Unmanned aerial vehicle airport liftable platform optimization control method

Abstract

The invention discloses an unmanned aerial vehicle airport liftable platform optimization control method, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing an unmanned aerial vehicle airport liftable platform control system model; step 2, improving the salmons swarm optimization algorithm, wherein the specific improvement strategy is as follows: D1, improving a mathematical model of an exploration stage of the salmons swarm optimization algorithm by using a triangular walk strategy; d2, using a self-adaptive Levy flight migration strategy to improve a mathematical model in a development stage of a samson swarm optimization algorithm; step 3, optimizing PID controller parameters of the unmanned aerial vehicle airport liftable platform control system model by using an improved saltcat swarm optimization algorithm to obtain three optimal parameters Kp, Ki and Kd; and 4, applying the three optimal parameters Kp, Ki and Kd obtained in the step 3 to an unmanned aerial vehicle airport liftable platform control system model, and optimizing the control effect of the unmanned aerial vehicle airport liftable platform.

Description

Technical Field

[0001] The invention belongs to the technical field of PID control optimization, and in particular relates to an optimization control method for a liftable platform at an unmanned aerial vehicle airport. Background Art

[0002] The UAV airport lift platform is a lifting device specially designed for UAVs. It is usually used for automatic take-off and landing, storage, maintenance and charging of UAVs. It can adapt to the take-off and landing needs of UAVs by raising and lowering the platform, improving the automation and efficiency of operations. This lifting platform has broad application prospects in UAV airports, UAV distribution centers and some special application scenarios (such as offshore and mountainous environments). The use of PID (proportional-integral-differential) control technology to control the height of the UAV lifting platform can achieve smooth and precise lifting of the platform to meet the take-off and landing needs of the UAV. PID control achieves dynamic matching of the target height and the current height by continuously adjusting the input of the platform motor or hydraulic system.

[0003] PID control technology is a classic feedback control method. It adjusts the system error through three control functions: proportional (P), integral (I) and differential (D) to achieve precise and stable control effects. Proportional control quickly responds to the error size, integral control accumulates the error to eliminate steady-state deviation, and differential control predicts and reduces overshoot and oscillation based on the error change trend. PID control is widely used in industrial automation, drone control and other fields. With its simplicity, stability and high precision, it effectively improves the system's response speed and control accuracy.

[0004] The Sand Cat Swarm Optimization (SCSO) algorithm is a metaheuristic algorithm inspired by the foraging and attacking behaviors of sand cats. The algorithm simulates the detection ability of sand cats to low-frequency noise, updates the positions of sand cats in the exploration and exploitation phases to find the optimal solution. After initialization, the sand cats use randomness to expand the search range in the prey search phase, and focus more on optimizing the current solution in the prey attack phase. Summary of the invention

[0005] The present invention aims to improve the standard sand cat swarm optimization algorithm, improve its optimization accuracy and speed, enhance the adaptability and overall effect of the algorithm, and apply the improved sand cat swarm optimization algorithm to the PID controller parameter setting of the control system of the unmanned aerial vehicle airport lift platform, which significantly improves the parameter setting efficiency and system response speed, reduces the overshoot amplitude in the control process, and thus enhances the stability of the system. The proposed unmanned aerial vehicle airport lift platform optimization control method effectively solves the problems of easy fluctuation and low precision of the lift platform control, and achieves a more efficient and precise control effect.

[0006] In order to achieve the above object, the present invention adopts the following technical solution.

[0007] A method for optimizing and controlling a liftable platform of an unmanned aerial vehicle airport, the specific steps are as follows.

[0008] Step 1: Construct a control system model of the UAV airport liftable platform. The model includes a height signal receiving module, an error calculation module, a PID controller, an improved sand cat swarm optimization algorithm module, a lifting motor module, and a height detection module.

[0009] Step 2: Improve the sand cat group optimization algorithm. The specific improvement strategy is: D1. A mathematical model for improving the exploration phase of the sand cat swarm optimization algorithm using a triangle walk strategy; D2. A mathematical model for the development phase of improving the sand cat swarm optimization algorithm using an adaptive Levy flying walk strategy.

[0010] Step 3: Use the improved sand cat swarm optimization algorithm to optimize the PID controller parameters of the UAV airport liftable platform control system model and obtain the optimal Kp, Ki, and Kd parameters.

[0011] Step 4: Use the optimal parameters Kp, Ki, and Kd obtained in step 3 to the control system model of the UAV airport lift platform to optimize the control effect of the UAV airport lift platform.

[0012] Furthermore, in the step 1, in the constructed drone airport lift platform control system model, the altitude signal receiving module receives the signal of the drone requesting to land, records the landing starting position, altitude, and current environmental conditions of the drone, and calculates the target altitude that the platform needs to lift based on the recorded parameters; the error calculation module calculates the error e(t) between the target altitude and the actual altitude detected by the altitude detection module; the PID controller outputs the control quantity u(t) through the error e(t); the improved sand cat group optimization algorithm module adjusts the parameters of the PID controller through the improved sand cat group optimization algorithm to obtain an optimal set of parameter values; the lifting motor module controls the lifting of the platform based on the control quantity u(t) obtained by PID control; the altitude detection module detects the platform altitude in real time; the calculation formula of u(t) is shown in formula (1); and the calculation formula of the target altitude is shown in formula (2); In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; h target =h1+(h2-h1)+h env (2); In formula (2), h target represents the target height, h2 represents the predicted landing height of the drone, h1 represents the current platform height, and h envIndicates the environmental correction amount.

[0013] Furthermore, in D1, a triangle wandering strategy is used to improve the mathematical model of the exploration phase of the sand cat swarm optimization algorithm. The strategy is to simulate the sand cats performing triangle wandering around the prey while approaching it. The specific mathematical model is shown in formula (3): Pos_new=pos1(iter)+rand()·P (3); In formula (3), Pos_new represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current number of iterations, rand() represents a random number between [0,1], and P represents the control variable of the triangle walk as shown in formula (4): P = L1 2 +L2 2 -2·L1·L2·cos(β) (4); In formula (4), L1 represents the difference between the optimal position and the current position in the population, L2 = rand()·L1, β = 2π·rand().

[0014] Furthermore, the triangle walk strategy can avoid directly approaching the target, thereby increasing the diversity of searched solutions and helping to prevent the algorithm from falling into the situation of local optimal solutions. It provides an effective path search method for the sand cat swarm optimization algorithm, enables the algorithm to converge effectively in a large-scale search space, and helps to improve the quality of the solution and optimization performance.

[0015] Furthermore, in D2, an adaptive Levy flight and wandering strategy is used to improve the mathematical model of the development stage of the sand cat swarm optimization algorithm. The strategy adaptively adjusts the step size of the Levy flight to update the population. The specific mathematical model is shown in formula (5): Pos_new=pos1(iter)+(pos1(iter)-pos2(iter))·C·Levy() (5); In formula (5), Posnew represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current iteration number, pos2(iter) represents the current position, C represents the adaptive parameter, C = 1-iter / Maxiter, Maxiter represents the maximum iteration number, and Levy() represents the Levy flight random number function.

[0016] Furthermore, the adaptive Levy flying strategy provides a unique search mode by introducing a random step size that conforms to the Levy distribution. This enables the algorithm to perform large-scale long-distance jumps to achieve global exploration, and to perform fine searches with smaller steps near the target to improve local development capabilities. The randomness and step size diversity of this strategy can effectively prevent the algorithm from falling into the local optimum, while accelerating the convergence speed, allowing the algorithm to find the optimal solution more efficiently in a complex search space.

[0017] Furthermore, in step three, the PID controller parameters of the control system model of the UAV airport liftable platform are optimized using the improved sand cat swarm optimization algorithm, and the specific steps are as follows: S1. Initialize algorithm parameters: set the maximum boundary ub, minimum boundary lb, population size N, dimension dim, and maximum number of iterations Maxiter for the sand cat swarm optimization algorithm search; S2, using the parameter value range of the PID controller as the search space of the search individual of the sand cat swarm optimization algorithm, and converting the parameter value of the PID controller as the initial position of the search individual; S3, set the objective function, calculate the fitness values ​​of individuals in the initial population, sort them, select the optimal fitness value and the optimal position, and the objective function is shown in formula (6); In formula (6), J represents the fitness value, e(t) represents the error of the control system at time t, and T represents the total operation time of the system; S4, updating the position by improving the mathematical model of the exploration phase and the development phase of the sand cat group optimization algorithm, and updating the optimal position and optimal fitness value in the population after the update is completed; S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to execute S4. If the optimal parameter value is reached, stop the optimization search.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows.

[0019] The present invention proposes an optimization control method for a liftable platform of an unmanned aerial vehicle airport. By improving the mathematical models of the exploration phase and the development phase in the mathematical model of a sand cat swarm optimization algorithm, an organic balance between global exploration and local development can be achieved through the improvement of the algorithm, the adaptability and robustness of the algorithm can be enhanced, the convergence speed and accuracy can be improved, and the risk of premature convergence can be reduced. By optimizing the parameters of a PID controller in the control system model of a liftable platform of an unmanned aerial vehicle airport by using the improved sand cat swarm optimization algorithm, the adaptability and robustness of the system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1The present invention is a flow chart of an optimization control method for a liftable platform of a UAV airport.

[0021] Figure 2 This is the model diagram of the control system of the UAV airport liftable platform.

[0022] Figure 3 This is a comparison chart of fitness values ​​during the optimization process of the standard sand cat swarm optimization algorithm and the improved sand cat swarm optimization algorithm.

[0023] Figure 4 Comparison chart of PID controller response optimized by standard sand cat swarm optimization algorithm and improved sand cat swarm optimization algorithm. Specific implementation methods

[0024] The present invention provides a technical solution: a method for optimizing the control of a liftable platform at an unmanned aerial vehicle airport, the specific steps of which are as follows: Figure 1 shown.

[0025] Step 1: Construct the control system model of the UAV airport liftable platform. The model includes the height signal receiving module, error calculation module, PID controller, improved sand cat group optimization algorithm module, lifting motor module, and height detection module. Figure 2 shown.

[0026] Furthermore, in the step 1, in the constructed drone airport lift platform control system model, the altitude signal receiving module receives the signal of the drone requesting to land, records the landing starting position, altitude, and current environmental conditions of the drone, and calculates the target altitude that the platform needs to lift based on the recorded parameters; the error calculation module calculates the error e(t) between the target altitude and the actual altitude detected by the altitude detection module; the PID controller outputs the control quantity u(t) through the error e(t); the improved sand cat group optimization algorithm module adjusts the parameters of the PID controller through the improved sand cat group optimization algorithm to obtain an optimal set of parameter values; the lifting motor module controls the lifting of the platform based on the control quantity u(t) obtained by PID control; the altitude detection module detects the platform altitude in real time; the calculation formula of u(t) is shown in formula (1); and the calculation formula of the target altitude is shown in formula (2); In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; h target =h1+(h2-h1)+h env (2); In formula (2), h target represents the target height, h2 represents the predicted landing height of the drone, h1 represents the current platform height, and h env Indicates the environmental correction amount.

[0027] Step 2: Improve the sand cat group optimization algorithm. The specific improvement strategy is: D1. A mathematical model for improving the exploration phase of the sand cat swarm optimization algorithm using a triangle walk strategy; D2. A mathematical model for the development phase of improving the sand cat swarm optimization algorithm using an adaptive Levy flying walk strategy.

[0028] Furthermore, in D1, a triangle wandering strategy is used to improve the mathematical model of the exploration phase of the sand cat swarm optimization algorithm. The strategy is to simulate the sand cats performing triangle wandering around the prey while approaching it. The specific mathematical model is shown in formula (3): Pos_new=pos1(iter)+rand()·P (3); In formula (3), Pos_new represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current number of iterations, rand() represents a random number between [0,1], and P represents the control variable of the triangle walk as shown in formula (4): P=L1 2 +L2 2 -2·L1·L2·cos(β) (4); In formula (4), L1 represents the difference between the optimal position and the current position in the population, L2 = rand()·L1, β = 2π·rand().

[0029] Furthermore, in D2, an adaptive Levy flight and wandering strategy is used to improve the mathematical model of the development stage of the sand cat swarm optimization algorithm. The strategy adaptively adjusts the step size of the Levy flight to update the population. The specific mathematical model is shown in formula (5): Pos_new=pos1(iter)+(pos1(iter)-pos2(iter))·C·Levy() (5); In formula (5), Posnew represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current iteration number, pos2(iter) represents the current position, C represents the adaptive parameter, C = 1-iter / Maxiter, Maxiter represents the maximum iteration number, and Levy() represents the Levy flight random number function.

[0030] Step 3: Use the improved sand cat swarm optimization algorithm to optimize the PID controller parameters of the UAV airport liftable platform control system model and obtain the optimal Kp, Ki, and Kd parameters.

[0031] Furthermore, in step three, the PID controller parameters of the control system model of the UAV airport liftable platform are optimized using the improved sand cat swarm optimization algorithm, and the specific steps are as follows: S1. Initialize algorithm parameters: set the maximum boundary ub, minimum boundary lb, population size N, dimension dim, and maximum number of iterations Maxiter for the sand cat swarm optimization algorithm search; S2, using the parameter value range of the PID controller as the search space of the search individual of the sand cat swarm optimization algorithm, and converting the parameter value of the PID controller as the initial position of the search individual; S3, set the objective function, calculate the fitness values ​​of individuals in the initial population, sort them, select the optimal fitness value and the optimal position, and the objective function is shown in formula (6); In formula (6), J represents the fitness value, e(t) represents the error of the control system at time t, and T represents the total operation time of the system; S4, updating the position by improving the mathematical model of the exploration phase and the development phase of the sand cat group optimization algorithm, and updating the optimal position and optimal fitness value in the population after the update is completed; S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to execute S4. If the optimal parameter value is reached, stop the optimization search.

[0032] Step 4: Use the optimal parameters Kp, Ki, and Kd obtained in step 3 to the control system model of the UAV airport lift platform to optimize the control effect of the UAV airport lift platform.

[0033] Furthermore, in order to verify that the proposed optimization control method for the liftable platform of an unmanned aerial vehicle airport has better performance than other methods, the present invention uses Matlab to improve the sand cat swarm optimization algorithm, and uses the improved sand cat swarm optimization algorithm to optimize the parameters of the PID controller in the control system model of the liftable platform of the unmanned aerial vehicle airport. At the same time, the simulation software Simulink is used to construct the control system model of the liftable platform of the unmanned aerial vehicle airport. The model includes a height signal receiving module, an error calculation module, a PID controller, an improved sand cat swarm optimization algorithm module, a lifting motor module, and a height detection module. The program is run to obtain experimental results, such as Figure 3 and Figure 4 shown.

[0034] Furthermore, Figure 3This is a comparison chart of the fitness values ​​of the standard sand cat swarm optimization algorithm and the improved sand cat swarm optimization algorithm during the optimization process. It can be seen from the figure that the improved sand cat swarm optimization algorithm has a faster convergence speed than the standard sand cat swarm optimization algorithm. The improved sand cat swarm optimization algorithm quickly drops to a lower value in the early iterations during the optimization process. The improved sand cat swarm optimization algorithm is relatively stable after converging to a steady state, showing good stability. Figure 4 This is a comparison chart of the responses of the PID controller optimized by the standard sand cat swarm optimization algorithm and the improved sand cat swarm optimization algorithm. The target value is set to 1 unit. It can be seen from the figure that the PID controller optimized by the improved sand cat swarm optimization algorithm has a faster response speed, rises rapidly in the early stage of the algorithm, and the time to approach the target value is significantly shorter than the response curve time of the PID control optimized by the standard sand cat swarm optimization algorithm, indicating that it exhibits higher control accuracy and control stability.

Claims

1. A method for optimizing and controlling a liftable platform at an unmanned aerial vehicle airport, characterized in that: The specific steps are as follows: Step 1: Construct a control system model of the UAV airport liftable platform, which includes a height signal receiving module, an error calculation module, a PID controller, an improved sand cat swarm optimization algorithm module, a lifting motor module, and a height detection module; Step 2: Improve the sand cat group optimization algorithm. The specific improvement strategy is: D1. A mathematical model for improving the exploration phase of the sand cat swarm optimization algorithm using a triangle walk strategy; D2, mathematical model of the development phase of improving the sand cat swarm optimization algorithm using an adaptive Levy flight-and-wander strategy; Step 3: Use the improved sand cat swarm optimization algorithm to optimize the PID controller parameters of the UAV airport liftable platform control system model to obtain the optimal Kp, Ki, and Kd parameters; Step 4: Use the optimal parameters Kp, Ki, and Kd obtained in step 3 to the control system model of the UAV airport lift platform to optimize the control effect of the UAV airport lift platform.

2. The optimization control method of the liftable platform of a drone airport according to claim 1 is characterized in that: In the step 1, in the constructed drone airport lift platform control system model, the altitude signal receiving module receives the signal of the drone requesting landing, records the landing starting position, altitude, and current environmental conditions of the drone, and calculates the target altitude that the platform needs to lift based on the recorded parameters. The error calculation module calculates the error e(t) between the target altitude and the actual altitude detected by the altitude detection module. The PID controller outputs the control quantity u(t) through the error e(t). The improved sand cat group optimization algorithm module adjusts the parameters of the PID controller through the improved sand cat group optimization algorithm to obtain an optimal set of parameter values. The lifting motor module controls the lifting of the platform based on the control quantity u(t) obtained by PID control. The altitude detection module detects the platform altitude in real time. The calculation formula of u(t) is shown in formula (1), and the calculation formula of the target altitude is shown in formula (2). In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; h target =h1+(h2-h1)+h env (2); In formula (2), h target represents the target height, h2 represents the predicted landing height of the drone, h1 represents the current platform height, and h env Indicates the environmental correction amount.

3. The optimization control method of the liftable platform of a drone airport according to claim 2 is characterized in that: In D1, a triangle wandering strategy is used to improve the mathematical model of the exploration phase of the sand cat swarm optimization algorithm. The strategy is to simulate the sand cats performing triangle wandering around the prey while approaching it. The specific mathematical model is shown in formula (3): Pos_new=posl(iter)+rand()·P (3); In formula (3), Pos_new represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current number of iterations, rand() represents a random number between [0, 1], and P represents the control variable of the triangle walk as shown in formula (4): P=L1 2 +L2 2 -2·L1·L2·cos(β) (4); In formula (4), L1 represents the difference between the optimal position and the current position in the population, L2 = rand()·L1, β = 2π·rand().

4. The method for optimizing and controlling the liftable platform of a drone airport according to claim 3 is characterized in that: In D2, an adaptive Levy flight and wandering strategy is used to improve the mathematical model of the development stage of the sand cat swarm optimization algorithm. The strategy adaptively adjusts the step size of the Levy flight to update the population. The specific mathematical model is shown in formula (5): Pos_new=pos1(iter)+(pos1(iter)-pos2(iter))·C·Levy() (5); In formula (5), Posnew represents the updated position of the individual in the population, pos1(iter) represents the optimal position in the population, iter represents the current iteration number, pos2(iter) represents the current position, C represents the adaptive parameter, C = 1-iter / Maxiter, Maxiter represents the maximum iteration number, and Levy() represents the Levy flight random number function.

5. The method for optimizing and controlling the liftable platform of a drone airport according to claim 4 is characterized in that: In the step 3, the PID controller parameters of the control system model of the UAV airport liftable platform are optimized using the improved sand cat swarm optimization algorithm, and the specific steps are as follows: S1, algorithm parameter initialization: set the maximum boundary ub, minimum boundary 1b, population size N, dimension dim, and maximum number of iterations Maxiter of the sand cat swarm optimization algorithm search; S2, using the parameter value range of the PID controller as the search space of the search individual of the sand cat swarm optimization algorithm, and converting the parameter value of the PID controller as the initial position of the search individual; S3, set the objective function, calculate the fitness values ​​of individuals in the initial population, sort them, select the optimal fitness value and the optimal position, and the objective function is shown in formula (6); In formula (6), J represents the fitness value, e(t) represents the error of the control system at time t, and T represents the total operation time of the system; S4, updating the position by improving the mathematical model of the exploration phase and the development phase of the sand cat group optimization algorithm, and updating the optimal position and optimal fitness value in the population after the update is completed; S5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to execute S4. If the optimal parameter value is reached, stop the optimization search.

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