Rolling stability robust control method
By setting up a nonlinear dynamic model and a second-order model in the aircraft, and using a linear expansion state observer and an optimal secondary controller, the problem of rolling attitude stability of the aircraft in high-speed flight and large angle of attack flight is solved, and the problem of rolling attitude stability of the aircraft is achieved. Fast convergence and high robustness control effects.
Patent Information
- Application Number
- CN202310959413.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-01
- Publication Date
- 2025-02-14
AI Technical Summary
The existing technology is difficult to effectively solve the stability of the aircraft's rolling posture in high-speed flight and large-angle-angle-attack flight, especially when performing large-scale flight missions, the high-frequency large-magnitude nonlinear change of the rolling disturbance coefficient on the aircraft. Stability has had a serious impact.
By setting up a nonlinear dynamic model of the rolling channel, a second-order model is established, and a linear expansion state observer is used to observe external disturbances, and a controller is set up in combination with the estimated value, and a linear observation system is finally set up with the optimal observation system based on a linearized observation system. Secondary controller realizes robust control of the aircraft rolling channel.
The aircraft rolling angle is quickly converged to 0, with small steady-state error and fast response speed, avoiding the differential explosion phenomenon caused by traditional inverse steps, and maintaining reasonable range and good stability at the actuator deflection angle. .
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Figure CN119439705A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a rolling stability robust control method, belonging to the technical field of navigation control. Background Art
[0002] Usually, precision-guided aircraft are divided into rolling aircraft and non-rolling aircraft. For non-rolling aircraft with an X-shaped layout, the non-coplanarity of the control surface and the oncoming airflow causes the lateral incoming flow to flow around the aircraft or actuator asymmetrically, inducing a rolling moment, thereby breaking the stability of the rolling attitude. The aircraft is prone to induce a rolling moment in high-angle attack and high-maneuver flight, resulting in flight loss of control and affecting the accuracy of the landing point. However, the rolling of high-speed flying aircraft produces a Magnus moment, which causes a coupling effect in the pitch and yaw channels, increasing the difficulty of control while reducing the control accuracy. Especially when performing large-scale cross-domain flight missions, its roll disturbance coefficient changes nonlinearly with high-frequency and large-amplitude values of speed, altitude and angle of attack, which has a bad impact on the stability of the rolling channel of the guided aircraft. In addition, for guided aircraft using a TV seeker, the rolling of the aircraft body reduces the accuracy of target tracking and increases the complexity of the onboard computer solution, thereby reducing the strike accuracy of the guided aircraft.
[0003] For the roll attitude stabilization control of the aircraft in flight, a roll stabilization controller based on sliding mode control theory or an aircraft autopilot based on optimal control theory is often used to achieve attitude stabilization control. In this process, a sliding mode observer or an extended state observer is often used to estimate the internal uncertainty and external disturbance of the aircraft and compensate the controller. However, this method will bring about induced torque and interfere with the aircraft.
[0004] In addition, the traditional sliding mode observer or extended state observer is a first-order state space, which is complex in form and has a large amount of calculation.
[0005] Therefore, it is necessary to further study the rolling stability of aircraft to solve the above problems. Summary of the invention
[0006] In order to overcome the above problems, the inventors have conducted in-depth research and provided a method for robust control of roll stability, comprising the following steps:
[0007] S1. Setting a nonlinear dynamic model of the rolling channel, and establishing a second-order model of the rolling channel based on the dynamic model;
[0008] S2. Establish an observation system according to the second-order model of the rolling channel, use a linear extended state observer to observe the observation system, and obtain an estimated value of the external disturbance;
[0009] S3, set the controller based on the estimated value and linearize the observation system;
[0010] S4. Set up the optimal quadratic controller, obtain control instructions based on the linearized observation system, and realize robust control of the aircraft roll channel.
[0011] In a preferred embodiment, the nonlinear dynamic model of the rolling channel includes an induced torque factor introduced by the deflection of the actuator, a sensor dynamic hysteresis factor, and an external disturbance factor.
[0012] In a preferred embodiment, the nonlinear dynamic model of the rolling channel is expressed as:
[0013]
[0014] Where, φ is the roll angle; M δ is the rudder efficiency coefficient; ω p is the aircraft roll angular acceleration generated by the unit roll angular velocity, that is, the aircraft roll damping torque coefficient, δ c is the rudder deflection angle; The induced rolling moment coefficient introduced by the actuator deflection, Γ(ω g ,ζ g ,φ m ) is the sensor dynamic hysteresis, and Δ1 is the unknown bounded external disturbance.
[0015] In a preferred embodiment, the second-order model of the rolling channel is expressed as:
[0016]
[0017] Among them, x1, x2, and u are the state variables of the second-order model of the rolling channel, and b1, b2, d1, and d2 are intermediate variables.
[0018]
[0019]
[0020] b1=M δ
[0021]
[0022] d1=ω p Γ(ω g ,ζ g ,φ m )+Δ1
[0023] d2=Δ2
[0024] x1=φ
[0025] x2=δ c
[0026] u=δ
[0027] Among them, δ represents the deflection command of the actuator, μ represents the damping ratio of the actuator, and ω d represents the oscillation frequency, and Δ2 is the model error of the nonlinear dynamics of the roll channel.
[0028] In a preferred embodiment, the observation system includes a roll angle subsystem and a yaw angle subsystem. A first linear extended state observer is set to observe the roll angle subsystem to obtain an estimated value of external disturbance of the roll angle subsystem; and a second linear extended state observer is set to observe the yaw angle subsystem to obtain an estimated value of external disturbance of the yaw angle subsystem.
[0029] In a preferred embodiment, the roll angle subsystem is expressed as:
[0030]
[0031]
[0032] Among them, z1, z2, u1, z3 are state variables, A1, z1, b1, E1, C1 are intermediate variables, k0, k1 are control parameters, z1 = x1, u1=x2, z3=F1, F1 is the total disturbance of the roll angle subsystem.
[0033] In a preferred embodiment, the first linear extended state observer is configured as:
[0034]
[0035] in, is the estimated state variable of z1=[z1 z2 z3], L1 is the observer gain, is the observer estimation error, is the input vector, is the disturbance estimate.
[0036] In a preferred embodiment, the linearized observation system in S3 includes a linearized roll angle subsystem and a linearized yaw angle subsystem.
[0037] The acquisition of the linearized roll angle subsystem comprises the following sub-steps:
[0038] According to the roll angle subsystem, an objective function is set, the objective of which is to stabilize the roll angle of the aircraft at 0; a first feedback controller is set to minimize the objective function, thereby obtaining a linearized roll angle subsystem.
[0039] In a preferred embodiment, in S4, the optimal quadratic controller of the linearized roll angle subsystem is set to:
[0040]
[0041] in, represents the optimal feedback gain matrix, t represents the time series, R represents the positive definite matrix, and P(t) represents the solution of the differential Riccati equation.
[0042] In a preferred embodiment, in S4, the optimal secondary controller of the linearized deflection angle subsystem is set to:
[0043]
[0044] Where G(t) is a vector equation that satisfies:
[0045]
[0046] in,
[0047]
[0048] The beneficial effects of the present invention include:
[0049] (1) It can quickly control the roll angle to converge to 0 with a small steady-state error;
[0050] (2) Fast response speed, avoiding the differential explosion phenomenon caused by the traditional backstepping method;
[0051] (3) The deflection angle of the actuator is within a reasonable range and has good stability. BRIEF DESCRIPTION OF THE DRAWINGS
[0052] Figure 1 A schematic flow chart of a roll stability robust control method according to a preferred embodiment of the present invention is shown;
[0053] Figure 2 A graph showing the change of the aircraft roll angle over time in the simulation results in Example 1;
[0054] Figure 3 A comparison diagram of the aircraft roll angle variation over time in Example 1 and Comparative Example 1 is shown;
[0055] Figure 4 A comparison diagram of the rolling angular velocity of the aircraft over time in Example 1 and Comparative Example 1 is shown;
[0056] Figure 5 A comparison diagram of the rolling angular acceleration of the aircraft over time in Example 1 and Comparative Example 1 is shown;
[0057] Figure 6 The graphs showing the changes in the deflection angle of the aircraft actuator over time in Example 1 and Comparative Example 1 are shown. DETAILED DESCRIPTION
[0058] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.
[0059] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.
[0060] According to a roll stability robust control method provided by the present invention, Figure 1 As shown, the following steps are included:
[0061] S1. Setting a nonlinear dynamic model of the rolling channel, and establishing a second-order model of the rolling channel based on the dynamic model;
[0062] S2. Establish an observation system according to the second-order model of the rolling channel, use a linear extended state observer to observe the observation system, and obtain an estimated value of the external disturbance;
[0063] S3, setting the controller based on the estimated value to linearize the observation system;
[0064] S4. Set up the optimal quadratic controller, obtain control instructions based on the linearized observation system, and realize robust control of the aircraft roll channel.
[0065] Through a large number of studies, it was found that the deflection of the actuator will bring about an induced torque, while the traditional aircraft roll dynamics model, when dealing with external disturbances, mostly ignores the induced torque, resulting in poor control robustness.
[0066] According to the present invention, in the nonlinear dynamic model of the rolling channel, there are factors of induced torque introduced by the deflection of the actuator, factors of dynamic hysteresis of the sensor and factors of external disturbance.
[0067] Furthermore, in the present invention, the nonlinear dynamic model of the rolling channel is expressed as:
[0068]
[0069] Where, φ is the roll angle; M δ is the rudder efficiency coefficient; ω p is the aircraft roll angular acceleration generated by the unit roll angular velocity, that is, the aircraft roll damping moment coefficient, δ c is the rudder deflection angle; The induced rolling moment coefficient introduced by the actuator deflection, Γ(ω g ,ζg ,φ m ) is the sensor dynamic hysteresis, and Δ1 is the unknown bounded external disturbance.
[0070] Considering the modeling error of the dynamics and nonlinear dynamics of the rolling channel, set δ c The second-order dynamic differential equation is:
[0071]
[0072] Among them, δ represents the deflection command of the actuator, μ represents the damping ratio of the actuator, and ω d represents the oscillation frequency, and Δ2 is the model error of the nonlinear dynamics of the roll channel.
[0073] Furthermore, the model error Δ2 satisfies: Δ2≤Δ 2,max , where Δ 2,max Is an unknown constant value.
[0074] According to the nonlinear dynamic model of the rolling channel, and δ c The second-order dynamic differential of the rolling channel is used to establish the second-order model of the rolling channel. The second-order model of the rolling channel is expressed as:
[0075]
[0076] Among them, x1, x2, and u are the state variables of the second-order model of the rolling channel, and b1, b2, d1, and d2 are intermediate variables.
[0077]
[0078]
[0079] b1=M δ
[0080]
[0081] d1=ω p Γ(ω g ,ζ g ,φ m )+Δ1
[0082] d2=Δ2
[0083] x1=φ
[0084] x2=δ c
[0085] u=δ.
[0086] The observation system includes a roll angle subsystem and a deflection angle subsystem. A first linear extended state observer is set to observe the roll angle subsystem to obtain an estimated value of external disturbance of the roll angle subsystem; a second linear extended state observer is set to observe the deflection angle subsystem to obtain an estimated value of external disturbance of the deflection angle subsystem.
[0087] In a preferred embodiment, the roll angle subsystem is expressed as:
[0088]
[0089]
[0090] Among them, z1, z2, u1, z3 are state variables, A1, z1, b1, E1, C1 are intermediate variables, k0, k1 are control parameters, z1 = x1, u1=x2, z3=F1, F1 is the total disturbance of the roll angle subsystem.
[0091] According to the present invention, the roll angle subsystem is based on the first equation in the second-order model of the roll channel: Establish, consider the closed loop state is part of the total disturbance, add both ends of the first equation We can get:
[0092]
[0093]
[0094] According to the above formula, we define z1=x1, u1=x2, we can get:
[0095]
[0096] Its state space equation is:
[0097]
[0098] Define z3=F1 and expand the state space equation to obtain the roll angle subsystem.
[0099] In a preferred embodiment, the first linear extended state observer is configured as:
[0100]
[0101] in, is the estimated state variable of z1=[z1 z2 z3], L1 is the observer gain, is the observer estimation error, is the input vector, is the disturbance estimate.
[0102] Furthermore, the first linear extended state observer gain is:
[0103]
[0104] Among them, ω 01 is the observer bandwidth.
[0105] Through the first linear extended state observer, we can obtain That is, the estimated value of the total disturbance F1 of the roll angle subsystem is obtained
[0106] In a preferred embodiment, the deflection angle subsystem is expressed as:
[0107]
[0108]
[0109] Among them, z4, z5, u2, z6 are state variables, A2, z2, b2, E2, C2 are intermediate variables, k2, k3 are control parameters, z4 = x2, u2=u, z6=F2, F2 is the total disturbance of the deflection angle subsystem.
[0110] According to the present invention, the deflection angle subsystem is based on the second equation in the second-order model of the rolling channel: Establish, consider the closed loop state is part of the total disturbance, add both ends of the second equation We can get:
[0111]
[0112]
[0113] According to the above formula, we define z4=x2, u2=u, we can get:
[0114]
[0115] Its state space equation is:
[0116]
[0117] Define z6=F2 and expand the state-space equation to obtain the deflection angle subsystem.
[0118] In a preferred embodiment, the second linear extended state observer is configured as:
[0119]
[0120] in, is the estimated state variable of z2=[z4 z5 z6], L2 is the observer gain, is the observer estimation error, is the input vector, is the disturbance estimate.
[0121] Furthermore, the gain of the second linear extended state observer is:
[0122]
[0123] Among them, ω 02 is the bandwidth of the second linear extended state observer.
[0124] Through the second linear extended state observer, we can obtain That is, we get the estimated value of the total disturbance F2 of the deflection angle subsystem
[0125] The linearized observation system in S3 includes a linearized roll angle subsystem and a linearized yaw angle subsystem.
[0126] The acquisition of the linearized roll angle subsystem comprises the following sub-steps:
[0127] According to the roll angle subsystem, an objective function is set, the objective of which is to stabilize the roll angle of the aircraft at 0; a first feedback controller is set to minimize the objective function, thereby obtaining a linearized roll angle subsystem.
[0128] Specifically, according to the roll angle subsystem, since b1 = M δ ≠0, then the input and output system is as follows:
[0129]
[0130] Among them, y1 is the output, and the superscript (0~1) represents the 0th to 1st order derivative.
[0131] The goal is to stabilize the roll angle of the aircraft at 0, that is, the reference signal z(t) = 0, and the objective function J1 is set as:
[0132]
[0133] in, are two positive semidefinite matrices, is a positive definite matrix. The integral function is to minimize the rate of change of the system state and realize the state vector Smoothness and stability, so that the output y1 tracks the given fully differentiable signal z(t) to achieve the stability of the aircraft's roll angle at 0.
[0134] The first feedback controller is expressed as:
[0135]
[0136]
[0137] Among them, v1 is the optimal control rate, x 2d represents the virtual controller, K E1 is the coefficient.
[0138] According to the first feedback controller, the output-input system of the roll angle subsystem can be expressed as:
[0139]
[0140] Its state space is the linearized roll angle subsystem, expressed as:
[0141]
[0142] in,
[0143] According to the present invention, in the same manner, a linearized deflection angle subsystem is obtained based on the deflection angle subsystem.
[0144] In the traditional backstepping estimation, the first feedback controller is directly used for estimation, and then the control command is obtained, which is prone to the "differential explosion" phenomenon and amplifies the system noise.
[0145] In the present application, an optimal quadratic controller is set up to obtain control instructions based on a linearized observation system, thereby avoiding the differential explosion phenomenon, reducing system noise, and improving control robustness.
[0146] Specifically, in S4, the optimal quadratic controller of the linearized roll angle subsystem is set as:
[0147]
[0148] in, represents the optimal feedback gain matrix, t represents time, R represents a positive definite matrix, and P(t) represents the solution of the differential Riccati equation.
[0149] Specifically, the optimal secondary controller is obtained by:
[0150] Convert the objective function set in S3:
[0151]
[0152] The transformed objective function is solved by using the linear system minimum.
[0153] In S4, the optimal quadratic controller for the linearized deflection angle subsystem is set as:
[0154]
[0155] Where G(t) is a vector equation that satisfies:
[0156]
[0157] in,
[0158]
[0159] Further, in the present invention, P(t) is the solution of the following differential Riccati equation:
[0160]
[0161] Among them, C=I2, I2 represents a 2×2 unit matrix, and Q represents a semi-positive definite matrix.
[0162] Furthermore, in S4, the backstepping method is used to obtain control instructions based on the linearized observation system and the optimal quadratic controller, and the aircraft is controlled by the control instructions to achieve robust control of the roll channel. The backstepping method is a commonly used control method and will not be described in detail in the present invention.
[0163] Example
[0164] Example 1
[0165] The simulation experiment is carried out and the simulation parameters are shown in Table 1.
[0166] parameter <![CDATA[ω g ]]> <![CDATA[ζ g ]]> μ <![CDATA[ω d ]]> <![CDATA[ω 01 ]]> <![CDATA[ω 02 ]]> <![CDATA[k1]]> <![CDATA[k2]]> <![CDATA[k E1 ]]> <![CDATA[k E2 ]]> Value 0.5 200red / s 5 200red / s 30red / s 30red / s 100 10 1.1 1.1
[0167] Take the induced rolling moment coefficient m corresponding to the attack angle α=2,5,10,15 degrees 4d 、m 8d and rudder efficiency coefficient M δ , set the maximum uncertainty range of the rolling dynamics model to d 1max ≤200deg / s 2 , the maximum uncertainty range of the steering gear model d 2max ≤200deg / s 2 , the sensor noise is zero-mean white noise, and its value is 0.1red / s.
[0168] Set the initial roll angle to φ0 = 45 degrees, the initial roll angle rate φ0′ = -180 degrees / s, and the expected final value is φ d =0deg.
[0169] The vehicle roll channel robust control is performed by the following method:
[0170] S1. Setting a nonlinear dynamic model of the rolling channel, and establishing a second-order model of the rolling channel based on the dynamic model;
[0171] S2. Establish an observation system according to the second-order model of the rolling channel, use a linear extended state observer to observe the observation system, and obtain an estimated value of the external disturbance;
[0172] S3, setting the controller based on the estimated value and linearizing the observation system;
[0173] S4. Set up the optimal quadratic controller, obtain control instructions based on the linearized observation system, and realize robust control of the aircraft roll channel.
[0174] The nonlinear dynamic model of the rolling channel is expressed as:
[0175]
[0176] The second-order model of the rolling channel is expressed as:
[0177]
[0178] in,
[0179]
[0180] b1=M δ
[0181]
[0182] d1=ω p Γ(ω g ,ζ g ,φ m )+Δ1
[0183] d2=Δ2
[0184] x1=φ
[0185] x2=δ c
[0186] u=δ
[0187] The observation system includes a roll angle subsystem and a deflection angle subsystem. A first linear extended state observer is set to observe the roll angle subsystem to obtain an estimated value of external disturbance of the roll angle subsystem; a second linear extended state observer is set to observe the deflection angle subsystem to obtain an estimated value of external disturbance of the deflection angle subsystem.
[0188] The roll angle subsystem is expressed as:
[0189]
[0190]
[0191] The first linear extended state observer is set as:
[0192]
[0193] The first linear extended state observer gain is:
[0194]
[0195] The deflection angle subsystem is expressed as:
[0196]
[0197]
[0198] The second linear extended state observer is set as:
[0199]
[0200] The gain of the second linear extended state observer is:
[0201]
[0202] The linearized observation system in S3 includes a linearized roll angle subsystem and a linearized yaw angle subsystem.
[0203] The acquisition of the linearized roll angle subsystem comprises the following sub-steps:
[0204] According to the roll angle subsystem, an objective function is set, the objective of which is to stabilize the roll angle of the aircraft at 0; a first feedback controller is set to minimize the objective function, thereby obtaining a linearized roll angle subsystem.
[0205] In the same way, based on the deflection angle subsystem, a linearized deflection angle subsystem is obtained.
[0206] In S4, the optimal quadratic controller for the linearized roll angle subsystem is set as:
[0207]
[0208] In S4, the optimal quadratic controller for the linearized deflection angle subsystem is set as:
[0209]
[0210] The simulation results are as follows Figure 2As shown in the figure, it can be seen that the curves of the roll angle converge to the expected value within 0.4s. As the angle of attack increases, the corresponding convergence time difference range is less than 0.1s, which fully demonstrates the robustness of the method of Example 1.
[0211] Comparative Example 1
[0212] Using the same simulation parameters as in Example 1, simulations were performed using the STA method, the RBC method, and the TSM method, all of which are existing classical robust control methods.
[0213] Among them, the STA method is a robust controller control method based on the super-twisting (ST) algorithm.
[0214] The RBC method is a robust backstepping control method.
[0215] The TSM method is a control method that combines non-singular terminal sliding mode and backstepping method.
[0216] The results in Example 1 and Comparative Example 1 are as follows Figure 3-6 As shown, Figure 3 shows the change of the aircraft roll angle over time. Figure 4 The figure shows the change of the aircraft's roll angular velocity over time. Figure 5 The figure shows the change of the aircraft's roll angular acceleration over time. Figure 6 A graph showing the change in the deflection angle of the aircraft actuator over time is shown.
[0217] from Figure 3 It can be seen that, considering the modeling error, external disturbance and actuator uncertainty, the method in Example 1 can quickly control the roll angle to converge to 0. Among them, although the STA and RBC methods have fast convergence speed, the steady-state errors are large, which are 1 degree and 0.5 degree respectively. Although the TSM method has a small steady-state error, the convergence speed is slow. The methods in Example 1 are not as good as those in Example 1, indicating that the control method in Example 1 compensates for the model uncertainty better, which indirectly shows that the observer effect is better.
[0218] from Figure 4 , Figure 5 It can be seen that the method in Example 1 has a fast response speed and avoids the differential explosion phenomenon caused by the traditional backstepping method.
[0219] from Figure 6 It can be seen that, in the method of Example 1, the deflection angle of the actuator is within a reasonable range and has good stability.
[0220] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", etc. indicate positions or positional relationships based on the working state of the present invention, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", and "fourth" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.
[0221] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0222] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.
Claims
1. A roll stability robust control method, characterized in that: The following steps are involved: S1. Setting a nonlinear dynamic model of the rolling channel, and establishing a second-order model of the rolling channel based on the dynamic model; S2. Establish an observation system according to the second-order model of the rolling channel, use a linear extended state observer to observe the observation system, and obtain an estimated value of the external disturbance; S3, set the controller based on the estimated value and linearize the observation system; S4. Set up the optimal quadratic controller, obtain control instructions based on the linearized observation system, and realize robust control of the aircraft roll channel.
2. A roll stability robust control method according to claim 1, characterized in that: In the nonlinear dynamic model of the rolling channel, there are factors of induced torque introduced by the deflection of the actuator, factors of dynamic hysteresis of the sensor, and factors of external disturbance.
3. A roll stability robust control method according to claim 2, characterized in that: The nonlinear dynamic model of the rolling channel is expressed as: Where, φ is the roll angle; M δ is the rudder efficiency coefficient; ω p is the aircraft roll angular acceleration generated by the unit roll angular velocity, that is, the aircraft roll damping moment coefficient, δ c is the rudder deflection angle; The induced rolling moment coefficient introduced by the actuator deflection, Γ(ω g ,ζ g ,φ m ) is the sensor dynamic hysteresis, and Δ1 is the unknown bounded external disturbance.
4. A method for robust roll stability control according to claim 1, characterized in that: The second-order model of the rolling channel is expressed as: Among them, x1, x2, and u are the state variables of the second-order model of the rolling channel, and b1, b2, d1, and d2 are intermediate variables. b1=M δ d1=ω p C(ω g ,g g ,f m )+Δ1 d2=Δ2 x1=φ x2=δ c u=δ Among them, δ represents the deflection command of the actuator, μ represents the damping ratio of the actuator, and ω d represents the oscillation frequency, and Δ2 is the model error of the nonlinear dynamics of the roll channel.
5. The method for robust roll stability control according to claim 1, characterized in that: The observation system includes a roll angle subsystem and a deflection angle subsystem. A first linear extended state observer is set to observe the roll angle subsystem to obtain an estimated value of external disturbance of the roll angle subsystem; a second linear extended state observer is set to observe the deflection angle subsystem to obtain an estimated value of external disturbance of the deflection angle subsystem.
6. A method for robust roll stability control according to claim 5, characterized in that: The roll angle subsystem is expressed as: Among them, z1, z2, u1, z3 are state variables, A1, z1, b1, E1, C1 are intermediate variables, k0, k1 are control parameters, z1 = x1, u1=x2, z3=F1, F1 is the total disturbance of the roll angle subsystem.
7. A method for robust roll stability control according to claim 5, characterized in that: The first linear extended state observer is set as: in, is the estimated state variable of z1=[z1 z2 z3], L1 is the observer gain, is the observer estimation error, is the input vector, is the disturbance estimate.
8. The method for robust roll stability control according to claim 1, characterized in that: The linearized observation system in S3 includes the linearized roll angle subsystem and the linearized yaw angle subsystem. The acquisition of the linearized roll angle subsystem comprises the following sub-steps: According to the roll angle subsystem, an objective function is set, the objective of which is to stabilize the roll angle of the aircraft at 0; a first feedback controller is set to minimize the objective function, thereby obtaining a linearized roll angle subsystem.
9. A method for robust roll stability control according to claim 8, characterized in that: In S4, the optimal quadratic controller for the linearized roll angle subsystem is set as: in, represents the optimal feedback gain matrix, t represents the time series, R represents the positive definite matrix, and P(t) represents the solution of the differential Riccati equation.
10. The method for robust roll stability control according to claim 8, characterized in that: In S4, the optimal quadratic controller for the linearized deflection angle subsystem is set as: Where G(t) is a vector equation that satisfies: in,