Unmanned aerial vehicle flight control method based on adaptive quasi-optimal high-order sliding mode control

Abstract

The application discloses a kind of unmanned aerial vehicle flight control methods based on adaptive quasi-optimal high-order sliding mode control, including obtaining the first pose vector of unmanned aerial vehicle to be controlled;The first pose vector is used as the input of preset pose control model, and the control signal output by the preset pose control model is obtained according to the preset integral sliding surface of the preset pose control model and preset control rate, wherein the preset pose control model is the preset second-order integral chain model with pose as state variable, and the preset control rate makes the state variable of the preset second-order integral chain model reach the preset equilibrium state, and the preset linear quadratic index is minimum;The unmanned aerial vehicle to be controlled is controlled according to the control signal.The method of the application can guarantee that the tracking error of closed-loop system converges in finite time.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle flight control, and particularly relates to an unmanned aerial vehicle flight control method based on adaptive quasi-optimal high-order sliding mode control. BACKGROUND

[0002] Small unmanned helicopters have fast dynamic response, high sensitivity to external interference input, and very high requirements for the rapidity index of control systems.

[0003] In the robust autonomous flight control method of small unmanned helicopters, the traditional control methods mainly include classical feedback control (such as PD or PID) and linear control method. The advantage of PID control is simple structure and no need for accurate system model, which can avoid the complex modeling process of small unmanned helicopters, but it cannot fully exert the maneuverability of unmanned helicopters and has low robustness, and the parameter adjustment is entirely based on experience and the process is tedious. Linear control theory is mature and has many methods, but it relies on the linear model based on a single characteristic point linearization, ignores many system nonlinear characteristics, and when the helicopter state deviates from the equilibrium point, the control performance may decrease, and even leads to instability of the closed-loop system, and cannot guarantee the full envelope flight. These two control methods still have a large space for improvement in control performance. Sliding mode control is an effective control method for nonlinear systems in uncertain environments, and its design principle is to make the system reach a sliding surface in a limited time, and then maintain the dynamic characteristics of the system on the sliding surface through discontinuous control. However, the high-speed switching function in the traditional sliding mode controller may not be realized in actual mechatronic systems. The control chattering phenomenon caused by non-ideal switching not only significantly reduces the performance of the system, but also reduces the stability of the system.

[0004] Therefore, many scholars have devoted to designing autonomous flight control systems by using various advanced nonlinear control methods. Due to the difficulty in designing and implementing nonlinear control systems, there are relatively few experimental results in the research field. The most similar implementation schemes to the present application mainly include the following categories:

[0005] The first category is to combine the traditional sliding mode control and saturation function, and to design a linear sliding surface according to the system state error. This method has simple control structure and good control performance, but needs prior knowledge of uncertainty, and the control gain needs to be manually adjusted by engineers. If the gain is too small, the performance may not meet the requirements, and if the gain is too large, it will cause severe control chattering, and the saturation function has limited ability to suppress chattering, and cannot guarantee the convergence of the closed-loop system state in a limited time.

[0006] The second type is to replace the traditional linear sliding mode surface with a nonlinear sliding mode surface, so that the system state can converge to zero in a finite time. This method has a complex control structure and can improve the system response speed to a certain extent, but the convergence speed of the system is uncontrollable. When the initial state of the system is far from the equilibrium point, the convergence speed of the closed-loop system will become very slow, and it does not have global fast convergence ability, and it also cannot guarantee the optimal or quasi-optimal system performance index. For convenience of discussion, the following will take an affine system in the following general form as an example to investigate the convergence of this method.

[0007]

[0008] wherein x is R n , u is R m , f(x), g(x) are continuous smooth functions. Take the following nonlinear function as the sliding mode surface

[0009]

[0010] wherein κ>0, p and q are both odd numbers, and p>q>0.

[0011] The time for the system to converge to the equilibrium point (origin) is

[0012]

[0013] wherein x0 is the initial state of the system. According to formula (3), the convergence time is strongly related to the system state. When the initial state of the system x0 is far from the equilibrium point, the convergence speed will be slow, so this method cannot guarantee that the system always converges to the equilibrium point in the optimal form.

[0014] At present, there is no effective solution to the problem of slow convergence speed in the control process in the related art. SUMMARY

[0015] The main purpose of the present application is to provide a UAV flight control method based on adaptive quasi-optimal high-order sliding mode control, so as to solve at least the above problems.

[0016] In order to achieve the above purpose, according to one aspect of the present application, a UAV flight control method based on adaptive quasi-optimal high-order sliding mode control is provided, which comprises:

[0017] acquire a first pose vector of a to-be-controlled unmanned aerial vehicle; take the first pose vector as an input of a preset pose control model, acquire a control signal output by the preset pose control model according to a preset integral sliding surface and a preset control rate of the preset pose control model, wherein the preset pose control model is a preset second-order integral chain model with a pose as a state variable, and the preset control rate makes a state variable of the preset second-order integral chain model minimum when reaching a preset equilibrium state; and control the to-be-controlled unmanned aerial vehicle according to the control signal.

[0018] Further, in the method as described above, the preset pose control model comprises a preset attitude control model, a preset vertical position control model and a preset horizontal position control model, and the first pose vector comprises a first attitude vector, a first vertical position vector and a first horizontal position vector; and the step of taking the first pose vector as an input of a preset pose control model, acquiring a control signal output by the preset integral chain control model according to a preset integral sliding surface and a preset control rate of the preset pose control model comprises: taking the first attitude vector, the first vertical position vector and the first horizontal position vector as inputs of the preset attitude control model, the preset vertical position control model and the preset horizontal position control model respectively, and acquiring attitude control signals, vertical position control signals and horizontal position control signals output by the preset integral chain control models according to the preset integral sliding surface and the preset control rate of each model respectively.

[0019] Further, in the method as described above, the control signal output by the preset pose control model is a sum of a nominal control signal and an adaptive control signal, the nominal control signal is acquired according to the preset integral sliding surface and the preset control rate, and the adaptive control signal is acquired according to the preset integral sliding surface, the preset control rate and an adaptive parameter, wherein an update rate of the adaptive parameter is acquired according to the preset integral sliding surface and the preset control rate.

[0020] Further, in the method as described above, the preset pose control model is acquired according to the following model: wherein i = 1, 2, …, l-1, l is an integer greater than 1, z i , …, z l is a state variable, v n is a preset control rate, v n is acquired according to the following formula: wherein, is a positive definite diagonal matrix, P is a positive definite solution of the following state Riccati equation, wherein, 0 (·)×(·) and I(·)×(·) is a zero matrix and a unit matrix for the corresponding dimension, δ is a positive real number, α i = diag(α i1 ,…,α in ), i = 2,..., l, j = 1, 2,..., n, α (l+1)j = 1, α lj = α, j = 1, 2,..., n ; for any ε ∈ ( 0, 1 ), α ∈ ( 1 - ε, 1 ), the preset linear quadratic form index is obtained according to the following formula: where t is a time variable, the equilibrium state is obtained according to the following formula: v n0 = - K1Sgn(z1) -... - K i Sgn(z i ) -... - K l Sgn(z l ), wherein v n0 represents an equilibrium state, K1, K2,... K l are all normal numbers, and the polynomial λ l + k lj λ l-1 +... + k 2j λ + k 1j is Hurwitz stable, K i = diag(k i1 ,…,k ij ,…,k in ), i = 1, 2,..., l, j = 1, 2,..., n.

[0021] Further, as the method described above, the state variable of the preset attitude control model is:

[0022] x1= [Θ T , ω T ] T

[0023] wherein Θ = [φ, θ, ψ] T is an attitude angle vector containing roll, pitch and yaw angles, and ω = [p, q, r] T is an angular velocity vector.

[0024] The variable of the preset integral sliding surface of the preset attitude control model is:

[0025] z att,1 = s att = Θ - Θ d

[0026]

[0027] wherein Θ d is a desired attitude angle vector;

[0028] The preset control rate of the preset attitude control model is:

[0029]

[0030] wherein, is a positive definite matrix to be designed, and P1 is a positive definite solution of the following algebraic Riccati equation

[0031]

[0032] wherein δ1 is a positive real number to be designed, wherein α1=diag(α 11 ,α 12 ,α 13 ) and α2=diag(α 21 ,α 22 ,α 23 ), φ e =φ-φ d , θ e =θ-θ d , ψ e =ψ-ψ d , φ d , θ d and ψ d are desired roll angle, pitch angle and yaw angle respectively;

[0033] The preset integral sliding surface of the preset attitude control model is:

[0034] σ1=[σ 11 ,σ 12 ,σ 13 ] T =z att,2 -ν ns

[0035] wherein,

[0036] The control signal output by the preset attitude control model is:

[0037] δ aer =u att,n +u att,a

[0038] where u att,n is the nominal control signal, u att,a is the adaptive control signal,

[0039]

[0040] where K τ1 and K η1 are positive definite diagonal matrices,

[0041]

[0042]

[0043] where T(Θ) is a 3x3 matrix defined as

[0044]

[0045] J n is the measured nominal moment of inertia matrix, Sk(ω) is a skew-symmetric matrix with respect to angular velocity, k ped,ctr and k r,ctr are positive real numbers to be designed, s(·), c(·), t(·) are the abbreviations of sin(·), cos(·) and tan(·), respectively.

[0046]

[0047]

[0048]

[0049]

[0050] k β is the main rotor blade stiffness coefficient, H tr is the vertical distance from the center of gravity to the tail rotor hub, D tr is the longitudinal horizontal distance from the center of gravity to the tail rotor hub, H mr is the vertical distance from the center of gravity to the main rotor hub, T hov is the main rotor pull force when the unmanned helicopter hovers; the adaptive control signal u att,a is

[0051]

[0052] k a1 is a designed constant, and the update rates of the three adaptive parameters are designed as

[0053]

[0054] where o i > 0, p i > 0, i = 1, 2, 3 are design parameters.

[0055] Further, in the method as described previously, the variable of the preset integral sliding surface of the preset vertical position control model is:

[0056] z ver,1 = s ver = z - z d

[0057]

[0058] where z d is the desired vertical height;

[0059] The control signal output by the preset vertical position control model is:

[0060] δ col = u ver,n + u ver,a

[0061] where u ver,n is the nominal control signal, u ver,a is the adaptive control signal, the nominal control signal u ver,n is obtained according to the preset posture control model:

[0062]

[0063] where, K τ2 and K η2 are positive real numbers; the adaptive control signal u ver,a is obtained according to the preset posture control model:

[0064]

[0065] where k a2 is a designed constant, and the update rates of the three adaptive parameters are

[0066]

[0067] where o i > 0, p i > 0, i = 1, 2, 3 are design parameters.

[0068] Further, in the method as described previously, the variable of the preset integral sliding surface of the preset vertical position control model is:

[0069] z hon,1 = s hon= P h - P hd

[0070]

[0071] wherein P hd is the desired horizontal position vector,

[0072] The control signal output by the preset horizontal position control model is:

[0073] a hd = u hon,n + u hon,a

[0074] wherein u hon,n is a nominal control signal, u hon,a is an adaptive control signal, and the nominal control signal u hon,n is obtained according to the preset posture control model:

[0075]

[0076] wherein, K τ3 and K η3 are positive real numbers; and the adaptive control signal u hon,a is obtained according to the preset posture control model:

[0077]

[0078] wherein the update rates of the three adaptive parameters are

[0079]

[0080] wherein o i > 0, p i > 0, and i = 7, 8, 9 are design parameters.

[0081] To achieve the above object, according to another aspect of the present application, there is provided a UAV flight control device, comprising: a pose vector acquisition module, a pose model control module and a flight control module; the pose vector acquisition module is configured to acquire a first pose vector of a UAV to be controlled; the pose model control module is configured to take the first pose vector as an input of a preset pose control model, and obtain a control signal output by the preset pose control model according to a preset integral sliding surface and a preset control rate of the preset pose control model, wherein the preset pose control model is a preset second-order integral chain model with a pose as a state variable, and the preset control rate is configured to make a state variable of the preset second-order integral chain model reach a preset equilibrium state, and a preset linear quadratic index is minimum; and the flight control module is configured to control the UAV to be controlled according to the control signal.

[0082] To achieve the above object, according to another aspect of the present application, there is provided an electronic device, comprising: a memory configured to store a program; and a processor configured to run the program stored in the memory to execute the method according to any one of the above.

[0083] To achieve the above object, according to another aspect of the present application, there is provided a computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method according to any one of the above.

[0084] In the embodiments of the present application, the technical problem to be solved by the present application is that a general controller cannot guarantee fast finite time convergence of a closed loop system of a small unmanned helicopter. A multi-input multi-output adaptive high-order integral sliding mode control method suitable for engineering implementation is proposed based on finite time convergence quasi-optimal control. The method does not need to know the bound of uncertainty in advance and can avoid overestimation of sliding mode gain. In addition, the proposed adaptive high-order sliding mode control is continuous and fast convergent, and the convergence speed can be specified. BRIEF DESCRIPTION OF DRAWINGS

[0085] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and to make apparent the other features and advantages of the present application. The illustrative embodiments of the present application, as well as the explanations of the present application, are intended to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0086] Figure 1 is a flowchart of a UAV flight control method based on adaptive quasi-optimal high-order sliding mode control according to an embodiment of the present application;

[0087] Figure 2 is a system design flowchart of a UAV flight control method based on adaptive quasi-optimal high-order sliding mode control according to the present application.

[0088] Figure 3 is a structural schematic diagram of a flight control device of a UAV provided by an embodiment of the present application. DETAILED DESCRIPTION

[0089] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0090] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0091] In the present application, the terms "up", "down", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0092] In addition, in addition to being used to indicate the orientation or positional relationship, the above-mentioned part of the terms can also be used to indicate other meanings, for example, the term "up" can also be used to indicate a certain dependent relationship or connection relationship in some cases. For those skilled in the art, the specific meaning of these terms in the present application can be understood according to the specific circumstances.

[0093] In addition, the terms "mounting", "arrangement", "provided with", "connected", "linked", "sleeved" should be interpreted broadly. For example, it can be fixed connection, detachable connection, or integral structure; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood according to the specific circumstances by those skilled in the art.

[0094] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0095] The present application designs a quadratic performance index as shown in formula (6) and a finite time control (7) with a switching function as shown in formula (8). When the initial state of the system is far away from the equilibrium point, the system state will converge to the equilibrium point in the form of an exponential function not lower than the set value, which can significantly accelerate the convergence speed of the system state, so as to ensure the linear quadratic optimal performance index of the system. When the system state is close to the equilibrium point, the control law (7) is switched to the equivalent control of exponential convergence sliding mode.

[0096] According to the embodiment of the present application, an unmanned aerial vehicle flight control method based on adaptive quasi-optimal high-order sliding mode control is provided, as shown in the figure, which comprises the following steps S102 to S106:

[0097] S102. Obtain a first pose vector of an unmanned aerial vehicle to be controlled.

[0098] S104. Take the first pose vector as the input of a preset pose control model, and obtain the control signal of the output of the preset pose control model according to the preset integral sliding surface and the preset control rate of the preset pose control model, wherein the preset pose control model is a preset second-order integral chain model with pose as the state variable, and the preset control rate makes the state variable of the preset second-order integral chain model reach the preset equilibrium state, and the preset linear quadratic index is minimum.

[0099] S106. Control the unmanned aerial vehicle to be controlled according to the control signal.

[0100] Figure 2 is a system design flowchart of the unmanned aerial vehicle flight control method based on adaptive quasi-optimal high-order sliding mode control of the present application. As Figure 2As shown, first, a finite time convergent feedback control is designed for a general class of integral chain systems (S202), then a linear quadratic performance index is proposed based on the finite time control (S204), on this basis, the integral sliding surface is determined (S206), the final control is defined as the sum of nominal control and adaptive control, and then they are designed respectively, the attitude dynamics and position dynamics of small unmanned helicopter are converted into integral chain form (S208), so as to apply the proposed control method to the attitude and position integral chain model, the attitude kinematics and dynamics model of small unmanned helicopter is integrated and converted into integral chain form (S210), and a whole quasi-optimal finite time adaptive high-order sliding mode controller is obtained (S212).

[0101] The specific embodiments of the application are described in detail below: Figure 2

[0102] Step one: considering the following integral chain system

[0103]

[0104]

[0105] Wherein, i = 1, 2,..., l-1, Suppose K1, K2,..., K l are normal numbers, and the polynomial λ l +k lj λ l-1 +…+k 2j λ+k 1j is Hurwitz stable, then for any ε ∈ (0, 1), α ∈ (1-ε, 1), the following finite time feedback control can be designed to make the system (4) stable to the origin in finite time

[0106] v n0 =-K1Sgn(z1)-…-K i Sgn(z i )-…-K l Sgn(z l ) (5)

[0107] Wherein

[0108]

[0109] K i =diag(k i1 ,…,k ij ,…,k in ), i = 1, 2,..., l, j = 1, 2,..., n,

[0110] ​

[0111] α (l+1)j = 1, α lj = α, j = 1, 2, …, n

[0112] Step two: Based on finite-time control (5), the following linear quadratic performance index is designed for the integral chain system (4)

[0113]

[0114] where is a positive definite diagonal matrix, and δ is a positive real number. The finite-time control

[0115]

[0116] can make the system state z(t) approach zero in finite time, and the convergence speed is not less than the exponential e -δt . Where,

[0117]

[0118] α i = diag(α i1 ,…,α in ), P is the positive definite solution of the following state Riccati equation

[0119]

[0120] where, 0 (·)×(·) and I (·)×(·) are zero matrix and identity matrix of corresponding dimension.

[0121] Step three: Integrate and convert the small unmanned helicopter attitude kinematics and dynamics model into integral chain form

[0122]

[0123] where, Θ = [φ, θ, ψ] T is the attitude angle vector containing roll, pitch and yaw angles, ω = [p, q, r] T is the angular velocity vector, d is the external disturbance torque, Sk(ω) is a skew-symmetric matrix about angular velocity, and T(Θ) is a 3 × 3 matrix defined as

[0124]

[0125] J = diag{I x ,I y ,I z} is the body inertia matrix of the unmanned helicopter, and the total external torque is represented by M.

[0126] Since the main rotor flapping motion is much faster than the rigid body motion of the fuselage, the transient response of the main rotor flapping motion can be ignored if the response speed is fast enough, and the following simplified static flapping motion model can be obtained

[0127]

[0128] where,

[0129]

[0130]

[0131]

[0132] a,b are the longitudinal and lateral flapping angles of the unmanned helicopter, δ lat ,δ lon are the lateral and longitudinal cyclic control input signals, θ lat ,θ lon are the lateral and longitudinal cyclic control deflections of the main rotor. Since the tail rotor drag calculation is too complex to be used in control design, some small quantities are ignored according to the model characteristics, and only the main input variables are considered, and the following feedback control is designed

[0133] T tr,ctr = k ped,ctr δ ped - k r,ctr r (12)

[0134] k ped,ctr and k r,ctr are positive real numbers to be designed, δ ped is the rudder input signal. Then the moment of the tail rotor drag around the z-axis of the body coordinate system can be approximately expressed as

[0135] N tr,ctr = T tr,ctr D tr (13)

[0136] Since the moment of the unmanned helicopter is mainly derived from the main rotor lift and the tail rotor drag, other small moment components can be treated as disturbances, and the flapping angle is generally a small angle, so s(a)≈a and s(b)≈b can be assumed. Therefore, the external combined moment acting on the small unmanned helicopter can be expressed as

[0137]

[0138] where d is the lumped external disturbance moment,

[0139] Substituting the simplified flapping motion equation (11) and the tail rotor drag control (12) and (13) with angular velocity feedback into equation (14), we can obtain

[0140] M = (M1A - k r,ctr M2) ω + (k ped,ctr M2 - M1B) δ aer - M1B d θ aer + M Δ (15)

[0141] where,

[0142]

[0143]

[0144]

[0145]

[0146] L mr , M mr and N mr are the projections of the main rotor moment on the body coordinate x, y and z axes respectively, k β is the main rotor blade stiffness coefficient, H tr is the vertical distance from the center of gravity to the tail rotor hub, D tr is the longitudinal horizontal distance from the center of gravity to the tail rotor hub, H mr is the vertical distance from the center of gravity to the main rotor hub, T hov is the main rotor pull force when the unmanned helicopter is hovering.

[0147] Substituting equation (15) into equation (10) can transform equation (10) into

[0148]

[0149] Let the system state variable x1 = [Θ T , ω T ] T , the known system function can be rewritten as

[0150]

[0151]

[0152] The unknown uncertain function is

[0153]

[0154]

[0155] where, J = J n + J Δ , J nThe nominal inertia matrix J Δ is measured. Let the sliding variable z att,1 = s att = Θ - Θ d , Θ d is the desired attitude angle vector, then equation (16) can be rewritten as

[0156]

[0157]

[0158] Define α1=diag(α 11 ,α 12 ,α 13 ) and α2=diag(α 21 ,α 22 ,α 23 ), the nonlinear function

[0159]

[0160]

[0161] where φ e = φ - φ d , θ e = θ - θ d , ψ e = ψ - ψ d , φ d , θ d and ψ d are the desired roll, pitch and yaw angles, respectively. The virtual control

[0162]

[0163] where is a positive definite matrix to be designed, and P1 is the positive definite solution of the following Riccati equation

[0164]

[0165] δ1 is a positive real number to be designed,

[0166] The integral sliding surface σ1 is designed as

[0167] σ1= [σ 11 ,σ 12 ,σ 13 ] T = z att,2 -ν ns (24)

[0168] where Let the final input signal of the rudder be composed of nominal control and adaptive control

[0169] δ aer = u att,n + u att,a (25)

[0170] Since (k ped,ctr M2-M1B) is invertible, the nominal control u att,n is designed as

[0171]

[0172] where K τ1 and K η1 are positive definite diagonal matrices.

[0173] The adaptive control u att,a is designed as

[0174]

[0175] k a1 is a designed constant, and the update rates of the three adaptive parameters are designed as

[0176]

[0177] where, o i > 0, p i > 0, i = 1, 2, 3 are designed parameters.

[0178] Step four: define the vertical height position sliding mode variable z ver,1 = s ver = z - z d , Thus the vertical position dynamics of the small unmanned helicopter can be rewritten as

[0179]

[0180]

[0181] Similar to the attitude controller, let the total distance rudder control input be composed of nominal control and adaptive control

[0182] δ col = u ver,n + u ver,a (30)

[0183] Obviously Y 2n ≠ 0, the nominal control u ver,n is designed as

[0184]

[0185] where K τ2 and K η2 are positive real numbers. The adaptive control u ver,a is designed as

[0186]

[0187] k a2 is a designed constant, and the update rates of the three adaptive parameters are designed as

[0188]

[0189] where o i > 0, p i > 0, and i = 1, 2, 3 are designed parameters.

[0190] Step five: The horizontal position dynamics model can be approximated as a typical second-order integral chain model, which is suitable for the proposed finite-time convergent adaptive high-order sliding mode control method. The longitudinal-lateral horizontal position model is extracted and rewritten as

[0191]

[0192]

[0193] where z hon,1 = s hon = P h -P hd , P hd is the desired horizontal position vector, Let the horizontal position virtual control consist of a nominal control and an adaptive control

[0194] a hd = u hon,n + u hon,a (35)

[0195] The nominal control u hon,n is designed as

[0196]

[0197] where The adaptive control u hon,a is designed as

[0198]

[0199] wherein

[0200]

[0201] o i > 0, p i > 0, i = 7, 8, 9 are design parameters.

[0202] From the above description, it can be seen that the adaptive quasi-optimal high-order continuous sliding mode control method with a specified convergence speed is adopted, the tracking error of the closed-loop system can be guaranteed to converge in a limited time, the convergence speed is adjustable, the response rapidity and robustness of the flight control system of the small unmanned helicopter are effectively improved.In addition, the adaptive law does not need prior knowledge of known uncertainty, and can avoid the problem of overestimation of the sliding mode gain, and can effectively suppress the sliding mode chattering.

[0203] The application is tested in the control system design of the small unmanned helicopter Heli800E, and the actual flight test proves the feasibility of the application, and the actual autonomous flight result shows that the control precision and robustness of the control system designed by using the technology meet the index requirements of the system.

[0204] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from here.

[0205] According to the embodiments of the application, a device for implementing the above method is also provided, as shown in Figure 3 The device comprises:

[0206] A pose vector acquisition module is configured to acquire a first pose vector of a to-be-controlled unmanned aerial vehicle.

[0207] A pose model control module is configured to take the first pose vector as an input of a preset pose control model, and obtain a control signal output by the preset pose control model according to a preset integral sliding surface and a preset control rate of the preset pose control model, wherein the preset pose control model is a preset second-order integral chain model with a pose as a state variable, and the preset control rate makes a state variable of the preset second-order integral chain model minimum when reaching a preset equilibrium state.

[0208] A flight control module is configured to control the to-be-controlled unmanned aerial vehicle according to the control signal.

[0209] According to the embodiments of the present application, an electronic device for implementing the above method is also provided, which comprises a memory for storing a program, and a processor for running the program stored in the memory to execute the above method.

[0210] According to the embodiments of the present application, a computer readable storage medium for implementing the above method is also provided, which has computer program instructions stored thereon, and the computer program instructions are executed by a processor to implement the above method.

[0211] Obviously, those skilled in the art should understand that each module or each step of the present application described above can be implemented by a general computing device, which can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, each module or each step can be implemented by program codes executable by a computing device, so that each module or each step can be stored in a storage device and executed by a computing device, or each module or each step can be made into an individual integrated circuit module, or multiple modules or steps can be made into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

[0212] The above only describes the preferred embodiments of the present application and is not used to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A UAV flight control method based on adaptive quasi-optimal higher order sliding mode control, characterized in that, The method comprises: acquiring a first pose vector of a to-be-controlled unmanned aerial vehicle; inputting the first pose vector into a preset pose control model, and obtaining a control signal output by the preset pose control model according to a preset integral sliding surface and a preset control rate of the preset pose control model, wherein the preset pose control model is a preset second-order integral chain model with a pose as a state variable, and the preset control rate makes a state variable of the preset second-order integral chain model reach a preset equilibrium state, and a preset linear quadratic index is minimum; controlling the to-be-controlled unmanned aerial vehicle according to the control signal; the preset pose control model comprises a preset attitude control model, a preset vertical position control model, and a preset horizontal position control model, and the first pose vector comprises a first attitude vector, a first vertical position vector, and a first horizontal position vector; inputting the first pose vector into a preset pose control model, and obtaining a control signal output by the preset pose control model according to a preset integral sliding surface and a preset control rate of the preset pose control model, comprises: respectively inputting the first attitude vector, the first vertical position vector, and the first horizontal position vector into the preset attitude control model, the preset vertical position control model, and the preset horizontal position control model, respectively obtaining attitude control signals, vertical position control signals, and horizontal position control signals output by the respective preset attitude control models according to preset integral sliding surfaces and preset control rates of the respective models; the control signal output by the preset pose control model is a sum of a nominal control signal and an adaptive control signal, the nominal control signal is obtained according to the preset integral sliding surface and the preset control rate, and the adaptive control signal is obtained according to the preset integral sliding surface, the preset control rate, and an adaptive parameter, wherein an update rate of the adaptive parameter is obtained according to the preset integral sliding surface and the preset control rate; the preset pose control model is obtained according to the following model: ； wherein , l is an integer greater than 1, , is a state variable, , is a preset control rate and is obtained from the following equation: ； wherein is a positive definite diagonal matrix, , is the positive definite solution of the following Riccati equation ； wherein , and are zero matrices and identity matrices of the respective dimensions, is a positive real number, ; , , , ; for any , , the preset linear quadratic index is obtained according to the following formula: ； where t is a time variable, ; the equilibrium state is obtained according to the following formula: ； wherein equilibrium state, are normal numbers, and the polynomial is Hurwitz stable, , .

2. The UAV flight control method based on adaptive quasi-optimal higher order sliding mode control according to claim 1, characterized in that, a state variable of the preset attitude control model is: ； wherein, is a vector of attitude angles comprising roll, pitch and yaw angles, is a vector of angular velocities; a variable of the preset integral sliding surface of the preset attitude control model is: ； ； wherein is the desired attitude angle vector; the preset control rate of the preset attitude control model is: ； wherein is a positive definite matrix to be designed, is a positive definite solution of the following algebraic Riccati equation; ； wherein is a positive real number to be designed, , , , ， ， wherein and , , , , , and are the desired roll, pitch and yaw angles, respectively. the preset integral sliding surface of the preset attitude control model is: ； wherein ; the control signal output by the preset attitude control model is: ； wherein is a nominal control signal, is an adaptive control signal, ； wherein , and are positive definite diagonal matrices, ； wherein is a matrix defined as: ； the measured nominal moment of inertia matrix, is a skew-symmetric matrix with respect to the angular velocity, and is a positive real number to be designed, , , are the abbreviations for , and respectively; ； ； Cp is the main rotor blade stiffness coefficient, Cv is the vertical distance from the center of gravity to the tail rotor hub, Cv is the vertical distance from the center of gravity to the tail rotor hub, Cv is the vertical distance from the center of gravity to the tail rotor hub, Cp is the main rotor blade stiffness coefficient, Adaptive control signal For: ； , , is a constant for a design, and the update rates of the three adaptive parameters are designed as: ； wherein , , are design parameters.

3. The UAV flight control method based on adaptive quasi-optimal higher order sliding mode control according to claim 2, characterized in that, a variable of the preset integral sliding surface of the preset vertical position control model is: ； ； wherein, is the desired vertical height; the control signal output by the preset vertical position control model is: ； wherein, is a nominal control signal, is an adaptive control signal, the nominal control signal obtaining, according to the preset posture control model: ； wherein, , and are positive real numbers; adaptive control signal obtaining, according to the preset posture control model: ； wherein, is a design constant, and the update rates of the three adaptive parameters are: ； wherein , , are design parameters.

4. The UAV flight control method based on adaptive quasi-optimal higher order sliding mode control according to claim 3, characterized in that, a variable of the preset integral sliding surface of the preset horizontal position control model is: ； ； wherein is the desired horizontal position vector, , ; the control signal output by the preset horizontal position control model is: ； wherein, is a nominal control signal, is an adaptive control signal, the nominal control signal obtained according to the preset posture control model: ； wherein, , and are positive real numbers; adaptive control signal obtaining, according to the preset posture control model: ； the update rates of the three adaptive parameters are: ； wherein , , are design parameters.

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