Variable-pitch multi-rotor unmanned aerial vehicle and flight attitude control method thereof

Abstract

The application belongs to the field of unmanned aerial vehicle and its flight control technology, and discloses a variable-pitch multi-rotor unmanned aerial vehicle and a flight attitude control method thereof; wherein the variable-pitch multi-rotor unmanned aerial vehicle is provided with a self-disturbance rejection attitude control system, the self-disturbance rejection attitude control system is provided with three control loops of roll, pitch and yaw, each control loop comprises an extended state observer, a differential tracker and a nonlinear state error feedback, and when the control parameters are optimized, an improved kingfisher optimization algorithm is used to optimize the control parameters, and the dynamic updating strategy of the improved kingfisher optimization algorithm adopts a kingfisher position updating rule based on a variable helical sine-cosine strategy. The technical scheme of the application solves the technical problem that the control parameters are difficult to adjust due to factors such as model uncertainty, external disturbance and nonlinear coupling characteristics caused by variable-pitch rotors of the unmanned aerial vehicle in a complex dynamic environment, and can improve the flight performance of the unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of unmanned aerial vehicle and its flight control technology, and particularly relates to a variable-pitch multi-rotor unmanned aerial vehicle and a flight attitude control method thereof. BACKGROUND

[0002] The unmanned aerial vehicle has great strategic value. It can take off and land from short-distance take-off platforms such as islands, large ships and offshore oil fields, and perform various tasks such as rescue, patrol and emergency transportation. In order to further expand the application value of the unmanned aerial vehicle and improve the strong wind interference resistance of the unmanned aerial vehicle, it is an important direction of the current unmanned aerial vehicle control research to enable the unmanned aerial vehicle to complete the offshore flight task in harsh environment.

[0003] The variable-pitch multi-rotor unmanned aerial vehicle directly adjusts the thrust by changing the pitch angle through the variable-pitch system, and the response speed is superior to the traditional scheme which depends on the motor speed regulation. It is superior to the traditional fixed-pitch design in terms of energy efficiency, adaptability, stability and economy, and is particularly suitable for multi-task, long-endurance and complex environment application scenarios. At present, the PID (Proportional-Integral-Derivative Controller) controller is the most widely used controller in the traditional flight control system, but it faces core bottlenecks such as nonlinearity, coupling, delay and energy efficiency optimization when applied to the variable-pitch multi-rotor unmanned aerial vehicle, and cannot accurately compensate for nonlinear effects, making it difficult to obtain ideal flight quality.

[0004] In view of the above situation, the industry has proposed various unmanned aerial vehicle control methods, such as nonlinear control technology, including sliding mode control, feedback linearization, robust control, etc., but these control methods are difficult to achieve excellent results in actual flight. Further, the industry has found that the variable-pitch multi-rotor unmanned aerial vehicle system has strong nonlinearity and time-varying nature, and the active disturbance rejection controller does not require an accurate controlled object model. It can estimate the system state and total disturbance in real time through the extended state observer (ESO) for observer design. The core idea is to regard the internal uncertainty of the system and external disturbance as total disturbance, and to dynamically estimate and compensate through the extended state. However, the active disturbance rejection controller has many parameters, and parameter optimization is required under different environments and different targets. The parameter space has nonlinear, complex and multi-peak distribution, and is easy to fall into local optimum. At present, the industry has proposed various optimization methods to find the optimal parameters of the active disturbance rejection controller more quickly, such as genetic algorithm, particle swarm optimization method, genetic optimization method, etc., but all still have technical defects such as long exploration time, low optimization efficiency and difficulty in ensuring to find the global optimal solution. SUMMARY

[0005] The present application aims to provide a variable-pitch multi-rotor unmanned aerial vehicle and a flight attitude control method thereof to solve one or more of the above technical problems.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] In the first aspect of the present application, a variable-pitch multi-rotor unmanned aerial vehicle is provided, which is provided with a flight control system for realizing flight control of the variable-pitch multi-rotor unmanned aerial vehicle.

[0008] The flight control system is provided with a self-disturbance attitude control system for realizing flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment.

[0009] In each control loop, the optimization steps of the control parameters of the extended state observer, the differential tracker and the nonlinear state error feedback include: based on the established objective function, the control parameters are optimized by using an improved peacock optimization algorithm to obtain the optimization results of the control parameters.

[0010] Further improvement of the technical scheme of the present application is that,

[0011] The variable spiral sine-cosine strategy tends to global search in the early stage of optimization and tends to local search in the later stage of optimization.

[0012] Further improvement of the technical scheme of the present application is that,

[0013] A local escape mechanism is provided in the process of local search.

[0014] Further improvement of the technical scheme of the present application is that,

[0015] The expression of the objective function is:

[0016] ;

[0017] In the formula, J is the total objective function of the control loop; J is the root mean square error, which is used to measure the tracking accuracy; J is the system stability time, which is used to measure the dynamic response; J is the overshoot, which is used to measure the system stability; J is the weight coefficient.

[0018] Further improvement of the technical scheme of the present application is that,

[0019] The step of using the improved kingfisher optimization algorithm to optimize the control parameters to obtain the control parameter optimization result comprises:

[0020] According to the preset target function value constraint condition, the minimum value of the target function is found by using the improved kingfisher optimization algorithm, and the control parameter optimization result is obtained after iterative updating to meet the preset condition; wherein, the kingfisher optimization algorithm population and population parameters are initialized first, and each individual in the population represents a control parameter to be optimized; then in each iteration, the kingfisher position updating rule based on the variable spiral sine-cosine strategy is used to obtain a new population, and the fitness value of the population is evaluated according to the established kingfisher fitness function; finally, after iterative updating to meet the preset condition, the population corresponding to the fitness value closest to the preset optimal fitness value is taken as the control parameter optimization result.

[0021] Further improvement of the technical scheme of the present application is that,

[0022] In the kingfisher position updating rule based on the variable spiral sine-cosine strategy, the development stage updating formula is as follows:

[0023] ;

[0024] In the formula, is the position of the individual in the first generation; is the position of the individual in the first generation; is the variable spiral coefficient; are the hybrid adaptive coefficient, the exploration factor and the step coefficient, respectively; b is the reference offset; is the reference point or target position of the individual in the first generation; is the dynamic scaling factor, which is used to control the step of the search; is the random angle; and are random numbers, and both of them obey the uniform distribution of [0, 1].

[0025] The further improvement of the technical scheme of the present application is that,

[0026] Variable helix coefficient The expression is:

[0027] ;

[0028] In the formula, is a parameter for controlling the helix; is a parameter representing the period of the helix; l is a linear attenuation factor; t is the number of iterations; is the maximum number of iterations.

[0029] The further improvement of the technical scheme of the present application is that,

[0030] In the early stage of optimization, is 1, which is used to enable the search path to cover a larger range to enhance the global search ability; in the later stage of optimization, as the number of iterations increases, is gradually reduced, which is used to reduce the search range to improve the local optimization precision.

[0031] The further improvement of the technical scheme of the present application is that,

[0032] In each control loop, in the optimization step of the control parameters of the extended state observer, the differential tracker and the nonlinear state error feedback,

[0033] The control parameters to be optimized of the extended state observer include state error feedback gain, observer nonlinear strength parameter and observer linear and nonlinear region switching threshold parameter;

[0034] The control parameters to be optimized of the nonlinear state error feedback include error feedback gain, nonlinear strength and linear and nonlinear region switching threshold parameter;

[0035] The control parameters to be optimized of the differential tracker include filtering factor and speed factor for determining the fast and slow of the system tracking signal;

[0036] In addition, it also includes global parameters to be optimized, and the global parameters include global control gain compensation coefficient.

[0037] In the second aspect of the present application, a flight attitude control method of a variable-pitch multi-rotor unmanned aerial vehicle is provided, comprising:

[0038] In the process of realizing flight control of the variable-pitch multi-rotor unmanned aerial vehicle by the flight control system, the expected pitch angle, the expected yaw angle and the expected roll angle are input into the active disturbance rejection attitude control system and the rudder deflection value is obtained, so as to realize flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment by the active disturbance rejection attitude control system.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] The variable-pitch multi-rotor unmanned aerial vehicle specifically disclosed in the present application is provided with the newly designed active disturbance rejection attitude control system of the present application, and the improved Modified PiedKingfisher Optimization (MPKO) algorithm is used in the active disturbance rejection attitude control system for control parameter optimization, solving the problem of difficult adjustment of control parameters existing in the prior art due to factors such as model uncertainty, external disturbance and nonlinear coupling characteristics caused by variable-pitch rotors of the unmanned aerial vehicle in a complex dynamic environment, having the advantages of shorter exploration time, higher optimization efficiency and ability to ensure finding a global optimal solution, and being capable of improving the flight performance of the variable-pitch multi-rotor unmanned aerial vehicle. Further specifically and explainatively, the improved Modified PiedKingfisher Optimization algorithm used in the newly designed active disturbance rejection attitude control system of the present application establishes a Modified PiedKingfisher position update rule based on a variable helical sine-cosine strategy to balance global and local search, and the global search and fine local development capability can well adapt to the parameter tuning and optimization requirements of the active disturbance rejection attitude control system, effectively avoiding falling into a local optimum; in addition, the improved Modified PiedKingfisher Optimization algorithm has fewer core parameters (exemplarily, such as only diving speed and angle adjustment step), reducing the difficulty of parameter adjustment and shortening the optimization time, and being capable of efficiently finding a global optimal solution.

[0041] In the flight attitude control method disclosed in the present application, the improved Modified PiedKingfisher Optimization algorithm can more quickly find an optimal solution of the control parameters, improving the optimization efficiency; wherein the improved Modified PiedKingfisher Optimization algorithm tends to global search in the early optimization stage, increasing the opportunity to find a global optimal solution and improving the reliability of the optimization result, and tends to local search in the late optimization stage (the exemplary preferred technical solution can also be provided with a local escape mechanism, capable of more finely adjusting parameters), improving the optimization accuracy. Further summarily, the technical solution of the present application is particularly suitable for attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment, being capable of more accurately controlling the flight attitude of the unmanned aerial vehicle and improving the flight stability and safety of the unmanned aerial vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below; obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0043] Figure 1 is a basic structure schematic block diagram of a disturbance rejection attitude control system in an embodiment of the present application;

[0044] Figure 2 is a flowchart of an improved kingfisher optimization algorithm in an embodiment of the present application;

[0045] Figure 3 is a present position update flowchart of a disturbed particle swarm in an embodiment of the present application;

[0046] Figure 4 is a pitch angle control comparison curve schematic diagram in an embodiment of the present application;

[0047] Figure 5 is a roll angle control comparison curve schematic diagram in an embodiment of the present application;

[0048] Figure 6 is a yaw angle control comparison curve schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application more clear, the technical solutions in the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application; obviously, the described embodiment technical solutions are some embodiments of the present application, and are not all the embodiments.

[0050] Based on the technical solutions disclosed in the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0051] The variable-pitch multi-rotor unmanned aerial vehicle provided by the embodiment of the application is provided with a flight control system, which is used to realize flight control of the variable-pitch multi-rotor unmanned aerial vehicle; the flight control system is provided with a self-disturbance rejection attitude control system, which is used to realize flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment; specifically, the self-disturbance rejection attitude control system is provided with three control loops of roll, pitch and yaw, each control loop includes an extended state observer, a differential tracker and a nonlinear state error feedback; further, the self-disturbance rejection attitude control system is used to input a desired pitch angle, a desired yaw angle and a desired roll angle, and output a rudder deflection value.

[0052] The optimization step of the control parameters of the extended state observer, the differential tracker and the nonlinear state error feedback includes: based on the established objective function, the control parameters are optimized by using the improved kingfisher optimization algorithm to obtain the optimization result of the control parameters; the dynamic updating strategy of the improved kingfisher optimization algorithm adopts a kingfisher position updating rule based on a variable helical sine-cosine strategy; further, the variable helical sine-cosine strategy tends to global search in the early stage of optimization and tends to local search in the later stage of optimization, so as to balance global search and local search; in the further preferred technical solution, the local search process is further provided with a local escape mechanism.

[0053] In the technical solution provided by the embodiment of the application, the differential tracker is used to arrange a transition process, solve the contradiction between fast response and overshoot of the system, and extract a tracking signal and an accurate differential signal of the input signal, so as to design a reasonable controller; the extended state observer is used to estimate the total disturbance by using the extended state variable, to compensate the control signal by using the estimated value of the total disturbance, and to simplify the nonlinear control system into a high-order series integral linear control system in this way; the nonlinear state error feedback uses the tracking signal generated by the differential tracker and the observation signal generated by the extended state observer to calculate the tracking error of the system, and then generates a preliminary control amount by using a nonlinear combination, and finally, the extended state variable observed by the extended state observer is compensated, that is, the total disturbance is reduced, to obtain an actual control amount.

[0054] In the technical scheme disclosed by the embodiment of the application, the improved kingfisher optimization algorithm is introduced to solve the technical problems of long exploration time, low optimization efficiency and difficulty in finding a global optimal solution in the parameter optimization process of the active disturbance rejection controller. Specifically, the active disturbance rejection attitude control system in the technical scheme of the application includes an extended state observer, a differential tracker and a nonlinear state error feedback. The control parameters of these components need to be optimized by an optimization algorithm. The technical scheme of the application adopts a control parameter optimization method based on the improved kingfisher optimization algorithm, which can more efficiently find the optimal solution of the control parameters, thereby improving the optimization efficiency, shortening the exploration time and increasing the possibility of finding a global optimal solution. Further explanation, the core technical means of the technical scheme of the embodiment of the application is the improved kingfisher optimization algorithm. The algorithm is improved on the basis of the traditional kingfisher optimization algorithm and introduces the kingfisher position updating rule of the variable spiral sine-cosine strategy. This strategy makes the algorithm tend to global search in the early optimization stage, can more extensively explore the parameter space and increase the opportunity to find a global optimal solution. In the later optimization stage, the algorithm tends to local search and can more finely adjust the parameters to improve the optimization accuracy. In addition, a local escape mechanism is set in the local search process to prevent the algorithm from falling into a local optimal solution. In summary, the technical scheme of the embodiment of the application improves the traditional kingfisher optimization algorithm, introduces the kingfisher position updating rule of the variable spiral sine-cosine strategy, realizes the balance between global search and local search through the improved algorithm, ensures that the algorithm can extensively explore the parameter space and finely adjust the parameters, improves the optimization efficiency and accuracy, and is particularly suitable for the flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment, can more accurately control the flight attitude of the unmanned aerial vehicle and improve the flight stability and safety of the unmanned aerial vehicle. In addition, the further optimized technical scheme of the embodiment of the application sets a local escape mechanism in the local search process, which is a further solution to the problem that the algorithm may fall into a local optimal solution.

[0055] As a preferred embodiment of the application, the expression of the target function is:

[0056] ;

[0057] In the formula, is the total target function of the control loop; is the root mean square error (used to measure the tracking accuracy); is the system stable time (used to measure the dynamic response); is the overshoot (used to measure the system stability); is the weight coefficient.

[0058] The target function set by the embodiment of the application balances tracking accuracy, dynamic response and system stability to realize multi-objective optimization by weighted combination of multiple key indicators.

[0059] As a preferred technical solution of the embodiment of the application, the improved kingfisher optimization algorithm is used to optimize the control parameters, and the step of obtaining the optimization result of the control parameters specifically includes:

[0060] According to the preset target function value constraint condition, the improved kingfisher optimization algorithm is used to find the minimum value of the target function, and the control parameter optimization result is obtained after iterative updating to meet the preset condition; wherein,

[0061] The kingfisher optimization algorithm population and population parameters are initialized first, and each individual in the population represents a control parameter to be optimized;

[0062] In each iteration, the kingfisher position updating rule based on the variable spiral sine-cosine strategy is used to obtain a new population, and the fitness value of the population is evaluated according to the established kingfisher fitness function.

[0063] After iterative updating to meet the preset condition, the population corresponding to the fitness value closest to the preset optimal fitness value is taken as the control parameter optimization result.

[0064] In the embodiment of the application, the kingfisher position updating rule based on the variable spiral sine-cosine strategy (used to balance global and local search) includes:

[0065] To improve the global exploration and fine local development capability of the algorithm, the improved kingfisher optimization algorithm introduces the variable spiral sine-cosine strategy; wherein,

[0066] The mathematical model of the sine-cosine algorithm is as follows:

[0067] ;

[0068] In the formula, is the position of the individual in the first generation; is the position of the individual in the first generation; is the reference point or target position of the individual in the first generation; is a dynamic scaling factor used to control the step size of the search, which is usually gradually reduced with the increase of the number of iterations; is a random angle between [0, 2𝜋], which determines the fluctuation mode of the position update; and are random numbers, and are subject to uniform distribution of [0, 1], which are used to introduce randomness and diversity in the update formula.

[0069] By gradually narrowing the search range, the algorithm can further explore the previously discovered favorable areas, thereby improving the search ability for local optimal solutions; the dynamic adjustment of the spiral factor enables the search process to gradually transition from initial global exploration to later local fine search, achieving a balance between global search and local optimization.

[0070] The mathematical model of the spiral factor is as follows:

[0071] ;

[0072] In the formula, is a variable spiral factor, which is a key parameter for dynamically adjusting the range of spiral search to improve the effectiveness of the algorithm at different search stages; in the initial stage, is 1, enabling the search path to cover a larger range and thereby enhancing the global search capability; in the later stage of search, will gradually decrease, thereby narrowing the search range to improve the accuracy of local optimization. is a parameter for controlling the spiral, which is 1 in the early stage and decreases with the increase of the iteration number in the later stage. is a parameter representing the period of the spiral, which is usually considered is a parameter that linearly decreases from 1 to -1 according to the iteration number. l is a linear decay factor; t is the iteration number; is the maximum iteration number.

[0073] The improved variable spiral cosine algorithm establishes an update formula for the development stage of the Kingfisher algorithm, which further enhances the development capability of the Kingfisher optimization algorithm by utilizing the powerful development capability of the cosine algorithm, and improves the balance capability of exploration and development by using the oscillating spiral line of the variable spiral factor;

[0074] Finally, the update formula of the improved Kingfisher algorithm in the development stage is as follows:

[0075] ;

[0076] In the formula, is the position of the individual in the first generation; is the position of the individual in the first generation; is a variable spiral factor; are hybrid adaptive coefficients, exploration factors, and step coefficients, respectively; b is a reference offset; is an individual In the first reference point or target position of the generation; is a dynamic scaling factor used to control the step size of the search, which is usually gradually reduced as the number of iterations increases; is a random angle between [0, 2𝜋], which determines the fluctuation mode of the position update; and are random numbers, both subject to uniform distribution [0, 1], used to introduce randomness and diversity in the update formula.

[0077] In the specific exemplary technical solution of the application, the active disturbance rejection attitude control system includes three control loops of roll, pitch and yaw, each control loop includes a differential tracker, an extended state observer and a nonlinear state error feedback; wherein the differential tracker is used to receive error signals to generate smooth transition trajectory signals and their first derivative signals; the extended state observer is used to estimate the internal state of the system and the total disturbance in real time according to the output signal of the controlled object and the control input; the nonlinear state error feedback is used to combine the output of the differential tracker and the estimated value of the extended state observer module to generate a nonlinear compensation control quantity; the improved kingfisher optimization algorithm is used to optimize the parameters of the extended state observer , the parameters of the nonlinear state error feedback , the parameters of the differential tracker and the global control gain compensation coefficient b .

[0078] Compared with the traditional parameter setting method, the technical solution disclosed in the embodiment of the application breaks through the local convergence limit of the traditional optimization and realizes global coverage of the multi-dimensional parameter space based on the improved kingfisher optimization algorithm, significantly speeds up the convergence speed through the parallel heuristic search architecture, reduces the time cost of the method optimization, and improves the parameter optimization efficiency.

[0079] Referring to Figures 1 to 3 , in the specific exemplary technical solution of the application, a variable-pitch multi-rotor unmanned aerial vehicle is provided, which adopts an attitude active disturbance rejection control parameter setting method based on an improved kingfisher optimization algorithm; wherein the attitude active disturbance rejection control parameter setting method specifically includes the following steps:

[0080] Step 1: build a variable-pitch multi-rotor unmanned aerial vehicle dynamics model and a variable-pitch multi-rotor unmanned aerial vehicle active disturbance rejection (ADRC, Active Disturbances Rejection Controller) attitude control system;

[0081] In a specific example, a variable-pitch multi-rotor unmanned aerial vehicle model is established to replace an actual controlled object. To ensure a balance between model precision and operation complexity, a simplified state space model of a variable-pitch multi-rotor unmanned aerial vehicle roll, pitch and yaw channel is established. A three-channel active disturbance rejection controller model is established, and a control system block diagram is shown in FIG. 1, which is used as a variable-pitch multi-rotor unmanned aerial vehicle flight attitude control system. Figure 2

[0082] An extended state observer is established, and an expression is as follows:

[0083]

[0084] In the expression, x is an estimated value of a state variable attitude angle, attitude angle velocity and total disturbance; is a state error feedback gain, is an estimation affecting the attitude angle, is an estimation affecting the total disturbance of the system, is an estimation affecting the response of the system; is a nonlinear strength parameter of the extended state observer, is a threshold parameter of linear and nonlinear zone switching of the extended state observer; is an observation error; is an output signal of the system; is a nonlinear function, is a global control gain compensation coefficient, is a control input item of the system; Specifically,

[0085] is expressed as An expression of the extended state observer is as follows:

[0086]

[0087] In the expression, x is a function variable. Specifically, the error signal, the nonlinear factor and the threshold are sequentially expressed. A differential tracker is established, and an expression is as follows:

[0088]

[0089]

[0090] In the expression, x represents a current attitude angle, x represents a current attitude angle velocity, is a time step, x represents a next attitude angle; x represents a next attitude angle velocity, is a filtering factor, ​​​​​​​is a speed factor that determines the speed of the system tracking the signal; for a given input signal; is the fastest comprehensive function;

[0091] The nonlinear feedback is established, and the expression is:

[0092]

[0093] In the formula, is the error feedback gain, is the attitude angle tracking error, is the attitude angular velocity tracking error, is the nonlinear strength of the nonlinear feedback, is the threshold parameter of the linear and nonlinear region switching of the nonlinear feedback.

[0094] The final compensated control quantity is represented as:

[0095]

[0096] In the formula, is the global control gain compensation coefficient.

[0097] Step two: establish the fitness function of the pheasant, initialize the active disturbance rejection attitude control system parameters, create an initial pheasant optimization algorithm population, and initialize the population parameters;

[0098] In the specific exemplary technical solution, when initializing the active disturbance rejection attitude control system parameters, the control parameters required to be optimized include: the extended state observer , the differential tracker parameter , the nonlinear feedback controller parameter , and the global control gain compensation coefficient , and the parameter range is defined so that the optimization parameter process is within a reasonable range.

[0099] In the embodiment of the application, the search process is initialized by randomly generating a set of initial solutions in the search space, and the formula used to generate the initial population is as follows.

[0100] ;

[0101] In the formula, represents the position of the th bird (the total number is n ) in the th dimension (the total number is m ) in the pheasant population, represents a random number between 0 and 1; , represents the search upper bound and the search lower bound.

[0102] Once the initial population is generated, each individual is evaluated using a fitness function that assesses their problem-solving ability. The individual with the best fitness value is selected and used to generate a new population.

[0103] Step three: Improve the dynamic updating strategy of the kingfisher, establish a kingfisher position updating rule that balances global and local search through a variable spiral sine-cosine strategy, and adapt to the parameter tuning requirements of the self-disturbance controller.

[0104] The formula for establishing the kingfisher position updating rule is as follows:

[0105] ;

[0106] In the formula, represents the position of the next iteration of the kingfisher, represents the current position of the kingfisher, represents a random number following a normal distribution, represents the population size; the value of the parameter is dynamically determined according to the current strategy, and according to whether it is greater than 0.5, select "habitat" or "hover", the calculation of is tailored for each strategy to ensure optimal performance in different modes,

[0107] In the habitat strategy, the calculation formula is as follows:

[0108] ;

[0109] In the formula, where is the maximum number of iterations, represents a constant value of 8, is a random number between 0 and 1.

[0110] In the hover strategy, the calculation formula is as follows.

[0111] ;

[0112] In the formula, , represents the fitness value of the , th kingfisher.

[0113] The improved spotbird development stage updating formula is established by using the strong development capability of the cosine algorithm to further enhance the development capability of the spotbird optimization algorithm, and by using the spiral line with variable spiral coefficient oscillation to improve the balance capability of exploration and development of the algorithm, and the improved spotbird development stage updating formula is as follows:

[0114] .

[0115] Step four: fitness evaluation, comparing the updated fitness value, retaining the optimal position and fitness, establishing a local escape mechanism, and comparing the updated position;

[0116] The development stage mechanism is established, and the calculation formula is as follows.

[0117] ;

[0118] In the formula, is the average position of the group, is a random number uniformly distributed between 0 and 1; is a preset optimal fitness value;

[0119] The local escape stage mechanism is established.

[0120] ;

[0121] In the formula, is the maximum and minimum exploration probability, is the position of the two individuals selected at random at time t.

[0122] Two individuals are randomly selected from the population, and their positions are represented by and The predation efficiency of the spotbird is represented by , wherein and The constant values of and are set to 0.5 and 0, respectively.

[0123] Step five: the spotbird optimization algorithm is used to respectively tune the parameters of the three attitude control loops of the unmanned aerial vehicle, and the verified parameters are compared with the original parameters.

[0124] In the technical scheme provided by the embodiment of the application, the weight coefficient of the target function is used to balance the tracking accuracy, dynamic response and stability requirements, and ensure the overall optimization target. Combined with random disturbance and dynamic step adjustment, the local optimum is avoided, and the global search capability is improved. According to the ADRC theory and practical experience, the parameter search range is reasonably set, and invalid search is reduced.

[0125] In the specific exemplary technical solutions of the application, a simulation platform of the active disturbance rejection control system is built in MATLAB, including an active disturbance rejection controller and a three-loop attitude model; the active disturbance rejection controller compensates disturbance based on an extended state observer dynamics. The active disturbance rejection control parameters are taken as optimization variables, the population of the initialization kingfisher optimization algorithm is initialized, each "kingfisher" individual represents a set of active disturbance rejection parameter combinations, and the search range and iteration number are set. The diving hunting behavior of the kingfisher is simulated, the active disturbance rejection parameter combinations are updated through random disturbance, and the parameter search range is expanded. According to the position of the current optimal individual, the parameter step is adjusted, and the potential optimal solution in the neighborhood is finely searched. Each set of parameters is substituted into the simulation model to calculate the fitness value, and the active disturbance rejection parameter combination with better performance is selected. The active disturbance rejection parameters are updated through the "hunting-adjustment" mechanism of the kingfisher in a cycle until the convergence condition (such as the fitness threshold or the maximum iteration number) is met, and the optimal active disturbance rejection parameter combination is output.

[0126] Please refer to Figures 4 to 6 , Figures 4 to 6 After optimization, the expected attitude step of 0.1 rad is given to the actual pitch, roll and yaw angle changes of the unmanned aerial vehicle running simulation according to the optimal parameters, it can be seen that the active disturbance rejection controller shows faster response speed and higher tracking accuracy in the three attitude channels of pitch, roll and yaw, significantly improving the dynamic performance and control accuracy of the system. The comparison verifies the effectiveness and superiority of the parameter setting method based on the kingfisher optimization algorithm in the optimization of the attitude controller.

[0127] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the application and not to limit it, although the application has been described in detail with reference to the above examples, those skilled in the art should understand that the specific embodiments of the application can be modified or replaced by equivalents without departing from the spirit and scope of the application, any modification or equivalent replacement without departing from the spirit and scope of the application should be covered in the protection scope of the claims of the application.

Claims

1. A variable-pitch multi-rotor unmanned aerial vehicle, characterized in that, the variable-pitch multi-rotor unmanned aerial vehicle is provided with a flight control system for realizing flight control of the variable-pitch multi-rotor unmanned aerial vehicle; the flight control system is provided with a self-disturbance rejection attitude control system for realizing flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in a disturbed environment; wherein the self-disturbance rejection attitude control system is provided with three control loops of roll, pitch and yaw, each control loop comprising an extended state observer, a differential tracker and a nonlinear state error feedback; wherein in each control loop, the optimization steps of the control parameters of the extended state observer, the differential tracker and the nonlinear state error feedback comprise: based on the established objective function, the control parameters are optimized by using an improved peregrine falcon optimization algorithm to obtain the optimization results of the control parameters; wherein the dynamic update strategy of the improved peregrine falcon optimization algorithm adopts a peregrine falcon position update rule based on a variable helical sine-cosine strategy; the expression of the objective function is: ； In the formula, is the total target function of the control loop; is the root mean square error, used to measure the tracking accuracy; is the system stability time, used to measure the dynamic response; is the overshoot, used to measure the system stability; are all weight coefficients. 2.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 1, characterized in that, the variable helical sine-cosine strategy tends to global search in the early stage of optimization and tends to local search in the later stage of optimization. 3.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 2, characterized in that, a local escape mechanism is provided in the process of local search. 4.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 1, characterized in that, the step of optimizing the control parameters by using the improved peregrine falcon optimization algorithm to obtain the optimization results of the control parameters comprises: according to a preset target function value constraint condition, the minimum value of the objective function is found by using the improved peregrine falcon optimization algorithm, and the control parameter optimization results are obtained after iterative updating to meet the preset condition; wherein the peregrine falcon optimization algorithm population and population parameters are initialized first, and each individual in the population represents a control parameter to be optimized; then in each iteration, the peregrine falcon position update rule based on the variable helical sine-cosine strategy is used to obtain a new population, and the fitness value of the population is evaluated according to the established peregrine falcon fitness function; finally, after iterative updating to meet the preset condition, the population corresponding to the fitness value closest to the preset optimal fitness value is taken as the control parameter optimization result. 5.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 1, characterized in that, in the peregrine falcon position update rule based on the variable helical sine-cosine strategy, the development stage update formula is as follows: ； In the formula, is an individual at the position of the th generation; is an individual at the position of the th generation; is a variable spiral coefficient; are respectively a mixed adaptive coefficient, an exploration factor, and a step coefficient; b is a reference offset; is an individual at the reference point or target position of the th generation; is a dynamic scaling factor for controlling the step of the search; is a random angle; and are both random numbers and both subject to a uniform distribution of [0, 1]. 6.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 5, characterized in that, Variable helix coefficient The expression for the variable helix coefficient is: ； wherein is a parameter for controlling the spiral; is a parameter representing the period of the spiral; l is a linear decay factor; t is the number of iterations; is the maximum number of iterations. 7.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 6, characterized in that, In the early stage of optimization, 1, used to enable the search path to cover a larger range to enhance the global search ability; in the later stage of optimization, with the increase of the number of iterations, Gradually reduce, used to narrow the search range to improve the local optimization accuracy. 8.The variable-pitch multi-rotor unmanned aerial vehicle according to claim 1, characterized in that, in the optimization steps of the control parameters of the extended state observer, the differential tracker and the nonlinear state error feedback in each control loop, The control parameters to be optimized of the expansion state observer include state error feedback gain, observer non-linear strength parameter and threshold parameter of linear and non-linear region switching of the observer; The control parameters to be optimized of the non-linear state error feedback include error feedback gain, non-linear strength and threshold parameter of linear and non-linear region switching; The control parameters to be optimized of the differential tracker include filtering factor and speed factor for determining the fast and slow of the system tracking signal; In addition, a global parameter to be optimized is further included, and the global parameter includes a global control gain compensation coefficient.

9. The flight attitude control method of the variable-pitch multi-rotor unmanned aerial vehicle of claim 1, characterized in that, Comprise: In the process of realizing the flight control of the variable-pitch multi-rotor unmanned aerial vehicle through the flight control system, the expected pitch angle, the expected yaw angle and the expected roll angle are input into the active disturbance rejection attitude control system and the rudder deflection value is obtained, so as to realize the flight attitude control of the variable-pitch multi-rotor unmanned aerial vehicle in the disturbed environment through the active disturbance rejection attitude control system.

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