Helicopter hysteresis inverse compensation method and system based on obstacle Lyapunov function

Abstract

The invention discloses a helicopter hysteresis inverse compensation method and system based on an obstacle Lyapunov function, and the method comprises the steps: constructing a nonlinear kinetic equation of a two-degree-of-freedom helicopter system according to a Lagrange mechanical model; defining a tracking error variable and a sliding mode variable with convergence, introducing a radial basis function neural network and a hysteresis inverse model, and constructing a specified performance function based on obstacle Lyapunov; and performing inverse compensation on an unknown nonlinear function of the nonlinear kinetic equation of the two-degree-of-freedom helicopter system through a specified performance function based on the obstacle Lyapunov to obtain a helicopter hysteresis inverse compensation result. According to the method, the unknown nonlinear function of the two-degree-of-freedom helicopter system can be compensated, and the control precision and stability of the two-degree-of-freedom helicopter system are improved. The helicopter hysteresis inverse compensation method and system based on the obstacle Lyapunov function can be widely applied to the technical field of two-degree-of-freedom helicopter control.

Description

Technical Field

[0001] The present invention relates to the technical field of two - degree - of - freedom helicopter control, and particularly to a helicopter hysteresis inverse compensation method and system based on a barrier Lyapunov function. Background Art

[0002] Helicopters can achieve vertical take - off and landing in confined spaces. Therefore, they have been widely used in many fields such as rescue, cargo transportation, agricultural spraying, and reconnaissance missions. Among them, the attitude control of the helicopter system is the key to successfully completing these tasks. However, due to the system's uncertainty, strong non - linearity, and the coupling effect between inputs and outputs, the control of this multi - input multi - output system becomes extremely challenging, especially when achieving precise attitude control.

[0003] Related technologies construct highly robust non - linear controllers by using neural networks to approximate unknown system dynamics. Among them, radial basis function neural networks are widely used in the field of real - time control due to their fast convergence speed and good generalization ability. Such neural networks can effectively handle system uncertainties, improve control accuracy and system robustness. In addition, preset performance control (PPC) introduces an error performance function to clarify objectives such as the system's response time, steady - state accuracy, and overshoot, thereby guiding the system to achieve the desired dynamic and steady - state performance. However, the PPC method based on error transformation is usually complex in the design stage. Especially when deriving the error transformation and constructing an appropriate control law, a large amount of time and effort are required. In addition, the helicopter system also faces the problem of input hysteresis effect. This hysteresis effect may cause system response lag or even instability, seriously affecting the accuracy and safety of attitude control. Summary of the Invention

[0004] In order to solve the above - mentioned technical problems, the objective of the present invention is to provide a helicopter hysteresis inverse compensation method and system based on a barrier Lyapunov function, which can compensate for the unknown non - linear functions of a two - degree - of - freedom helicopter system and improve the control accuracy and stability of the two - degree - of - freedom helicopter system.

[0005] The first technical solution adopted by the present invention is: a helicopter hysteresis inverse compensation method based on a barrier Lyapunov function, including the following steps:

[0006] According to the Lagrangian mechanics model, construct the non - linear dynamic equation of the two - degree - of - freedom helicopter system;

[0007] Define the tracking error variable and the convergent sliding - mode variable, and introduce a radial basis neural network and a hysteresis inverse model to construct a specified performance function based on the barrier Lyapunov;

[0008] The inverse compensation of the unknown nonlinear function in the nonlinear dynamic equation of the two-degree-of-freedom helicopter system is performed through a specified performance function based on the barrier Lyapunov, and the helicopter hysteresis inverse compensation result is obtained.

[0009] Furthermore, the expression of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system is specifically as follows:

[0010]

[0011] In the above formula, θ and ψ respectively represent the pitch angle and yaw angle of the helicopter, and respectively represent the pitch angular velocity and yaw angular velocity, and respectively represent the pitch angular acceleration and yaw angular acceleration, J p and J y respectively represent the moments of inertia about the pitch axis and yaw axis, κ pp 、κ py 、κ yp and k yy all represent the thrust gains of the propellers, D p and D y are respectively the viscous friction coefficients in the pitch motion and yaw motion, M and g are respectively the mass and gravitational acceleration, L d represents the distance from the center of mass to the origin of the body-fixed coordinate system, V f and V y are respectively the input voltages of the two motors.

[0012] Furthermore, the step of defining the tracking error variable and the sliding mode variable with convergence, and introducing the radial basis neural network and the hysteresis inverse model, and constructing the specified performance function based on the barrier Lyapunov specifically includes:

[0013] Define the tracking error variable and the sliding mode variable, and construct the barrier Lyapunov function;

[0014] Based on the barrier Lyapunov function, verify the convergence of the sliding mode variable, and construct a preliminarily convergent barrier Lyapunov function;

[0015] Based on approximating the unknown nonlinear function in the two-degree-of-freedom helicopter system, design the radial basis neural network;

[0016] Based on the preliminarily convergent barrier Lyapunov function, verify the weight convergence of the radial basis neural network, and construct a convergent barrier Lyapunov function;

[0017] Introduce a hysteresis model, solve the hysteresis parameters by an adaptive parameter solving method, and construct a hysteresis inverse model;

[0018] Embed the hysteresis inverse model into a convergent barrier Lyapunov function to construct a specified performance function based on the barrier Lyapunov.

[0019] Furthermore, the expression of the barrier Lyapunov function is specifically as follows:

[0020]

[0021] In the above formula, V1 represents the barrier Lyapunov function, represents the time-varying upper bound of the constraint, represents the time-varying lower bound of the constraint, e i (t) represents the tracking error, Ξ(·) represents the switching function, when e>0, Ξ(·)=1, when e<0, Ξ(·)=0, and t represents the time variable.

[0022] Furthermore, the expression of the radial basis neural network is specifically as follows:

[0023]

[0024] In the above formula, f represents the radial basis neural network, α and β represent constants, represents an unknown continuous function, Λ represents an unknown continuous function, E represents the tracking error vector, represents the first derivative of the tracking error vector, represents the second derivative of the reference trajectory.

[0025] Furthermore, the step of introducing the hysteresis model, solving the hysteresis parameters by an adaptive parameter solving method, and constructing the hysteresis inverse model specifically includes:

[0026] Based on the hysteresis model, considering the invariance of the input signal, construct an ideal hysteresis inverse model;

[0027] Obtain the unknown hysteresis parameters of the ideal hysteresis inverse model and solve them by an adaptive parameter solving method to construct the hysteresis inverse model.

[0028] Furthermore, the expression of the hysteresis inverse model is specifically as follows:

[0029]

[0030] In the above formula, u(t) represents the hysteresis inverse model, ρ, B r and B l both represent the hysteresis parameters of the hysteresis model, represents the clearance inverse model, τd denotes an ideal controller, δ l denotes a function.

[0031] The second technical solution adopted by the present invention is: a helicopter hysteresis inverse compensation system based on a barrier Lyapunov function, including:

[0032] A first module, configured to construct a nonlinear dynamic equation of a two-degree-of-freedom helicopter system according to a Lagrangian mechanics model;

[0033] A second module, configured to define a tracking error variable and a convergent sliding mode variable, and introduce a radial basis neural network and a hysteresis inverse model to construct a specified performance function based on a barrier Lyapunov;

[0034] A third module, configured to perform inverse compensation on the unknown nonlinear function of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system through the specified performance function based on a barrier Lyapunov to obtain a helicopter hysteresis inverse compensation result.

[0035] The beneficial effects of the method and system of the present invention are: by constructing a nonlinear dynamic equation of a two-degree-of-freedom helicopter system according to a Lagrangian mechanics model, further defining a tracking error variable and a convergent sliding mode variable, and introducing a radial basis neural network and a hysteresis inverse model, constructing a specified performance function based on a barrier Lyapunov, designing a specified performance function based on a barrier Lyapunov, enabling the tracking error to converge to zero within a time-varying constraint, and then, on the premise of a completely unknown system model, introducing a radial basis neural network to compensate for the unknown nonlinear function of the two-degree-of-freedom helicopter system, combining with a hysteresis inverse compensation method, alleviating the hysteresis phenomenon of the control input signal through the idea of reverse cancellation, and finally performing inverse compensation on the unknown nonlinear function of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system through the specified performance function based on a barrier Lyapunov to improve the control accuracy and stability of the two-degree-of-freedom helicopter system. Description of the Drawings

[0036] Figure 1 is a flowchart of the steps of the helicopter hysteresis inverse compensation method based on a barrier Lyapunov function of the present invention;

[0037] Figure 2 is a structural block diagram of the helicopter hysteresis inverse compensation system based on a barrier Lyapunov function of the present invention;

[0038] Figure 3 is a schematic diagram of the control of a two-degree-of-freedom helicopter based on a barrier Lyapunov specified performance and hysteresis inverse compensation provided by a specific embodiment of the present invention;

[0039] Figure 4It is a schematic diagram of the angle tracking trajectory of the pitch angle provided by a specific embodiment of the present invention;

[0040] Figure 5 It is a schematic diagram of the angle tracking trajectory of the yaw angle provided by a specific embodiment of the present invention;

[0041] Figure 6 It is a schematic diagram of the pitch angle tracking error provided by a specific embodiment of the present invention;

[0042] Figure 7 It is a schematic diagram of the pitch angle tracking error trajectory provided by a specific embodiment of the present invention;

[0043] Figure 8 It is a schematic diagram of the first control signal with and without backlash compensation provided by a specific embodiment of the present invention;

[0044] Figure 9 It is a schematic diagram of the second control signal with and without backlash compensation provided by a specific embodiment of the present invention. Detailed implementation manners

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, they are only set for the convenience of elaboration and explanation, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0046] First of all, it should be noted that in the prior art, although the control method designed based on the linear model (such as the linear quadratic regulator LQR) can compensate for the uncertainty of the system to a certain extent, for a helicopter system with strong nonlinear and coupling effects, its control effect is often not ideal. The nonlinear characteristics are not fully modeled or compensated, resulting in low control accuracy and robustness. In addition, the existing preset performance control (PPC) technology usually adopts error transformation design to implement, and such methods are complex to derive and difficult to implement. Moreover, the hysteresis effect in the helicopter system may cause the system response to lag or oscillate, and this phenomenon is usually ignored in the control design, which may lead to dynamic mismatch between the input and the output, thus affecting the stability and control performance of the system.

[0047] Based on this, in the embodiments of the present invention, the barrier Lyapunov function (BLF) technology is first applied to preset performance control. By restricting the error range, the error transformation design process is simplified. At the same time, it is ensured that the system output reaches the specified dynamic performance target within a given time, and the PID sliding mode surface design is introduced to enhance the robustness of the system. Moreover, by adopting an adaptive control strategy based on a radial basis function neural network (RBFNN), the present invention can approximate unknown nonlinear dynamics and system uncertainties in real time, significantly improving the robustness and adaptability of the controller. Furthermore, a clearance inverse compensation technology is proposed to achieve the smoothing of the control signal.

[0048] Referring to Figure 1 , the present invention provides a helicopter hysteresis inverse compensation method based on a barrier Lyapunov function, and the method includes the following steps:

[0049] S100. Construct a nonlinear dynamic equation of a two-degree-of-freedom helicopter system according to the Lagrangian mechanics model;

[0050] In this embodiment, according to the Lagrangian mechanics model, the nonlinear dynamic equation of the system is as follows:

[0051]

[0052] In the above formula, θ and ψ respectively represent the pitch angle and yaw angle of the helicopter, and respectively represent the pitch angular velocity and yaw angular velocity, and respectively represent the pitch angular acceleration and yaw angular acceleration, J p and J y respectively represent the moments of inertia about the pitch axis and yaw axis, κ pp , κ py , κ yp and κ yy all represent the thrust gains of the propellers, D p and D y are respectively the viscous friction coefficients in pitch motion and yaw motion, M and g are respectively the mass and gravitational acceleration, L d represents the distance from the center of mass to the origin of the body-fixed coordinate system, V f and V y are respectively the input voltages of the two motors.

[0053] Define the state variable X1 = [θ, ψ] T and The dynamic equation of the two-degree-of-freedom helicopter system can be rewritten as the following state-space equation:

[0054]

[0055] Among them, and Λ(X1) represent unknown continuous functions related to the state variables X1 and X2. Their expressions are as follows:

[0056]

[0057] In the above formula, X1 and X2 represent state variables, and Λ(X1) represent unknown continuous functions related to the state variables X1 and X2, J p and J y represent the moments of inertia about the pitch axis and the yaw axis respectively, κ pp , κ py , κ yp and κ yy all represent the thrust gains of the propellers, D p and D y are the viscous friction coefficients in the pitch motion and the yaw motion respectively, M and g are the mass and the acceleration due to gravity respectively, and L d represents the distance from the center of mass to the origin of the body-fixed coordinate system.

[0058] S200. Define the tracking error variable and the sliding mode variable with convergence, introduce the radial basis neural network and the hysteresis inverse model, and construct the specified performance function based on the barrier Lyapunov;

[0059] S210. Define the tracking error variable and the sliding mode variable, and construct the barrier Lyapunov function;

[0060] In this embodiment, define the tracking error variable E = X1 - X d ∈R 2 and the sliding mode variable X d is the reference tracking trajectory, and α, β, and λ are all designed positive constants, is the differential of E. Design the barrier Lyapunov function to satisfy the specified performance constraint:

[0061]

[0062] In the above formula, V1 represents the barrier Lyapunov function, represents the time-varying upper bound of the constraint, represents the time-varying lower bound of the constraint, e i (t) represents the tracking error, Ξ(·) represents the switching function, Ξ(·) = 1 when e > 0, Ξ(·) = 0 when e < 0, and t represents the time variable.

[0063] Among them, is the time-varying upper bound of the constraint, ζ(t) = (ζ0 - ζ∞ )e -κt +ζ ∞ is the time-varying constraint lower bound, ζ0>0, ζ ∞ >0 are all set constants.

[0064] S220. Based on the barrier Lyapunov function, verify the convergence of the sliding mode variable and construct a preliminarily convergent barrier Lyapunov function;

[0065] In this embodiment, to ensure the convergence of the sliding mode variable, the following Lyapunov function is further defined. Therefore, the expression of the preliminarily convergent barrier Lyapunov function is specifically as follows:

[0066] V2 = V1 + S T ηS

[0067] where η = Λ -1 is the inverse matrix of Λ.

[0068] S230. Design a radial basis neural network based on the unknown nonlinear functions in the approximate two-degree-of-freedom helicopter system;

[0069] In this embodiment, to approximate all the unknown nonlinear functions in the system, a radial basis neural network is designed to approximate the following functions:

[0070]

[0071] Then, the weight update rate of the radial basis neural network is designed as

[0072]

[0073] where Γ w is the learning rate, γ w is a very small positive constant. H(Z) represents the Gaussian function with the input vector Z. represents the approximate weight of the radial basis neural network, is the update change rate of.

[0074] S240. Based on the preliminarily convergent barrier Lyapunov function, verify the weight convergence of the radial basis neural network and construct a convergent barrier Lyapunov function;

[0075] In this embodiment, for the convergence of the radial basis neural network weights, the following Lyapunov function is defined. Therefore, the expression of the convergent barrier Lyapunov function is specifically as follows:

[0076]

[0077] Among them, Γ -1 is a positive definite matrix.

[0078] S250. Introduce a hysteresis model, solve the hysteresis parameters through an adaptive parameter solution method, and construct a hysteresis inverse model;

[0079] Specifically, based on the hysteresis model, considering the invariance of the input signal, construct an ideal hysteresis inverse model; obtain the unknown hysteresis parameters of the ideal hysteresis inverse model, and solve them through an adaptive parameter solution method to construct a hysteresis inverse model.

[0080] In this embodiment, a common hysteresis model is considered, and its mathematical model is as follows:

[0081]

[0082] Among them, u represents the input of the hysteresis model, and τ B represents the output of the hysteresis model, and ρ, B r , B l both represent the hysteresis parameters of the hysteresis model. τ B (t_) indicates that the input signal is invariant.

[0083] For an ideal hysteresis inverse model of this model, the expression is as follows:

[0084]

[0085] Among them, p is a positive constant.

[0086] Considering that the hysteresis parameters ρ, B r , B l of the hysteresis model are unknown, an adaptive parameter method is introduced to solve the problem, and a hysteresis inverse model is proposed as follows:

[0087]

[0088] Furthermore, τ d can be expressed as:

[0089]

[0090] Among them, Φ = [u, δ r , δ l T .

[0091] S260. Embed the hysteresis inverse model into a barrier Lyapunov function with convergence, and construct a specified performance function based on the barrier Lyapunov.

[0092] ​In this embodiment, to ensure the convergence of the adaptive hysteresis parameter μ, the following Lyapunov function is further defined. Therefore, the expression of the specified performance function based on the barrier Lyapunov is specifically as follows:

[0093] V4 = V3 + μ T Qμ

[0094] where Q is a positive definite diagonal matrix. Finally, through all the above steps, it is proved that the expression of the specified performance function based on the barrier Lyapunov is uniformly bounded and stable, which also shows that the controller of this scheme is effective.

[0095] S300. Inverse compensation is performed on the unknown nonlinear function of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system through the specified performance function based on the barrier Lyapunov to obtain the helicopter hysteresis inverse compensation result.

[0096] In summary, as Figure 3 shown, this embodiment of the present invention proposes a control method for a two-degree-of-freedom helicopter system based on barrier Lyapunov specified performance and hysteresis inverse compensation. First, a specified performance function based on barrier Lyapunov is designed so that the tracking error can converge to zero within the time-varying constraints. Then, on the premise of a completely unknown system model (the system parameters are completely unknown), a radial basis neural network is introduced to compensate for the unknown nonlinear function of the two-degree-of-freedom helicopter system. Next, a hysteresis inverse compensation technology is developed to alleviate the hysteresis phenomenon of the control input signal through the idea of reverse cancellation. Finally, the effectiveness of the proposed method is verified through numerical simulation on the Matlab platform.

[0097] Finally, the embodiment of the present invention is verified by simulation. As Figure 4 shown, it is the angle tracking trajectory diagram of the pitch angle of the two-degree-of-freedom helicopter provided by the embodiment of the present invention; in the figure, the actual pitch angle (blue curve) tracks the reference pitch angle tracking trajectory (red curve) with a small error.

[0098] As Figure 5 shown, it is the angle tracking trajectory diagram of the yaw angle of the two-degree-of-freedom helicopter. In the figure, the actual yaw angle (blue curve) tracks the reference yaw angle tracking trajectory (red curve) with a small error.

[0099] As Figure 6 shown, it is the pitch angle tracking error diagram of the two-degree-of-freedom helicopter. The figure shows the PID control, the control strategy without backlash compensation, and the control strategy with backlash compensation. Among them, the PID control violates the specified performance constraint, and both the control strategies with and without backlash compensation are within the specified performance constraint range, but the pitch angle tracking error under the backlash compensation is smaller.

[0100] AsFigure 7 As shown, it is the yaw angle tracking error diagram of a two-degree-of-freedom helicopter. The diagram shows the PID control, the control strategy without backlash compensation, and the control strategy with backlash compensation. Among them, the PID control violates the specified performance constraints. The control strategies with and without backlash compensation are both within the specified performance constraints, but the yaw angle tracking error with backlash compensation is smaller.

[0101] As Figure 8 shown, it is the comparison diagram of the first control signal with and without backlash compensation; the diagram shows that the first control signal with backlash compensation is smoother. As Figure 9 shown, it is the comparison diagram of the second control signal with and without backlash compensation; the diagram shows that the second control signal with backlash compensation is smoother.

[0102] Referring to Figure 2 , the helicopter hysteresis inverse compensation system based on the barrier Lyapunov function includes:

[0103] The first module 201 is used to construct the nonlinear dynamic equation of the two-degree-of-freedom helicopter system according to the Lagrangian mechanical model;

[0104] The second module 202 is used to define the tracking error variable and the convergent sliding mode variable, and introduce the radial basis neural network and the hysteresis inverse model to construct the specified performance function based on the barrier Lyapunov;

[0105] The third module 203 is used to perform inverse compensation on the unknown nonlinear function of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system through the specified performance function based on the barrier Lyapunov to obtain the helicopter hysteresis inverse compensation result.

[0106] The content in the above method embodiments is applicable to the present system embodiment. The functions specifically implemented in the present system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0107] The above is a specific description of the preferred embodiment of the present invention. However, the present invention is not limited to the described embodiment. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention. These equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A helicopter hysteresis inverse compensation method based on a barrier Lyapunov function, characterized in that, It includes the following steps: According to the Lagrangian mechanics model, construct the nonlinear dynamic equation of the two-degree-of-freedom helicopter system; Define the tracking error variable and the convergent sliding mode variable, introduce the radial basis neural network and the hysteresis inverse model, and construct the specified performance function based on the barrier Lyapunov; Through the specified performance function based on the barrier Lyapunov, perform inverse compensation on the unknown nonlinear function in the nonlinear dynamic equation of the two-degree-of-freedom helicopter system to obtain the helicopter hysteresis inverse compensation result.

2. The helicopter hysteresis inverse compensation method based on the barrier Lyapunov function according to claim 1, characterized in that, The expression of the nonlinear dynamic equation of the two-degree-of-freedom helicopter system is specifically as follows: In the above formula, θ and ψ respectively represent the pitch angle and yaw angle of the helicopter. and respectively represent the pitch angular velocity and yaw angular velocity. and respectively represent the pitch angular acceleration and yaw angular acceleration. J p and J y respectively represent the moments of inertia about the pitch axis and yaw axis. κ pp 、κ py 、κ yp and κ yy all represent the thrust gains of the propellers. D p and D y are respectively the viscous friction coefficients in pitch motion and yaw motion. M and g are respectively the mass and gravitational acceleration. L d represents the distance from the center of mass to the origin of the body-fixed coordinate system. V f and V y are respectively the input voltages of the two motors.

3. The method for helicopter hysteresis inverse compensation based on the barrier Lyapunov function according to claim 2, characterized in that, The step of defining the tracking error variable and the convergent sliding mode variable, introducing the radial basis neural network and the hysteresis inverse model, and constructing the specified performance function based on the barrier Lyapunov specifically includes: Define the tracking error variable and the sliding mode variable, and construct the barrier Lyapunov function; Based on the barrier Lyapunov function, verify the convergence of the sliding mode variable, and construct the barrier Lyapunov function with preliminary convergence; Based on approximating the unknown nonlinear function in the two-degree-of-freedom helicopter system, design the radial basis neural network; Based on the barrier Lyapunov function with preliminary convergence, verify the weight convergence of the radial basis neural network, and construct the barrier Lyapunov function with convergence; Introduce the hysteresis model, solve the hysteresis parameters through the adaptive parameter solution method, and construct the hysteresis inverse model; Embed the hysteresis inverse model into the barrier Lyapunov function with convergence to construct the specified performance function based on the barrier Lyapunov.

4. The method for helicopter hysteresis inverse compensation based on the barrier Lyapunov function according to claim 3, characterized in that, The expression of the barrier Lyapunov function is specifically as follows: In the above formula, V1 represents the barrier Lyapunov function, represents the time-varying upper bound of the constraint, represents the time-varying lower bound of the constraint, e i (t) represents the tracking error, represents the switching function. When e > 0, When e < 0, t represents the time variable.

5. The helicopter hysteresis inverse compensation method based on the barrier Lyapunov function according to claim 4, wherein The expression of the radial basis neural network is specifically as follows: In the above formula, f represents a radial basis neural network, α and β represent constants, represents an unknown continuous function, Λ represents an unknown continuous function, E represents a tracking error vector, represents the first derivative of the tracking error vector, represents the second derivative of the reference trajectory.

6. The method for helicopter hysteresis inverse compensation based on the barrier Lyapunov function according to claim 5, wherein The step of introducing the hysteresis model, solving the hysteresis parameters through the adaptive parameter solution method, and constructing the hysteresis inverse model specifically includes: Based on the hysteresis model, considering the invariance of the input signal, construct the ideal hysteresis inverse model; Obtain the unknown hysteresis parameters of the ideal hysteresis inverse model, and solve them through the adaptive parameter solution method to construct the hysteresis inverse model.

7. The helicopter hysteresis inverse compensation method based on the barrier Lyapunov function according to claim 6, characterized in that, The expression of the hysteresis inverse model is specifically as follows: In the above formula, u(t) represents the hysteresis inverse model, ρ, B r and B l both represent the hysteresis parameters of the hysteresis model, represents the clearance inverse model, τ d represents the ideal controller, δ l represents a function.

8. The helicopter hysteresis inverse compensation system based on the barrier Lyapunov function is characterized in that It includes the following modules: The first module is used to construct the nonlinear dynamic equation of the two-degree-of-freedom helicopter system according to the Lagrangian mechanics model; The second module is used to define the tracking error variable and the convergent sliding mode variable, introduce the radial basis neural network and the hysteresis inverse model, and construct the specified performance function based on the barrier Lyapunov; The third module is used to perform inverse compensation on the unknown nonlinear function in the nonlinear dynamic equation of the two-degree-of-freedom helicopter system through the specified performance function based on the barrier Lyapunov to obtain the helicopter hysteresis inverse compensation result.

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