Automatic re-flight control method based on helicopter

Abstract

The invention discloses an automatic re-flight control method based on a helicopter. The automatic re-flight control method comprises the following steps: acquiring flight state parameters of the helicopter; inputting the flight state parameters into a pre-established mathematical model, and calculating the ratio range of the rotor speed and the engine power under the current load condition; whether the current ratio meets the conditions of hovering and fuel efficiency or not is judged, and if the ratio does not meet the conditions, the ratio of the rotor rotating speed to the engine power is dynamically adjusted based on the ratio range output by the mathematical model; judging whether the posture change of the aircraft body is within a preset threshold range or not through a real-time monitoring system; and if the attitude change exceeds the threshold value, recalculating the ratio of the rotor speed to the engine power, performing secondary adjustment, acquiring helicopter flight state parameters including the rotor torque, the engine speed, the fuel flow, the body attitude and the like in real time, and inputting the helicopter flight state parameters into a pre-established mathematical model to obtain the attitude change of the helicopter. And calculating the optimal ratio range of the rotor rotating speed to the engine power under the current load condition.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an automatic go-around control method based on a helicopter. Background Art

[0002] During helicopter automated go-around maneuvers, the ratio of rotor speed to engine power directly impacts flight stability and fuel efficiency. Traditional control strategies often fix the rotor speed at a preset value and adjust engine output to maintain flight stability. However, this fixed-speed approach presents significant technical contradictions: On the one hand, under high load conditions, maintaining a high rotor speed requires greater engine power output, resulting in increased fuel consumption. On the other hand, under low load conditions, while reducing rotor speed can reduce energy consumption, it can also compromise flight stability, potentially leading to aircraft wobbling or even loss of control. This contradiction is particularly acute in complex weather conditions or during precision missions. To address this issue, a coordinated mechanism combining dynamic power scheduling and hovering control is required, monitoring flight parameters such as rotor torque, engine speed, fuel flow, and aircraft attitude in real time. The system must collect and process data within milliseconds, assess the current operating conditions based on a pre-set algorithm, and dynamically adjust the ratio of rotor speed to engine power. This adjustment must ensure a stable hover under all operating conditions while minimizing fuel consumption. However, this real-time optimization faces several technical difficulties: first, there is a complex nonlinear relationship between rotor speed and engine power, making it difficult to establish an accurate mathematical model; second, the dynamic changes in the flight environment increase the complexity of the control system; finally, energy consumption optimization needs to be achieved while ensuring flight safety. Summary of the Invention

[0003] The present application provides an automatic go-around control method based on a helicopter. By acquiring the helicopter flight status parameters in real time, including rotor torque, engine speed, fuel flow and body attitude, etc., and inputting them into a pre-established mathematical model, the optimal ratio range of rotor speed and engine power under the current load conditions is calculated.

[0004] The present application provides a method for controlling an automatic go-around of a helicopter, the method comprising:

[0005] S101, obtaining helicopter flight status parameters, including rotor torque, engine speed, fuel flow, and body attitude;

[0006] S102, inputting the flight state parameters into a pre-established mathematical model to calculate the ratio range of the rotor speed and engine power under the current load condition;

[0007] S103: Using a preset control algorithm, determine whether the current ratio meets the requirements for hovering and fuel efficiency. If the ratio does not meet the requirements, dynamically adjust the ratio of rotor speed to engine power based on the ratio range output by the mathematical model.

[0008] S104: The real-time monitoring system obtains the adjusted flight status parameters and determines whether the aircraft attitude change is within a preset threshold range. If the attitude change exceeds the threshold, the ratio of rotor speed to engine power is recalculated and a secondary adjustment is performed.

[0009] In step S105, the adjusted ratio data is input into the control algorithm, the flight state parameters are updated, and new control instructions are generated. According to the control instructions, the engine output power and rotor speed are adjusted to complete the dynamic optimization process.

[0010] Preferably, the obtaining of helicopter flight state parameters, including rotor torque, engine speed, fuel flow and body attitude, includes: using sensors to obtain the helicopter's rotor torque and engine speed, recording fuel consumption data through a fuel flow meter, and using an inertial measurement unit to collect body attitude information; and calculating the power system efficiency based on the rotor torque and engine speed.

[0011] Preferably, the flight state parameters are input into a pre-established mathematical model to calculate the ratio range of the rotor speed and the engine power under the current load conditions, including: obtaining the flight state parameters and converting them into a format recognizable by the mathematical model; using a preset mathematical algorithm to process the converted flight parameter data; determining the initial value range of the rotor speed according to the load conditions; calculating the engine power value within the speed range; if the power value exceeds a preset threshold, adjusting the speed value and recalculating; and determining the optimal ratio of speed and power through an iterative algorithm.

[0012] Preferably, the method of using a preset control algorithm to determine whether the current ratio satisfies the hovering and fuel efficiency conditions includes: obtaining a preset control algorithm; determining, based on an output result of the control algorithm, whether the current ratio reaches a preset threshold for hovering; if the hovering reaches the preset threshold, no adjustment is required; if the hovering does not reach the preset threshold, adjusting the ratio parameters and re-acquiring new ratio data; and determining, using a fuel efficiency calculation model, whether the current ratio satisfies the fuel efficiency conditions; if the fuel efficiency meets the conditions, no adjustment is required; if the fuel efficiency does not meet the conditions, adjusting the ratio using the algorithm.

[0013] Preferably, if the ratio does not meet the conditions, the ratio of rotor speed to engine power is dynamically adjusted based on the ratio range output by the mathematical model, including: obtaining the current ratio value of rotor speed to engine power; judging whether the ratio value meets the preset optimization value; if the ratio value meets the optimization value, no adjustment is required; if the ratio value does not meet the optimization value, a dynamic adjustment algorithm is used to calculate the adjustment values ​​of rotor speed and engine power, and the adjusted rotor speed and engine power values ​​are input into the control system.

[0014] Preferably, the real-time monitoring system is used to obtain the adjusted flight status parameters and determine whether the change in the aircraft attitude is within a preset threshold range. If the attitude change does not exceed the threshold, no adjustment is performed. If the attitude change exceeds the threshold, the ratio of the rotor speed to the engine power is recalculated and a secondary adjustment is performed, including: obtaining the current attitude value, and comparing the attitude value with a preset threshold value using a comparison method; if the attitude value exceeds the threshold value, obtaining the rotor speed and engine power values; recalculating the ratio of the rotor speed to the engine power value through a formula; adjusting the rotor speed and engine power values ​​according to the ratio value; and performing a secondary adjustment using an adjustment method to obtain new rotor speed and engine power values.

[0015] Preferably, the step of inputting the adjusted matching data into the control algorithm, updating the flight status parameters, and generating new control instructions includes: using the adjusted matching data, inputting the control algorithm, updating the flight status parameters according to the algorithm calculation results, and generating new control instructions; obtaining the change value of the flight status parameter, determining whether the parameter exceeds a preset threshold, and if so, adjusting the control algorithm weight and regenerating the control instruction; if not, making no adjustment.

[0016] Preferably, the engine output power and rotor speed are adjusted according to the control instruction to complete the dynamic optimization process, including: obtaining the control instruction, extracting the instruction value of the control instruction, determining the adjustment value according to the instruction value, inputting the adjustment value into the calculation model, and obtaining the power value and speed value.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: by acquiring helicopter flight state parameters in real time, including rotor torque, engine speed, fuel flow, and body attitude, etc., and inputting them into a pre-established mathematical model, the optimal ratio range of rotor speed and engine power under current load conditions is calculated. The present invention adopts a preset control algorithm to determine whether the current ratio meets the optimization conditions of hovering stability and fuel efficiency, and dynamically adjusts the ratio if not. The real-time monitoring system determines whether the change in body attitude is within the preset threshold range, and performs secondary adjustments when necessary. Finally, the present invention generates new control instructions based on the optimized ratio data, adjusts the engine output power and rotor speed, realizes dynamic optimization of the helicopter in the hovering state, and improves flight stability and fuel efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of an automatic go-around control method based on a helicopter according to the present invention;

[0019] Figure 2 This is a flow chart of determining whether a change in an aircraft posture is within a preset threshold range according to an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the process of performing secondary adjustment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To facilitate understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0022] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are for illustrative purposes only and do not represent the only implementation method.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains; the terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0024] Example 1: Figure 11 is a flow chart of an automatic go-around control method for a helicopter according to an embodiment of the present invention, including an airspeed control method and a vertical speed control method in the automatic go-around control method. The airspeed control method is specifically as follows:

[0025] S101, obtaining helicopter flight status parameters, including rotor torque, engine speed, fuel flow, and body attitude;

[0026] Furthermore, a rotor torque sensor is used to measure the torque force generated by the rotor during rotation. The rotor torque sensor is installed on the rotor shaft. The rotor torque sensor converts the real-time measured torque data into an electrical signal. An engine speed sensor is used to monitor the engine speed change. The engine speed sensor is installed on the engine crankshaft. The engine speed sensor senses the rotational motion of the engine and uses the rotor torque sensor and the rotor speed sensor to measure the current rotor torque T and rotor speed ω respectively. The formula for calculating the rotor power is: P r =T×ω, the formula for calculating the power system efficiency is: Among them, P e =f(n),P e is the output power of the engine, n is the engine speed, f is a function determined by the engine characteristics, η is the power system efficiency; a fuel flow meter is used to record the fuel consumption data. The flow meter measures the volume of fuel flowing through the pipeline. The fuel flow meter records the fuel consumption data in real time and obtains the amount of fuel flowing through the fuel system per unit time. The flight time or flight distance of the helicopter is recorded, and the fuel flow rate is divided by the flight time or flight distance to obtain the fuel consumption rate; an inertial measurement unit (IMU) is used to collect the attitude information of the body. The IMU usually integrates sensors such as accelerometers and gyroscopes and is installed on the body of the helicopter. The accelerometer measures the linear acceleration of the body, and the gyroscope measures the angular velocity of the body. Flight control By processing data, the system calculates attitude information such as the pitch angle, roll angle and yaw angle of the aircraft. According to the design characteristics of the helicopter and the requirements of the flight control system, the pitch angle threshold range, roll angle threshold range, yaw angle threshold range and change rate threshold range are set for the pitch angle, roll angle, yaw angle and change rate respectively. By observing the collected pitch angle, roll angle and yaw angle and change rate data in real time, the actual values ​​collected are compared with the set thresholds. If all attitude angles (including pitch angle, roll angle and yaw angle) and their change rates are within the set threshold range, the helicopter is judged to be in a stable flight state; if any attitude angle or change rate exceeds the set threshold, it indicates that the helicopter is in an unstable state.

[0027] The flight control system monitors the helicopter's flight altitude and speed in real time, and compares the actual flight altitude and speed with the preset safety range. If the actual value exceeds the preset range, the engine speed adjustment mechanism is triggered. Depending on the degree of the exceedance, the flight control system sends an adjustment instruction to the engine to increase or decrease the engine speed to adjust the helicopter's lift and forward speed so that it returns to the preset range. During the adjustment process, the flight altitude and speed changes are continuously monitored, and fine-tuning is performed as needed to ensure stable flight of the helicopter; basic flight performance data of the helicopter under different ambient temperatures and air pressures is collected. When the onboard sensors monitor changes in ambient temperature and air pressure in real time, the flight control system analyzes the specific impact of the changes on the rotor torque and flight performance. Based on the collected basic data and environmental impact analysis, a correction model is established to calculate the rotor torque value that needs to be corrected under different ambient temperatures and air pressures. The correction model is a mathematical formula or algorithm. The ambient temperature and air pressure data monitored in real time by the onboard sensors are input into the correction model. Based on the correction model, the rotor torque value that needs to be corrected under the current ambient temperature and air pressure is calculated. The correction value is an increase or decrease in torque, which is used to adjust the torque output of the rotor to adapt to the current environmental conditions. Before the correction value is actually applied, its accuracy can be verified through simulation or actual testing to ensure that the correction value can effectively adjust the rotor torque so that the helicopter maintains optimal flight performance under the current environmental conditions; the wind speed value and azimuth angle are monitored in real time by the onboard anemometer and gyroscope, and the actual wind speed value and azimuth angle are compared with the preset threshold value. If the threshold value is exceeded, the optimization mechanism of the pitch angle and roll angle control strategy is triggered, and the influence of the wind speed value and azimuth angle on the pitch angle and roll angle of the helicopter is analyzed. According to the analysis results, the control algorithm of the pitch angle and roll angle is adjusted through the flight control system, including adjusting the control gain, adding a feedforward control item or adopting an adaptive control strategy.

[0028] For example, during a flight, the rotor torque was measured at 1200 N·m. The current engine speed was 2800 rpm. The fuel flow meter measured 200 liters per hour. The accelerometer and gyroscope measured the current pitch angle at 5 degrees, the roll angle at 3 degrees, and the yaw angle at 10 degrees. During the Kalman filter process, through two steps, state prediction and measurement update, the covariance matrix of system noise and measurement noise is combined to continuously adjust the state estimate, ultimately obtaining more accurate aircraft attitude parameters. By comparing historical data with current data, regression analysis is used to predict future flight state trends. For example, within the next five minutes, the rotor torque may increase to 1300 N·m and the engine speed may drop to 2700 rpm, ensuring safe and efficient flight of the helicopter.

[0029] S102: Inputting the flight state parameters into a pre-established mathematical model to calculate the ratio range of the rotor speed and the engine power under the current load condition.

[0030] Specifically, the flight status parameters such as Figure 2 As shown, the flight state parameters include flight altitude, speed, ambient temperature, air pressure, rotor angle, load weight, etc. The flight state parameters are preprocessed, such as removing noise and calibrating data, to ensure data accuracy and reliability. The preprocessed flight state parameters are converted into a format that can be recognized by the mathematical model, such as converting analog signals into digital signals, or organizing the data into a specific data structure or matrix form. The flight parameters are processed using a filtering algorithm to extract the stability of the flight state and the change trend of the load. Based on the weight and distribution of the load of the flight mission and the change of the load during flight, the initial value range of the rotor speed is determined. The initial value range takes into account factors such as the mechanical performance of the rotor, the output capacity of the engine, and flight safety to ensure that the rotor and engine can operate within the normal operating range during flight. Within the determined initial value range of the rotor speed, several speed points are selected as calculation points. The calculation points are evenly distributed throughout the speed range. For each selected speed point, the power value required by the engine at that speed is calculated based on the aerodynamic characteristics of the rotor and the performance curve of the engine. The formula is: Where P represents the engine's output power, n represents the engine speed, T1 represents the engine torque, and η represents the transmission efficiency. After calculating the engine power value, the power value is checked to see if it exceeds a preset threshold. If it does, it indicates that the current speed is too high or the load conditions have changed, requiring the engine to provide more power to maintain flight. In this case, the speed should be appropriately reduced based on the degree of power overload and the change in load conditions, and the calculation should be repeated. This adjustment process may require multiple iterations until a speed point that meets the power threshold is found. To find the optimal speed-to-power ratio, an iterative algorithm can be used for adjustment and calculation. During the speed adjustment process, the engine power calculation step is repeated, gradually approaching the optimal ratio through multiple iterations. When the iterative algorithm converges to a stable solution, the final calculated speed-to-power ratio range is output. This range may include an optimal speed point and corresponding power value, as well as an allowable speed fluctuation range and a corresponding power variation range.

[0031] S103: Using a preset control algorithm, it is determined whether the obtained ratio meets the optimization conditions of hovering stability and fuel efficiency.

[0032] Specifically, a preset control algorithm PID (proportional-integral-differential) is obtained. The proportional (P) term reflects the magnitude of the current error, that is, the difference between the current ratio and the ideal ratio; the integral (I) term reflects the accumulation of past errors and helps eliminate static errors in the system; the differential (D) term reflects the rate of change of the error and helps predict future errors and make adjustments in advance. The calculated ratio data is input into the control algorithm PID, and the control algorithm PID outputs a control signal. The output control signal is compared with a preset hovering stability threshold. If the control signal is within the threshold range, it is considered that the current ratio meets the hovering stability requirements.

[0033] Otherwise, if the ratio does not meet the optimization conditions, the ratio parameters are adjusted according to the output error of the PID algorithm. According to the current ratio error, an adjustment value proportional to the error is directly calculated. The adjustment value = proportional coefficient × current error + integral coefficient × accumulation of past errors + differential coefficient × rate of change of error. The proportional coefficient determines the magnitude of the adjustment force. The integral coefficient takes into account the accumulation of ratio errors over a period of time in the past to eliminate the static deviation of the system. The integral coefficient determines the degree of influence of the cumulative error on the adjustment value. The differential part predicts the future error trend according to the rate of change of the ratio error and makes adjustments in advance. The differential coefficient determines the degree of influence of the rate of change on the adjustment value; determine the direction in which the rotor speed and engine power need to be adjusted, and adjust the ratio parameters according to the obtained adjustment value and adjustment direction. If the error is positive, the rotor speed needs to be reduced and the engine power needs to be increased according to the adjustment value to reduce the error; if the error is negative, the rotor speed needs to be increased and the engine power needs to be reduced according to the adjustment value. After adjusting the blending parameters, experiments or simulations are repeated to obtain new blending data. The fuel efficiency under the current blending is calculated using an empirical data model constructed based on data such as engine power, rotor efficiency, aircraft weight, and air resistance. The calculated fuel efficiency is then compared with preset optimization conditions. If the fuel efficiency meets the optimization conditions, the optimized blending parameters are input into the PID control algorithm and the fuel efficiency calculation model to determine hovering stability and fuel efficiency, respectively. If both the PID control algorithm and the fuel efficiency calculation model meet the requirements, the current blending parameters are considered the final blending parameters. Otherwise, further blending parameters are adjusted. If the fuel efficiency does not meet the optimization conditions, a global optimization algorithm, such as a genetic algorithm, particle swarm optimization (PSO), or simulated annealing, is used to iteratively search for blending parameters that maximize fuel efficiency. A set of blending parameters is initialized as candidate solutions, and the fuel efficiency calculation model is used to evaluate the fuel efficiency of each candidate solution. Based on the evaluation results, excellent candidate solutions are selected for crossover, mutation, or update operations to generate a new set of candidate solutions. This process is repeated until blending parameters that meet the fuel efficiency optimization conditions are found or the preset number of iterations is reached.

[0034] A specific example is: select the PID control algorithm and set the proportional coefficient K p , integral coefficient K i and differential coefficient K d The initial value of the current ratio (the ratio of rotor speed to engine power) and the difference between the ideal ratio is the error e. According to the PID algorithm, the control signal u is calculated: Among them, ∑e is the accumulation of past errors, is the rate of change of the error. The calculated control signal u is compared with the preset hovering stability threshold. If u is within the threshold range, it is considered that the current ratio meets the hovering stability requirement; otherwise, the next adjustment step is entered. According to the output error e of the PID algorithm, the adjustment values ​​ΔP (rotor speed adjustment value) and ΔE (engine power adjustment value) of the ratio parameters are calculated: ΔE=-ΔP (because the adjustment directions of rotor speed and engine power are opposite), where K p ′、K i ′、K d ' represents the proportional, integral, and differential coefficients used to adjust the fuel mix parameters. These coefficients can be set based on actual conditions. If e is positive, the rotor speed is reduced while the engine power is increased; if e is negative, the rotor speed is increased while the engine power is reduced. The fuel mix parameters are adjusted based on the calculated adjustment value and direction. After the adjustment, new experiments or simulations are conducted to obtain new fuel mix data. The empirical data model is used to calculate the fuel efficiency for the current fuel mix. This model is constructed based on data such as engine power, rotor efficiency, aircraft weight, and air resistance. The calculated fuel efficiency is compared with pre-set optimization criteria. If the fuel efficiency meets the criteria, the next step is reached. Otherwise, further adjustments are made using a global optimization algorithm. This algorithm is used to further adjust an initial set of fuel mix parameters as candidate solutions. The fuel efficiency calculation model is used to evaluate the fuel efficiency of each candidate solution. Based on the evaluation results, excellent candidate solutions are selected for crossover, mutation, or update operations to generate a new set of candidate solutions. This process is repeated until a fuel mix parameter that meets the fuel efficiency optimization criteria is found or the pre-set number of iterations is reached.

[0035] Assume that the initial ratio is rotor speed 1000 rpm, engine power 10kW, and the ideal ratio is rotor speed 950 rpm, engine power 10.5kW. Initial setting K p =1,K i =0.1,K d=0.01. After one PID control, the control signal u is calculated to be 0.5 (exceeding the threshold range), and the error e is 50. According to the adjustment formula, ΔP is calculated to be -10 rpm, and ΔE is calculated to be 10% of 10kW = 1kW (assuming that the engine power adjustment range is ±10%). After adjusting the ratio parameters, the experiment is repeated to obtain new ratio data, and the fuel efficiency is calculated to be 85% (lower than the optimization condition of 90%). Therefore, the genetic algorithm is used for further adjustment, a set of candidate solutions is initialized and iterative search is performed, and finally the ratio parameters that meet the fuel efficiency optimization conditions are found: rotor speed 945 rpm, engine power 10.6kW. At this time, the PID control algorithm also meets the hovering stability requirements. Therefore, this ratio parameter is the final optimization result.

[0036] S104, obtaining the adjusted flight status parameters through the real-time monitoring system, and determining whether the change in the aircraft attitude is within a preset threshold range.

[0037] First, if Figure 3 As shown, the attitude parameters of the adjusted aircraft are obtained in real time through the monitoring system. The parameters usually include pitch angle, roll angle, yaw angle, etc., which describe the attitude of the aircraft in space. The acquired real-time attitude parameters are preprocessed, which may include denoising, filtering and other steps to ensure the accuracy and reliability of the data. If there are missing or abnormal values ​​in the data, corresponding processing is required, such as interpolation, replacement or deletion. A reference attitude is selected as the benchmark. The reference time is the attitude at the beginning of the flight, or it can be the attitude at a specific time. The specific choice depends on the application requirements and flight mission. For each attitude parameter (such as pitch angle, roll angle, yaw angle), calculate the current time The difference between the current moment and the reference moment can be obtained by a simple subtraction operation, that is, the attitude value at the current moment minus the attitude value at the reference moment, and the calculated attitude difference is quantified to obtain the attitude change. The attitude change is an absolute value, which indicates the magnitude of the attitude change; it can also be a signed value, which indicates the direction and magnitude of the attitude change. The attitude change is further processed, such as taking the average value, calculating the rate of change, etc. If the attitude change exceeds the preset range value, it means that the attitude of the aircraft has changed significantly. In this case, the change is recalculated through the adjusted data, and the state parameters of the flight attitude are updated to reflect the actual attitude of the aircraft.

[0038] According to the design specifications and flight requirements of the aircraft, a normal range of attitude changes is preset. Based on the preset value, a judgment threshold is generated for comparing the real-time data with the range value. The real-time attitude data of the aircraft is compared and analyzed with the range value to determine whether the attitude change is within the preset range. Based on the preset value and the real-time data, a decision tree model is constructed. The decision tree model can determine whether the attitude change is within the range based on input features (such as attitude change amount, speed, altitude, etc.). The decision tree model is used to judge the real-time data and output a result indicating whether the attitude change is within the range. If the attitude change is within the range, no adjustment is made. Conversely, if the attitude change exceeds the threshold, the ratio of rotor speed to engine power is recalculated, a secondary adjustment is performed, and the current attitude value is obtained. The attitude value is compared with the preset threshold value using a comparison method. If the attitude value exceeds the threshold value, the rotor speed and engine power values ​​are obtained, and the rotor speed and engine power ratio is recalculated using the formula in step S102. The rotor speed and engine power values ​​are adjusted according to the ratio value. The adjustment method is used to perform a secondary adjustment to obtain new rotor speed and engine power values. The new rotor speed and engine power values ​​are applied to the system. Monitor the attitude value to determine whether it exceeds the threshold again.

[0039] A support vector machine algorithm is used to classify attitude changes. The support vector machine algorithm can identify different attitude change patterns and determine abnormal flight attitude states. Based on the support vector machine classification results, it is determined whether the aircraft's flight attitude is in an abnormal state. Based on the adjusted real-time data, a neural network model is constructed. The neural network model can learn the aircraft's flight patterns and predict attitude changes in the future. The adjusted data is used to predict the attitude change trend in the future. The predicted results of the neural network model are compared with the preset values. Based on the comparison results, it is determined whether the aircraft's body posture at the future moment is within the preset range. If the prediction results indicate that the attitude change in the future moment will exceed the range, timely adjustment measures are taken to ensure the safe flight of the aircraft.

[0040] S105, input the adjusted ratio data into the control algorithm, update the flight state parameters, generate new control instructions, and adjust the engine output power and rotor speed according to the control instructions to complete the dynamic optimization process.

[0041] Furthermore, the adjusted matching data is input into the control algorithm. The flight state parameters are updated based on the algorithm's calculation results, and new control instructions are generated. The flight state parameter changes are then determined to determine whether the parameters exceed the preset threshold. If so, the control algorithm weights are adjusted and the control instructions are regenerated. Based on the matching degree between the flight state parameters and the control instructions, the algorithm optimization direction is determined, and the algorithm parameters are updated using a machine learning model. The machine learning model is trained using historical flight data to optimize the control algorithm and improve parameter update accuracy. Based on the optimized control algorithm, the flight state parameters are updated in real time, generating more accurate control instructions. A deep learning model is used to analyze the relationship between flight state parameters and control instructions, further optimizing the algorithm generation logic. Through multiple iterative optimizations, the final control algorithm is obtained, enabling dynamic updates of flight state parameters and precise generation of control instructions.

[0042] Based on the acquired control instructions, the instruction value is extracted and the adjustment value is determined based on the instruction value. Using a preset calculation model, the adjustment value is input into the calculation model to obtain the power value and speed value. Based on the power value and speed value, the operating status of the engine and rotor is determined, and the optimization value is determined. If the optimization value exceeds the preset threshold range, the power value and speed value are recalculated; otherwise, the optimization value is applied to the dynamic optimization process. The optimized value is used to adjust the engine output power and the rotor speed at the same time to achieve dynamic optimization. The adjusted power value and speed value are recorded in the processed value, and a processing report is generated. Based on the processing report, the preset calculation model is updated to provide a reference for subsequent dynamic optimization.

[0043] Specifically, the dynamic optimization process begins with obtaining control commands. For helicopter flight control, for example, these commands include specific values ​​such as the desired climb rate of five meters per second, forward speed of fifteen meters per second, and heading angle rate of change of two degrees per second. These command values ​​are converted into engine power and rotor speed adjustment values ​​using a pre-set algorithm. A pre-set calculation model is established based on the engine performance curve and rotor aerodynamic characteristics. By inputting adjustment values ​​such as climb rate and speed, the required engine power value is determined. For example, a required power of 600 kilowatts corresponds to a main rotor speed of 350 rpm. This calculation method considers the matching relationship between engine operating characteristics and rotor efficiency. Determining the operating status of power and speed values ​​involves multiple factors. For the engine, the power output must be checked to ensure it is within the rated range and fuel consumption is within the economic range. For the rotor, the focus is on whether the tip speed exceeds the sonic limit and whether the blade loads are within the structural tolerances. The optimization values ​​are determined based on the optimal engine operating point and the maximum rotor efficiency point. When the optimization calculation determines that the main rotor speed should be adjusted to 380 rpm, the system first checks whether this value exceeds a pre-set threshold. If the threshold is exceeded, such as the maximum allowable speed of 370 rpm, recalculation is required, possibly through adjustments to the climb rate or other means. The optimized value that meets the threshold is used for actual adjustments. During dynamic optimization, the engine control system adjusts fuel supply based on the optimized value, gradually increasing power output to the target value. Simultaneously, the rotor blade angle is adjusted via the pitch control mechanism to achieve the optimized speed. This process requires coordination to avoid mismatches between engine and rotor speeds. The processing report records the changes in power and speed before and after optimization, as well as the dynamic response of various parameters. For example, the transient characteristics of the engine when adjusting from 500 kW to 600 kW, or the vibration characteristics of the rotor when increasing from 320 to 350 rpm, are recorded. This data is used to evaluate the optimization results. The preset calculation model is updated using an adaptive method, continuously revising model parameters based on actual operating data. For example, if actual power consumption is consistently 5% higher than predicted, the efficiency coefficient in the model is adjusted accordingly. This continuous optimization ensures model accuracy and provides a more reliable basis for subsequent dynamic optimization.

[0044] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: by acquiring the flight state parameters of the helicopter in real time, including rotor torque, engine speed, fuel flow and body attitude, etc., and inputting them into a pre-established mathematical model, the optimal ratio range of rotor speed and engine power under the current load conditions is calculated. The present invention adopts a preset control algorithm to determine whether the current ratio meets the optimization conditions of hovering stability and fuel efficiency, and dynamically adjusts the ratio if not. The real-time monitoring system is used to determine whether the change in the body attitude is within the preset threshold range, and a secondary adjustment is performed if necessary. Finally, the present invention generates new control instructions based on the optimized ratio data, adjusts the engine output power and rotor speed, realizes dynamic optimization of the helicopter in the hovering state, and improves flight stability and fuel efficiency.

[0045] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for controlling an automatic go-around of a helicopter, characterized in that: The method comprises: S101, obtaining helicopter flight status parameters, including rotor torque, engine speed, fuel flow, and body attitude; S102, inputting the flight state parameters into a pre-established mathematical model to calculate the ratio range of the rotor speed and engine power under the current load condition; S103: Using a preset control algorithm, determine whether the current ratio meets the requirements for hovering and fuel efficiency. If the ratio does not meet the requirements, dynamically adjust the ratio of rotor speed to engine power based on the ratio range output by the mathematical model. S104: The real-time monitoring system obtains the adjusted flight status parameters and determines whether the aircraft attitude change is within a preset threshold range. If the attitude change exceeds the threshold, the ratio of rotor speed to engine power is recalculated and a secondary adjustment is performed. In step S105, the adjusted ratio data is input into the control algorithm, the flight state parameters are updated, and new control instructions are generated. According to the control instructions, the engine output power and rotor speed are adjusted to complete the dynamic optimization process.

2. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: The method of obtaining helicopter flight state parameters, including rotor torque, engine speed, fuel flow, and body attitude, includes: using sensors to obtain the helicopter's rotor torque and engine speed, recording fuel consumption data through a fuel flow meter, and collecting body attitude information using an inertial measurement unit; and calculating the power system efficiency based on the rotor torque and engine speed.

3. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: The method of inputting flight state parameters into a pre-established mathematical model and calculating the ratio range of rotor speed and engine power under current load conditions includes: obtaining the flight state parameters and converting them into a format recognizable by the mathematical model; using a preset mathematical algorithm to process the converted flight parameter data; determining an initial value range of the rotor speed according to the load conditions; calculating the engine power value within the speed range; if the power value exceeds a preset threshold, adjusting the speed value and recalculating; and determining the optimal ratio of speed to power through an iterative algorithm.

4. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: The method of using a preset control algorithm to determine whether the current ratio meets the conditions of hovering and fuel efficiency includes: obtaining a preset control algorithm; determining whether the current ratio reaches a preset threshold for hovering based on an output result of the control algorithm; if hovering reaches the preset threshold, no adjustment is required; if hovering does not reach the preset threshold, adjusting the ratio parameters and re-acquiring new ratio data; and determining whether the current ratio meets the conditions of fuel efficiency using a fuel efficiency calculation model; if the fuel efficiency meets the conditions, no adjustment is required; if the fuel efficiency does not meet the conditions, adjusting the ratio using the algorithm.

5. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: If the ratio does not meet the conditions, the ratio of rotor speed to engine power is dynamically adjusted based on the ratio range output by the mathematical model, including: obtaining the current ratio value of rotor speed to engine power; judging whether the ratio value meets the preset optimization value; if the ratio value meets the optimization value, no adjustment is required; if the ratio value does not meet the optimization value, a dynamic adjustment algorithm is used to calculate the adjustment values ​​of rotor speed and engine power, and the adjusted rotor speed and engine power values ​​are input into the control system.

6. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: The real-time monitoring system is used to obtain adjusted flight status parameters and determine whether the change in the aircraft attitude is within a preset threshold range. If the attitude change does not exceed the threshold, no adjustment is performed. If the attitude change exceeds the threshold, the ratio of rotor speed to engine power is recalculated and a secondary adjustment is performed, including: obtaining a current attitude value, and comparing the attitude value with a preset threshold using a comparison method; if the attitude value exceeds the threshold, obtaining the rotor speed and engine power values; recalculating the ratio of rotor speed to engine power values ​​using a formula; adjusting the rotor speed and engine power values ​​according to the ratio value; and performing a secondary adjustment using an adjustment method to obtain new rotor speed and engine power values.

7. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: Inputting the adjusted matching data into the control algorithm, updating the flight state parameters, and generating new control instructions includes: using the adjusted matching data, inputting the control algorithm, updating the flight state parameters according to the algorithm calculation results, and generating new control instructions; obtaining the flight state parameter change value, determining whether the parameter exceeds a preset threshold value, and if so, adjusting the control algorithm weight and regenerating the control instruction; If not exceeded, no adjustment is made.

8. The automatic go-around control method based on a helicopter according to claim 1, characterized in that: The engine output power and rotor speed are adjusted according to the control instruction to complete the dynamic optimization process, including: obtaining the control instruction, extracting the instruction value of the control instruction, determining the adjustment value according to the instruction value, inputting the adjustment value into the calculation model, and obtaining the power value and speed value.

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