apparatus for UAV altitude hold control. The method includes the following steps: acquiring electrooculogram (EOG) signals; determining the signal source and detecting blink events based on the EOG signals to obtain an updated desired flight altitude change; acquiring electroencephalogram (EEG) signals; decoding the EEG signals to obtain a determined desired flight direction; determining the desired flight altitude based on the desired flight altitude change and the desired flight direction; inputting the desired flight altitude to a model predictive controller to solve for the optimal control input to generate a desired thrust value; and mapping the thrust value to the speed control signals of each control motor through a power conversion module. This invention converts EEG signals into a desired flight direction and EOG signals into a dynamically adjustable desired flight altitude change, comprehensively determining the desired flight altitude, thus achieving excellent precision and stability in UAV altitude hold control.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, and relates to a method and device for UAV altitude hold control. Background Technology

[0002] In drone control technology, traditional solutions still rely on remote controllers, smart terminals such as mobile phones and tablets, or preset automatic control programs. These control methods mainly depend on the operator's physical input or the flight parameters set by the ground station, requiring the operator to have certain operating skills and experience.

[0003] For people with physical disabilities, limited motor function, or those working in specific industries, traditional manual control methods may be unusable. EEG and EEG-based control methods allow users to directly control drones through thought and eye movements, eliminating the need for manual operation. This is more natural and intuitive, lowering the barrier to entry and making drone use easier for more people. EEG and EEG control technology provides them with a completely new interaction method, enabling them to operate drones and, to some extent, restore or enhance their autonomy, for example, for assisting rehabilitation training or carrying everyday items.

[0004] In existing UAV altitude hold control systems, the use of fixed adjustment values ​​makes it difficult to dynamically adjust the adjustment values ​​during the altitude hold process, thus limiting the precision and stability of UAV altitude hold control. Summary of the Invention

[0005] This invention provides a method and apparatus for altitude hold control of unmanned aerial vehicles (UAVs), which solves the above-mentioned technical problems.

[0006] To achieve the above objectives, the specific technical solution provided by the present invention is as follows:

[0007] A method for altitude hold control of an unmanned aerial vehicle (UAV) includes:

[0008] Acquiring electrooculogram (EOG) signals,

[0009] The source of the electrooculogram (EOG) signal is determined and blink events are detected. Based on the results of the source determination and blink event detection, the expected change in flight altitude is determined. The expected change in flight altitude has multiple candidate values. When the source of the EOG signal is the left eye, the current expected change in flight altitude is decreased for each blink event to obtain an updated expected change in flight altitude. When the source of the EOG signal is the right eye, the current expected change in flight altitude is increased for each blink event to obtain an updated expected change in flight altitude.

[0010] EEG signals are acquired, and signal decoding is performed based on the EEG signals to obtain the desired flight direction. The desired flight altitude is then determined based on the change in desired flight altitude and the desired flight direction.

[0011] The desired flight altitude is input to the model predictive controller to solve for the optimal control input, thereby generating the desired thrust value. The thrust value is then mapped to the required speed control signals for each control motor via the power conversion module, which are used to drive the UAV to perform altitude adjustments.

[0012] Furthermore, before determining the signal source and detecting blink events, the electrooculogram (EOG) signal is processed using a notch filter and a bandpass filter to extract low-frequency feature components related to eye movement, thus obtaining a preprocessed EOG signal.

[0013] Furthermore, the blink event detection includes the following steps:

[0014] Within a fixed-length sliding time window, segments of vertical electrooculogram (EOG) voltage signals are extracted. The median of the average value of all segments of vertical EOG voltage signals within the sliding time window is determined. Based on the median of the average value, the median absolute deviation of each vertical EOG voltage signal value relative to the median of the average value and a dynamic detection threshold are determined. When the vertical EOG voltage signal value within the fixed-length sliding time window is greater than the dynamic detection threshold, a blinking event is determined to have occurred.

[0015] Furthermore, based on the EEG signals, signal decoding is performed to determine the desired flight direction, including the following steps:

[0016] Notch filters and bandpass filters are used to process the EEG signal to obtain a preprocessed EEG signal. Within a fixed-length time window, standard canonical correlation analysis is applied to each sub-band signal of the preprocessed EEG signal to generate correlation values ​​between the sub-band signal and predefined reference signals corresponding to all stimulus frequencies. Based on the correlation values, the weighted sum of the squared correlation values ​​is determined for all sub-band signals by weighted summation to generate a correlation vector. The category with the highest correlation in the correlation vector is used as the decoding category within the current time window to represent the current expected flight direction.

[0017] Furthermore, the desired flight altitude is determined using the following formula:

[0018] ;

[0019] in, The desired flight altitude at the current moment, in kilometers. The expected flight altitude at the previous moment, in kilometers. The maximum value in the correlation vector generated after EEG decoding is... As a category of EEG decoding, The expected change in flight altitude is determined based on the results of each blink detection. The maximum flight altitude of the drone, in kilometers. Minimum altitude for drone flight, expressed in kilometers.

[0020] Furthermore, the mathematical model of the model predictive controller is:

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] in, For terminal augmentation status, To augment the state weight matrix, It is the steady-state augmented input weight matrix. It is the terminal augmented state weight matrix. For the first The augmented state matrix at time t. For the first The steady-state input error at time t will The first item is used as the input pull value for the drone. , For the control time domain, in the model predictive controller, the prediction time domain is equal to the control time domain; Indicates the first Time for the first The predicted state value at time 10:00. Indicates the first Time for the first The predicted state value at time 10:00. Indicates the first Time for the first The predicted steady-state input value at time t. Here is the state transition matrix. For the input matrix, For the first Time for the first Predicted altitude value at any time For the first Time for the first Predicted flight speed of the drone at any given time. The maximum flight altitude of the drone, in kilometers. Minimum flight altitude for drones, in kilometers. This represents the maximum flight speed of the drone, expressed in kilometers per hour. This represents the minimum flight speed of the drone, expressed in kilometers per hour. Input the maximum value of the pull force for the drone, in Newtons. Input the minimum pulling force value for the drone, in Newtons.

[0027] Furthermore, the tension value is mapped to the required speed control signal for each control motor via the power conversion module using the following formula:

[0028] ;

[0029] in, ;

[0030] The radius of the fuselage is in meters. This is the propeller thrust coefficient. This is the propeller torque coefficient. This is the tensile force, measured in Newtons. The torque about each axis, expressed in Newton-meters. These represent the angular velocities of the four control motors, in radians per second. The force-torque mapping matrix is ​​used in constant-height control to maintain a stable height. All values ​​are set to 0.

[0031] An unmanned aerial vehicle (UAV) altitude hold control system, based on the aforementioned UAV altitude hold control method, includes:

[0032] The first acquisition module is used to acquire electrooculogram (EOG) signals.

[0033] The first determining module is used to determine the signal source of the electrooculogram (EOG) signal and detect blink events. Based on the results of the signal source determination and blink event detection, it determines the expected change in flight altitude. The expected change in flight altitude has multiple candidate values. When the EOG signal originates from the left eye, the current expected change in flight altitude is decreased with each blink event to obtain an updated expected change in flight altitude. When the EOG signal originates from the right eye, the current expected change in flight altitude is increased with each blink event to obtain an updated expected change in flight altitude.

[0034] The second acquisition module is used to acquire electroencephalogram (EEG) signals.

[0035] The decoding module is used to decode signals based on electroencephalogram (EEG) signals to determine the desired flight direction.

[0036] The second determining module is used to input the desired flight altitude into the model predictive controller, solve for the optimal control input to generate the desired thrust value, and map the thrust value into the speed control signal required by each control motor through the power conversion module, so as to drive the UAV to perform altitude adjustment.

[0037] An electronic device includes a storage device, a processor, and a computer program stored in the storage device and executable on the processor. The processor executes the computer program to implement the above-described drone altitude hold control method.

[0038] A communication interface is established between the memory and the processor to establish signal connections and enable data transfer. This communication interface can be either a serial interface or a parallel interface.

[0039] The processor may be a central processing unit (CPU), a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0040] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the above-described UAV altitude hold control method.

[0041] Compared with existing technologies, the present invention provides a drone altitude hold control method and device. The control method collects both electroencephalogram (EEG) and electrooculogram (EOG) signals. The EEG signals are used to control the drone's vertical flight direction. The EOG signals are analyzed to determine the signal source and detect blink events. Based on the results of these analyses, the desired flight altitude change is determined. Multiple values ​​are available for this desired flight altitude change. When the EOG signal originates from the left eye, the current desired flight altitude change is decreased with each blink event to obtain an updated desired flight altitude change. When the EOG signal originates from the right eye, the current desired flight altitude change is increased with each blink event to obtain an updated desired flight altitude change. The desired flight altitude is determined by combining the desired flight altitude change with the flight direction, enabling dynamic adjustment of the adjustment amount during drone altitude hold. Compared to previous methods using fixed-amplitude adjustments, this method achieves more flexible and precise control of the flight state, significantly improving the accuracy and stability of the brain-controlled system. It provides a new technical means for the widespread application of drones in military rescue, environmental monitoring, and civilian logistics, demonstrating strong practicality and worthy of promotion. Attached Figure Description

[0042] Figure 1 This is a flowchart of the present invention.

[0043] Figure 2The structure of the present invention Figure 1 .

[0044] Figure 3 The structure of the present invention Figure 2 .

[0045] Figure 4 This is a partial view of the interactive interface of the present invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the technical solutions of the present invention will be clearly and thoroughly described below in conjunction with the accompanying drawings.

[0047] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0048] Furthermore, it should be further explained that in the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0049] The terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0050] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0051] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0052] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0053] See Figure 1 This invention provides a method for altitude hold control of an unmanned aerial vehicle (UAV), comprising the following steps:

[0054] The electrooculogram (EOG) signal is acquired, and signal source determination and blink event detection are performed on the EOG signal. Based on the results of signal source determination and blink event detection, the expected change in flight altitude is determined. The expected change in flight altitude has multiple candidate values. When the source of the EOG signal is the left eye, the current expected change in flight altitude is decreased for each blink event to obtain an updated expected change in flight altitude. When the source of the EOG signal is the right eye, the current expected change in flight altitude is increased for each blink event to obtain an updated expected change in flight altitude.

[0055] The system acquires EEG signals, decodes them to determine the desired flight direction, and then determines the desired flight altitude based on the desired flight altitude change and the desired flight direction. The desired flight altitude is then input to the model predictive controller to solve for the optimal control input, thereby generating the desired thrust value. The thrust value is then mapped to the required speed control signals for each control motor through the power conversion module, which are used to drive the UAV to perform altitude adjustments.

[0056] The following description combines the structure and method of the UAV altitude hold control device to illustrate the specific implementation method of this solution:

[0057] 1. Multimodal brain-computer interface

[0058] 1.1 Human-Computer Interaction Interface

[0059] This invention utilizes a PyQt5-based human-computer interaction interface to present stimuli, which is displayed via an external screen. The stimulus method chosen in this invention is based on brain vision, employing a low-flicker, steady-state motion flipping visual stimulus paradigm. This experimental paradigm mainly includes two chessboards, each flipped at a different frequency, with flicker frequencies of 13Hz and 11Hz respectively.

[0060] 1.2 Signal Acquisition Equipment

[0061] This invention utilizes the iRecorder W32 wireless EEG amplifier provided by Shanghai Niantong Intelligent Technology Co., Ltd. for EEG signal acquisition. EEG signals are transmitted via Ag / AgCl electrodes, arranged in eight channels according to the international 10-20 system standard: P7, Pz, P8, PO3, PO4, Oz, O1, and O2, covering the occipital and parietal lobes of the brain. This arrangement is suitable for processing signals related to visual attention. During electrooculography (EOG) signal acquisition, vertical EOG signals from both eyes are acquired, monitoring the potential changes caused by vertical eye movements. The ground electrode is positioned at the midpoint between CPz and AFz to reduce common-mode interference. To ensure signal quality, the impedance of all electrodes is controlled below 5kΩ. After amplification and digitization, the acquired EEG signals are recorded at a sampling rate of 500Hz.

[0062] 1.3 Signal Decoding

[0063] (1) Signal preprocessing

[0064] To improve the signal-to-noise ratio of neural signals, the acquired electroencephalogram (EEG) and electrooculogram (EOG) signals need to be preprocessed.

[0065] For EEG signals, a 50Hz notch filter is first used to effectively suppress power frequency interference. Then, based on the frequency bands involved in the experimental task, an appropriate bandpass filter range is selected to remove high-frequency noise and DC drift. In this embodiment, the bandpass filter range is 0.5Hz–40Hz. This filtering process can preserve specific neural activities related to the stimulation frequency or brain control intention. Afterward, the preprocessed signal is divided into fixed-length time windows, each time window corresponding to one brain control intention judgment, thereby generating a control command, laying the foundation for subsequent feature extraction and classification decoding.

[0066] For electrooculogram (EOG) signals, a 50Hz notch filter is first used to remove power frequency interference. Then, a bandpass filter suitable for the characteristics of EOG signals is used to further filter out high-frequency noise and low-frequency drift, thereby extracting low-frequency characteristic components related to eye movement. Specifically, the frequency of the bandpass filter is 0.1Hz–15Hz, and the filter parameters should be flexibly adjusted according to actual experimental conditions to ensure that effective eye movement information is preserved.

[0067] (2) Method for detecting blinking signals using electrooculography

[0068] During blink event detection, the eyelid closure caused by blinking typically manifests as a short, high-amplitude pulse waveform in the electrooculogram (EOG) signal. A fixed-length sliding time window is defined, and the vertical EOG signal segment within each sliding time window is extracted, denoted as . ,in This indicates the number of sampling points.

[0069] First, determine the median mean of all vertical electrooculography (EOG) voltage signal segments within the window, using the Hodges-Lehmann estimation method, defined as follows:

[0070] ;

[0071] in, The first one in the sliding window The and the first Each electrooculometry voltage signal sampling value, It is the median of the mean.

[0072] A dynamic detection threshold is constructed based on this estimate. To adapt to signal fluctuations under different backgrounds:

[0073] The median absolute deviation of each vertical electrooculography (EOG) voltage signal value relative to the median mean is determined based on the median mean. This is known as Median Absolute Deviation (MAD) and is defined as follows:

[0074] ;

[0075] Then, the dynamic detection threshold is calculated:

[0076] ;

[0077] in, This is the sensitivity coefficient. It is a dynamic detection threshold. It is the median of the mean. It is the absolute deviation of the median, set to [default value]. The larger the value, the more missed detections but fewer false positives.

[0078] A blink event is determined to have occurred when the vertical electrooculography voltage signal value within a fixed-length sliding time window exceeds the dynamic detection threshold.

[0079] Specifically, the criteria for judging a single blink event are:

[0080] In a certain window Memory at any time satisfy

[0081] ;

[0082] in, Indicates time The voltage signal value of the electrooculogram (EOG) signal. The time window length, It is a dynamic detection threshold; if the above conditions are met, it is determined to be a blink event.

[0083] (3) Decoding of EEG signals:

[0084] The electroencephalogram (EEG) signals collected within a fixed-length time window are denoted as... ,in Represents the number of channels. This represents the number of sampling points. To extract specific frequency band components from the signal, a zero-phase Chebyshev type I infinite impulse response filter is used as the bandpass filter for the signal. Filtering is performed to obtain the signals of each sub-band. ,in It is the first The body carries a signal. This represents the total number of sub-bands. After completing the filter bank analysis, for each sub-band signal... Standard canonical correlation analysis was applied. This process generates correlation values ​​between the sub-band signal and predefined reference signals corresponding to all stimulus frequencies. For the... one reference signal ,in, The unit is Hz. Representing no stimulation, calculate a set of lengths. Correlation vector The formula is shown below:

[0085] ;

[0086] in, express and The correlation coefficient between them and To The projection matrix obtained after applying standard canonical correlation analysis makes the projected variables... and The correlation between them is the strongest.

[0087] Corresponding to all sub-band signals The weighted sum of squared correlation values ​​is calculated by weighted summation. The calculation formula is as follows:

[0088] ;

[0089] in, Subband index, weight of subband signal The definition is as follows:

[0090] ;

[0091] in, and It is a constant. It is the weight of the sub-band signal. This is the sub-band index. In practical applications, these constants can be determined using grid search methods in offline analysis.

[0092] These steps generate a correlation vector. That is, the one with the highest correlation in the correlation vector. The corresponding category is used as the decoding category within the current time window, representing the current expected flight direction, which represents the user's intent or control command.

[0093] 1.4 Desired Flight Altitude Generation

[0094] Blink detection is used to adjust the desired flight altitude change of the drone, thereby enabling altitude hold control at different flight altitude levels. In this system, the default desired flight altitude change... The baseline value is set at 1 meter, and it can adaptively adjust according to the user's blinking behavior. The preset height variation range is [missing value]. The unit for the range of height variation is meters.

[0095] This range is a recommended setting and can be adjusted in actual use according to specific task scenarios or user-specific needs. Specifically, a left-eye blink event can be recorded as... It is used to reduce the current expected change in flight altitude. The right eye blink event can be recorded as It is used to increase the current expected change in flight altitude. The drone in the Final expected flight altitude at the next update It can be represented as follows:

[0096] ;

[0097] in, The desired flight altitude at the current moment, in kilometers. The expected flight altitude at the previous moment, in kilometers. The maximum value in the correlation vector generated after EEG decoding is... As a category of EEG decoding, The expected change in flight altitude is determined based on the results of each blink detection. The maximum flight altitude of the drone, in kilometers. Minimum altitude for drone flight, expressed in kilometers.

[0098] 2. Model Predictive Controller

[0099] 2.1 UAV Altitude Model

[0100] The drone is subjected to downward gravity. and the motivation to move upward The power comes from the rotating motor of the propeller. According to Newton's second law, the following dynamic equations for the UAV in the vertical direction are obtained:

[0101] ;

[0102] in, For the quality of drones, It is a time function of altitude. The drone is experiencing upward force. It is gravitational acceleration, taken as the system's state variable. The altitude of the drone ,speed as well as If the input is the driving force, then the following state equations can be obtained.

[0103] ;

[0104] in, , This is the tensile force value. , , Here is the state transition matrix. The input matrix is ​​denoted as .

[0105] 2.2 Model Predictive Controller Design

[0106] This invention employs a controller design method based on a steady-state non-zero reference value to determine the desired flight altitude. Used as the reference expected value for the model predictive controller, the control objective The target state transition matrix is ​​a constant. Meanwhile, it is assumed that when the system input is the target input... At that time, the state variable will stabilize at .

[0107] Define the state error as

[0108] ;

[0109] Define the error of steady-state input as

[0110] ;

[0111] Discretizing the state equations and substituting the state error and steady-state input error into the discretized state equations, we can obtain:

[0112] ;

[0113] The system performance index is defined as follows:

[0114] ;

[0115] in, Indicates the prediction of the first time domain Step state error, This corresponds to the steady-state input error. For the state weight matrix, It is the steady-state input weight matrix. It is the terminal state weight matrix.

[0116] Define the new augmented state matrix as

[0117] ;

[0118] Substitution We can obtain,

[0119] ;

[0120] in, , , , can be obtained Substituting into the definition of the system's performance index, we can obtain...

[0121] ;

[0122] in, , , , , To control the time domain, For the first The augmented state matrix at time t. For the first Steady-state input error at time t, To augment the state weight matrix, It is the steady-state augmented input weight matrix. If the terminal augmented state weight matrix is ​​given, then the final model predictive controller can be obtained as follows:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] in, For terminal augmentation status, To augment the state weight matrix, It is the steady-state augmented input weight matrix. It is the terminal augmented state weight matrix. For the first The augmented state matrix at time t. For the first The steady-state input error at time t will The first item is used as the input pull value for the drone. , For the control time domain, in the model predictive controller, the prediction time domain is equal to the control time domain. Indicates the first Time for the first The predicted state value at time 10:00. Indicates the first Time for the first The predicted state value at time 10:00. Indicates the first Time for the first The predicted steady-state input value at time t. Here is the state transition matrix. For the input matrix, For the first Time for the first Predicted altitude value at any time For the first Time for the first Predicted flight speed of the drone at any given time. The maximum flight altitude of the drone, in kilometers. Minimum flight altitude for drones, in kilometers. This represents the maximum flight speed of the drone, expressed in kilometers per hour. This represents the minimum flight speed of the drone, expressed in kilometers per hour. Input the maximum value of the pull force for the drone, in Newtons. Input the minimum pulling force value for the drone, in Newtons. , , and The resulting physical constraints reflect the performance and safety requirements that aircraft must meet in practical applications, and can be determined based on specific missions and various performance parameters of the UAV.

[0129] This problem can be transformed into a standard quadratic programming problem, abbreviated as QP, which is an optimization problem with a quadratic objective function and linear constraints. To achieve real-time control, this invention uses the quadprog toolbox to solve this QP problem online.

[0130] 2.3 Conversion from tension value to UAV rotation speed

[0131] The parameters of the unmanned aerial vehicle system include the fuselage radius. The propeller thrust coefficient is propeller torque coefficient Based on the total tensile force value and the torque about each axis Calculate the angular velocities of the four control motors Construct the following matrix equation:

[0132] ;

[0133] in, .

[0134] The radius of the fuselage is in meters. This is the propeller thrust coefficient. This is the propeller torque coefficient. This is the tensile force, measured in Newtons. The torque about each axis, expressed in Newton-meters. These represent the angular velocities of the four control motors, in radians per second. This is the force-torque mapping matrix.

[0135] After obtaining the optimal control input, the square of the corresponding control motor speed can be calculated by matrix inversion. Finally, the angular velocity of each control motor is obtained by combining the square root operation, which is used to drive the UAV to perform altitude adjustment. During altitude hold control, it is desirable to maintain a stable altitude, therefore... All values ​​are set to 0.

[0136] Based on the above-described UAV altitude hold control method, this invention also provides a UAV altitude hold control system, the structure of which is as follows: Figure 2 As shown, it specifically includes:

[0137] The first acquisition module is used to acquire electrooculogram (EOG) signals.

[0138] The first determining module is used to determine the source of the electrooculogram (EOG) signal and detect blink events. Based on the results of the signal source determination and blink event detection, the desired change in flight altitude is determined. The desired change in flight altitude has multiple candidate values. When the source of the EOG signal is the left eye, the current desired change in flight altitude is decreased for each blink event to obtain an updated desired change in flight altitude. When the source of the EOG signal is the right eye, the current desired change in flight altitude is increased for each blink event to obtain an updated desired change in flight altitude.

[0139] The second acquisition module is used to acquire EEG signals.

[0140] The decoding module is used to decode signals based on EEG signals to determine the desired flight direction.

[0141] The second determining module is used to input the desired flight altitude into the model predictive controller, solve for the optimal control input to generate the desired thrust value, and map the thrust value into the speed control signal required by each control motor through the power conversion module, so as to drive the UAV to perform altitude adjustment.

[0142] The above methods can also be implemented using electronic devices, such as... Figure 3 As shown, the structure of this electronic device specifically includes a storage device, a processor, and a computer program stored in the storage device and capable of running on the processor. The processor executes the computer program to implement the aforementioned drone altitude hold control method.

[0143] A communication interface is established between the memory and the processor to establish signal connections and enable data transfer. This communication interface can be either a serial interface or a parallel interface.

[0144] The processor may preferably be a central processing unit, or a specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of this application. Since this technology can be implemented using existing technology, it will not be elaborated here.

[0145] The above method can also be implemented using a computer-readable storage medium that stores a computer program, which is executed by a processor to implement the above-mentioned UAV altitude hold control method.

[0146] Compared with existing technologies, this invention provides a method and device for altitude hold control of unmanned aerial vehicles (UAVs), enabling precise control of the UAV's flight altitude. The overall control process is as follows: The system first detects blink events from the collected electrooculogram (EOG) signals, extracts feature information reflecting the user's current adjustment intention, and determines the desired change in flight altitude based on this feature information. Subsequently, the user selects a visual stimulus target through a human-computer interaction interface to induce a specific EEG response. The system collects and decodes the EEG signals in real time to identify the user's specific control direction. The system is designed with features such as... Figure 4 The three stimulus targets shown—"ascend," "descend," and "maintain altitude"—correspond to three altitude control commands. If the user is not focused on any target, the default intention is "maintain current altitude." After completing multi-class decoding of the EEG signals, the system obtains the desired flight direction and combines the desired flight altitude change with the desired flight direction to determine the desired flight altitude at the current moment. The generated desired flight altitude is used as a reference trajectory input to the Model Predictive Controller (MPC). The MPC solves for the optimal control input to generate the desired pull value. Finally, this pull is mapped through the power conversion module to the speed control signals required by each control motor of the quadcopter, driving the UAV to precisely perform altitude adjustment tasks. This system introduces a multimodal brain-computer interface into UAV control, fully utilizing the natural interaction advantages of eye-tracking signals and the active control characteristics of EEG signals to construct an efficient, intuitive, and stable human-machine collaborative control system, providing an effective solution for achieving multi-level autonomous altitude-holding flight of UAVs.

[0147] This invention enables continuous adjustment of control increments, resulting in refined and stable altitude control for UAVs. It also effectively reduces reliance on traditional remote control equipment, giving UAVs higher reliability and intelligent control capabilities in complex environments and for specific user groups. Furthermore, it diversifies and flexibly adapts operating methods, providing a new technical means for the widespread application of UAVs in military rescue, environmental monitoring, and civilian logistics. It is highly practical and worthy of promotion.

[0148] It is understood that the present invention has been described through some embodiments, and those skilled in the art will know that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the present invention.

[0149] Furthermore, based on the teachings of this invention, modifications can be made to these features and embodiments to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims are protected by this invention.

Claims

1. A method for altitude hold control of an unmanned aerial vehicle (UAV), characterized in that, Includes the following steps: Acquire electrooculogram (EOG) signals; The source of the electrooculogram (EOG) signal is determined and blink events are detected. Based on the results of the source determination and blink event detection, the expected change in flight altitude is determined. The expected change in flight altitude has multiple candidate values. When the source of the EOG signal is the left eye, the current expected change in flight altitude is decreased for each blink event to obtain an updated expected change in flight altitude. When the source of the EOG signal is the right eye, the current expected change in flight altitude is increased for each blink event to obtain an updated expected change in flight altitude. Acquiring EEG signals; Signal decoding based on electroencephalogram (EEG) signals yields a determined desired flight direction; The desired flight altitude is determined based on the desired change in flight altitude and the desired flight direction. The desired flight altitude is input to the model predictive controller to solve for the optimal control input, thereby generating the desired thrust value. The thrust value is then mapped to the required speed control signal for each control motor through the power conversion module, which is used to drive the UAV to perform altitude adjustment. Before determining the signal source and detecting blink events, the electrooculogram (EOG) signal is processed using a notch filter and a bandpass filter to extract low-frequency feature components related to eye movement and obtain the preprocessed EOG signal. The blink event detection includes the following steps: Extract segments of vertical electrooculography voltage signal values ​​within a fixed-length sliding time window; Determine the median of the average value of all vertical electrooculography voltage signal segments within the sliding time window; The median absolute deviation of each vertical electrooculogram voltage signal value relative to the median mean and the dynamic detection threshold are determined based on the median mean. A blink event is determined to have occurred when the vertical electrooculography voltage signal value within a fixed-length sliding time window is greater than the dynamic detection threshold. The mathematical model of the model predictive controller is: ； ； ； ； ； in, For terminal augmentation status, To augment the state weight matrix, It is the steady-state augmented input weight matrix. It is the terminal augmented state weight matrix. For the first The augmented state matrix at time t. For the first The steady-state input error at time t will The first item is used as the input pull value for the drone. , For the control time domain, in the model predictive controller, the prediction time domain is equal to the control time domain; Indicates the first Time for the first The predicted state value at time 10:

00. Indicates the first Time for the first The predicted state value at time 10:

00. Indicates the first Time for the first The predicted steady-state input value at time t. Here is the state transition matrix. For the input matrix, For the first Time for the first Predicted altitude value at any time For the first Time for the first Predicted flight speed of the drone at any given time. The maximum flight altitude of the drone, in kilometers. Minimum flight altitude for drones, in kilometers. This represents the maximum flight speed of the drone, expressed in kilometers per hour. This represents the minimum flight speed of the drone, expressed in kilometers per hour. Input the maximum value of the pull force for the drone, in Newtons. Input the minimum pulling force value for the drone, in Newtons.

2. The UAV altitude hold control method according to claim 1, characterized in that, Decoding brainwave signals to determine the desired flight direction involves the following steps: Notch filters and bandpass filters were used to process the EEG signals to obtain preprocessed EEG signals; Within a fixed-length time window, standard canonical correlation analysis is applied to each subband signal of the preprocessed EEG signal to generate correlation values ​​between the subband signal and predefined reference signals corresponding to all stimulation frequencies. Based on the correlation value, corresponding to all sub-band signals, the weighted sum of the squared correlation values ​​is determined by weighted summation to generate a correlation vector. The category with the highest correlation in the correlation vector is taken as the decoding category within the current time window to represent the current expected flight direction.

3. The UAV altitude hold control method according to claim 1, characterized in that, The desired flight altitude is determined using the following formula: ; in, The desired flight altitude at the current moment, in kilometers. The expected flight altitude at the previous moment, in kilometers. The maximum value in the correlation vector generated after EEG decoding is... As a category of EEG decoding, The expected change in flight altitude is determined based on the results of each blink detection. The maximum flight altitude of the drone, in kilometers. Minimum altitude for drone flight, expressed in kilometers.

4. The UAV altitude hold control method according to claim 1, characterized in that, The tensile force value is mapped to the required speed control signal for each control motor via the power conversion module using the following formula: ； in, ; The radius of the fuselage is in meters. This is the propeller thrust coefficient. This is the propeller torque coefficient. This is the tensile force, measured in Newtons. The torque about each axis, expressed in Newton-meters. These represent the angular velocities of the four control motors, in radians per second. The force-torque mapping matrix is ​​used in constant-height control to maintain a stable height. All values ​​are set to 0.

5. A drone altitude hold control system, based on the drone altitude hold control method according to any one of claims 1-4, characterized in that, include: The first acquisition module is used to acquire electrooculogram (EOG) signals; The first determining module is used to determine the source of the electrooculogram (EOG) signal and detect blink events. Based on the results of the signal source determination and blink event detection, it determines the expected change in flight altitude. The expected change in flight altitude has multiple candidate values. When the source of the EOG signal is the left eye, the current expected change in flight altitude is decreased for each blink event to obtain an updated expected change in flight altitude. When the source of the EOG signal is the right eye, the current expected change in flight altitude is increased for each blink event to obtain an updated expected change in flight altitude. The second acquisition module is used to acquire electroencephalogram (EEG) signals; The decoding module is used to decode signals based on electroencephalogram (EEG) signals to determine the desired flight direction. The second determining module is used to input the desired flight altitude into the model predictive controller, solve for the optimal control input to generate the desired thrust value, and map the thrust value into the speed control signal required by each control motor through the power conversion module, so as to drive the UAV to perform altitude adjustment.

6. An electronic device, characterized in that, include: The storage device, the processor, and the computer program stored on the storage device and executable on the processor, wherein the processor executes the computer program to implement the UAV altitude hold control method as described in claim 1.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the UAV altitude hold control method as described in claim 1.

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