A gain adaptive tail-sitter aircraft control system and control method based on distributed dynamic pressure detection

Abstract

The application discloses a gain self-adaptive tail seat aircraft control system and control method based on distributed dynamic pressure detection, and first acquires dynamic pressure distribution data according to a distributed dynamic pressure detection subsystem, and calculates average dynamic pressure, and then adjusts the gain of a controller output according to the average dynamic pressure, and compensates and corrects the output. The application preferably solves the problem of irregular model change in the control of the tail seat aircraft in the vertical plane transition conversion, and improves the safety and smoothness of the transition conversion control of the tail seat aircraft.

Description

Technical Field

[0001] This invention is a gain-adaptive tail-seat aircraft control system based on distributed dynamic pressure detection, belonging to the field of flight control. On the one hand, this patent effectively solves the problem of irregular model changes in the transition control of tail-seat aircraft with control surfaces, improving the safety and smoothness of the transition process. This allows tail-seat vertical take-off and landing aircraft to achieve single-mode continuous control during the transition process without the need for mode switching and gain scheduling between level flight cruise and vertical take-off and landing. At the same time, this method can be applied to larger tail-seat UAVs, exhibiting good platform versatility. Background Technology

[0002] Vertical takeoff and landing (VTOL) fixed-wing aircraft are a type of aircraft that combines the characteristics of multi-rotor and fixed-wing aircraft. They possess the advantages of both multi-rotor and helicopter VTOL, offering convenient takeoff and landing with low requirements for deployment environment and facilities, while also possessing the high-speed level flight capability, long range, and long loiter time of fixed-wing aircraft. Tail-seat aircraft are the simplest type in terms of configuration, lacking both the complex tilting mechanism of tilt-lift VTOL aircraft and the excessive power and wind resistance of compound VTOL aircraft, making them an important research direction in the field of VTOL fixed-wing aircraft. They have significant application value in fields such as aerial surveying, military reconnaissance, and logistics transportation. Tail-seat aircraft are generally unmanned aerial vehicles (UAVs), and their flight states are generally divided into level flight cruise, vertical hovering, and transitional flight between these two phases. While the level flight and vertical hovering phases can be controlled as fixed-wing and multi-rotor aircraft respectively, and both have mature and stable control methods, the control of the transition phase, especially the transition from high-speed level flight to hovering takeoff and landing, involves drastic model changes and presents the highest level of flight control difficulty.

[0003] In previous transition control, especially the transition from level flight to hovering, to avoid control problems at high angles of attack, a flight mode of vertical pull-up followed by slow descent was generally chosen. This approach is slow and energy-intensive. To achieve vertical-to-level transition control for tail-seat vertical takeoff, common control methods can be divided into multi-mode switching and parameter dynamic estimation methods to cope with changes in model structural parameters. Multi-mode switching control is discontinuous and prone to problems during switching, while parameter dynamic estimation requires an accurate model. Neither method possesses good versatility and robustness. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a gain-adaptive tailplane control system and control method based on distributed dynamic pressure detection.

[0005] To achieve the above objectives, the technical solution of the present invention is summarized as follows:

[0006] A gain-adaptive tail-seat aircraft control system based on distributed dynamic pressure detection is characterized by mainly consisting of a distributed dynamic pressure detection subsystem and an angular velocity control algorithm, wherein the distributed dynamic pressure detection subsystem outputs average control surface dynamic pressure. The angular velocity control algorithm is based on the average rudder surface dynamic pressure. The control gain is adjusted. The distributed dynamic pressure detection subsystem consists of a hardware detection section and a software processing section. The hardware detection section of the distributed dynamic pressure detection subsystem consists of a dynamic pressure probe array arranged on the wing surface and in front of the control surface shaft, and several corresponding differential pressure airspeed sensors. The dynamic pressure probe array and the several differential pressure airspeed sensors are connected through an air duct. The software processing section of the distributed dynamic pressure detection subsystem receives the dynamic pressure distribution data acquired by the hardware detection section of the distributed dynamic pressure detection subsystem as input. and the corresponding rudder surface area, The output is the average dynamic pressure of the control surface. The specific calculation method is as follows:

[0007]

[0008] in The average dynamic pressure of the control surface. The dynamic pressure of the control surface detected by the i-th dynamic pressure probe is... Let be the area of ​​the rudder surface corresponding to the i-th dynamic pressure probe. The total area of ​​the rudder surface is denoted by , and its value is . .

[0009] The input to the angular velocity control algorithm is the measured angular velocity obtained from the gyroscope. and the given target angular velocity The output is the uncompensated control quantity. Where k is the axis number of the machine body. One of them. The angular velocity control algorithm allows parameters to vary within a certain range. If the distributed dynamic pressure detection subsystem malfunctions or the measurement is inaccurate, as long as it can still output approximate measurement parameters following changes in flight status, the final transition control effect will not be significantly affected.

[0010] Furthermore, the angular velocity control algorithm can specifically be an active disturbance rejection control algorithm, which includes an extended state observer, a tracking differentiator, state feedback, and total disturbance compensation; the steps of the active disturbance rejection control algorithm are as follows:

[0011] Step 1: Expanded State Observer Update: Based on the angular velocity measured by the gyroscope The control quantity output by the controller at the previous moment and the average dynamic pressure of the control surface Substitute the values ​​into the extended state observer to update the state variables. Where j is the order of the state quantity corresponding to each body axis k, and k is the body axis label. One of them;

[0012] Step 2: Arrange the transition process: Use a tracking differentiator to process the given input angular velocity. Preprocessing is performed to plan a uniform acceleration and deceleration process for a given step change in angular velocity, and the preprocessed given angular velocity and angular acceleration are output. Where k is the axis number of the machine body. One of them;

[0013] Step 3: Calculate the state feedback: Based on the state quantity output by the extended state observer described in Step 1. The given angular velocity and given angular acceleration after preprocessing as described in Step 2 Substituting the state feedback rate, we obtain the ideal control quantity for the pure integral model design. Where k is the axis number of the machine body. One of them;

[0014] Step 4: Total Disturbance Compensation: Based on the estimate of the equivalent total disturbance output by the extended state observer described in Step 1. The ideal control quantity described in Step 3 is subjected to disturbance compensation to cancel it out, so that the actual dynamics of the aircraft approach the ideal pure integral model. Where k is the axis number of the machine body. One of them;

[0015] Furthermore, the control method of the gain adaptive tail-spinner aircraft control system of the present invention is characterized by comprising the following steps:

[0016] Step 1: Obtain the dynamic pressure distribution data of the control surface through the hardware component of the distributed dynamic pressure detection subsystem. ;

[0017] Step 2: Based on the dynamic pressure distribution data of the control surface obtained in Step 1 and the corresponding rudder surface area The average rudder surface dynamic pressure is calculated through the software portion of the distributed dynamic pressure detection subsystem. ;

[0018] Step 3: Measure the angular velocity obtained from the gyroscope. and the given angular velocity input The angular velocity control algorithm outputs the uncompensated control quantity. Where k is the axis number of the machine body. One of them;

[0019] Step 4: Based on the average dynamic pressure of the control surface obtained in Step 2 The output obtained in step 3 is subjected to dynamic voltage gain compensation to obtain a suitable control quantity. This enables precise control of the aircraft, where k is the axis number of the aircraft. One of them;

[0020] Furthermore, the specific method for dynamic voltage gain compensation in step 4 is as follows:

[0021]

[0022] in These are the final pitch and roll control values ​​after compensation. These are the uncompensated control quantities. The mean dynamic pressure of the control surface. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a block diagram of a gain adaptive control method for distributed dynamic pressure detection and compensation;

[0025] Figure 2 This is a schematic diagram of the aircraft's coordinate axes;

[0026] Figure 3 This is a schematic diagram of the aircraft's coordinate axes;

[0027] Figure 4 This is the simplest two-region airfoil flow field distribution diagram;

[0028] Figure 5 This is a simplified distribution diagram of two-region dual dynamic pressure probes on the wing;

[0029] Figure 6 This is a schematic diagram of the side mounting method for a single dynamic pressure probe;

[0030] Figure 7 This is a block diagram of a one-dimensional second-order active disturbance rejection control algorithm;

[0031] Figure 8 This is a graph showing the simulated control effect of angular velocity when the dynamic pressure changes and its measured value remains constant.

[0032] Figure 9 It is a curve of the angular velocity simulation control effect, which is relatively slow due to dynamic pressure follow-up measurement;

[0033] Figure 10 It is a curve of the angular velocity simulation control effect with accurate dynamic pressure follow-up measurement;

[0034] Reference numerals: 1. Left motor; 2. Right motor; 3. Left servo; 4. Right servo; 5. Left propeller; 6. Right propeller; 7. Left wing; 8. Right wing; 9. Left control surface; 10. Right control surface; 11. Fuselage; 101. Propeller slipstream external dynamic pressure probe; 102. Propeller slipstream internal dynamic pressure probe; a. Distributed dynamic pressure detection subsystem; a1. Hardware detection part of distributed dynamic pressure detection subsystem; a2. Software processing part of distributed dynamic pressure detection subsystem; b. Angular velocity control algorithm; c. Dynamic pressure gain adjustment of control quantity; d. Gyroscope. Detailed Implementation

[0035] To make the above-mentioned objectives, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to specific examples.

[0036] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0038] This invention proposes a gain-adaptive tailseat aircraft control system and control method based on distributed dynamic pressure detection. The tailseat aircraft mainly consists of 1 left motor, 2 right motor, 3 left servo motor, 4 right servo motor, 5 left propeller, 6 right propeller, 7 left wing, 8 right wing, 9 left control surface, 10 right control surface, and 11 fuselage.

[0039] like Figure 1 As shown, firstly, distributed dynamic pressure detection sensors are arranged according to the wing flow field distribution to obtain dynamic pressure distribution data of the control surface; based on the dynamic pressure distribution data of the control surface obtained by the distributed dynamic pressure detection sensors, the average control surface dynamic pressure is calculated; based on the measured angular velocity obtained by the gyroscope and the input given angular velocity, the control quantity is output through the control algorithm; based on the average control surface dynamic pressure, dynamic pressure compensation is performed on the output quantity to obtain a suitable control deflection, thereby achieving precise control of the aircraft;

[0040] To facilitate subsequent algorithm description, the body coordinate axes are defined as needed, as follows: Figure 2 and Figure 3 As shown.

[0041] The following is a specific implementation step.

[0042] The hardware component of the distributed dynamic pressure detection subsystem obtains the dynamic pressure distribution data of the control surface.

[0043] like Figure 4 As shown, the wing flow field is divided into regions not covered by the propeller slipstream, and the control surface coverage area is... ,like Figure 5 As shown at position 101, a dynamic pressure probe is placed in this area to obtain the dynamic pressure of the control surface in this area. And the propeller slipstream coverage area, its control surface coverage area is ,like Figure 5 As shown at position 202, a dynamic pressure probe is placed in this area to obtain the dynamic pressure of the control surface in this area. The sides of the dynamic pressure probes in both regions are as follows Figure 6 It is positioned in front of the rudder shaft as shown.

[0044] 2) The average dynamic pressure of the control surface is calculated through the software part of the distributed dynamic pressure detection subsystem.

[0045] Based on the dynamic pressure distribution of the control surfaces, the average dynamic pressure of the control surfaces is calculated. The calculation method for the average dynamic pressure of the control surfaces is as follows:

[0046] (1)

[0048] in The average dynamic pressure of the control surface. These are the rudder surface dynamic pressures outside the propeller slipstream coverage area and the rudder surface dynamic pressures inside the propeller slipstream coverage area, respectively. These refer to the control surface area outside the propeller slipstream coverage area and the control surface area inside the propeller slipstream coverage area, respectively.

[0049] 3) Employ active disturbance rejection control algorithm to obtain uncompensated control quantity.

[0050] For example Figure 7 Taking the one-dimensional second-order active disturbance rejection control algorithm block diagram shown as an example, a specific angular velocity control algorithm is designed. As the reference input signal, The reference signal is processed by the tracking differentiator. For state error, For ideal control quantity, This represents the actual control quantity after disturbance compensation. This is an estimate of the gain coefficient of the control quantity. External disturbances For controlled quantity, The measured filter value of the controlled variable is obtained by the measurement filtering system by default. For the observations corresponding to the state variables, The extended state observations are used to track and estimate internal and external disturbances at the input, in addition to the control force.

[0051] Based on the model, design expansion state observers on three body axes, and then handle the subsequent operations. Function description:

[0052] It is a nonlinear function, and its specific expression is:

[0053] (2)

[0055] It is a nonlinear factor. This is the filter factor. The function is composed of segments of proportional and exponential functions, where the power of the exponential function is... Generally, the value is between 0 and 1. The straight line truncates the exponential function, retaining... The part is then symmetrical about the origin to form... Function, linear interval width range .

[0056] Therefore, the process by which the active disturbance rejection control algorithm calculates the control output is as follows:

[0057] Step 1: First, construct a three-axis extended state observer and update the extended state observer: Based on the angular velocity measured by the gyroscope, the control quantity output by the controller at the previous moment, and the average dynamic pressure of the control surface, substitute them into the extended observer model to update the state.

[0058] The x-axis is:

[0059] (3)

[0061] in The angular velocity observer error along the x-axis. Angular velocity along the x-axis and its rate of change The corresponding observations, The observation is the equivalent total disturbance along the x-axis. The gain coefficient of the x-axis observer. These are the second-order coefficients of the x-axis angular velocity observer. The average dynamic pressure of the control surface. This is the pitch control surface coefficient.

[0062] The y-axis is:

[0063] (4)

[0065] in The error is the angular velocity observer error along the y-axis. Angular velocity along the y-axis and its rate of change The corresponding observations, The observation is the equivalent total disturbance along the y-axis. This represents the gain coefficient of the y-axis observer. The coefficients of the second-order term on the y-axis are... This is the gain for differential control.

[0066] The z-axis is:

[0067] (5)

[0069] in The error of the z-axis angular velocity observer. The z-axis angular velocity and its rate of change The corresponding observations, The observation is the equivalent total disturbance along the z-axis. The z-axis observer gain coefficient. These are the second-order coefficients of the z-axis angular velocity observer. The average dynamic pressure of the control surface. This is the roll control surface coefficient.

[0070] Step 2: Arrange the transition process: Use a tracking differentiator to preprocess the input given angular velocity, plan a uniform acceleration and deceleration process for the step change of the given angular velocity, and output the preprocessed given angular velocity and angular acceleration.

[0071] Based on the aircraft's maneuverability, a tracking differentiator is designed on three body axes, employing... The differential tracker constructed by the function is used as a feedforward link to preprocess the input signal and extract the processed differential signal in order to design a second-order state feedback link and obtain better input response dynamics.

[0072] (6)

[0074] in Given the triaxial angular velocities as input, and Target angular velocity The pre-planned command signal and the rate of change of the command signal and To track the fastest synthesis function in the differentiator The parameters. Among them, the steepest synthesis function. It defines a two-input nonlinear function, and its operation process is as follows:

[0075] (7)

[0077] in These are all temporary variables in the calculation process. For function parameters, For the function output value, For input values. For symbolic functions, the expression is:

[0078] (8)

[0080] Step 3: Calculate the state feedback: Based on the angular velocity and angular acceleration output by the extended state observer as described in Step 1, and the preprocessed given angular velocity and given angular acceleration as described in Step 2, substitute them into the state feedback rate to obtain the ideal control quantity designed for the pure integral model.

[0081] use The function design uses nonlinear feedback for three body axes to obtain the ideal control variables for the three axes. :

[0082] (9)

[0084] in The damping coefficient for state feedback. and The fastest synthesis function in state feedback The parameters.

[0085] Step 4: Total Disturbance Compensation: Based on the estimated equivalent total disturbance output by the extended state observer described in Step 1, the ideal control quantity described in Step 3 is compensated for to make the actual dynamics of the aircraft approach the ideal pure integral model.

[0086] The total disturbance of the three axes is compensated to obtain the actual y-axis differential control value. and control surfaces on the x and z axes without hydrodynamic compensation and :

[0087] (10)

[0089] in The rate of change of angular velocity along the x-axis The corresponding observations, The observation is the equivalent total disturbance along the x-axis. These are the second-order coefficients of the x-axis angular velocity observer. For pitch control surface coefficients, The rate of change of angular velocity along the y-axis The corresponding observations, The observation is the equivalent total disturbance along the y-axis. These are the second-order coefficients of the y-axis angular velocity observer. For differential control gain The rate of change of z-axis angular velocity The corresponding observations, The observation is the equivalent total disturbance along the z-axis. These are the second-order coefficients of the z-axis angular velocity observer. This is the roll control surface coefficient.

[0090] 4) Perform dynamic pressure compensation on the control quantity and allocate the control quantity.

[0091] The dynamic pressure compensation method is as follows:

[0092] (11)

[0094] in These are the final pitch and roll control values ​​after compensation. These are the uncompensated pitch and roll control values, respectively. The mean dynamic pressure of the control surface.

[0095] The obtained control quantities are then allocated to obtain the control signal for left motor 1. and the control signal of the right motor 2 and the control signal of the left servo motor 3 and the control signal of the right servo motor 4 :

[0096] (12)

[0098] in This is the master throttle signal, which can be manually input via the throttle lever or automatically adjusted by the altitude / speed controller. These are the neutral point zero values ​​for the left and right servos, respectively, which are obtained through on-site adjustments during aircraft assembly.

[0099] Figure 8 The display shows the changes in dynamic pressure and angle of attack during actual transition flight. Under the condition of constant dynamic pressure estimation, that is, fixed control gain, the pitch control effect of the aircraft relies entirely on the total disturbance estimation of the active disturbance rejection controller to cope with the large changes in the model. The pitch angular velocity response exhibits large oscillations and delays. Figure 9 The effect of dynamic pressure estimation lag is shown. Even if there is a small error in the average dynamic pressure measurement estimation, as long as the distributed dynamic pressure detection can reflect the real-time change pattern of the average dynamic pressure, the control effect is hardly weakened with the assistance of the wide parameter characteristics of the active disturbance rejection controller. It can be seen that as long as the distributed dynamic pressure detection system provides a roughly accurate dynamic control gain, it can have a good effect throughout the entire flight process. Figure 10 The distributed dynamic pressure detection system accurately estimates the dynamic pressure changes during the simulated transition process, achieving ideal control results with low latency.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A gain-adaptive tail-seat aircraft control system based on distributed dynamic pressure detection, characterized in that, It mainly consists of a distributed dynamic pressure detection subsystem and an angular velocity control algorithm. The distributed dynamic pressure detection subsystem outputs the average dynamic pressure of the control surface, and the angular velocity control algorithm is based on the average dynamic pressure of the control surface. The control gain is adjusted. The distributed dynamic pressure detection subsystem consists of a hardware detection section and a software processing section. The hardware detection section of the distributed dynamic pressure detection subsystem consists of a dynamic pressure probe array arranged on the wing surface and in front of the control surface shaft, and several corresponding differential pressure airspeed sensors. The dynamic pressure probe array and the several differential pressure airspeed sensors are connected through an air duct. The software processing section of the distributed dynamic pressure detection subsystem receives the dynamic pressure distribution data acquired by the hardware detection section of the distributed dynamic pressure detection subsystem as input. and the corresponding rudder surface area, The output is the average dynamic pressure of the control surface. The specific calculation method is as follows: in The average dynamic pressure of the control surface. The dynamic pressure of the control surface detected by the i-th dynamic pressure probe is... Let be the area of ​​the rudder surface corresponding to the i-th dynamic pressure probe. The total area of ​​the rudder surface is denoted by , and its value is . ; The input to the angular velocity control algorithm is the measured angular velocity obtained from the gyroscope. and the given target angular velocity The output is the uncompensated control quantity. Where k is the axis number of the machine body. One of them; The angular velocity control algorithm is an active disturbance rejection control algorithm, which includes an extended state observer, a tracking differentiator, state feedback, and total disturbance compensation. The steps of the active disturbance rejection control algorithm are as follows: Step 1: Expanded State Observer Update: Based on the angular velocity measured by the gyroscope The control quantity output by the controller at the previous moment and the average dynamic pressure of the control surface Substitute the values ​​into the extended state observer to update the state variables. Where j is the order of the state quantity corresponding to each body axis k, and k is the body axis label. One of them; Step 2: Arrange the transition process: Use a tracking differentiator to process the given input angular velocity. Preprocessing is performed to plan a uniform acceleration and deceleration process for a given step change in angular velocity, and the preprocessed given angular velocity and angular acceleration are output. Where k is the axis number of the machine body. One of them; Step 3: Calculate the state feedback: Based on the state quantity output by the extended state observer described in Step 1. The given angular velocity and given angular acceleration after preprocessing as described in Step 2 Substituting the state feedback rate, we obtain the ideal control quantity for the pure integral model design. Where k is the axis number of the machine body. One of them; Step 4: Total Disturbance Compensation: Based on the estimate of the equivalent total disturbance output by the extended state observer described in Step 1. The ideal control quantity described in Step 3 is subjected to disturbance compensation to cancel it out, so that the actual dynamics of the aircraft approach the ideal pure integral model. Where k is the axis number of the machine body. One of them.

2. A control method based on the gain adaptive tail-seat aircraft control system as described in claim 1, characterized in that, Includes the following steps: Step 1: Obtain the dynamic pressure distribution data of the control surface through the hardware component of the distributed dynamic pressure detection subsystem. ; Step 2: Based on the dynamic pressure distribution data of the control surface obtained in Step 1 and the corresponding rudder surface area The average rudder surface dynamic pressure is calculated through the software portion of the distributed dynamic pressure detection subsystem. ; Step 3: Measure the angular velocity obtained from the gyroscope. and the given angular velocity input The angular velocity control algorithm outputs the uncompensated control quantity. Where k is the axis number of the machine body. One of them; Step 4: Based on the average dynamic pressure of the control surface obtained in Step 2 The output obtained in step 3 is subjected to dynamic voltage gain compensation to obtain a suitable control quantity. This enables precise control of the aircraft, where k is the axis number of the aircraft. One of them.

3. The control method according to claim 2, characterized in that, The specific method for dynamic voltage gain compensation in step 4 is as follows: in These are the final pitch and roll control values ​​after compensation. These are the uncompensated control quantities. The mean dynamic pressure of the control surface.

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