Indoor flight control method for unmanned aerial vehicle

Abstract

The invention provides an indoor flight control method for an unmanned aerial vehicle, and belongs to the field of PID control optimization, and the method specifically comprises the steps: 1, converting an indoor flight control problem of the unmanned aerial vehicle into a mathematical model to be optimized; step 2, establishing a PID control model of an indoor flight speed control system of the unmanned aerial vehicle; step 3, improving a snake optimization algorithm, wherein the specific improvement strategy is as follows: D1, introducing a weight coefficient to improve a position updating expression when the snake searches for food; d2, introducing a balance factor to balance the probability that the snakes are in the combat mode and the mating mode; 4, optimizing an indoor flight speed PID controller of the unmanned aerial vehicle by using an improved snake optimization algorithm to obtain optimal Kp, Ki and Kd parameters of the indoor flight speed PID controller of the unmanned aerial vehicle; and 5, inputting the obtained optimal parameters into a PID control model of an indoor flight speed control system of the unmanned aerial vehicle to obtain an optimal effect of indoor flight control of the unmanned aerial vehicle.

Description

Technical Field

[0001] The invention belongs to the field of PID control optimization, and in particular relates to an indoor flight control method for a drone. Background Art

[0002] A drone is an aircraft controlled by remote control or autonomous program, and is widely used in military, commercial, scientific research and entertainment fields. The flight control system of a drone uses sensors, electronic speed regulators (ESC) and brushless motors to achieve precise attitude control, navigation and mission execution. Most modern drones use brushless DC motors (BLDC) with multi-rotor structures to obtain stable flight capabilities. The core of a drone includes a flight controller (FC), GPS navigation system, communication system, camera and various sensors. Through a ground control station (GCS), users can remotely control drones for aerial photography, mapping, reconnaissance and logistics distribution tasks. Automatic return, fixed-point hovering and path planning are common intelligent functions. Drones have been widely used in agriculture, environmental monitoring, security and transportation due to their flexibility, high efficiency and relatively low cost, and with the advancement of technology, the use scenarios and intelligence of drones will continue to expand.

[0003] PID (proportional-integral-differential) control is a classic feedback control algorithm widely used in industrial control and automation fields. PID control adjusts the system error (i.e. the difference between the expected value and the actual value) and controls the output to make the system reach a stable target state. In the face of the indoor flight environment of drones, the performance of PID control may be limited. In order to solve this problem, an intelligent optimization algorithm is used to automatically adjust the PID parameters, which can not only effectively find the optimal parameter value, but also significantly improve the performance and applicability of PID control.

[0004] Snake Optimizer (SOA) is a bionic heuristic algorithm that simulates the behavior of snakes searching for food in complex environments. The snake optimization algorithm achieves global search and local development through the movement and deformation of individuals (snakes), aiming to solve the global optimization problem. The algorithm is divided into "combat mode" and "development mode". Combat mode: simulates the rapid response of snakes when facing obstacles, and conducts global search with large step size and direction changes to avoid falling into local optimality; development mode: simulates the fine adjustment of snakes when approaching food, and optimizes the accuracy of the solution through small step size and local search. The snake optimization algorithm balances exploration and development by introducing adaptive weights and environmental feedback to improve global search efficiency and convergence speed. Summary of the invention

[0005] The purpose of the present invention is to improve the global search ability of the algorithm in the early stage and the local search ability in the later stage during the optimization process by improving the snake optimization algorithm, reduce the possibility of falling into the local optimum, thereby improving the optimization accuracy and response speed of the algorithm, and optimize the control parameters of the indoor flight speed PID controller of the unmanned aerial vehicle by improving the snake optimization algorithm, improve the accuracy and stability of speed control, and reduce the adverse effects of dynamic errors on the control effect during the parameter setting process.

[0006] To achieve the above purpose, the present invention adopts a method for controlling indoor flight of a UAV, and the specific steps are as follows.

[0007] Step 1: Convert the UAV indoor flight control problem into a mathematical model to be optimized.

[0008] Step 2: Establish the PID control model of the UAV indoor flight speed control system.

[0009] Step 3: Improve the snake optimization algorithm. The specific improvement strategy is: D1. In the algorithm exploration phase, weight coefficients are introduced to improve the position update expression when the snake is looking for food; D2. The balance factor is introduced in the algorithm development stage to balance the probability of the snake being in fighting mode and mating mode, as follows.

[0010] Step 1. In the algorithm exploration stage, the weight coefficient w is introduced to improve the position update expression when the snake is looking for food. The improved position update expression is: In the formula, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X rand,m (t), X rand，f (t) are the positions of randomly selected male and female snakes, respectively. A m , A f They represent the ability of male and female snakes to find food, w is the weight coefficient, X max Search for the upper bound of the algorithm, X min To search for a lower bound for the algorithm, rand is a random number between 0 and 1, and c2 is a constant set to 0.05; Step 2: During the algorithm development phase, a balance factor is introduced to balance the probability of the snake being in fighting mode and mating mode. The balance factor expression is: Where η is the weight coefficient, t is the current number of iterations, T is the maximum number of iterations, N is the algorithm population size, and rand(N, 1) represents a random number between N and 1.

[0011] Step 4: Use the improved snake optimization algorithm to optimize the UAV indoor flight speed PID controller and obtain the optimal Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller.

[0012] Step 5: Input the obtained optimal parameters Kp, Ki, and Kd into the PID control model of the UAV indoor flight speed control system to obtain the best effect of the UAV indoor flight control.

[0013] Preferably, the mathematical model to be optimized in step 1 is: Where f(t) is the system output, e(t) is the system error input, t is the current time, and T is the total system running time.

[0014] Preferably, the PID control model of the indoor flight speed control system of the UAV in step 2 includes an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module; the expected speed value is input from the input module, and is input into the error calculation module together with the actual speed value detected by the speed detection module to obtain a speed error value by difference, the speed PID controller receives the speed error value, and applies the PID parameters optimized by the improved snake optimization algorithm module to calculate and obtain a reference speed value, thereby controlling the motor to run at high speed and realizing the control of the indoor flight speed of the UAV.

[0015] Preferably, in the algorithm exploration phase described in step 3, a weight coefficient is introduced to improve the position update expression when the snake is looking for food. During the optimization process, the weight coefficient can help the snake optimize the step size and direction selection when approaching the target solution, making the position update more accurate, thereby accelerating the convergence speed of the algorithm and reducing unnecessary exploration steps.

[0016] Preferably, in the algorithm development phase described in step three, a balance factor is introduced to balance the probability of the snake being in combat mode and mating mode. By dynamically adjusting the balance factor, the two modes can be switched in time according to the characteristics of the problem and the optimization stage; this not only retains the fast global search capability of the combat mode, but also utilizes the local development advantages of the mating mode, thereby enhancing the adaptability and stability of the algorithm.

[0017] Preferably, the improved snake optimization algorithm is used to optimize the indoor flight speed PID controller of the UAV in step 4, and the specific steps are as follows: S1. Simulate the working conditions of the indoor flight speed control system of the UAV and design the transfer function to describe the dynamic characteristics of the system; S2, given the input of the UAV indoor flight speed control system, the input is the expected speed value of the motor in the control system; S3. Initialize the population size N of the improved snake optimization algorithm, the problem dimension d, and the algorithm search upper bound X max , the algorithm searches for the lower bound X min , maximum number of iterations T; S4, encoding the parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller as the solution of the improved snake optimization algorithm search space. As the algorithm is iterated, the updated population individual positions reflect the corresponding parameters of the UAV indoor flight speed PID controller. S5. Calculate the individual fitness values ​​of the current iteration population according to the fitness function, record the minimum fitness value, and compare it with the optimal fitness value of the population in the previous iteration, and retain the optimal fitness value; S6, simulating the optimization process of each stage of the improved snake optimization algorithm, that is, updating the Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller; S7. If the updated population individual is better than the one updated in the previous iteration, the current optimal solution is retained; S8. Determine whether the current number of iterations reaches the maximum number of iterations. If so, exit the loop, output the global optimal solution, and assign it to the three parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller. Otherwise, return to execute S2.

[0018] Preferably, the S1 simulates the working condition of the indoor flight speed control system of the UAV, designs a transfer function to describe the dynamic characteristics of the system, and the transfer function formula is: Where s represents the complex frequency domain variable in Laplace transform.

[0019] Preferably, in S4, the parameters Kp, Ki, and Kd of the indoor flight speed PID controller of the drone are encoded as the solution of the search space of the improved snake optimization algorithm. As the algorithm is iterated, the updated positions of the individual populations are the solutions of the search space of the updated algorithm, that is, the parameters of the indoor flight speed PID controller of the drone are updated. The encoding vector is: x = [Kp, Ki, Kd]; Where x is the encoding vector, i.e., the solution of the search space of the improved snake optimization algorithm, and Kp, Ki, and Kd are the proportional parameter, integral parameter, and differential parameter of the UAV indoor flight speed PID controller, respectively.

[0020] Preferably, the S6 simulates and improves the optimization process of each stage of the snake optimization algorithm, and the specific steps are: Step 1, simulate the algorithm exploration stage, introduce the weight coefficient w to improve the position update expression when the snake searches for food, and execute formulas (1)-(2); Step 2: During the simulation algorithm development phase, a balance factor is introduced to balance the probability of the snake being in the fighting mode and the mating mode. When the balance factor η is greater than 0.5, the snake is in the fighting mode. When the balance factor η is less than or equal to 0.5, the snake is in the mating mode. Formulas (3)-(5) are executed. In the formula, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X i，m (t), X i，f (t) are the positions of the ith male snake and the ith female snake at the tth iteration, respectively. m 、F f Respectively represent the fighting ability of male snakes and female snakes, M m 、M f They represent the mating ability of male and female snakes respectively, rand is a random number between 0 and 1, c3 is a constant set to 0.05, Q is the amount of food, X best，f is the position of the best individual in the male snake group, X best，m is the position of the best individual in the female snake group, and η is the balance factor.

[0021] In summary, due to the adoption of the above technical scheme, the beneficial effects of the present invention are: the snake optimization algorithm is improved by introducing weight coefficients and balance factors, so as to improve the accuracy and performance of the algorithm; the improved snake optimization algorithm is used to perform parameter tuning on the speed PID controller in the indoor flight speed control system of the UAV, and the optimal control parameters Kp, Ki, and Kd are obtained through algorithm optimization. The three optimal control parameters are set as the parameters of the indoor flight speed PID controller of the UAV, so as to optimize the motor speed control effect, thereby better exerting the indoor flight control effect of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flowchart of the steps of the UAV indoor flight control method.

[0023] Figure 2 This is the PID control model diagram of the UAV indoor flight speed control system.

[0024] Figure 3Comparison chart of the best fitness functions between the basic snake optimization algorithm and the improved snake optimization algorithm.

[0025] Figure 4 A comparison chart of the effects of optimizing PID parameters using the basic snake optimization algorithm and the improved snake optimization algorithm. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The present invention provides a technical solution: a method for controlling indoor flight of a drone specifically comprises the following steps: Figure 1 shown.

[0028] Step 1: Convert the UAV indoor flight control problem into a mathematical model to be optimized.

[0029] Furthermore, the mathematical model to be optimized in step 1 is: Where f(t) is the system output, e(t) is the system error input, t is the current time, and T is the total system running time.

[0030] Step 2: Establish the PID control model of the UAV indoor flight speed control system.

[0031] Furthermore, the PID control model of the indoor flight speed control system of the UAV in step 2 is as follows: Figure 2 As shown, it includes an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module; the expected speed value is input from the input module, and is input into the error calculation module together with the actual speed value detected by the speed detection module to obtain a speed error value by difference, the speed PID controller receives the speed error value, and uses the PID parameters optimized by the improved snake optimization algorithm module to calculate and obtain a reference speed value, thereby controlling the motor to run at a high speed and realizing the control of the indoor flight speed of the UAV.

[0032] Step 3: Improve the snake optimization algorithm. The specific improvement strategy is: D1. In the algorithm exploration phase, weight coefficients are introduced to improve the position update expression when the snake is looking for food; D2. The balance factor is introduced in the algorithm development stage to balance the probability of the snake being in fighting mode and mating mode, as follows.

[0033] Step 1. In the algorithm exploration stage, the weight coefficient w is introduced to improve the position update expression when the snake is looking for food. The improved position update expression is: Where, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X rand,m (t), X rand，f (t) are the positions of randomly selected male and female snakes, respectively. A m , A f They represent the ability of male and female snakes to find food, w is the weight coefficient, X max Search for the upper bound of the algorithm, X min To search for a lower bound for the algorithm, rand is a random number between 0 and 1, and c2 is a constant set to 0.05; Step 2: During the algorithm development phase, a balance factor is introduced to balance the probability of the snake being in fighting mode and mating mode. The balance factor expression is: Where η is the weight coefficient, t is the current number of iterations, T is the maximum number of iterations, N is the algorithm population size, and rand(N, 1) represents a random number between N and 1.

[0034] Step 4: Use the improved snake optimization algorithm to optimize the UAV indoor flight speed PID controller to obtain the optimal Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller. The specific steps are as follows: S1. Simulate the working conditions of the indoor flight speed control system of the UAV and design the transfer function to describe the dynamic characteristics of the system; S2, given the input of the UAV indoor flight speed control system, the input is the expected speed value of the motor in the control system; S3. Initialize the population size N of the improved snake optimization algorithm, the problem dimension d, and the algorithm search upper bound X max , the algorithm searches for the lower bound X min , maximum number of iterations T; S4, encoding the parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller as the solution of the improved snake optimization algorithm search space. As the algorithm is iterated, the updated population individual positions reflect the corresponding parameters of the UAV indoor flight speed PID controller. S5. Calculate the individual fitness values ​​of the current iteration population according to the fitness function, record the minimum fitness value, and compare it with the optimal fitness value of the population in the previous iteration, and retain the optimal fitness value; S6, simulating the optimization process of each stage of the improved snake optimization algorithm, that is, updating the Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller; S7. If the updated population individual is better than the one updated in the previous iteration, the current optimal solution is retained; S8. Determine whether the current number of iterations reaches the maximum number of iterations. If so, exit the loop, output the global optimal solution, and assign it to the three parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller. Otherwise, return to execute S2.

[0035] Furthermore, the S1 simulates the working condition of the indoor flight speed control system of the UAV, and designs a transfer function to describe the dynamic characteristics of the system. The transfer function formula is: Where s represents the complex frequency domain variable in Laplace transform.

[0036] Furthermore, in S4, the parameters Kp, Ki, and Kd of the indoor flight speed PID controller of the drone are encoded as the solution of the search space of the improved snake optimization algorithm. As the algorithm is iterated, the updated positions of the individual populations are the solutions of the search space of the updated algorithm, that is, the parameters of the indoor flight speed PID controller of the drone are updated. The encoding vector is: x = [Kp, Ki, Kd]; Where x is the encoding vector, i.e., the solution of the search space of the improved snake optimization algorithm, and Kp, Ki, and Kd are the proportional parameter, integral parameter, and differential parameter of the UAV indoor flight speed PID controller, respectively.

[0037] Furthermore, the S6 simulates and improves the optimization process of each stage of the snake optimization algorithm, and the specific steps are as follows: Step 1, simulate the algorithm exploration stage, introduce the weight coefficient w to improve the position update expression when the snake searches for food, and execute formulas (1)-(2); Step 2: During the simulation algorithm development phase, a balance factor is introduced to balance the probability of the snake being in the fighting mode and the mating mode. When the balance factor η is greater than 0.5, the snake is in the fighting mode. When the balance factor η is less than or equal to 0.5, the snake is in the mating mode. Formulas (3)-(5) are executed. Where, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f(t+1) is the position of the ith female snake at the t+1th iteration, X i，m (t), X i，f (t) are the positions of the ith male snake and the ith female snake at the tth iteration, respectively. m 、F f Respectively represent the fighting ability of male snakes and female snakes, M m 、M f They represent the mating ability of male and female snakes respectively, rand is a random number between 0 and 1, c3 is a constant set to 0.05, Q is the amount of food, X best，f is the position of the best individual in the male snake group, X best，m is the position of the best individual in the female snake group, and η is the balance factor.

[0038] Step 5: Input the obtained optimal parameters Kp, Ki, and Kd into the PID control model of the UAV indoor flight speed control system to obtain the best effect of the UAV indoor flight control.

[0039] In order to verify the indoor flight control method of unmanned aerial vehicle proposed in the present invention, the present invention conducts simulation experiments through Matlab and Simulink, and compares the PID control optimized by the improved snake optimization algorithm and the basic snake optimization algorithm. Firstly, the mathematical model of the snake optimization algorithm is improved by Matlab, and the PID control model of the indoor flight speed control system of the unmanned aerial vehicle is established by Simulink. The parameters of the improved snake optimization algorithm are initialized, including the population size N=50, the problem dimension d=3, and the algorithm search upper bound X. max =1, the algorithm searches for the lower bound X min =0, the maximum number of iterations T = 30, run the Matlab program, and get the best fitness value comparison curve of the basic snake optimization algorithm and the improved snake optimization algorithm, as shown in Figure 3 As shown in the figure, the improved snake optimization algorithm reaches the optimal fitness value of 14.55 at about the 18th iteration. Compared with the basic snake optimization algorithm, it has better optimization accuracy and convergence speed, which can better improve the control performance of the UAV indoor flight control system.

[0040] Furthermore, Figure 4 The comparison chart of PID control effect optimization between basic snake optimization algorithm and improved snake optimization algorithm is shown in the figure. The target input value is set to 100 units. Figure 4 It can be seen that the improved snake optimization algorithm optimizes the PID control response curve with lower overshoot and faster response speed. Compared with the basic snake optimization algorithm, the parameters of PID control are better, which can make the UAV speed quickly reach a stable state and improve the indoor flight control effect of the UAV.

Claims

1. A method for controlling indoor flight of a drone, characterized in that: The specific steps are: Step 1: Convert the UAV indoor flight control problem into a mathematical model to be optimized; Step 2: Establish the PID control model of the UAV indoor flight speed control system; Step 3: Improve the snake optimization algorithm. The specific improvement strategy is: D1. In the algorithm exploration phase, weight coefficients are introduced to improve the position update expression when the snake is looking for food; D2. In the algorithm development phase, a balancing factor is introduced to balance the probability of the snake being in fighting mode and mating mode; Step 4: Use the improved snake optimization algorithm to optimize the UAV indoor flight speed PID controller to obtain the optimal Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller; Step 5: Input the obtained optimal parameters Kp, Ki, and Kd into the PID control model of the UAV indoor flight speed control system to obtain the best effect of the UAV indoor flight control.

2. The method for controlling indoor flight of a drone according to claim 1, characterized in that: In the step 2, a PID control model of the indoor flight speed control system of the UAV is established, and the model includes an input module, an error calculation module, a speed PID controller module, an improved snake optimization algorithm module, a speed detection module, and a motor module; The expected speed value is input from the input module and input into the error calculation module together with the actual speed value detected by the speed detection module to obtain the speed error value. The speed PID controller receives the speed error value and uses the PID parameters optimized by the improved snake optimization algorithm module to calculate the reference speed value, thereby controlling the motor to run at high speed and realizing the control of the indoor flight speed of the drone.

3. The method for controlling indoor flight of a drone according to claim 2, characterized in that: In step 3, the specific steps of improving the snake optimization algorithm are: Step 1. In the algorithm exploration stage, the weight coefficient w is introduced to improve the position update expression when the snake is looking for food. The improved position update expression is: Where, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X rand，m (t), X rand，f (t) are the positions of randomly selected male and female snakes, respectively. A m , A f They represent the ability of male and female snakes to find food, w is the weight coefficient, X max Search for the upper bound of the algorithm, X min To search for a lower bound for the algorithm, rand is a random number between 0 and 1, and c2 is a constant set to 0.05; Step 2: During the algorithm development phase, a balance factor is introduced to balance the probability of the snake being in fighting mode and mating mode. The balance factor expression is: Where η is the weight coefficient, t is the current number of iterations, T is the maximum number of iterations, N is the algorithm population size, and rand(N, 1) represents a random number between N and 1.

4. The method for controlling indoor flight of a drone according to claim 3, characterized in that: In step 4, the improved snake optimization algorithm is used to optimize the UAV indoor flight speed PID controller, and the specific steps are as follows: S1. Simulate the working conditions of the indoor flight speed control system of the UAV and design the transfer function to describe the dynamic characteristics of the system; S2, given the input of the UAV indoor flight speed control system, the input is the expected speed value of the motor in the control system; S3. Initialize the population size N of the improved snake optimization algorithm, the problem dimension d, and the algorithm search upper bound X max , the algorithm searches for the lower bound X min , maximum number of iterations T; S4, encoding the parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller as the solution of the improved snake optimization algorithm search space. As the algorithm is iterated, the updated population individual positions reflect the corresponding parameters of the UAV indoor flight speed PID controller. S5. Calculate the individual fitness values ​​of the current iteration population according to the fitness function, record the minimum fitness value, and compare it with the optimal fitness value of the population in the previous iteration, and retain the optimal fitness value; S6, simulating the optimization process of each stage of the improved snake optimization algorithm, that is, updating the Kp, Ki, and Kd parameters of the UAV indoor flight speed PID controller; S7. If the updated population individual is better than the one updated in the previous iteration, the current optimal solution is retained; S8. Determine whether the current number of iterations reaches the maximum number of iterations. If so, exit the loop, output the global optimal solution, and assign it to the three parameters Kp, Ki, and Kd of the UAV indoor flight speed PID controller. Otherwise, return to execute S2.

5. The method for controlling indoor flight of a drone according to claim 4, characterized in that: Simulate the optimization process of each stage of the improved snake optimization algorithm. The specific steps are: Step 1: Simulate the algorithm exploration phase, introduce the weight coefficient w to improve the position update expression when the snake is looking for food, and execute formulas (1)-(2); Step 2: During the simulation algorithm development phase, a balance factor is introduced to balance the probability of the snake being in the fighting mode and the mating mode. When the balance factor η is greater than 0.5, the snake is in the fighting mode. When the balance factor η is less than or equal to 0.5, the snake is in the mating mode. Formulas (3)-(5) are executed. Where, X i，m (t+1) is the position of the ith male snake at the t+1th iteration, X i，f (t+1) is the position of the ith female snake at the t+1th iteration, X i，m (t), X i，f (t) are the positions of the ith male snake and the ith female snake at the tth iteration, respectively. m 、F f Respectively represent the fighting ability of male snakes and female snakes, M m 、M f They represent the mating ability of male and female snakes respectively, rand is a random number between 0 and 1, c3 is a constant set to 0.05, Q is the amount of food, X best，f is the position of the best individual in the male snake group, X best，m is the position of the best individual in the female snake group, and η is the balance factor.

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