Aircraft attitude control methods, systems, equipment and media

Abstract

This invention relates to an attitude control method, system, device, and medium for aircraft. The attitude control method, used for attitude control of a wide-range unpowered aircraft, includes: S101 using an MPC as the attitude controller; S102 using a simplified state space as the required state space model for the attitude controller; S103 obtaining the state space deviation caused by the simplification of the state space observed by an extended state observer; S104 superimposing the observed state space deviation onto the state space as the input to the attitude controller; S105 constructing a predicted state space for a predetermined number of future iterations using the state space deviation through iterative optimization; and S106 constructing a loss function and using a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal control output. This invention eliminates the need for separate designs for different operating conditions, reducing design workload and computational load, and allows for real-time operation within a flight control computer.

Description

Technical Field

[0001] This invention relates to the field of attitude control technology for wide-area unpowered aircraft. In particular, it relates to an attitude control method, system, device, and medium for aircraft. Background Technology

[0002] Wide-range aircraft have large airspace and speed envelopes. Traditionally, this involves designing corresponding controllers for different speeds and altitudes, and selecting different controllers and control parameters based on flight conditions. This approach requires extensive work during the design phase, designing controllers for different operating conditions for selection during flight. However, in actual flight, aircraft are affected by various external environments. If the actual operating conditions differ significantly from the design conditions, and the designed controller cannot meet the actual requirements, serious flight accidents may occur. How to design attitude controllers that cannot adapt to different operating conditions, and how to address the computational complexity of state-space algorithms that make real-time operation difficult, are urgent problems that need to be solved. Summary of the Invention

[0003] This invention provides an attitude control method, system, device, and medium for aircraft, to solve the problems that attitude controllers cannot adapt to different operating conditions and that the computational complexity of state-space algorithms makes real-time operation difficult.

[0004] To achieve the above objectives, in a first aspect, the present invention relates to an attitude control method for an aircraft, used for attitude control of a wide-range unpowered aircraft, comprising:

[0005] The S101 uses an MPC as its attitude controller;

[0006] The simplified state space of S102 serves as the required state space model for the attitude controller. The pitch angle θ and pitch rate q two-dimensional signals, which are accurately measured by the sensors, are used as the state variables of the attitude controller. The simplified state space omits at least the aircraft's speed, angle of attack, and altitude.

[0007] S103 acquires the state space deviation caused by the simplification of the state space as observed by the extended state observer ESO;

[0008] S104 superimposes the observed state space deviation onto the state space as the input to the attitude controller;

[0009] S105 uses the state space deviation to construct a future preset number of predicted state spaces through iterative optimization;

[0010] S106 constructs a loss function and uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal rudder output.

[0011] Preferably, the method includes: acquiring the state space deviation observed by the extended state observer (ESO) due to the simplification of the state space, including:

[0012] The linearized formulas for pitch angle θ and pitch rate q are obtained as follows: in

[0013] M q For the partial derivative of the pitch moment M with respect to the pitch rate q, M δe Let M be the pitch moment relative to the elevator amount δ e The partial derivative;

[0014] The state space is as follows:

[0015] The influence of velocity V, angle of attack α, height H, and other omitted state variables affecting angle and angular rate is collectively referred to as deviation f. The linearization formula can be described as follows:

[0016]

[0017] The step of superimposing the observed state space deviation onto the state space as the input to the attitude controller specifically involves:

[0018] The elevator amount δ e As the system input u, with the addition of a third state x3 = f, the state space is: Where x1 in the first state is the pitch angle θ, x2 in the second state is the angular velocity q, and the derivative of the deviation is... The output is y = θ = x1, and f is the state-space deviation.

[0019] It also includes introducing an error feedback coefficient L to ensure the stability of the extended state observer (ESO) and to estimate the state space deviation in real time, specifically:

[0020] The extended state observer (ESO) is constructed as follows:

[0021] L is the feedback vector.

[0022] Configure the observer poles to have the same root to obtain the feedback vector L:

[0023] s 3 +β1s 2 +β2s+β3=(s+w0) 3 ,β1=3w0, in w0 is

[0024] The observer band has widths β1, β2, and β3, which are the coefficients in front of the observer, and s is a complex variable.

[0025] Preferably, the step of using the state space deviation to construct the predicted state quantity for a predetermined number of future iterations through iterative optimization includes:

[0026] Using the state space with state space bias as a reference, n predictions are made about the future state. The state space with state space bias is:

[0027] Let the predicted initial state be x(k);

[0028] x(k+n|k) represents the state of the nth subsequent prediction based on time k, yielding X. k =Mx(k)+FU k +P,

[0029] in:

[0030]

[0031] X k Both M and F are 2(n+1)×1 dimensional vectors, and F is a 2(n+1)×n dimensional vector. k Let P be an n×1 dimensional vector, and let P be a 2(n+1)×1 dimensional vector.

[0032] Preferably, the construction of the loss function, using a quadratic programming algorithm to find the input that minimizes the loss function, and using it as the optimal steering output, includes:

[0033] Define R as the system's input command. Then, the input matrix R is composed of 2(n+1)×1 dimensional components. k =(R RR...R) T ;

[0034] Define Q as the weight matrix of the state variable (θ, q). Where a1 is the weighting coefficient of the state variable θ, and a2 is the weighting coefficient of the state variable q, they can be combined to form a 2(n+1)×2(n+1) dimensional diagonal weight matrix.

[0035] Let I be the weight coefficient for the input. Then, the n×n weight diagonal matrix is ​​obtained from the weight coefficient I.

[0036] Construct the loss function: Where, E=Mx(k)-R k ,

[0037] The quadratic programming algorithm is used to find the input that minimizes the loss function, which serves as the optimal control output for the current aircraft state.

[0038]

[0039] Preferably, it also includes S107: using S103 to S106 in real time to obtain the optimal control output of the aircraft flying in the air at any given time.

[0040] To achieve the above objectives, in a second aspect, the present invention relates to an aircraft attitude control system for attitude control of a wide-range unpowered aircraft, comprising:

[0041] Attitude control module, used to employ MPC as an attitude controller;

[0042] A simplified state space module is used to simplify the state space as the required state space model of the attitude controller. The pitch angle θ and pitch rate q two-dimensional signals accurately measured by the sensor are used as the state variables of the attitude controller. The simplified state space omits at least the aircraft's speed, angle of attack, and altitude.

[0043] An error acquisition module is used to acquire the state space deviation caused by the simplification of the state space observed by the extended state observer (ESO).

[0044] An error superposition module is used to superimpose the observed state space deviation onto the state space as the input of the attitude controller;

[0045] The prediction module is used to construct the state quantity to be predicted a preset number of times in the future by using the state space deviation through iterative optimization;

[0046] The optimal rudder output module is used to construct a loss function and uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal rudder output.

[0047] To achieve the above objectives, in a third aspect, the present invention also relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the above-described aircraft attitude control method.

[0048] To achieve the above objectives, in a fourth aspect, the present invention also relates to a computer-readable storage medium storing instructions that, when executed, perform the above-described aircraft attitude control method.

[0049] The present invention relates to an aircraft attitude control method, system, device, and medium, which has the following advantages compared to the prior art:

[0050] This invention uses MPC (Model Predictive Control) as the attitude controller. Due to limited flight control computer resources, to reduce computational load, a simplified state space is used as the required state space model for MPC, and the two-dimensional signals of angle θ and angular rate q, which can be accurately measured by sensors, are used as state variables. To compensate for errors caused by state simplification, an Extended State Observer (ESO) is used to observe the state space deviation caused by the omission of states, and the observed error is superimposed on the state space as the model input for MPC.

[0051] The attitude controller designed in this invention only needs to know the simplified state space of the aircraft. It obtains the optimal control output through iterative optimization, eliminating the need for separate designs for different operating conditions and greatly reducing the design workload. An extended state observer is used to compensate for state space output deviations caused by model simplification and the external environment, making the linearized aircraft model more closely resemble reality and facilitating MPC calculations that are more adapted to actual flight conditions. The simplified state space significantly reduces the computational complexity of the algorithm, enabling real-time operation in the flight control computer. The controller input consists of two command signals: angle θ and angular rate q. The angular rate command uses proportional control to further correct control errors and reduce static errors. Attached Figure Description

[0052] Figure 1 The following is a flowchart of an aircraft attitude control method according to Embodiment 1 of the present invention. Figure 1 ;

[0053] Figure 2 The following is a flowchart of an aircraft attitude control method according to Embodiment 1 of the present invention. Figure 2 ;

[0054] Figure 3 This is the attitude angle response curve of an aircraft attitude control method according to Embodiment 1 of the present invention;

[0055] Figure 4 This is a velocity variation curve of an aircraft attitude control method according to Embodiment 1 of the present invention;

[0056] Figure 5 This is the altitude change curve of an aircraft attitude control method according to Embodiment 1 of the present invention;

[0057] Figure 6 This is a schematic diagram of the structure of an aircraft attitude control system according to Embodiment 2 of the present invention;

[0058] Figure 7This is a schematic diagram of the structure of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0059] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0060] Example 1

[0061] For an aircraft attitude control method, please refer to [link / reference]. Figures 1-5 It is used for attitude control of wide-range unpowered aircraft and is implemented by the flight control computer, such as... Figure 1 As shown, the procedure includes the following steps: S101 to S106.

[0062] The S101 uses MPC (Model Predictive Control) as its attitude controller.

[0063] The simplified state space of S102 serves as the required state space model for the attitude controller. The pitch angle θ and pitch rate q two-dimensional signals, which are accurately measured by the sensors, are used as the state variables of the attitude controller. The simplified state space omits at least the aircraft's speed, angle of attack, and altitude, and may also omit some other state variables that affect the angle and angular rate.

[0064] Due to limited flight control computer resources, a simplified state space is used as the required state space model for MPC in order to reduce computational load.

[0065] The attitude controller inputs are two types of command signals: angle θ and angular rate q. The angular rate command uses proportional control to further correct the control error and reduce static error.

[0066] S103 acquires the state space bias caused by the simplification of the state space observed by the Extended State Observer (ESO).

[0067] In this embodiment, in order to compensate for the error caused by state simplification, an extended state observer (ESO) is used to observe the state space deviation caused by the omission of states, and the observed error is superimposed on the state space as the model input of MPC.

[0068] S103 specifically includes S1031-S1032:

[0069] S1031 yields the linearized formulas for the pitch angle θ and pitch rate q: Among them, M q Let M be the partial derivative of the pitch moment M with respect to the pitch rate q, i.e.: M δeLet M be the pitch moment and δ be the elevator amount. e The partial derivative, i.e.:

[0070] The state space is: This state space does not include velocity V, angle of attack α, height H, or other state variables that affect angle and angular rate.

[0071] S1032 refers to the influence of velocity V, angle of attack α, altitude H, and other omitted state variables affecting angle and angular rate as the state-space deviation f, which can be described by the linearization formula as:

[0072]

[0073] The state-space deviation f is a function of velocity V, angle of attack α, height H, and other omitted state variables that affect angle and angular rate, f = f(V, α, H, ...).

[0074] S104 superimposes the observed state space deviations onto the state space as input to the attitude controller.

[0075] Specifically, the elevator displacement δ e As the system input u, with the addition of a third state x3 = f, the state space is:

[0076] Where x1 in the first state is the pitch angle θ, x2 in the second state is the angular velocity q, and the derivative of the deviation is... The output is y = θ = x1, and f is the state-space deviation.

[0077] It also includes introducing an error feedback coefficient L to ensure the stability of the extended state observer (ESO) and to estimate the state space deviation in real time, specifically:

[0078] The state space can be simplified to:

[0079] in:

[0080]

[0081] The extended state observer (ESO) is constructed as follows:

[0082] L is the feedback vector;

[0083] Configure the observer poles to have the same root to obtain the feedback vector L:

[0084] s 3 +β1s 2 +β2s+β3=(s+w0) 3 ,β1=3w0, in w0 is

[0085] The observer bandwidth, β1, β2, and β3 are the coefficients in front of the observer, and s is a complex variable.

[0086] The specific derivation process is as follows, therefore the extended differential equation can be written as:

[0087]

[0088] Transform it into a state-space form:

[0089]

[0090] y = Cx

[0091] in:

[0092]

[0093] The following forms of state observers can be constructed:

[0094]

[0095] By introducing an error feedback coefficient, both the stability of the observer and the real-time estimation of the system's deviation can be achieved. The feedback vector L is obtained by configuring the observer poles to have the same root, i.e.:

[0096] s 3 +β1s 2 +β2s+β3=(s+w0) 3

[0097] β1=3w0,

[0098] The feedback vector L can then be expressed as:

[0099] Where w0 is the observer bandwidth, β1, β2, and β3 are the coefficients before the observer, and s is a complex variable.

[0100] S105 uses state-space bias to construct the state variables for future preset number of predictions through iterative optimization.

[0101] Specifically, it includes:

[0102] Using a state space with state space bias as a baseline, n predictions are made about the future state. The state space with state space bias is:

[0103] Let the predicted initial state be x(k);

[0104] x(k+n|k) represents the state of the nth subsequent prediction based on time k, yielding X. k =Mx(k)+FU k +P,

[0105] in:

[0106]

[0107] X k Both M and F are 2(n+1)×1 dimensional vectors, and F is a 2(n+1)×n dimensional vector. k Let P be an n×1 dimensional vector, and let P be a 2(n+1)×1 dimensional vector.

[0108] In this embodiment, the specific prediction derivation process is as follows:

[0109] Using the state observer constructed in the first section, we can obtain the bias term f, which can be introduced into the state space for model prediction.

[0110]

[0111] in,

[0112] The state space described above can then be written as:

[0113]

[0114] Using this state space as a reference, n predictions are made about the future state. Let the initial state be x(k); x(k+n|k) is the nth subsequent prediction based on time k.

[0115] Current state: x(k|k) = x(k)

[0116] First prediction:

[0117]

[0118] Second prediction:

[0119]

[0120] Third prediction:

[0121]

[0122] nth prediction:

[0123]

[0124] Represent it in state-space form:

[0125]

[0126] It can be abbreviated as:

[0127] X k =Mx(k)+FU k +P

[0128] in:

[0129]

[0130] X k Both M and F are 2(n+1)×1 dimensional vectors; F is a 2(n+1)×n dimensional vector; U k P is an n×1 dimensional vector; P is a 2(n+1)×1 dimensional vector.

[0131] S106 constructs a loss function and uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal rudder output.

[0132] The construction of the loss function includes:

[0133] Define R as the system's input command. Then R can be used to construct a 2(n+1)×1 dimensional input matrix R k =(RR R...R) T ;

[0134] Define Q as the weight matrix of the state variable (θ, q). Where a1 is the weighting coefficient of the state variable θ, and a2 is the weighting coefficient of the state variable q, they can be combined to form a 2(n+1)×2(n+1) dimensional diagonal weight matrix.

[0135] Let I be the weight coefficient for the input, then an n×n weight diagonal matrix can be obtained from the weight coefficient I.

[0136] Construct the loss function:

[0137] Where, E=Mx(k)-R k ,

[0138] The quadratic programming algorithm is used to find the input that minimizes the loss function, which is then used as the optimal rudder output, including:

[0139] The simplified loss function is used to find the input UkAct that minimizes J using a quadratic programming algorithm.

[0140] This yields a set of optimal elevator values ​​for the current aircraft condition: δ e =U kAct (1) = u(k).

[0141] In this embodiment, U is taken. kAct The first value u(k) in the equation is used as the current output value and given to the aircraft elevator.

[0142] In this embodiment, the final loss function can be simplified to a standard form. The derivation process is as follows:

[0143] The n×n weight diagonal matrix defined above Construct the loss function J(UK)

[0144]

[0145] Let E = Mx(k) - R k ,but

[0146] because Since it is a constant and does not affect the final result, it can be ignored. Let... The final loss function can then be simplified to its standard form.

[0147]

[0148] In some embodiments, S107 (not shown in the figures) is included after S106: using S103 to S106 in real time to obtain the optimal control output for the aircraft flying in the air at any given moment.

[0149] To better illustrate the invention, such as Figure 3-5 The following is an example:

[0150] The following is an example of a non-powered aircraft. Using the ESO-MPC shown in Example 1 of this invention, we applied a 5-degree pitch angle attitude step command to it and observed its step response.

[0151] During a 20-second unpowered attitude step response flight, the speed decreased by 25 m / s and the altitude decreased by 800 meters. The pitch attitude step response showed no overshoot and was able to achieve stable tracking, demonstrating excellent performance and achieving good results on wide-range unpowered aircraft.

[0152] Example 2

[0153] An aircraft attitude control system is provided for attitude control of a wide-range unpowered aircraft. In this embodiment, a flight control computer is used for implementation. Please refer to [link / reference]. Figure 6It includes an attitude control module 61, a simplified state space module 62, an error acquisition module 63, an error superposition module 64, a prediction module 65, and an optimal rudder output module 66.

[0154] Attitude control module 61, used to employ MPC as an attitude controller;

[0155] The simplified state space module 62 is used to simplify the state space as the required state space model for the attitude controller. The pitch angle θ and pitch rate q two-dimensional signals accurately measured by the sensor are used as the state variables of the attitude controller. The simplified state space omits at least the aircraft's speed, angle of attack, and altitude.

[0156] Error acquisition module 63 is used to acquire the state space deviation caused by the simplification of the state space observed by the extended state observer (ESO).

[0157] Error superposition module 64 is used to superimpose the observed state space deviation onto the state space as the input of the attitude controller;

[0158] Prediction module 65 is used to construct the state quantity to be predicted a preset number of times in the future by using state space deviation through iterative optimization;

[0159] The optimal rudder output module 66 is used to construct the loss function. It uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal rudder output.

[0160] In some embodiments, the error acquisition module 63 is specifically used for:

[0161] The linearized formulas for pitch angle θ and pitch rate q are obtained as follows: in

[0162] M q For the partial derivative of the pitch moment M with respect to the pitch rate q, M δe Let M be the pitch moment and δ be the elevator amount. e The partial derivative;

[0163] The state space is:

[0164] The influence of velocity V, angle of attack α, height H, and other omitted state variables affecting angle and angular rate are collectively referred to as deviation f. The linearization formula can be described as follows:

[0165]

[0166] Error superposition module 64 is specifically used to: superimpose elevator value δ e As the system input u, with the addition of a third state x3 = f, the state space is: Where x1 in the first state is the pitch angle θ, x2 in the second state is the angular velocity q, and the derivative of the deviation is... The output is y = θ = x1, and f is the state-space deviation.

[0167] It also includes: an error feedback coefficient introduction module 67 (not shown in the figure) for connecting the error acquisition module 63, which is used to introduce the error feedback coefficient L to ensure the stability of the extended state observer ESO;

[0168] Introducing the error feedback coefficient L, specifically:

[0169] Constructing an Extended State Observer (ESO):

[0170] L is the feedback vector.

[0171] Configure the observer poles to have the same root to obtain the feedback vector L:

[0172] s 3 +β1s 2 +β2s+β3=(s+w0) 3 ,β1=3w0,

[0173] in w0 is the observer bandwidth, β1, β2, and β3 are the coefficients before the observer, and s is a complex variable.

[0174] In some embodiments, the prediction module 65 is specifically used for:

[0175] Using a state space with state space bias as a baseline, n predictions are made about the future state. The state space with state space bias is:

[0176] Let the predicted initial state be x(k);

[0177] x(k+n|k) represents the state of the nth subsequent prediction based on time k, yielding X. k =Mx(k)+FU k +P,

[0178] in:

[0179]

[0180] X k Both M and F are 2(n+1)×1 dimensional vectors, and F is a 2(n+1)×n dimensional vector. k Let P be an n×1 dimensional vector, and let P be a 2(n+1)×1 dimensional vector.

[0181] In some embodiments, the optimal steering output module 66 is specifically used for:

[0182] Define R as the system's input command. Then, the input matrix R is composed of 2(n+1)×1 dimensional components. k =(R RR...R) T ;

[0183] Define Q as the weight matrix of the state variable (θ, q). Where a1 is the weighting coefficient of the state variable θ, and a2 is the weighting coefficient of the state variable q, they can be combined to form a 2(n+1)×2(n+1) dimensional diagonal weight matrix.

[0184] Let I be the weight coefficient for the input. Then, the n×n weight diagonal matrix is ​​obtained from the weight coefficient I.

[0185] Construct the loss function: Where, E=Mx(k)-R k ,

[0186] Use a quadratic programming algorithm to find the input that minimizes J in the loss function. We obtain a set of optimal elevator values ​​for the current aircraft state, taking U... kAct The first value u(k) is used as the current output value and fed to the aircraft elevator δ. e =U kAct (1) = u(k).

[0187] It also includes a real-time optimization module 68 (not shown in the figure), which is used to execute the error acquisition module 63, the error superposition module 64, the prediction module and the optimal control output module 65 in real time to obtain the optimal control output of the aircraft flying in the air at any time.

[0188] The aircraft attitude control system of this embodiment is the same as the aircraft attitude control method described in Embodiment 1 in terms of implementation process, method and effect, and will not be repeated here.

[0189] Example 3

[0190] like Figure 7As shown, this embodiment relates to an electronic device including at least one processor and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to execute an aircraft attitude control method according to Embodiment 1, and to achieve the corresponding beneficial effects of the aircraft attitude control method, which will not be elaborated further here. The electronic device provided in this embodiment can be a personal computer, such as a desktop computer, all-in-one computer, laptop computer, tablet computer, etc., or it can be a mobile phone, wearable device, PDA, etc. In this embodiment, the electronic device is a flight control computer. The electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0191] The components of the electronic device 3 may include, but are not limited to: at least one processor 4, at least one memory 5, and a bus 6 connecting different system components (including memory 5 and processor 4).

[0192] Bus 6 includes a data bus, an address bus, and a control bus.

[0193] The memory 5 may include volatile memory, such as random access memory (RAM) 51 and / or cache memory 52, and may further include read-only memory (ROM) 53.

[0194] The memory 5 may also include a program / utility 55 having a set (at least one) of program modules 54, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0195] The processor 4 executes various functional applications and data processing by running computer programs stored in the memory 5, such as the aforementioned aircraft attitude control method.

[0196] Electronic device 3 can also communicate with one or more external devices 7 (e.g., keyboard, pointing device, etc.). This communication can be performed through input / output (I / O) interface 8. Furthermore, electronic device 3 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 9. Figure 5 As shown, network adapter 9 communicates with other modules of electronic device 3 via bus 6. It should be understood that, although... Figure 5Not shown, it can be combined with electronic device 3 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0197] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0198] Example 4

[0199] This invention relates to a computer-readable storage medium storing instructions that, when executed, perform an aircraft attitude control method according to Embodiment 1. The execution process and effects are the same as those of the aircraft attitude control method described in Embodiment 1, and will not be repeated here.

[0200] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0201] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An attitude control method for an aircraft, characterized in that, Attitude control for wide-range unpowered aircraft, including: The S101 uses an MPC as its attitude controller; The simplified state space of S102 serves as the required state space model for the attitude controller, containing the pitch angle precisely measured by the sensor. Pitch rate Two-dimensional signals are used as state variables of the attitude controller, and the simplified state space omits at least the aircraft's speed, angle of attack, and altitude. S103 acquires the state space deviation caused by the simplification of the state space as observed by the extended state observer ESO; The state space bias obtained by acquiring the extended state observer (ESO) observations due to the simplification of the state space includes: Obtain the pitch angle With pitch rate The linearization formula: ,in pitching moment pitch rate The partial derivative, pitching moment elevator volume The partial derivative; The state space is as follows: ; speed Angle of attack ,high The effects of other omitted state variables that influence angle and angular rate are collectively referred to as deviations. The linearization formula is described as follows: ； S104 superimposes the observed state space deviation onto the state space as the input to the attitude controller, specifically: The elevator amount As system input Additional third state The resulting state space is: Among them, the first state pitch angle Second state angular velocity The derivative of the deviation Output , This refers to the state-space deviation; It also includes introducing an error feedback coefficient L to ensure the stability of the extended state observer (ESO) and to estimate the state space deviation in real time, specifically: The extended state observer (ESO) is constructed as follows: , For the feedback vector, ; Configure the observer poles to have the same root to obtain the feedback vector. : , , in , For the observer bandwidth, , , These are the coefficients before the observer, and s is a complex variable; S105 uses the state space deviation to construct a state space for future preset number of predictions through iterative optimization; S106 constructs a loss function and uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal elevator output.

2. The aircraft attitude control method according to claim 1, characterized in that, The step of using the state space deviation to construct the predicted state quantity for a predetermined number of future iterations through iterative optimization includes: Using the state space with state space bias as a reference, future states are... The state space with state space bias in this prediction is: ; Let the predicted initial state be... ; Therefore The subsequent steps based on time The predicted state is obtained. , in: ， In the above formula, , for dimensional vector, for 3D matrix for 3D matrix for dimensional vector, for Dimensional vector.

3. The aircraft attitude control method according to claim 2, characterized in that, The construction of the loss function, using a quadratic programming algorithm to find the input that minimizes the loss function, serves as the optimal elevator output, including: definition Input commands for the system Then by composition 3D input matrix ; definition State variables weight matrix ,in State variables The weighting coefficients, State variables The weighting coefficients can be derived from... composition 3D diagonal weight matrix ; definition For the weighting coefficients of the input, then the weighting coefficients are... get Weight diagonal matrix , ; Construct the loss function: ,in, , , ; The input that minimizes the loss function is used as the optimal elevator input for the current state of the aircraft. 。 4. The aircraft attitude control method according to claim 1, characterized in that, It also includes S107: using S103 to S106 in real time to obtain the optimal elevator output for the aircraft flying in the air at any given moment.

5. An aircraft attitude control system, characterized in that, Attitude control for wide-range unpowered aircraft, including: Attitude control module, used to employ MPC as an attitude controller; A simplified state-space module is used to simplify the state space as the required state-space model for the attitude controller, which will accurately measure the pitch angle by the sensor. Pitch rate Two-dimensional signals are used as state variables of the attitude controller, and the simplified state space omits at least the aircraft's speed, angle of attack, and altitude. An error acquisition module is used to acquire the state space deviation caused by the simplification of the state space observed by the extended state observer (ESO). The error acquisition module is specifically used for: Obtain the pitch angle With pitch rate The linearization formula: ,in pitching moment pitch rate The partial derivative, pitching moment elevator volume The partial derivative; The state space is as follows: ; speed Angle of attack ,high The effects of other omitted state variables that influence angle and angular rate are collectively referred to as deviations. The linearization formula is described as follows: ； The error superposition module is used to superimpose the observed state space deviation onto the state space as input to the attitude controller, specifically: The step of superimposing the observed state space deviation onto the state space as the input to the attitude controller specifically involves: The elevator amount As system input Additional third state The resulting state space is: Among them, the first state pitch angle Second state angular velocity The derivative of the deviation Output , This refers to the state-space deviation; It also includes introducing an error feedback coefficient L to ensure the stability of the extended state observer (ESO) and to estimate the state space deviation in real time, specifically: The extended state observer (ESO) is constructed as follows: , For the feedback vector, ; Configure the observer poles to have the same root to obtain the feedback vector. : , , in , For the observer bandwidth, , , These are the coefficients before the observer, and s is a complex variable; The prediction module is used to construct the state quantity to be predicted a preset number of times in the future by using the state space deviation through iterative optimization; The optimal rudder output module is used to construct a loss function and uses a quadratic programming algorithm to find the input that minimizes the loss function, which is then used as the optimal elevator output.

6. The aircraft attitude control system according to claim 5, characterized in that: The prediction module is specifically used for: Using the state space with state space bias as a reference, future states are... The state space with state space bias in this prediction is: ; Let the predicted initial state be... ; Therefore The subsequent steps based on time The predicted state is obtained. , in: ， In the above formula, , Think dimensional vector, for 3D matrix for 3D matrix for dimensional vector, for Dimensional vector.

7. The aircraft attitude control system according to claim 6, characterized in that, The optimal steering output module is specifically used for: definition Input commands for the system Then by composition 3D input matrix ; definition State variables weight matrix ,in State variables The weighting coefficients, State variables The weighting coefficients can be derived from... composition 3D diagonal weight matrix ; definition For the weighting coefficients of the input, then the weighting coefficients are... get Weight diagonal matrix , ; Construct the loss function: ,in, , , ; The loss function is solved using a quadratic programming algorithm. Minimum input This yields a set of optimal elevator values ​​for the current aircraft state. The first value in This current output value is given to the aircraft elevator. ; It also includes a real-time optimization module, which is used to execute the error acquisition module, error superposition module, prediction module and optimal control output module in real time to obtain the optimal elevator control output of the aircraft at any time in the air.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements an aircraft attitude control method as described in any one of claims 1-4.

9. A computer-readable storage medium, characterized in that: The storage medium stores instructions that, when executed, perform an aircraft attitude control method as described in any one of claims 1-4.

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