Target projectile launching device attitude control method

Abstract

The invention relates to a target projectile launching device attitude control method, which is based on integral separation PID (Proportion Integration Differentiation) target projectile launching device attitude control, and comprises the following steps: 1) setting the number of particle swarms and the initial position and speed of each particle; 2) for each particle, calculating the corresponding performance of the control system according to the current position of the particle; 3) for each particle, comparing the current fitness with the historical optimal fitness, and updating the optimal position of the individual; 4) finding out the particle with the best fitness from all the particles, and calling the position of the particle as a global optimal position; 5) updating the speed and the position of the particle based on the individual optimal position and the global optimal position of each particle; according to the method, the attitude control which is rapid, stable and high in precision is realized, and the working efficiency of preparation work before launching is improved. The method comprises the following steps of: (1) carrying out the following steps of: (2) repeatedly executing the steps (2) to (5) until a preset stop condition is met, and (7) outputting a PID parameter corresponding to a global optimal position as a final optimal solution when the stop condition is met.

Description

Technical Field

[0001] This invention relates to the field of target missile launching technology, specifically to an attitude control method for a target missile launching device. This enables the rapid and adaptable execution of launching missions. Background Technology

[0002] The launching device is a crucial component of the target missile or weapon system, and its performance directly impacts the weapon system's combat effectiveness. Due to the uncertain timing of exercise missions and short launch windows, the time allotted for the launching device to adjust is limited, requiring it to rapidly adjust the target missile's initial attitude. Traditional launching device attitude control suffers from insufficient attitude control stability, poor dynamic performance, and slow angle adjustment speed, severely restricting the weapon system's rapid response capability.

[0003] Traditional launching devices suffer from poor ground adaptability and slow angle adjustment speed, severely restricting the rapid response capability of weapon systems. Traditional PID control methods cannot meet the overshoot-free requirement of target missile launching device attitude control. Overshoot is a method in traditional control methods that introduces integrals to eliminate static errors. However, due to the large error during startup, integrals accumulate in a short period of time, causing the control quantity to exceed the limit, resulting in a large overshoot in the control system, and even causing oscillations. Summary of the Invention

[0004] Based on the above-mentioned technical problems, the present invention proposes to provide a launching device based on a hydraulic system to solve the problems of insufficient attitude control stability and slow angle adjustment speed of traditional launching devices. The present invention enables rapid adjustment of the launching angle under complex terrain conditions.

[0005] To address the aforementioned technical problems, one objective of this invention is to provide an attitude control method for a target missile launching device. This method is based on integral separation PID control of the target missile launching device, and adaptively optimizes the PID parameters based on particle swarm optimization. The specific steps are as follows: S1: Set the number of particles and the initial position and velocity of each particle. The initial position of the particle represents the initial guess of the PID controller parameters, while the velocity determines the search direction of the particle. The initialization of position and velocity is random to ensure the diversity of the particle population, thereby covering a wider search space. S2: Calculate fitness: For each particle, calculate the corresponding control system performance based on its current position (i.e., the current value of the PID parameters); S3: Update individual optimal position: For each particle, update its individual optimal position by comparing its current fitness with its historical best fitness; S4: Update the global optimal position: Among all particles, find the particle with the best fitness, and its position is called the global optimal position; S5: Update velocity and position: Update the velocity and position of each particle based on its individual optimal position and global optimal position; S6: Determine the stopping condition: Repeat S2 to S5 until the predetermined stopping condition is met; S7: Output optimal PID parameters: When the stopping condition is met, output the PID parameters corresponding to the globally optimal position as the final optimal solution. This parameter configuration should provide the best performance in the control system.

[0006] Furthermore, the S2 fitness calculation specifically involves: calculating the sum of squared errors between the output and the reference input by simulating the response of the control system, using this sum as a measure of controller performance; and using the fitness function to evaluate the particle's performance, i.e.: ; Where ERROR represents the sum of squared errors, y represents the input, and r identifies the reference input.

[0007] Furthermore, the optimal position of an individual in S3 refers to the position where the particle finds the best performance during the search process, that is, the performance index of the control system is optimal at this position.

[0008] Furthermore, the globally optimal position in S4 refers to the best-performing solution in the entire particle swarm, representing the optimal PID parameter configuration in the current iteration.

[0009] Furthermore, the S5 update of speed and position specifically involves: the particle speed update taking into account the influence of its historical best position (individual best position) and global best position, thereby adjusting the particle's search direction and pace; the position update adjusting the particle's position according to the new speed, so as to further explore potential high-quality solutions in the solution space.

[0010] Furthermore, the S6 stopping condition is reaching the maximum number of iterations, the fitness function value reaching a certain threshold, or other termination criteria in the optimization process. Setting the stopping condition helps control the computational load of the algorithm and ensure the convergence of the optimization.

[0011] The above-described one or more technical solutions of the present invention have at least one or more of the following technical effects: This invention is applicable to adaptive attitude control under different environmental conditions, meets the overshoot-free requirement of attitude control, and can achieve fast, stable and high-precision attitude control, thereby improving the efficiency of pre-launch preparation work.

[0012] This invention relates to attitude control of a target launcher based on integral-separated PID control, which can overcome problems such as overshoot and oscillation in attitude control. Error thresholds for the pitch and yaw angles of the target launcher are set. When the current attitude deviates significantly from the set thresholds, integral control is canceled; when the current attitude is within the set thresholds, integral control is introduced, thereby eliminating static errors and improving control accuracy.

[0013] This invention is based on particle swarm optimization (PSO) to optimize PID parameters. It avoids the need for manual parameter adjustment relying on experience and trial-and-error processes. The control method adaptively tunes the PID parameters to the controlled model, enabling rapid parameter selection and reducing adjustment time. PSO optimizes by simulating a swarm of particles searching for the optimal solution in the solution space. Each particle represents a possible solution and is guided by both the swarm optimal solution and the individual optimal solution during its search. Attached Figure Description

[0014] Figure 1 Simulation structure diagram of the particle swarm optimization tuning PID control method; Figure 2 : Pitch angle output value; Figure 3 : Deflection angle output value. Detailed Implementation

[0015] The control scheme of this invention combines the traditional PID control method with particle swarm optimization to achieve rapid adjustment of the pitch and yaw angles of the launching device.

[0016] Attitude control of a target launcher based on integral-separated PID can overcome problems such as overshoot and oscillation in attitude control. Error thresholds for the pitch and yaw angles of the target launcher are set. When the current attitude deviates significantly from the set thresholds, integral control is canceled; when the current attitude is within the set thresholds, integral control is introduced, thereby eliminating static errors and improving control accuracy.

[0017] Particle swarm optimization (PSO) avoids the need for manual parameter tuning based on experience and trial-and-error processes. The control method adaptively tunes the PID parameters to the controlled model. PSO optimizes by simulating a swarm of particles searching for the optimal solution in the solution space. Each particle represents a possible solution and is guided by both the swarm optimal solution and the individual optimal solution during its search.

[0018] The effectiveness and reliability of the control method were verified through simulation experiments. The control method can quickly and smoothly adjust the attitude to the set value, meeting the overshoot-free requirement of the attitude control of the target missile launcher.

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments obtained. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0020] In the attitude control of the elevation and yaw angles of a target missile launcher, the control performance requirements are quite stringent. This invention introduces a particle swarm optimization algorithm into the traditional PID control method to quickly solve for the control parameters, thereby achieving rapid angle control. The particle swarm optimization algorithm optimizes the target missile by simulating a swarm of particles searching for the optimal solution in the solution space.

[0021] The control algorithm of this invention includes the following key steps: (1) First, set the number of particles and the initial position and velocity of each particle. The initial position of the particle represents the initial guess of the PID controller parameters, while the velocity determines the search direction of the particle. The initialization of position and velocity is random to ensure the diversity of the particle swarm, thereby covering a wider search space.

[0022] (2) Calculate fitness: For each particle, calculate the corresponding control system performance based on its current position (i.e., the current value of the PID parameters). The fitness function is used to evaluate the performance of the particle, as shown in Equation (1). By simulating the response of the control system, the sum of squared errors between the output and the reference input is calculated as a measure of the controller performance.

[0023] (1); Where ERROR represents the sum of squared errors, y represents the input, and r identifies the reference input.

[0024] (3) Update the individual optimal position: For each particle, its current fitness is compared with its historical best fitness, and its individual optimal position is updated. The individual optimal position refers to the position where the particle finds the best performance during the search process, that is, the performance index of the control system is optimal at this position.

[0025] (4) Update the global optimal position: Among all particles, find the particle with the best fitness, and its position is called the global optimal position. The global optimal position refers to the best-performing solution in the entire particle swarm, representing the optimal PID parameter configuration in the current iteration.

[0026] (5) Update velocity and position: Based on the individual best position and the global best position of each particle, update the velocity and position of the particle. The velocity update of the particle takes into account the influence of its historical best position (individual best position) and the global best position, thereby adjusting the particle's search direction and pace. The position update adjusts the particle's position according to the new velocity, so as to further explore potential high-quality solutions in the solution space.

[0027] (6) Determine the stopping condition: Repeat steps 2 to 5 until the predetermined stopping condition is met. The stopping condition can be reaching the maximum number of iterations, the fitness function value reaching a certain threshold, or other termination criteria in the optimization process. Setting the stopping condition helps control the computational load of the algorithm and ensure the convergence of the optimization.

[0028] (7) Output optimal PID parameters: When the stopping condition is met, output the PID parameters corresponding to the globally optimal position as the final optimal solution. This parameter configuration should provide the best performance in the control system.

[0029] Simulations were performed in MATLAB Simulink to compare the attitude control performance of the target missile launcher with traditional PID control, integral separation PID control, and particle swarm optimization PID control, thereby verifying the achievement of zero overshoot and tracking of preset parameters. Figure 1 The diagram shown is a simulation structure diagram of the particle swarm optimization tuning PID control method.

[0030] from Figure 2 As can be seen, the particle swarm optimization tuning PID control method reaches the set value in 8 minutes with no overshoot, thus quickly and smoothly achieving pitch angle attitude adjustment.

[0031] from Figure 3 As can be seen, the particle swarm optimization tuning PID control method reaches the set value in 3 minutes with no overshoot, thus achieving yaw angle attitude adjustment.

[0032] This invention has undergone flight testing of the target missile system. The launch system operates normally in low-temperature, high-wind, and sandy ground environments, and the target missile system can take off from the launch device according to the predetermined launch attitude. Addressing the problems of insufficient attitude control stability, poor dynamic performance, and slow adjustment speed in existing target missile launch devices, this invention employs hydraulic drive and angle feedback control technology. This control method achieves rapid adjustment of the launch attitude, which is of great significance for the rapid launch of target missiles and weapons.

Claims

1. A method of attitude control of a target projectile launcher, characterized by, The control method is based on the target missile launcher posture control of integral separation PID, and PID parameters are adaptively optimized based on particle swarm, and the specific steps are as follows: S1: set the number of particle swarms and the initial position and speed of each particle, the initial position of the particle represents the initial guess of the PID controller parameter, and the speed determines the search direction of the particle, and the initialization of the position and the speed is random, so as to ensure the diversity of the particle swarm and cover a wider search space; S2: calculate the fitness: for each particle, calculate the corresponding control system performance according to its current position (i.e. the current value of the PID parameter); S3: update the individual optimal position: for each particle, compare its current fitness with the historical optimal fitness, and update its individual optimal position; S4: update the global optimal position: among all particles, find the particle with the best fitness, and its position is called the global optimal position; S5: update the speed and position: based on the individual optimal position and the global optimal position of each particle, update the speed and position of the particle; S6: judge the stop condition: repeat S2 to S5 until the predetermined stop condition is met; S7: output the optimal PID parameter: when the stop condition is met, output the PID parameter corresponding to the global optimal position as the final optimal solution, and the parameter configuration should be able to provide the best performance in the control system.

2. The method according to claim 1, wherein The S2 calculates the fitness, specifically: by simulating the response of the control system, the sum of error squares between the output and the reference input is calculated as the measure of the controller performance, and the fitness function is used to evaluate the performance of the particle, that is: ； Where ERROR represents the sum of error squares, y represents the input, and r represents the reference input.

3. The method of claim 1, wherein: The individual optimal position in S3 refers to the position found by the particle in the search process, that is, in this position, the performance index of the control system is optimal.

4. The method of claim 1, wherein: The global optimal position in S4 refers to the best solution in the whole particle swarm, which represents the optimal PID parameter configuration in the current iteration.

5. The method of claim 1, wherein: The S5 updates the speed and position, specifically: the speed update of the particle considers the influence of its historical best position (individual optimal position) and global best position, so as to adjust the search direction and pace of the particle; the position update adjusts the position of the particle according to the new speed, so as to further explore the potential high-quality solution in the solution space.

6. The method of claim 1, wherein: The S6 stop condition is to reach the maximum number of iterations, the fitness function value reaches a certain threshold, or other termination criteria in the optimization process, and the setting of the stop condition helps to control the calculation amount of the algorithm and ensure the convergence of the optimization.

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