Four-rotor unmanned aerial vehicle cascade control method

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and particularly provides a four-rotor unmanned aerial vehicle cascade control method. The method comprises the following steps: acquiring a mathematical model of the quad-rotor unmanned aerial vehicle; designing a cascade control framework of position incremental model predictive control-attitude fractional order PID (Proportion Integration Differentiation) control; according to the method, a non-linear cross iteration strategy is designed, controller parameters in a cascade control framework are optimized by taking the minimum tracking error square as a target, the optimal parameters can be quickly and accurately searched, and the external disturbance resistance is improved while the tracking performance is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a cascade control method for a quad-rotor UAV. Background Art

[0002] Quadcopter drones have become indispensable in a variety of applications, including monitoring, mapping, emergency rescue, and environmental observation. In complex environments, especially those with strong wind disturbances, quadcopter controllers must be able to react quickly. Furthermore, due to the inherent mechanical limitations of quadcopters, such as the upper speed limit of brushless motors and the requirement for relatively smooth speed changes, the quadcopter can easily lose its stability against disturbances, leading to loss of balance and damage to the aircraft and mission failure.

[0003] Model predictive control (MPC) can perform real-time rolling optimization calculations and has strong adaptability. Fractional-order proportional-integral-differential (PID) control is a variant of the PID controller. It expands the order of the integral and differential terms in the PID controller to fractional values. Compared with traditional PID control, fractional-order PID controllers can better adapt to the characteristics of nonlinear systems, providing more flexible and precise control. Parameter adjustment is crucial. Different controller parameters can have a significant impact on quadcopter flight. In severe cases, improper controller adjustment can lead to system instability. Although MPC and fractional-order PID have been proven in quadcopter control, their parameter adjustment process is very cumbersome. Especially when MPC is combined with fractional-order PID technology, the number of parameters that need to be adjusted increases further. When a large number of parameters need to be adjusted, trial-and-error methods are time-consuming and may not guarantee optimal parameter settings. Summary of the Invention

[0004] In view of this, the present invention provides a cascade control method for a quadrotor unmanned aerial vehicle, which is used to quickly and accurately find the optimal parameters and improve the ability to resist external disturbances while ensuring tracking performance.

[0005] In a first aspect, the present invention provides a cascade control method for a quadrotor drone, the method comprising: Step 1: Obtain the mathematical model of the quadrotor drone; Step 2: Based on step 1, design a cascade control framework of position incremental model predictive control and attitude fractional-order PID control; Step 3: Based on step 2, a nonlinear cross-iteration strategy is designed to optimize the controller parameters in the cascade control framework with the goal of minimizing the square of the tracking error.

[0006] Optionally, the mathematical model of step 1 includes: ; ; ; ; ; ; Among them, the quadrotor take-off position is taken as the origin, Indicates the distance traveled due east. Indicates the speed in the east direction. represents the acceleration toward the east; Indicates the distance traveled in the south direction. Indicates the speed in the south direction. represents the acceleration toward due south; Indicates the height of the flight in the vertical direction. Indicates the speed of flying in the vertical direction. Indicates the acceleration of flying upward in the vertical direction; Indicates the angle of rotation towards the east direction. represents the angular velocity of rotation toward the east direction, It represents the angular acceleration of rotation toward due east; Indicates the angle of rotation towards due south. represents the angular velocity of rotation toward due south, Indicates the angular acceleration of rotation toward due south; Indicates the angle of rotation of the quadrotor from west to east, represents the angular velocity of the quadrotor rotating from west to east, represents the angular acceleration of the quadrotor rotating from west to east; is the overall mass of the quadrotor; is the sum of the speeds of the four propellers; , , Four rotors in , , Coefficient of moment of inertia in direction; , , Four rotors in , , Directional fuselage drag coefficient; , , Four rotors in , , Fuselage damping moment coefficient in direction; , , , , , Four rotors in , , , , , Disturbance in direction; is the total lift generated by the rotation of the four propellers, , , Four rotors in , , Rotational torque in direction; is the propeller moment of inertia, g is the acceleration due to gravity; Decompose the mathematical model into two subsystem models: Position subsystem: ; ; ; Attitude subsystem: ; ; ; In the quadrotor system, the attitude subsystem is the inner loop and the position subsystem is the outer loop; the position on the horizontal plane , Depends on attitude angle , , by controlling the attitude angle , , to control the position , .

[0007] Optionally, step 2 includes: The position incremental model predictive control: The mathematical model of the quadrotor position subsystem is converted into the following expression: ; ; ; The mathematical model of the converted quadrotor position subsystem is simplified and its expression is: ; ; ; in, , , g is the acceleration due to gravity, , , , , that is, the product term 、 and Recorded as 、 and ; At a time step of When , the discrete state space equations of the position subsystem are: ; ; ; ; ; ; in, , , , , ; , , , , ; , , , , ; At a time step of When , the incremental discrete state space equations of the position subsystem are: ; ; ; ; ; ; in, , , ; , , ; , , ; Using the nonlinear extended state observer, the disturbance is estimated , , The numerical value of is expressed as: ; in, , , Used to estimate the values of position, velocity and disturbance respectively, is the difference between the estimated position and the actual position, is the time step, is the control input of the design, , , , , , as well as These are all adjustable parameters that affect the performance of the observer; Nonlinear functions The expression is: ; ; in, is a symbolic function; The incremental control output is obtained by using the rolling optimization solution of the incremental model predictive control. , , , and the control output is obtained, and its expression is: ; ; ; The discrete expression of the attitude fractional-order PID controller is: ; in, is the error input sequence of the controller, is a positive integer, is the output sequence of the controller, is the discrete time step, is the integration order, is the differential order, , , are proportional, integral, and differential coefficients respectively; and is the binomial coefficient and takes the following values: ; ; Based on this, the virtual control input of the attitude subsystem is obtained , , , which is then converted into torque input , , .

[0008] Optionally, the designing of a nonlinear crossover iteration strategy in step 3 includes: ; ; ; in, , Inertia weight The minimum and maximum values of is the number of iterations of the current beetle swarm optimization, Maximum number of iterations optimized for beetle swarms; is an adjustable parameter, , Acceleration factors The minimum and maximum values of , Acceleration factors The minimum and maximum values of .

[0009] Optionally, the improved beetle swarm optimization algorithm is: ; in, is the random search direction, is a random function, is the dimension of the population; ; in, is the location of the population, is the step length, is the cost function of the right antenna of the longicorn, is the cost function of the left antenna of the longicorn; ; ; in, is the speed at which the population advances, and for A random number between is the optimal position of an individual, The best position for the population.

[0010] In a second aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the four-rotor drone cascade control method in the first aspect or any possible implementation of the first aspect.

[0011] In a third aspect, an embodiment of the present invention provides an electronic device comprising: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, enable the device to execute the four-rotor drone cascade control method in the first aspect or any possible implementation of the first aspect.

[0012] In the technical solution provided by the present invention, the method includes obtaining a mathematical model of a quadrotor unmanned aerial vehicle; designing a cascade control framework of position incremental model predictive control-attitude fractional-order PID control; designing a nonlinear cross-iteration strategy, with the goal of minimizing the square of the tracking error, and optimizing the controller parameters in the cascade control framework. This method can quickly and accurately find the optimal parameters, and while ensuring tracking performance, it improves the ability to resist external disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A flow chart of a cascade control method for a quadrotor drone provided by an embodiment of the present invention; Figure 2 A framework diagram of the cascade control of a quadrotor drone provided by an embodiment of the present invention; Figure 3 An improved beetle swarm optimization algorithm flow chart provided by an embodiment of the present invention; Figure 4 A nonlinear update change diagram of the inertia weight provided in an embodiment of the present invention; Figure 5A nonlinear update change diagram of the acceleration factor provided by an embodiment of the present invention; Figure 6 A comparison chart of optimization results of different optimization algorithms provided by the embodiment of the present invention; Figure 7 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0016] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0017] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings.

[0018] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0019] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0020] Figure 1 The flowchart of the cascade control method of the quadrotor drone provided by the embodiment of the present invention is as follows: Figure 1 As shown, the method includes: Step 1: Obtain the mathematical model of the quadrotor drone.

[0021] In the embodiment of the present invention, the mathematical model of step 1 includes: ; ; ; ; ; ; Among them, the quadrotor take-off position is taken as the origin, Indicates the distance traveled due east. Indicates the speed in the east direction. represents the acceleration toward the east; Indicates the distance traveled in the south direction. Indicates the speed in the south direction. represents the acceleration in the south direction; Indicates the height of the flight in the vertical direction. Indicates the speed of flying in the vertical direction. Indicates the acceleration of flying upward in the vertical direction; Indicates the angle of rotation towards the east direction. represents the angular velocity of rotation toward the east direction, represents the angular acceleration of rotation toward the east; Indicates the angle of rotation towards due south. represents the angular velocity of rotation toward due south, Indicates the angular acceleration of rotation toward due south; Indicates the angle of rotation of the quadrotor from west to east, represents the angular velocity of the quadrotor rotating from west to east, represents the angular acceleration of the quadrotor rotating from west to east; is the overall mass of the quadrotor; is the sum of the speeds of the four propellers; , , Four rotors in , , Coefficient of moment of inertia in direction; , , Four rotors in , , Directional fuselage drag coefficient; , , Four rotors in , , Fuselage damping moment coefficient in direction; , , , , , Four rotors in , , , , , Disturbance in direction; is the total lift generated by the rotation of the four propellers, , , Four rotors in , , The torque in the direction; is the propeller moment of inertia, g is the acceleration due to gravity; Decompose the mathematical model into two subsystem models: Position subsystem: ; ; ; Attitude subsystem: ; ; ; In the quadrotor system, the attitude subsystem is the inner loop and the position subsystem is the outer loop; the position on the horizontal plane , Depends on attitude angle , , by controlling the attitude angle , , to control the position , .

[0022] Step 2: Based on step 1, design a cascade control framework of position incremental model predictive control and attitude fractional-order PID control.

[0023] In the embodiment of the present invention, Figure 2 As shown, step 2 includes: The position incremental model predictive control, that is, the design of a position incremental MPC controller: The mathematical model of the quadrotor position subsystem is converted into the following expression: ; ; ; The mathematical model of the converted quadrotor position subsystem is simplified and its expression is: ; ; ; in, , , g is the acceleration due to gravity, , , , , that is, the product term 、 and Recorded as 、 and ; At a time step of When , the discrete state space equations of the position subsystem are: ; ; ; ; ; ; in, , , , , ; , , , , ; , , , , ; At a time step of When , the incremental discrete state space equations of the position subsystem are: ; ; ; ; ; ; in, , , ; , , ; , , ; Using the nonlinear extended state observer, the disturbance is estimated , , The numerical value of is expressed as: ; ; ; in, , , Used to estimate the values of position, velocity and disturbance respectively, is the difference between the estimated position and the actual position, is the time step, is the control input of the design, , , , , , as well as These are all adjustable parameters that affect the performance of the observer; Nonlinear functions The expression is: ; ; in, is a symbolic function; The incremental control output is obtained by using the rolling optimization solution of the incremental model predictive control. , , , and the control output is obtained, and its expression is: ; ; ; The discrete expression of the attitude fractional-order PID controller is: ; in, is the error input sequence of the controller, is a positive integer, is the output sequence of the controller, is the discrete time step, is the integration order, is the differential order, , , are proportional, integral, and differential coefficients respectively; and is the binomial coefficient and takes the following values: ; ; Based on this, the virtual control input of the attitude subsystem is obtained , , , which is then converted into torque input , , .

[0024] Step 3: Based on step 2, a nonlinear cross-iteration strategy is designed to optimize the controller parameters in the cascade control framework with the goal of minimizing the square of the tracking error.

[0025] In the embodiment of the present invention, designing a nonlinear crossover iteration strategy in step 3 includes: ; ; ; in, , Inertia weight The minimum and maximum values of is the number of iterations of the current beetle swarm optimization, Maximum number of iterations optimized for beetle swarms; is an adjustable parameter, , Acceleration factors The minimum and maximum values of , Acceleration factors The minimum and maximum values of .

[0026] In the embodiment of the present invention, the improved beetle swarm optimization algorithm (IBSO) is: ; in, is the random search direction, is a random function, is the dimension of the population; ; in, is the location of the population, is the step length, is the cost function of the right antenna of the longicorn, is the cost function of the left antenna of the longicorn; ; ; in, is the speed at which the population advances, and for A random number between is the optimal position of an individual, The best position for the population.

[0027] In the cascade control strategy of the quadrotor UAV, the position subsystem and attitude subsystem are both integral and independent, so the optimization of the controller parameters of the two subsystems should be considered accordingly.

[0028] In the embodiment of the present invention, Figure 3 As shown in Figure 2, the conditions for population update are explained as follows: (1) When optimizing the parameters of the incremental model predictive controller for position, keep the parameters of the fractional-order PID controller unchanged, and find the incremental model predictive control controller parameters that make the fitness function of the position subsystem smaller. Based on this, update the individual optimal position and global optimal position of the incremental model predictive control population; (2) When optimizing the parameters of the fractional-order PID controller, keep the parameters of the incremental model predictive control controller unchanged. Since the desired attitude angle is solved, the desired attitude angle is changing. However, the ultimate goal of controller parameter optimization is to make the quadrotor have high tracking performance of the posture subsystem and high stability performance of the attitude subsystem. Therefore, only when the parameters of the fractional-order PID controller make the fitness function of the position subsystem smaller and the fitness function of the attitude subsystem smaller, will the individual optimal position of the fractional-order PID controller be updated, rather than only considering the fitness function of the position subsystem and not considering the fitness function of the attitude subsystem when optimizing the parameters of the fractional-order PID controller.

[0029] In the optimization process of the basic Brain Storming Optimization Algorithm (BSO), if the inertia weight There are two possible situations: On the one hand, The larger the value, the faster the beetle swarm moves, which may enhance the beetle swarm's global search ability and avoid falling into the local optimum. However, this will reduce the accuracy of the beetle swarm's search for the global optimal solution, causing the beetle swarm to eventually find a pseudo global optimal solution; on the other hand, smaller This will cause the beetle swarm to move at a slower speed, thereby enhancing the swarm's local search ability and finding a more accurate optimal solution within a certain range. However, this will reduce the swarm's global movement range, resulting in the swarm ultimately finding only one optimal solution in a local area.

[0030] In the embodiment of the present invention, Figure 4 As shown, the inertia weight under the nonlinear change strategy is shown The changing trend from Figure 4 It can be seen that they meet the requirements for the change of inertia weight, that is: in the initial optimization stage, the inertia weight A larger value is beneficial for the beetle group to maintain a larger moving speed, relying more on the individual experience of the population, searching for the optimal solution in the entire solution space, and avoiding falling into the local optimal solution. As the number of iterations increases, the inertia weight The decrease in the value of is conducive to the beetle swarm maintaining a lower movement speed, relying more on the social experience of the population, and conducting more detailed exploration near the best solution that has been obtained, which is conducive to improving the accuracy of the optimal solution.

[0031] In addition, the beetle swarm seeks the optimal solution by sharing information among individuals. and Represent the tendency of the beetle group to move towards the individual optimal position and the global optimal position respectively. Similarly, in the early stages of the optimization algorithm, the beetle swarm can rely more on its own experience to conduct global searches and avoid falling into the dilemma of local optimal solutions; in the later stages of the optimization algorithm, the beetle swarm will rely more on social experience to approach the global optimum and conduct more detailed searches near the global optimal position, thereby improving the accuracy of the optimal solution.

[0032] In the embodiment of the present invention, Figure 5 As shown, the acceleration factor under the nonlinear change strategy is shown , The changing trend from Figure 5 It can be seen that they meet the requirements for the change of the acceleration factor, that is: in the initial optimization stage, the acceleration factor Get a larger value, and the acceleration factor A smaller value is beneficial for the beetle swarm to maintain a larger moving speed, relying more on the individual experience of the swarm, searching for the optimal solution in the entire solution space, and avoiding falling into the local optimal solution. As the number of iterations increases, the acceleration factor The value of decreases, the acceleration factor The increase of is conducive to the beetle swarm maintaining a lower movement speed, relying more on the social experience of the population, and conducting more detailed exploration near the best solution that has been obtained, which is conducive to improving the accuracy of the optimal solution.

[0033] In the embodiment of the present invention, Figure 6 As shown in the figure, the optimization results of the improved beetle swarm optimization algorithm (IBSO), the ordinary beetle swarm optimization algorithm (BSO), and the ordinary particle swarm optimization algorithm (PSO) are shown in the position incremental model predictive control-attitude fractional order PID control framework. Figure 6 It can be seen from the figure that the present invention has the fastest convergence speed and the best search accuracy.

[0034] The present invention proposes a position incremental model prediction-attitude fractional-order PID control framework, which realizes more flexible dynamic response adjustment and improves the robustness of the system to disturbances. It improves the existing optimization algorithm, takes the minimization of the square of the tracking error as the goal, designs a nonlinear cross-iteration strategy, and improves the convergence speed and search accuracy of the existing algorithm.

[0035] In the technical solution provided by the present invention, the method includes obtaining a mathematical model of a quadrotor unmanned aerial vehicle; designing a cascade control framework of position incremental model predictive control-attitude fractional-order PID control; designing a nonlinear cross-iteration strategy, with the goal of minimizing the square of the tracking error, and optimizing the controller parameters in the cascade control framework. This method can quickly and accurately find the optimal parameters, and while ensuring tracking performance, it improves the ability to resist external disturbances.

[0036] Each step of the embodiment of the present invention may be performed by an electronic device, including but not limited to a tablet computer, a portable PC, a desktop computer, etc.

[0037] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the electronic device where the computer-readable storage medium is located is controlled to execute the embodiment of the above-mentioned four-rotor drone cascade control method.

[0038] Figure 7 A schematic diagram of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 7As shown, the electronic device 21 includes: a processor 211, a memory 212, and a computer program 213 stored in the memory 212 and executable on the processor 211. When the computer program 213 is executed by the processor 211, the quadcopter cascade control method in the embodiment is implemented. To avoid repetition, they are not described here one by one.

[0039] The electronic device 21 includes, but is not limited to, a processor 211 and a memory 212. Those skilled in the art will understand that Figure 7 It is only an example of the electronic device 21 and does not constitute a limitation of the electronic device 21. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0040] The processor 211 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0041] The memory 212 can be an internal storage unit of the electronic device 21, such as the hard drive or memory of the electronic device 21. The memory 212 can also be an external storage device of the electronic device 21, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 21. Furthermore, the memory 212 can include both the internal storage unit of the electronic device 21 and an external storage device. The memory 212 is used to store computer programs and other programs and data required by the network device. The memory 212 can also be used to temporarily store data that has been output or is about to be output.

[0042] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cascade control method for a quadrotor drone, characterized in that: The method comprises: Step 1: Obtain the mathematical model of the quadrotor drone; Step 2: Based on step 1, design a cascade control framework of position incremental model predictive control and attitude fractional-order PID control; Step 3: Based on step 2, a nonlinear cross-iteration strategy is designed to optimize the controller parameters in the cascade control framework with the goal of minimizing the square of the tracking error.

2. The method according to claim 1, characterized in that The mathematical model of step 1 includes: ； ； ； ； ； ； Among them, the quadrotor take-off position is taken as the origin, Indicates the distance traveled due east. Indicates the speed in the east direction. represents the acceleration toward the east; Indicates the distance traveled in the south direction. Indicates the speed in the south direction. represents the acceleration toward due south; Indicates the height of the flight in the vertical direction. Indicates the speed of flying in the vertical direction. Indicates the acceleration of flying upward in the vertical direction; Indicates the angle of rotation towards the east direction. represents the angular velocity of rotation toward the east direction, It represents the angular acceleration of rotation toward due east; Indicates the angle of rotation towards due south. represents the angular velocity of rotation toward due south, Indicates the angular acceleration of rotation toward due south; Indicates the angle of rotation of the quadrotor from west to east, represents the angular velocity of the quadrotor rotating from west to east, represents the angular acceleration of the quadrotor rotating from west to east; is the overall mass of the quadrotor; is the sum of the speeds of the four propellers; , , Four rotors in , , Coefficient of moment of inertia in direction; , , Four rotors in , , Directional fuselage drag coefficient; , , Four rotors in , , Fuselage damping moment coefficient in direction; , , , , , Four rotors in , , , , , Disturbance in direction; is the total lift generated by the rotation of the four propellers, , , Four rotors in , , Rotational torque in direction; is the propeller moment of inertia, g is the acceleration due to gravity; Decompose the mathematical model into two subsystem models: Position subsystem: ； ； ； Attitude subsystem: ； ； ； In the quadrotor system, the attitude subsystem is the inner loop and the position subsystem is the outer loop; the position on the horizontal plane , Depends on attitude angle , , by controlling the attitude angle , , to control the position , .

3. The method according to claim 2, characterized in that The step 2 includes: The position incremental model predictive control: The mathematical model of the quadrotor position subsystem is converted into the following expression: ； ； ； The mathematical model of the converted quadrotor position subsystem is simplified and its expression is: ； ； ； in, , , g is the acceleration due to gravity, , , , , that is, the product term 、 and Recorded as 、 and ; At a time step of When , the discrete state space equations of the position subsystem are: ； ； ； ； ； ； in, , , , , ; , , , , ; , , , , ; At a time step of When , the incremental discrete state space equations of the position subsystem are: ； ； ； ； ； ； in, , , ; , , ; , , ; Using the nonlinear extended state observer, the disturbance is estimated , , The numerical value of is expressed as: ； ； ； in, , , Used to estimate the values of position, velocity and disturbance respectively, is the difference between the estimated position and the actual position, is the time step, is the control input of the design, , , , , , as well as These are all adjustable parameters that affect the performance of the observer; Nonlinear functions The expression is: ； ； in, is a symbolic function; The incremental control output is obtained by using the rolling optimization solution of the incremental model predictive control. , , , and the control output is obtained, and its expression is: ； ； ； The discrete expression of the attitude fractional-order PID controller is: ； in, is the error input sequence of the controller, is a positive integer, is the output sequence of the controller, is the discrete time step, is the integration order, is the differential order, , , are proportional, integral, and differential coefficients respectively; and is the binomial coefficient and takes the following values: ； ； Based on this, the virtual control input of the attitude subsystem is obtained , , , which is then converted into torque input , , .

4. The method according to claim 1, wherein The nonlinear crossover iteration strategy designed in step 3 includes: ； ； ； in, , Inertia weight The minimum and maximum values of is the number of iterations of the current beetle swarm optimization, Maximum number of iterations optimized for beetle swarms; is an adjustable parameter, , Acceleration factors The minimum and maximum values of , Acceleration factors The minimum and maximum values of .

5. The method according to claim 4, characterized in that The improved beetle swarm optimization algorithm is: ； in, is the random search direction, is a random function, is the dimension of the population; ； in, is the location of the population, is the step length, is the cost function of the right antenna of the longicorn, is the cost function of the left antenna of the longicorn; ； ； in, is the speed at which the population advances, and for A random number between is the optimal position of an individual, The best position for the population.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the quadrotor drone cascade control method according to any one of claims 1 to 5.

7. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, enable the device to execute the quadrotor drone cascade control method according to any one of claims 1 to 5.

Images