Two-degree-of-freedom helicopter system adaptive neural network control method based on time-varying output constraint

Abstract

The invention discloses a two-degree-of-freedom helicopter system adaptive neural network control method based on time-varying output constraint, and the method comprises the steps: constructing a nonlinear kinetic equation of a system according to a Lagrange mechanical model, and obtaining a two-degree-of-freedom helicopter system equation; according to the two-degree-of-freedom helicopter system equation, constructing an asymmetric time-varying obstacle Lyapunov function; constructing an adaptive rate according to the adaptive controller, and further completing the overall construction of the two-degree-of-freedom helicopter system according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate; and carrying out stability verification on the two-degree-of-freedom helicopter system, and carrying out flight control on a to-be-controlled target object aircraft based on the two-degree-of-freedom helicopter system passing the stability verification. According to the embodiment of the invention, high-precision tracking control can be realized on the premise of meeting complex time-varying output constraints, the overall stability and safety of the system can be ensured, and the method can be widely applied to the technical field of flight system control.

Description

Technical Field

[0001] The invention relates to the technical field of flight system control, and in particular to an adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints. Background Art

[0002] With the rapid development of aerospace and drone technology, the market demand for small, flexible and cost-effective drones continues to increase, and the application prospects of small unmanned helicopters in the military, commercial and civilian fields are becoming increasingly broad. Compared with traditional fixed-wing manned aircraft, small unmanned helicopters have significant advantages. They can not only achieve vertical take-off and landing, but also operate flexibly in narrow or complex environments, greatly improving the adaptability and flexibility of operations. These unmanned helicopters excel in low-altitude flight, complex maneuvers and hovering, making them ideal for monitoring, reconnaissance, search and rescue tasks.

[0003] However, due to the typical multi-input and multi-output nonlinear characteristics of small unmanned helicopter systems, involving complex dynamic modeling and mutual coupling effects, the design of robust control faces significant challenges. In practical applications, the control systems of small unmanned helicopters are often subject to time-varying output constraints. With the continuous changes in mission requirements, external environmental conditions and system operating status, these constraints change dynamically, placing higher requirements on the control of the system. Time-varying output constraints usually include dynamic restrictions on speed, attitude, position and acceleration to ensure the safety, stability and reliability of flight under different missions or environmental conditions.

[0004] If these time-varying output constraints are ignored when designing a control scheme, the system may show significant performance degradation under high load or sudden environments, and may even cause instability or loss of control of the aircraft. Therefore, in the design of helicopter control systems, the key to achieving safe and reliable control is to reasonably consider and meet the time-varying output constraints, which is also the core factor in improving the autonomy and execution of small unmanned helicopters in complex environments. Summary of the invention

[0005] The main purpose of the embodiment of the present invention is to propose an adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints, which can achieve high-precision tracking control while satisfying complex time-varying output constraints and ensure the overall stability and safety of the system.

[0006] To achieve the above object, an embodiment of the present invention provides an adaptive neural network control method for a two-degree-of-freedom helicopter system based on a time-varying output constraint, comprising the following steps:

[0007] According to the Lagrangian mechanics model, the nonlinear dynamic equation of the system is constructed to obtain the two-degree-of-freedom helicopter system equation;

[0008] According to the two-degree-of-freedom helicopter system equation, an asymmetric time-varying obstacle Lyapunov function is constructed;

[0009] The adaptive rate is constructed according to the adaptive controller, and then the overall construction of the two-degree-of-freedom helicopter system is completed according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate;

[0010] The stability of the two-degree-of-freedom helicopter system is verified, and based on the two-degree-of-freedom helicopter system that has passed the stability verification, flight control is performed on a target object aircraft to be controlled.

[0011] In some embodiments, constructing the nonlinear dynamic equation of the system according to the Lagrangian mechanics model to obtain the two-degree-of-freedom helicopter system equation includes the following steps:

[0012] Based on the nonlinear dynamic equation of the two-degree-of-freedom helicopter system, the two-degree-of-freedom helicopter system is expressed as a multi-input and multi-output nonlinear equation form;

[0013] According to the nonlinear equation form of multiple inputs and multiple outputs, radial basis function neural network is used to estimate the unknown functions of the two-degree-of-freedom helicopter system.

[0014] The angle error is configured, and a two-degree-of-freedom helicopter system equation is obtained according to the unknown function and the angle error.

[0015] In some embodiments, the nonlinear dynamic equation of the system is as follows:

[0016]

[0017] Among them, θ, Respectively represent the pitch angle, pitch angular velocity, and pitch angular acceleration; ψ, Represent the yaw angle, yaw angular velocity and yaw angular acceleration respectively, V a and V b Represent the input voltage of the front motor and the rear motor respectively, J a and J b represents the moment of inertia around the pitch axis and the yaw axis; P a and P b Represent the viscous friction coefficients of these axes; K aa , K ab , K ba and K bb They represent the thrust torque constants of the pitch and yaw axes respectively; m represents the mass, l d is the position of the center of mass, g represents the acceleration due to gravity;

[0018] The expression of the multi-input multi-output nonlinear equation form is:

[0019]

[0020] Where z = [z1, z2] T , z1=[α,β] T , ΔX(z) represents the unknown smooth continuous nonlinear function vector, G(u)=[V a ,V b ] T represents the control input of the system;

[0021] The expressions of X(z) and Y(z) are as follows:

[0022]

[0023] The expression for estimating the unknown function of the two-degree-of-freedom helicopter system using the radial basis function neural network is:

[0024]

[0025] Among them, S(Z) represents the basis function vector, is the input vector of the neural network; W *T represents the optimal weights of the neural network; represents the minimum approximation error;

[0026] The expression of the angle error of the pitch angle e1 and the yaw angle e2 is: e1 = z1-z d , e2=z2-ξ, where z d is the desired trajectory and ξ is the virtual controller.

[0027] In some embodiments, in the step of constructing an asymmetric time-varying obstacle Lyapunov function according to the two-degree-of-freedom helicopter system equation, the expression of the constructed asymmetric time-varying obstacle Lyapunov function is:

[0028]

[0029] in, and is a time-varying constraint function; p>1 and is an integer, h i (e 1i ) is a piecewise function, and the expression of the piecewise function is:

[0030] According to the unconstrained state variable z2, the expression of the quadratic Lyapunov function V2 is:

[0031] According to the quadratic Lyapunov function, the system controller expression is derived:

[0032] Where c2 is the control gain matrix,

[0033] In some embodiments, in the step of constructing an adaptive rate according to the adaptive controller, the construction expression of the adaptive rate is:

[0034] in, Approximately Γ=Γ T >0 indicates the gain matrix, δ>0 and is a small parameter; the network weight error is defined as

[0035] In some embodiments, the step of verifying the stability of the two-degree-of-freedom helicopter system is specifically as follows:

[0036] Construct Lyapunov function and verify the stability of the constructed controller on the system;

[0037] Among them, the expression of Lyapunov function V is: Among them, V2 represents the quadratic Lyapunov function; is the radial basis neural network weight error; Γ represents the gain matrix;

[0038] According to the Lyapunov function V, we derive the inequality for stability proof: in, λ min (Γ i ) is the matrix Γ i The minimum eigenvalue of i Represents a tiny parameter.

[0039] In some embodiments, the method further comprises the following steps:

[0040] The two-degree-of-freedom helicopter system is numerically simulated through the software Matlab to verify the control effect of the system and generate the corresponding optimization plan.

[0041] Another aspect of the embodiment of the present invention further provides a two-degree-of-freedom helicopter system adaptive neural network control system based on time-varying output constraints, comprising:

[0042] The first module is used to construct the nonlinear dynamic equation of the system according to the Lagrangian mechanics model and obtain the two-degree-of-freedom helicopter system equation;

[0043] The second module is used to construct an asymmetric time-varying obstacle Lyapunov function based on the two-degree-of-freedom helicopter system equation;

[0044] The third module is used to construct an adaptive rate according to the adaptive controller, and then complete the overall construction of the two-degree-of-freedom helicopter system according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate;

[0045] The fourth module is used to verify the stability of the two-degree-of-freedom helicopter system, and to perform flight control on the target object aircraft to be controlled based on the two-degree-of-freedom helicopter system that has passed the stability verification.

[0046] To achieve the above objective, another aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0047] To achieve the above objective, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0048] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0049] The embodiments of the present invention include at least the following beneficial effects: the present invention provides an adaptive neural network control method for a two-degree-of-freedom helicopter system based on a time-varying output constraint. The scheme constructs a nonlinear dynamic equation of the system according to the Lagrangian mechanics model to obtain a two-degree-of-freedom helicopter system equation; constructs an asymmetric time-varying barrier Lyapunov function according to the two-degree-of-freedom helicopter system equation; constructs an adaptive rate according to an adaptive controller, and then completes the overall construction of the two-degree-of-freedom helicopter system according to the asymmetric time-varying barrier Lyapunov function and the adaptive rate; performs stability verification on the two-degree-of-freedom helicopter system, and performs flight control on the target object aircraft to be controlled based on the two-degree-of-freedom helicopter system that has passed the stability verification. The embodiments of the present invention can achieve high-precision tracking control under the premise of satisfying complex time-varying output constraints, and can also ensure the overall stability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0051] Figure 2 is a flow chart of overall steps provided by an embodiment of the present invention;

[0052] Figure 3 It is a specific implementation process diagram provided by an embodiment of the present invention;

[0053] Figure 4 is an angle tracking trajectory diagram of the pitch angle provided by an embodiment of the present invention;

[0054] Figure 5 is an angle tracking trajectory diagram of the yaw angle provided by an embodiment of the present invention;

[0055] Figure 6 is a pitch angle tracking error diagram provided by an embodiment of the present invention;

[0056] Figure 7 is a yaw angle tracking error diagram provided by an embodiment of the present invention;

[0057] Figure 8 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0059] It is understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0060] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0062] The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology. The invention relates to the field of flight system control technology.

[0063] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0065] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0066] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0067] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0068] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides an adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints. The following is an example of applying the adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints in the server 101. It can be understood that the method can also be applied to the terminal 102.

[0069] Reference Figure 2 , Figure 2 The flowchart of the adaptive neural network control method of a two-degree-of-freedom helicopter system based on time-varying output constraints applied to a server provided by an embodiment of the present invention, the execution subject of the method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method may include the following steps:

[0070] According to the Lagrangian mechanics model, the nonlinear dynamic equation of the system is constructed to obtain the two-degree-of-freedom helicopter system equation;

[0071] According to the two-degree-of-freedom helicopter system equation, an asymmetric time-varying obstacle Lyapunov function is constructed;

[0072] The adaptive rate is constructed according to the adaptive controller, and then the overall construction of the two-degree-of-freedom helicopter system is completed according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate;

[0073] The stability of the two-degree-of-freedom helicopter system is verified, and based on the two-degree-of-freedom helicopter system that has passed the stability verification, flight control is performed on a target object aircraft to be controlled.

[0074] In some embodiments, constructing the nonlinear dynamic equation of the system according to the Lagrangian mechanics model to obtain the two-degree-of-freedom helicopter system equation includes the following steps:

[0075] Based on the nonlinear dynamic equation of the two-degree-of-freedom helicopter system, the two-degree-of-freedom helicopter system is expressed as a multi-input and multi-output nonlinear equation form;

[0076] According to the nonlinear equation form of multiple inputs and multiple outputs, radial basis function neural network is used to estimate the unknown functions of the two-degree-of-freedom helicopter system.

[0077] The angle error is configured, and a two-degree-of-freedom helicopter system equation is obtained according to the unknown function and the angle error.

[0078] In some embodiments, the nonlinear dynamic equation of the system is as follows:

[0079]

[0080] Among them, θ, Respectively represent the pitch angle, pitch angular velocity, and pitch angular acceleration; ψ, Represent the yaw angle, yaw angular velocity and yaw angular acceleration respectively, V a and V b Represent the input voltage of the front motor and the rear motor respectively, J a and J b represents the moment of inertia around the pitch axis and the yaw axis; P a and P b Represent the viscous friction coefficients of these axes; K aa , K ab , K ba and K bb They represent the thrust torque constants of the pitch and yaw axes respectively; m represents the mass, l d is the position of the center of mass, g represents the acceleration due to gravity;

[0081] The expression of the multi-input multi-output nonlinear equation form is:

[0082]

[0083] Where z = [z1, z2] T , z1=[α,β] T , ΔX(z) represents the unknown smooth continuous nonlinear function vector, G(u)=[Va ,V b ] T represents the control input of the system;

[0084] The expressions of X(z) and Y(z) are as follows:

[0085]

[0086] The expression for estimating the unknown function of the two-degree-of-freedom helicopter system using the radial basis function neural network is:

[0087]

[0088] Among them, S(Z) represents the basis function vector, is the input vector of the neural network; W *T represents the optimal weights of the neural network; represents the minimum approximation error;

[0089] The expression of the angle error of the pitch angle e1 and the yaw angle e2 is: e1 = z1-z d , e2=z2-ξ, where z d is the desired trajectory and ξ is the virtual controller.

[0090] In some embodiments, in the step of constructing an asymmetric time-varying obstacle Lyapunov function according to the two-degree-of-freedom helicopter system equation, the expression of the constructed asymmetric time-varying obstacle Lyapunov function is:

[0091]

[0092] in, and is a time-varying constraint function; p>1 and is an integer, h i (e 1i ) is a piecewise function, and the expression of the piecewise function is:

[0093] According to the unconstrained state variable z2, the expression of the quadratic Lyapunov function V2 is:

[0094] According to the quadratic Lyapunov function, the system controller expression is derived:

[0095] Where c2 is the control gain matrix,

[0096] In some embodiments, in the step of constructing an adaptive rate according to the adaptive controller, the construction expression of the adaptive rate is:

[0097] in, Approximately Γ=Γ T >0 indicates the gain matrix, δ>0 and is a small parameter; the network weight error is defined as

[0098] In some embodiments, the step of verifying the stability of the two-degree-of-freedom helicopter system is specifically as follows:

[0099] Construct Lyapunov function and verify the stability of the constructed controller on the system;

[0100] Among them, the expression of Lyapunov function V is: Among them, V2 represents the quadratic Lyapunov function; is the radial basis neural network weight error; Γ represents the gain matrix;

[0101] According to the Lyapunov function V, we derive the inequality for stability proof: in, λ min (Γ i ) is the matrix Γ i The minimum eigenvalue of i Represents a tiny parameter.

[0102] In some embodiments, the method further comprises the following steps:

[0103] The two-degree-of-freedom helicopter system is numerically simulated through the software Matlab to verify the control effect of the system and generate the corresponding optimization plan.

[0104] The overall implementation process of the embodiment of the present invention is described in detail below with reference to the accompanying drawings, taking a specific application scenario as an example:

[0105] A control method based on adaptive neural network is proposed for a nonlinear uncertain two-degree-of-freedom helicopter system with time-varying output constraints. This method constructs a new asymmetric time-varying barrier Lyapunov function and designs an adaptive controller, so that the neural network can effectively approximate the unknown nonlinear function in the system, ensuring the tracking performance and stability of the system under the time-varying output constraint. Specifically, when the two-degree-of-freedom helicopter system performs complex tasks (such as hovering, low-altitude flight and attitude adjustment), its output parameters (such as position, velocity and attitude) usually need to follow dynamic constraints to ensure the safety and reliability of the system under different tasks and environmental conditions. Since constant constraints are special cases of time-varying constraints, the time-varying output constraint method is more versatile than the traditional method and can better cope with the diverse needs in practical applications. To this end, the control scheme uses a neural network to approximate the unknown function of the system and prevents the violation of the output constraint through the time-varying barrier Lyapunov function, thereby improving the robustness and adaptability of the system in a dynamic environment. This method proves the stability of the closed-loop system through Lyapunov analysis, and verifies its effective control ability for the nonlinear two-degree-of-freedom helicopter system in simulation. The research results show that this control method can not only achieve high-precision tracking control while meeting complex time-varying output constraints, but also ensure the overall stability and safety of the system.

[0106] refer to Figure 3 The adaptive neural network control method of a two-degree-of-freedom helicopter system based on time-varying output constraints of an embodiment of the present invention specifically comprises the following steps:

[0107] Step 1: According to the Lagrangian mechanics model, the nonlinear dynamic equation of the system is as follows:

[0108]

[0109] Among them, θ, and ψ, Respectively represent the pitch angle, pitch angular velocity, pitch angular acceleration, yaw angle, yaw angular velocity and yaw angular acceleration, V a and V b Represent the input voltage of the front motor and the rear motor respectively, J a and J b represents the moment of inertia around the pitch and yaw axes, P a and P b Represent the viscous friction coefficients of these axes, K aa , K ab , K ba and K bb They represent the thrust torque constants of the pitch and yaw axes, m represents the mass, l d is the position of the center of mass, and g represents the acceleration due to gravity.

[0110] (1) Based on the nonlinear dynamic equation of the two-degree-of-freedom helicopter system, the system can be expressed as the following multi-input and multi-output nonlinear equation form.

[0111]

[0112] Where z = [z1, z2] T , z1=[α,β] T , ΔX(z) represents the unknown smooth continuous nonlinear function vector, and G(u) = [V a ,V b ] T represents the control input of the system. The expressions of X(z) and Y(z) are as follows:

[0113]

[0114] (2) According to equations (1.3) and (1.4), the radial basis function neural network is used to estimate the unknown function ΔX(z) of the system:

[0115]

[0116] Here, W *T The optimal weight of the neural network, S(Z) represents the basis function vector, is the input vector of the neural network, and Represents the minimum approximation error.

[0117] (3) Set the angle error e1 = z1-z d , e2=z2-ξ, where z d is the desired trajectory and ξ is the virtual controller.

[0118] Step 2: Based on the two-degree-of-freedom helicopter system equation obtained in step 1, construct the asymmetric time-varying obstacle Lyapunov function:

[0119]

[0120] Where p>1 and is an integer, h i (e 1i ) is a piecewise function, defined as follows:

[0121]

[0122] Since the state variable z2 has no constraints, the quadratic Lyapunov function is set as follows:

[0123]

[0124] Then the system controller expression is derived as follows:

[0125]

[0126] Where c2 is the control gain matrix,

[0127] Step 3: According to the adaptive controller, the adaptive rate can be constructed

[0128]

[0129] in, Approximately W i * , Γ=Γ T >0 indicates the gain matrix, δ>0 and is a small parameter. The network weight error is defined as

[0130] Step 4: Verify the stability of the constructed controller to the system and construct the following Lyapunov function

[0131]

[0132] is the weight error of the radial basis neural network, and finally the inequality required for the stability proof is derived in is the matrix Γ i The minimum eigenvalue of .

[0133] Step 6: Use Mat lab software to perform numerical simulation on the two-degree-of-freedom helicopter system to verify the effect of the proposed control algorithm and provide analysis.

[0134] The method adopted by the present invention is experimentally verified in a specific application scenario, with reference to the accompanying drawings, wherein: Figure 4 and Figure 5 The angle tracking trajectory of the helicopter at pitch angle θ and yaw angle ψ is shown, where kb11 and kb12 are time-varying output constraints. The smoothness and accuracy of these trajectories demonstrate the effectiveness of the control strategy. Figure 6 and Figure 7 Tracking error of pitch and yaw angles e 11 and e 12 This further confirms the high-precision nature of the control strategy.

[0135] In summary, facing the multi-input and multi-output nonlinear characteristics and complex dynamic coupling problems of small unmanned helicopter systems, designing robust control strategies is indeed a challenge. In actual operation, small unmanned helicopter systems often need to deal with dynamically changing output constraints, which change with mission requirements, environmental conditions and system states, and put forward higher requirements for control strategies. Time-varying output constraints cover multiple aspects such as speed, attitude, position and acceleration, which are important factors to ensure flight safety, stability and reliability. In the design of the control system, if these dynamically changing output constraints are not fully considered, the system may experience performance degradation under high load or emergency conditions, and may even cause the aircraft to lose stability or control.

[0136] In order to solve these problems, the present invention proposes a comprehensive control strategy to solve the nonlinear and time-varying output constraint problems in the two-degree-of-freedom helicopter system, and specifically adopts a method based on an adaptive neural network. This method constructs a new type of asymmetric time-varying barrier Lyapunov function and designs an adaptive controller, so that the neural network can approximate the unknown nonlinear part of the system, ensuring that the system can maintain tracking performance and stability under the time-varying output constraint. And the time-varying barrier Lyapunov function is used to prevent the violation of the output constraint, thereby enhancing the robustness and adaptability of the system in a dynamic environment. The stability of the closed-loop system is proved by Lyapunov analysis, and its effective control ability for the nonlinear two-degree-of-freedom helicopter system is verified in simulation. The research results show that this control method can not only achieve high-precision tracking control under the premise of satisfying complex time-varying output constraints, but also ensure the overall stability and safety of the system.

[0137] Another aspect of the embodiment of the present invention further provides a two-degree-of-freedom helicopter system adaptive neural network control system based on time-varying output constraints, comprising:

[0138] The first module is used to construct the nonlinear dynamic equation of the system according to the Lagrangian mechanics model and obtain the two-degree-of-freedom helicopter system equation;

[0139] The second module is used to construct an asymmetric time-varying obstacle Lyapunov function based on the two-degree-of-freedom helicopter system equation;

[0140] The third module is used to construct an adaptive rate according to the adaptive controller, and then complete the overall construction of the two-degree-of-freedom helicopter system according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate;

[0141] The fourth module is used to verify the stability of the two-degree-of-freedom helicopter system, and to perform flight control on the target object aircraft to be controlled based on the two-degree-of-freedom helicopter system that has passed the stability verification.

[0142] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0143] The embodiment of the present invention further provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0144] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0145] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0146] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0147] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints of the embodiment of the present invention;

[0148] Input / output interface 803, used to implement information input and output;

[0149] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0150] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0151] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0152] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints.

[0153] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0154] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] It should be noted that in various specific embodiments of the present invention, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present invention needs to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiment of the present invention.

[0156] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art can appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0157] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present invention and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0158] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0160] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0161] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0162] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0163] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0165] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store programs.

[0166] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the embodiments of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of the rights of the embodiments of the present invention.

Claims

1. An adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints, characterized in that: The following steps are involved: According to the Lagrangian mechanics model, the nonlinear dynamic equation of the system is constructed to obtain the two-degree-of-freedom helicopter system equation; According to the two-degree-of-freedom helicopter system equation, an asymmetric time-varying obstacle Lyapunov function is constructed; The adaptive rate is constructed according to the adaptive controller, and then the overall construction of the two-degree-of-freedom helicopter system is completed according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate; The stability of the two-degree-of-freedom helicopter system is verified, and based on the two-degree-of-freedom helicopter system that has passed the stability verification, flight control is performed on a target object aircraft to be controlled.

2. The adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints according to claim 1 is characterized in that: The nonlinear dynamic equation of the system is constructed according to the Lagrangian mechanics model to obtain the two-degree-of-freedom helicopter system equation, including the following steps: Based on the nonlinear dynamic equation of the two-degree-of-freedom helicopter system, the two-degree-of-freedom helicopter system is expressed as a multi-input and multi-output nonlinear equation form; According to the nonlinear equation form of multiple inputs and multiple outputs, radial basis function neural network is used to estimate the unknown functions of the two-degree-of-freedom helicopter system. The angle error is configured, and a two-degree-of-freedom helicopter system equation is obtained according to the unknown function and the angle error.

3. The adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints according to claim 2 is characterized in that: The nonlinear dynamic equation of the system is as follows: in, Respectively represent pitch angle, pitch angular velocity, and pitch angular acceleration; Represent the yaw angle, yaw angular velocity and yaw angular acceleration respectively, V a and K b Represent the input voltage of the front motor and the rear motor respectively, J a and J b represents the moment of inertia around the pitch axis and the yaw axis; P a and P b Represent the viscous friction coefficients of these axes; K aa , K ab , K ba and K bb They represent the thrust torque constants of the pitch and yaw axes respectively; m represents the mass, l d is the position of the center of mass, g represents the acceleration due to gravity; The expression of the multi-input multi-output nonlinear equation form is: Where z = [z1, z2] T , z1=[α,β] T , ΔX(z) represents the unknown smooth continuous nonlinear function vector, G(u)=[V a ,V b ] T represents the control input of the system; The expressions of X(z) and Y(z) are as follows: The expression for estimating the unknown function of the two-degree-of-freedom helicopter system using the radial basis function neural network is: Among them, S(Z) represents the basis function vector, is the input vector of the neural network; W *T represents the optimal weights of the neural network; represents the minimum approximation error; The expression of the angle error of the pitch angle e1 and the yaw angle e2 is: e1 = z1-z d , e2=z2-ξ, where z d is the desired trajectory and ξ is the virtual controller.

4. The adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints according to claim 1 is characterized in that: In the step of constructing an asymmetric time-varying obstacle Lyapunov function according to the two-degree-of-freedom helicopter system equation, the expression of the constructed asymmetric time-varying obstacle Lyapunov function is: in, and is a time-varying constraint function; p>1 and is an integer, h i (e 1i ) is a piecewise function, and the expression of the piecewise function is: According to the unconstrained state variable z2, the expression of the quadratic Lyapunov function V2 is: According to the quadratic Lyapunov function, the system controller expression is derived: Where c2 is the control gain matrix, 5. The adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints according to claim 1 is characterized in that: In the step of constructing the adaptive rate according to the adaptive controller, the construction expression of the adaptive rate is: in, Approximately Γ=Γ T >0 indicates the gain matrix, δ>0 and is a small parameter; the network weight error is defined as 6. The adaptive neural network control method for a two-degree-of-freedom helicopter system based on time-varying output constraints according to claim 1 is characterized in that: The step of verifying the stability of the two-degree-of-freedom helicopter system is specifically as follows: Construct Lyapunov function and verify the stability of the constructed controller on the system; Among them, the expression of Lyapunov function V is: Among them, V2 represents the quadratic Lyapunov function; is the radial basis neural network weight error; Γ represents the gain matrix; According to the Lyapunov function V, we derive the inequality for stability proof: in, is the matrix Γ i The minimum eigenvalue of i Represents a tiny parameter.

7. A two-degree-of-freedom helicopter system adaptive neural network control method based on time-varying output constraints according to any one of claims 1-6, characterized in that: The method further comprises the following steps: The two-degree-of-freedom helicopter system is numerically simulated through the software Matlab to verify the control effect of the system and generate the corresponding optimization plan.

8. An adaptive neural network control system for a two-degree-of-freedom helicopter system based on time-varying output constraints, characterized in that: include: The first module is used to construct the nonlinear dynamic equation of the system according to the Lagrangian mechanics model and obtain the two-degree-of-freedom helicopter system equation; The second module is used to construct an asymmetric time-varying obstacle Lyapunov function based on the two-degree-of-freedom helicopter system equation; The third module is used to construct an adaptive rate according to the adaptive controller, and then complete the overall construction of the two-degree-of-freedom helicopter system according to the asymmetric time-varying obstacle Lyapunov function and the adaptive rate; The fourth module is used to verify the stability of the two-degree-of-freedom helicopter system, and to perform flight control on the target object aircraft to be controlled based on the two-degree-of-freedom helicopter system that has passed the stability verification.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

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