Unmanned aerial vehicle pitching control method and system based on fuzzy immune control optimization PID (Proportion Integration Differentiation)

Abstract

The invention discloses an unmanned aerial vehicle pitching control method and system for optimizing PID through fuzzy immune control, and relates to the field of unmanned aerial vehicle control, and the analysis method comprises the steps: carrying out the decoupling and linear simplification of a motion equation of an unmanned aerial vehicle, obtaining a longitudinal motion mathematical model, carrying out the parameter initialization of a PID controller according to the response requirements and performance indexes of the system, and obtaining a longitudinal motion mathematical model; the method comprises the following steps: establishing an unmanned aerial vehicle PID control system, establishing a fuzzy control module, carrying out fuzzy reasoning adjustment, initializing immune algorithm parameters, determining an optimization termination condition of an immune algorithm, obtaining an initial unmanned aerial vehicle PID control system, optimizing the initial unmanned aerial vehicle PID control system, connecting the optimized unmanned aerial vehicle PID control system with a controlled object, and operating the optimized unmanned aerial vehicle PID control system. According to the method, the purpose of adaptively adjusting the PID parameters can be achieved, different flight environments and task requirements are adapted, and the flight performance and task execution efficiency of the unmanned aerial vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control, and in particular to a method and system for unmanned aerial vehicle pitch control by optimizing PID through fuzzy immune control. Background Art

[0002] The main functions of the pitch control system of fixed-wing UAVs are to maintain a stable flight attitude, control flight altitude, achieve attitude adjustment, and optimize flight performance. These functions enable UAVs to perform tasks efficiently and stably in various application scenarios. Due to the very complete development of the PID control theory system, the PID control system has the characteristics of strong stability and high practicality. It is widely used in UAV control systems and is of great significance for the precision control operation of UAVs.

[0003] With the rapid development of UAV technology, the application fields of UAVs are becoming more and more extensive, and higher requirements are placed on the flight performance and control accuracy of UAVs. As a common type of UAV, the performance of the pitch control system of fixed-wing UAVs directly affects the overall performance of the UAV. Traditional controllers have certain limitations in the pitch control of UAVs, such as sensitivity to parameter adjustment and weak anti-interference ability. In addition, UAVs must adapt to the interference of various uncertain factors during flight, such as complex and changeable air environments, changes in their own states, and adjustments to combat missions. These factors increase the difficulty of designing UAV flight control systems and place higher requirements on control systems. This requires the control system to have good adaptability and robustness, and to be able to quickly respond to changing environments and mission requirements to ensure the flight safety of the UAV and the effectiveness of mission completion.

[0004] Fuzzy algorithm is a computational method based on fuzzy logic principles, used to deal with uncertainty and ambiguity. It models and processes fuzzy information through fuzzy sets, fuzzy rules and fuzzy reasoning to perform tasks such as decision-making, control or optimization. Immune algorithm is a computational method based on the principle of immune system, which is used to solve optimization, search and pattern recognition problems by simulating the evolution, selection and adaptability of the immune system.

[0005] Therefore, there is an urgent need for a UAV pitch control method and system based on fuzzy immune control optimized PID to meet the high requirements of UAV control systems. Summary of the invention

[0006] To solve the above problems, the present application provides a UAV pitch control method and system based on fuzzy immune control optimized PID, with the goal of improving adaptability and robustness, aiming to be able to quickly respond to changing environments and mission requirements, so as to ensure the flight safety of the UAV and the completion of the mission.

[0007] In this application, a method for controlling the pitch of a UAV by optimizing PID using fuzzy immune control is provided, comprising the following steps:

[0008] S1. Decoupling and linearly simplifying the UAV motion equation to obtain a longitudinal motion mathematical model, which is a PID control object;

[0009] S2. Initialize the parameters of the PID controller according to the response requirements and performance indicators of the system;

[0010] S3, build a UAV PID control system, establish a fuzzy control module and perform fuzzy reasoning adjustment, the fuzzy control module includes a fuzzy set and a fuzzy rule, the fuzzy control module uses the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and uses the parameter adjustment amount of the UAV PID controller as output data;

[0011] S4, initialize the immune algorithm parameters, determine the optimization termination conditions of the immune algorithm, and obtain the initial UAV PID control system;

[0012] S5, optimizing the initial UAV PID control system to obtain an optimized UAV PID control system, connecting the optimized UAV PID control system to the controlled object and running them;

[0013] S6. Optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation results.

[0014] Preferably, in S1, the UAV motion equation is decoupled and linearly simplified to obtain the specific content of the longitudinal motion mathematical model as follows:

[0015] Establish the UAV motion equation to obtain the mathematical motion model, and decouple the mathematical motion model to obtain the longitudinal sub-model;

[0016] According to the small disturbance principle, the longitudinal sub-model is linearized to obtain a decoupled model;

[0017] According to the short-period motion characteristics of the UAV in the longitudinal direction, the degrees of freedom in the decoupling model are simplified to obtain a short-period decoupling model.

[0018] Preferably, the expression for building the drone PID control system is:

[0019]

[0020] Among them, k p is the proportionality coefficient, T i is the integration time constant, T d is the differential time constant, s is the Laplace operator, and G(s) is the transfer function.

[0021] Among them, the expression of the short-period decoupling model is:

[0022]

[0023] Among them, V is the velocity along the x-axis of the body, α is the angle of attack, θ is the pitch angle, q is the pitch angular velocity, δ e is the elevator deflection increment, is the force caused by the change in angular velocity at the angle of attack, is the torque caused by the change of angular velocity at the angle of attack, Z α is the force caused by the change in angle of attack, M α is the moment caused by the change in angle of attack, Z δe is the force caused by the incremental deflection of the elevator, M δe is the moment caused by the incremental deflection of the elevator.

[0024] Preferably, the specific content of the fuzzy reasoning adjustment is:

[0025] Obtain the precise value of the controlled quantity of the UAV PID control system to obtain the collected quantity, and compare the collected quantity with the given quantity to obtain the error signal E;

[0026] Fuzzifying the digital quantity of the error signal E to obtain a fuzzy quantity;

[0027] There are preset corresponding rules between fuzzy quantity and fuzzy language, and the corresponding fuzzy language represents the fuzzy quantity to obtain a subset e of the fuzzy language set;

[0028] Combining fuzzy control rules with fuzzy reasoning rules, fuzzy decision making is obtained;

[0029] Combining the fuzzy decision with the subset e, we get the fuzzy control quantity u;

[0030] The expression of fuzzy control quantity u is:

[0031] u=e*R

[0032] Among them, R is a fuzzy relationship;

[0033] Repeatedly collect the precise values ​​of the controlled variables of the UAV PID control system and repeatedly perform fuzzy reasoning and adjustment processing.

[0034] Preferably, in S4, an immune algorithm is used to calculate the proportionality coefficient k p Make adjustments.

[0035] In the present application, a fuzzy immune control optimized PID unmanned aerial vehicle pitch control system is also provided, including: a data preprocessing module: decoupling and linearly simplifying the unmanned aerial vehicle motion equation to obtain a longitudinal motion mathematical model, the longitudinal motion mathematical model is a PID control object, and the PID controller is initialized according to the response requirements and performance indicators of the system;

[0036] Model building module: build a UAV PID control system, establish a fuzzy control module and perform fuzzy reasoning adjustment. The fuzzy control module includes fuzzy sets and fuzzy rules. The fuzzy control module uses the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and the parameter adjustment amount of the UAV PID controller as output data. It initializes the immune algorithm parameters and determines the optimization termination conditions of the immune algorithm to obtain the initial UAV PID control system.

[0037] Model optimization module: optimize the initial UAV PID control system to obtain the optimized UAV PID control system, connect the optimized UAV PID control system to the controlled object and run it, and optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation effect.

[0038] To summarize, the UAV pitch control method and system of the fuzzy immune control optimized PID of the present invention, compared with the traditional PID control method, obtains the transfer function by linearly simplifying the longitudinal motion model of the UAV, introduces fuzzy control and immune feedback algorithm, and integrates the two with PID control, so that the UAV pitch control system can adaptively adjust the PID parameters to adapt to different flight environments and mission requirements. Due to the use of the fuzzy immune control algorithm, the system has strong anti-interference ability and robustness. The implementation of the UAV pitch control system mentioned in this application can improve the flight performance of the UAV and the efficiency of executing tasks.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A method step diagram of a UAV pitch control method using fuzzy immune control optimized PID according to the present invention;

[0041] Figure 2 This is a control flow chart of a UAV pitch control system using fuzzy immune control and PID optimization according to the present invention;

[0042] Figure 3 This is a fuzzy immune control principle diagram of a UAV pitch control method of the present invention that optimizes PID using fuzzy immune control.

[0043] Figure 4 The present invention discloses an immune control algorithm flow chart of a UAV pitch control method using fuzzy immune control to optimize PID. Figure 5 This is a step response simulation result diagram of a UAV pitch control method based on fuzzy immune control optimized PID according to the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​described in these embodiments do not limit the scope of the present application.

[0045] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0046] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0047] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0048] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0049] Since the pitch motion of the UAV needs to be controlled, it is necessary to first obtain the mathematical model of the pitch motion of the UAV. In order to better observe the motion trend of the UAV and design a simple and effective control system, the motion model of the UAV is approximately linearized. The following assumptions are made for this purpose: First, it is assumed that the aerodynamic shape of the aircraft is symmetrical and the mass distribution is uniform. In addition, it is assumed that the aircraft is in a state of horizontal stable flight, that is, the sideslip angle and roll angle are very close to zero. These assumptions can decouple the mathematical model of the aircraft into a longitudinal sub-model. Next, the decoupled longitudinal sub-model is linearized using the small perturbation principle. This means that the nonlinear system can be approximately converted into a linear system, so that the controller design is easier. For the pitch angle controller design of the longitudinal sub-model, the elevator is selected as the input. By controlling the angle of the elevator, the pitch attitude of the aircraft is adjusted. This design method can simplify the structure of the control system and provide effective pitch angle control.

[0050] like Figure 1As shown, a fuzzy immune control optimized PID UAV pitch control method is provided, comprising the following steps: S1, decoupling and linearly simplifying the UAV motion equation to obtain a longitudinal motion mathematical model, and the longitudinal motion mathematical model is a PID control object.

[0051] Furthermore, in S1, the UAV motion equation is decoupled and linearly simplified to obtain the specific content of the longitudinal motion mathematical model as follows: the UAV motion equation is established to obtain the mathematical motion model, and the mathematical motion model is decoupled to obtain the longitudinal sub-model.

[0052] The decoupled longitudinal model is as follows:

[0053] Where: X = [Vαθq] T is the state quantity, V is the velocity along the x-axis of the body, α is the angle of attack, θ is the pitch angle, and q is the pitch angle velocity.

[0054] U=δ e , U is the input, δ e is the elevator deflection increment.

[0055] The matrices E, A, and B are obtained by Taylor series expansion of the nonlinear formula and retaining the first-order terms.

[0056]

[0057] The longitudinal sub-model is linearized according to the small disturbance principle to obtain a decoupled model.

[0058] Except for the hover state, in general, due to is much smaller than V, so the Jacobian matrix E is always non-singular and can be written in standard form:

[0059]

[0060] In the initial stage of the aircraft's longitudinal motion, when it is disturbed or input, short-period motion mainly occurs, and its transition process is very rapid. In this short initial stage, the change amplitude of the flight speed and pitch angle is relatively small, that is, the increment ΔV = Δθ = 0. In this way, the longitudinal motion equation originally containing four degrees of freedom (speed, angle of attack, pitch angle and pitch angular velocity) is simplified to a short-period motion equation with only two degrees of freedom (angle of attack and pitch angular velocity).

[0061] According to the short-period motion characteristics of the UAV in the longitudinal direction, the degrees of freedom in the decoupling model are simplified to obtain a short-period decoupling model.

[0062] Furthermore, the expression of the short-period decoupling model is:

[0063]

[0064] Among them, V is the velocity along the x-axis of the body, α is the angle of attack, θ is the pitch angle, q is the pitch angular velocity, δ e is the elevator deflection increment, is the force caused by the change in angular velocity at the angle of attack, is the torque caused by the change of angular velocity at the angle of attack, Z α is the force caused by the change in angle of attack, M α is the moment caused by the change in angle of attack, Z δe is the force caused by the incremental deflection of the elevator, M δe is the moment caused by the incremental deflection of the elevator.

[0065] In the pitch attitude control loop, the servo transfer function is represented by the inertial link, and the longitudinal model of the UAV is represented by δ e As input, q is expressed as a transfer function with output.

[0066] S2. Initialize the parameters of the PID controller according to the system's response requirements and performance indicators.

[0067] S3. Use MATLAB to build a conventional fixed-wing UAV pitch control system, namely the UAV PID control system.

[0068] A fuzzy control module is established and fuzzy reasoning adjustment is performed. The fuzzy control module includes a fuzzy set and a fuzzy rule. The fuzzy control module takes the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and takes the parameter adjustment amount of the UAV PID controller as output data.

[0069] A simple block diagram of a fixed-wing drone using a PID controller is shown below: Figure 2 As shown in the figure. As a classic control theory, it can help the controlled object achieve the desired control effect. In traditional analog control systems, PID control has been widely adopted and has become an indispensable control strategy. Using the PID controller, the deviation percentage can be adjusted and the deviation integral can be introduced, thereby significantly improving the stability and anti-interference ability of the system. In addition, the use of differential branches can not only effectively suppress the inertia delay of the system, but also greatly improve the stability of the system, thereby greatly improving the performance and quality of the entire control system.

[0070] Furthermore, the expression for building the UAV PID control system is:

[0071] Among them, k p is the proportionality coefficient, T i is the integration time constant, T dis the differential time constant, s is the Laplace operator, and G(s) is the transfer function.

[0072] The basic structure of fuzzy control is fuzzy control module, such as Figure 3 As shown in the dotted box in the figure, by writing the corresponding program, the fuzzy control module can adjust the parameters to achieve the expected effect.

[0073] Furthermore, the specific content of the fuzzy reasoning adjustment is to obtain the precise value of the controlled quantity of the UAV PID control system to obtain the collected quantity, and compare the collected quantity with the given quantity to obtain the error signal E.

[0074] The digital quantity of the error signal E is fuzzified to obtain a fuzzy quantity.

[0075] There are preset corresponding rules between fuzzy quantities and fuzzy languages, and the corresponding fuzzy languages ​​represent the fuzzy quantities to obtain a subset e of the fuzzy language set.

[0076] Combining fuzzy control rules with fuzzy reasoning rules, fuzzy decision making is obtained.

[0077] Combining the fuzzy decision with the subset e, we get the fuzzy control quantity u.

[0078] The expression of the fuzzy control quantity u is u=e*R.

[0079] Among them, R is a fuzzy relationship.

[0080] Repeatedly collect the precise values ​​of the controlled variables of the UAV PID control system and repeatedly perform fuzzy reasoning and adjustment processing.

[0081] S4. Initialize the immune algorithm parameters and determine the optimization termination conditions of the immune algorithm to obtain the initial UAV PID control system.

[0082] The immune control algorithm is a method that imitates the biological immune system and is used to eliminate foreign viruses. In this process, T lymphocytes transmit antigens to B lymphocytes to achieve information exchange, and B cells restore the body's health by producing antibodies and engulfing antigens. This immune response is a complex and dynamic biological reaction. In the process of information transmission, a complex state of mutual regulation and feedback is formed between T cells and B cells. If both have changes or abnormalities, the relationship between the two needs to be adjusted. When there are a large number of antigens in the body, the promoting T cells will undergo orderly cell division and reproduction, thereby stimulating the proliferation of B cells and producing more antibodies. As the antigen decreases, the suppressive T cells begin to multiply, thereby inhibiting the generation of B cells to avoid energy consumption in the body.

[0083] like Figure 4 As shown, the immune process algorithm.

[0084] Assume that the number of antigens in the kth generation is ε(k), and the number of promoting T cells is T H (k), the number of suppressor T cells is T s (k), the stimulation received by B cells is S(k).

[0085] Among them, T H (k) = k1ε(k).

[0086] T S (k) = k2f(ΔS(k))ε(k).

[0087] S(k)=T H (k)-T S (k).

[0088] Wherein, k1 is the excitation factor, k2 is the inhibition factor, and f(ΔS(k)) is a nonlinear function representing the degree of inhibition.

[0089] If the number of antigens ε(k) is regarded as the system deviation e(k), and the stimulation of B cells S(k) is regarded as the output of the system, the relationship between the system output and the proportional parameter is obtained.

[0090] The relational expression is: S(k)=K(1-ηf(ΔS(k)))e(k).

[0091] In the formula, η=k2 / k1 is the stabilizing effect of control, and K=k1 is the reaction speed of control.

[0092] In the field of immune controllers, designing nonlinear functions f(·) is a very challenging task, but fuzzy control theory can be used to approximate various linear or nonlinear functions to achieve higher control accuracy. By identifying the fuzzy relationship between input variables and output variables, the membership function f(·) and fuzzy control rules corresponding to the fuzzified input variables can be queried during operation, so as to adjust the parameters in time and adjust the proportional coefficient of the immune PID controller in a timely manner, thereby achieving better control effects. The fuzzy controller is used to realize the integral time constant K I and the differential time constant K D Adaptive online tuning is performed to achieve fuzzy immune adaptive PID control.

[0093] Furthermore, in S4, the immune algorithm is used to calculate the proportionality coefficient k p Make adjustments.

[0094] S5. Optimize the initial UAV PID control system to obtain an optimized UAV PID control system, and connect the optimized UAV PID control system to the controlled object and run them.

[0095] S6. Optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation results.

[0096] In the present application, a fuzzy immune control optimized PID unmanned aerial vehicle pitch control system is also provided, including a data preprocessing module: decoupling and linearly simplifying the UAV motion equation to obtain a longitudinal motion mathematical model, wherein the longitudinal motion mathematical model is a PID control object, and the PID controller is initialized with parameters according to the system response requirements and performance indicators.

[0097] Model building module: build a UAV PID control system, establish a fuzzy control module and perform fuzzy reasoning adjustment. The fuzzy control module includes fuzzy sets and fuzzy rules. The fuzzy control module uses the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and the parameter adjustment amount of the UAV PID controller as output data. It initializes the immune algorithm parameters, determines the optimization termination conditions of the immune algorithm, and obtains the initial UAV PID control system.

[0098] Model optimization module: optimize the initial UAV PID control system to obtain the optimized UAV PID control system, connect the optimized UAV PID control system to the controlled object and run it, and optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation effect.

[0099] Table 1 Transient response performance index table

[0100] <![CDATA[Rise time t r / s]]> <![CDATA[Peak time t p / s]]> <![CDATA[Adjustment time t s / s]]> Overshoot σ% ZN method setting 1.6578 7.3230 2.5995 0.74％ Fuzzy Immunity 0.7886 3.5710 1.3147 0.19％

[0101] Combining Table 1 and Figure 5 As shown in the figure, the step signal response curves of the traditional ZN method and the fuzzy immune method show that when the expected pitch angle of 1° is input, the parameters obtained by optimizing the PID using the fuzzy immune algorithm are better than those obtained by adjusting the ZN method, with faster response speed, shorter rise time and smaller steady-state error.

[0102] To summarize, the UAV pitch control method and system of the fuzzy immune control optimized PID of the present invention, compared with the traditional PID control method, obtains the transfer function by linearly simplifying the longitudinal motion model of the UAV, introduces fuzzy control and immune feedback algorithm, and integrates the two with PID control, so that the UAV pitch control system can adaptively adjust the PID parameters to adapt to different flight environments and mission requirements. Due to the use of the fuzzy immune control algorithm, the system has strong anti-interference ability and robustness. The implementation of the UAV pitch control system mentioned in this application can improve the flight performance of the UAV and the efficiency of executing tasks.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A UAV pitch control method based on fuzzy immune control optimized PID, characterized in that: The following steps are involved: S1. Decoupling and linearly simplifying the UAV motion equation to obtain a longitudinal motion mathematical model, which is a PID control object; S2. Initialize the parameters of the PID controller according to the response requirements and performance indicators of the system; S3, build a UAV PID control system, establish a fuzzy control module and perform fuzzy reasoning adjustment, the fuzzy control module includes a fuzzy set and a fuzzy rule, the fuzzy control module uses the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and uses the parameter adjustment amount of the UAV PID controller as output data; S4, initialize the immune algorithm parameters, determine the optimization termination conditions of the immune algorithm, and obtain the initial UAV PID control system; S5, optimizing the initial UAV PID control system to obtain an optimized UAV PID control system, connecting the optimized UAV PID control system to the controlled object and running them; S6. Optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation results.

2. The method for controlling the pitch of an unmanned aerial vehicle using fuzzy immune control optimized PID according to claim 1 is characterized in that: In S1, the UAV motion equations are decoupled and linearly simplified, and the specific contents of the longitudinal motion mathematical model are obtained as follows: Establish the UAV motion equation to obtain the mathematical motion model, and decouple the mathematical motion model to obtain the longitudinal sub-model; According to the small disturbance principle, the longitudinal sub-model is linearized to obtain a decoupled model; According to the short-period motion characteristics of the UAV in the longitudinal direction, the degrees of freedom in the decoupling model are simplified to obtain a short-period decoupling model.

3. The method for controlling the pitch of an unmanned aerial vehicle using fuzzy immune control optimized PID according to claim 1 is characterized in that: The expression for building a drone PID control system is: Among them, k p is the proportionality coefficient, T i is the integration time constant, T d is the differential time constant, s is the Laplace operator, and G(s) is the transfer function.

4. The method for controlling the pitch of an unmanned aerial vehicle using fuzzy immune control optimized PID according to claim 2 is characterized in that: The expression of the short-period decoupling model is: Among them, V is the velocity along the x-axis of the body, α is the angle of attack, θ is the pitch angle, q is the pitch angular velocity, δ e is the elevator deflection increment, is the force caused by the change in angular velocity at the angle of attack, is the torque caused by the change of angular velocity at the angle of attack, Z α is the force caused by the change of angle of attack, M α is the moment caused by the change in angle of attack, Z δe is the force caused by the incremental deflection of the elevator, M δe is the torque caused by the incremental deflection of the elevator.

5. According to the fuzzy immune control optimized PID unmanned aerial vehicle pitch control method of claim 1, the specific content of the fuzzy reasoning adjustment is: Obtain the precise value of the controlled quantity of the UAV PID control system to obtain the collected quantity, and compare the collected quantity with the given quantity to obtain the error signal E; Fuzzifying the digital quantity of the error signal E to obtain a fuzzy quantity; There are preset corresponding rules between fuzzy quantity and fuzzy language, and the corresponding fuzzy language represents the fuzzy quantity to obtain a subset e of the fuzzy language set; Combining fuzzy control rules with fuzzy reasoning rules, fuzzy decision making is obtained; Combining the fuzzy decision with the subset e, we get the fuzzy control quantity u; The expression of fuzzy control quantity u is: u=e*R in, R is a fuzzy relationship; Repeatedly collect the precise values ​​of the controlled variables of the UAV PID control system and repeatedly perform fuzzy reasoning and adjustment processing.

6. The method for controlling the pitch of an unmanned aerial vehicle using fuzzy immune control optimized PID according to claim 1 is characterized in that: In S4, the immune algorithm is used to calculate the proportionality coefficient k p Make adjustments.

7. A UAV pitch control system based on fuzzy immune control optimized PID, characterized in that: include: Data preprocessing module: decouple and linearly simplify the UAV motion equation to obtain the longitudinal motion mathematical model, which is the PID control object. The PID controller is initialized according to the system response requirements and performance indicators. Model building module: build a UAV PID control system, establish a fuzzy control module and perform fuzzy reasoning adjustment. The fuzzy control module includes fuzzy sets and fuzzy rules. The fuzzy control module uses the error amount of the UAV PID control system and the error change rate of the UAV PID control system as input data, and the parameter adjustment amount of the UAV PID controller as output data. It initializes the immune algorithm parameters and determines the optimization termination conditions of the immune algorithm to obtain the initial UAV PID control system. Model optimization module: optimize the initial UAV PID control system to obtain the optimized UAV PID control system, connect the optimized UAV PID control system to the controlled object and run it, and optimize and adjust the fuzzy control module and immune algorithm parameters according to the real-time operation effect.

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