PID (Proportion Integration Differentiation) controller parameter adjusting method and device of photoelectric tracking system

Abstract

The invention relates to the technical field of photoelectric tracking and stability control, and provides a PID controller parameter adjusting method and device of a photoelectric tracking system, and the method comprises the steps: initializing the photoelectric tracking system; acquiring a PVZ feedback signal; calculating a fitness value of a particle swarm by using the obtained PVZ feedback signal; the parameters of the PID controller are dynamically adjusted through an HDPSO algorithm, and the position of each particle represents the parameters of one set of PID controller; calculating and outputting a control signal by using the PID parameter obtained after adjustment; the output control signal is used for updating the state of the photoelectric control system; judging whether any one of the termination conditions is met or not: reaching the maximum number of iterations; carrying out fitness value convergence; the target is tracked successfully; a manual stop instruction is provided; if yes, ending; and if not, skipping to the step of acquiring the PVZ feedback signal. According to the scheme, PID controller parameters are automatically adjusted based on PVZ feedback and hybrid dynamic particle swarm optimization, and the performance of the photoelectric tracking system is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of optoelectronic tracking and stabilization control, and particularly relates to a PID controller parameter adjustment method and device for an optoelectronic tracking system. BACKGROUND

[0002] As an important target detection and tracking technology, optoelectronic tracking systems have been widely used in military, civilian, industrial automation and other fields. The core function is to obtain the position information of the target in real time through optoelectronic sensors (such as radar, infrared camera, visible light camera, etc.), and to drive the actuator (such as motor) to adjust the attitude of the optoelectronic device through the control algorithm to realize high-precision tracking of the target.

[0003] Traditional PID (proportion integration differentiation) control method: through the adjustment of three parameters of proportion, integration and differentiation, the feedback control of the system is adjusted, which is difficult to adapt to the rapid movement of the target and the change of the complex environment, and the fixed parameter setting limits the dynamic response ability of the system.

[0004] Specifically, in the traditional PID control method, the PID parameters usually need to be set by manual adjustment or experience-based method. This process generally includes the following steps: 1. Initial parameter setting: set the initial PID parameters Kp, Ki and Kd according to experience or literature recommendation; 2. System debugging: run the PID controller in the actual system, observe the response behavior of the system; according to the response of the system, manually adjust the PID parameters; 3. Parameter optimization: through repeated experiments, gradually adjust the PID parameters to optimize the performance of the system. This process usually needs multiple experiments, which is time-consuming and depends on the experience of the operator; 4. Real-time adjustment: in actual operation, if the dynamic characteristics of the target change (such as rapid movement or environmental change), the PID parameters need to be adjusted in real time. This usually requires the operator to manually adjust the parameters according to the real-time feedback of the system, which is poor in real-time performance.

[0005] However, with the increase of target maneuverability and environmental complexity, the performance of traditional optoelectronic tracking systems in response speed, tracking accuracy and anti-interference ability gradually exposes the shortcomings. SUMMARY

[0006] To solve the above problems, the present disclosure provides a PID controller parameter adjustment method and device for an optoelectronic tracking system, which is used to improve the performance of the optoelectronic tracking system.

[0007] In a first aspect, a method for adjusting parameters of a PID controller of an optoelectronic tracking system is provided, comprising: initializing the optoelectronic tracking system, including initializing parameters of the PID controller and initializing a particle swarm, wherein a position of each particle in the particle swarm represents a set of parameters of the PID controller; obtaining a PVZ feedback signal, wherein PVZ represents position, velocity and acceleration; calculating a fitness value of the particle swarm using the obtained PVZ feedback signal; dynamically adjusting parameters of the PID controller using an HDPSO algorithm, wherein a position of each particle represents a set of parameters of the PID controller; calculating and outputting a control signal using the adjusted PID parameters; updating a state of the optoelectronic tracking system using the output control signal; determining whether any of a plurality of termination conditions is met, wherein the termination conditions include reaching a maximum number of iterations, the fitness value converging, the target being successfully tracked, and a manual stop instruction being received; if yes, ending; and if no, jumping to the step of obtaining the PVZ feedback signal.

[0008] Further, a fitness function used to calculate the fitness value represents a weighted sum of PVZ tracking errors.

[0009] Further, obtaining the PVZ feedback signal comprises: obtaining a target position; calculating the PVZ feedback signal of the target based on a constant acceleration model.

[0010] Further, calculating the PVZ feedback signal of the target based on the constant acceleration model comprises: calculating the PVZ feedback signal of the target using the constant acceleration model in combination with Kalman filtering.

[0011] Further, dynamically adjusting parameters of the PID controller using the HDPSO algorithm comprises: updating an individual optimal position and a global optimal position according to fitness values of the particles; updating a velocity and a position of each particle according to the individual optimal position and the global optimal position, while introducing an acceleration coefficient in a position updating formula.

[0012] In a second aspect, a device for adjusting parameters of a PID controller of an optoelectronic tracking system is provided, comprising: an initialization unit configured to initialize the optoelectronic tracking system, including initializing parameters of the PID controller and initializing a particle swarm, wherein a position of each particle in the particle swarm represents a set of parameters of the PID controller; The acquisition unit is configured to acquire a PVZ feedback signal, wherein PVZ represents position, velocity and acceleration. The fitness value calculation unit is configured to calculate the fitness value of the particle swarm by using the acquired PVZ feedback signal. The parameter adjustment unit is configured to dynamically adjust the parameters of the PID controller by using the HDPSO algorithm. The control signal output unit is configured to calculate and output a control signal by using the adjusted PID parameters. The update unit is configured to update the state of the photoelectric control system by using the output control signal. The judgment unit is configured to judge whether any of the termination conditions is met, wherein the termination conditions include: reaching a maximum number of iterations; the fitness value converging; the target being successfully tracked; having a manual stop instruction; if yes, ending; and if no, triggering the acquisition unit.

[0013] Further, the acquisition unit is specifically configured to acquire a target position; and calculate the PVZ feedback signal of the target based on a constant acceleration model.

[0014] Further, the parameter adjustment unit is specifically configured to update the individual optimal position and the global optimal position according to the fitness value of the particle; and update the velocity and position of each particle according to the individual optimal position and the global optimal position, while introducing an acceleration coefficient in the position update formula.

[0015] In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus. The memory is configured to store a computer program. The processor is configured to execute the program stored on the memory to implement the method described above.

[0016] In a fourth aspect, a computer storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method described above.

[0017] Compared with the prior art, the present disclosure has the following advantages: The automatic adjustment of the PID controller parameters based on the PVZ feedback and the hybrid dynamic particle swarm optimization can adapt to the fast movement and dynamic changes of the target, improve the response speed, anti-interference ability and tracking precision of the photoelectric tracking system, and reduce the tracking error. Meanwhile, compared with the traditional PID control method, the PID controller parameter adjustment method of the present disclosure automatically performs initialization of the particle swarm, evaluation of the fitness, updating of the individual optimal and global optimal positions, updating of the particle speed and position, dynamic adjustment of the PID parameters and iterative optimization, simplifies the parameter setting process, reduces the time cost of system debugging and optimization, and improves the practicability and operability of the system.

[0018] Other features and advantages of the present disclosure will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present disclosure. The objects and other advantages of the present disclosure can be achieved and obtained by the structures indicated in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0020] Figure 1 A PID controller parameter adjustment method flow diagram of a photoelectric tracking system according to an embodiment of the present disclosure is shown; Figure 2 A PID controller parameter adjustment device diagram of a photoelectric tracking system according to an embodiment of the present disclosure is shown; Figure 3 An electronic device block diagram according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0021] The present disclosure proposes a PID control method based on PVZ (Position, Velocity, Acceleration) feedback and hybrid dynamic particle swarm optimization (HDPSO, Hybrid Dynamic Particle Swarm Optimization) for improving the performance of photoelectric tracking systems. By obtaining the PVZ information of the target in real time and dynamically adjusting the parameters of the PID controller combined with the HDPSO algorithm, the anti-interference ability and tracking accuracy of the system can be significantly improved. Compared with the traditional PID control method, the present disclosure adapts to the rapid movement of the target and the change of the complex environment through dynamic parameter adjustment, simplifies the parameter setting process, and reduces the difficulty of system debugging and optimization. In addition, the PVZ feedback signal is filtered by a Kalman filter, further enhancing the stability and reliability of the system. The present invention is applicable to various photoelectric tracking scenarios, including military, civilian and industrial automation fields, and can achieve high-precision and high-robustness target tracking.

[0022] To make the purposes, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present disclosure.

[0023] Figure 1 A flowchart of a PID controller parameter adjustment method of a photoelectric tracking system according to an embodiment of the present disclosure is shown, as shown in Figure 1 The PID controller parameter adjustment method of the photoelectric tracking system of the present embodiment comprises: Step 101: initializing the photoelectric tracking system; Specifically, it includes initializing the parameters of the PID controller and initializing the particle swarm, wherein the position of each particle in the particle swarm represents a set of parameters of the PID controller. Initializing the PID controller includes initializing the parameters of the PID controller .

[0024] Initializing the particle swarm includes: 1) initializing the position of the particle swarm: The initial position of the particle is generated in the search space in a random way to ensure that the particles are uniformly distributed in the search space. The specific formula is: =L

[0025] where L and are the lower bounds of the jth dimension, and rand() is a random number in the interval [0, 1].

[0026] 2) Initialize the velocity of the particle swarm: The initial velocity of the particle is generated randomly, but needs to be controlled within a reasonable range to avoid the particle moving too fast or too slow in the initial stage. The specific formula is: = L factor where factor is a proportion less than 1, such as 0.1 or 0.2.

[0027] 3) Initialize the size of the particle swarm, the maximum number of iterations, the range of inertia weight ( and ), the learning factor ( and ), the mutation coefficient, and the acceleration coefficient.

[0028] Step 102: Obtain the PVZ feedback signal: Step 102-1: Target position acquisition.

[0029] Obtain the geographic position information of the target in real time through the sensor system (such as radar, infrared, visible light camera, GPS, sensor fusion, etc.), including the longitude, latitude, and height of the target.

[0030] Convert the position information of the target into the coordinate system relative to the photoelectric device. That is, the local coordinate system (x, y, z) with the photoelectric device as the origin.

[0031] Step 102-2: Target motion state calculation.

[0032] Use the constant acceleration (CA) model combined with Kalman filtering to calculate the position (P), velocity (V), and acceleration (Z) of the target, remove noise interference, and ensure the accuracy and stability of the feedback signal.

[0033] Step 102-3: Output the PVZ feedback signal: X=[ , , ].

[0034] Step 103: Calculate the fitness value of the particle swarm using the obtained PVZ feedback signal.

[0035] Use the PVZ parameters to calculate the fitness function to evaluate the performance of the PID parameters. The fitness function can be defined as the weighted sum of the tracking error: Fitness f =

[0036] wherein, , and are weight coefficients corresponding to the position, velocity and acceleration errors respectively, denotes the desired position at time t, denotes the desired velocity at time t, denotes the desired acceleration at time t.

[0037] Step 104: dynamically adjust the parameters of the PID controller using the HDPSO algorithm.

[0038] wherein the position of each particle represents a set of parameters of the PID controller.

[0039] Specifically comprising the following steps: Step 104-1: update the individual optimal position and the global optimal position according to the fitness value of the particle.

[0040] 1) update of the individual optimal position For each particle i, calculate the fitness value f( (t)) of the current position (t) of the particle. If the fitness value of the current position is better than the fitness value of the individual optimal position

[0041] of the particle, update the individual optimal position, that is: If f( (t)) < f( ), then = (t) 2) update of the global optimal position Iterate through the individual optimal positions of all particles to find the individual optimal position with the optimal fitness value. If the fitness value of the individual optimal position of a certain particle is better than the fitness value of the current global optimal position , update the global optimal position, that is:

[0042] If f( ) < f( ), then =

[0043] ​​Step 104-2: Update the velocity and position of each particle based on the individual optimal position and global optimal position, while introducing an acceleration coefficient in the position update formula to improve the convergence speed of the algorithm: Velocity update formula:

[0044] Where: vi(t+1): Velocity of the i-th particle at time t+1. w: Inertia weight, controls the inertia part of the particle. vi(t): Velocity of the i-th particle at time t. ci: Individual learning factor, controls the movement of the particle towards its own optimal position. ri: Random number in the interval [0, 1], increases the randomness of the search. pi(t): Individual optimal position of the i-th particle. xi(t): Position of the i-th particle at time t. ci: Social learning factor, controls the movement of the particle towards the global optimal position. ri: Random number in the interval [0, 1], increases the randomness of the search. pg: Global optimal position.

[0045] Position update formula:

[0046] Where: xi(t+1): Position of the i-th particle at time t+1. xi(t): Position of the i-th particle at time t. xi(t+1): Position of the i-th particle at time t+1.

[0047] Step 104-3: Dynamically adjust the PID parameters.

[0048] In the PID parameter optimization, the position of each particle xi(t) represents a set of PID parameters ].

[0049] Through the above update process, the PID parameters are dynamically adjusted to optimize the performance of the control system.

[0050] Step 105: Calculate and output the control signal using the adjusted PID parameters; Step 106: Update the state of the photoelectric control system using the output control signal; Step 107: Determine whether any of the termination conditions are met, including: reaching the maximum number of iterations; fitness value converges; target has been successfully tracked; manual stop command; if yes, end; if no, jump to step 102.

[0051] In the scheme of the embodiments of the present disclosure, the dynamic characteristics of the target are comprehensively captured by combining the PVZ feedback signal. The Kalman filter is used to filter the PVZ feedback signal, remove noise interference, and ensure the accuracy and stability of the feedback signal. The HDPSO algorithm is adopted to dynamically adjust the PID parameters. The PID parameters are optimized through the dynamic inertia weight, adaptive mutation mechanism and acceleration coefficient, the global search and local search capabilities are balanced, the local optimum is avoided, and the convergence speed and global optimization capability of the algorithm are improved. By dynamically adjusting the PID parameters, the dynamic changes of the target are quickly responded, and the dynamic response speed of the system is improved. The real-time optimization mechanism ensures that the system always maintains the optimal control performance in complex environments, especially in terms of fast-moving target tracking and anti-interference capability.

[0052] Based on the above method, the present disclosure further provides a PID parameter adjustment device of an optoelectronic tracking system corresponding to the above method, Figure 2 A block diagram of a PID parameter adjustment device of an optoelectronic tracking system according to an embodiment of the present disclosure is shown as follows: Figure 2 As shown in the figure, the PID parameter adjustment device of the optoelectronic tracking system comprises: An initialization unit 21 is configured to initialize the optoelectronic tracking system, including: initializing the parameters of the PID controller, initializing the particle swarm, wherein the position of each particle in the particle swarm represents a set of parameters of the PID controller; An acquisition unit 22 is configured to acquire a PVZ feedback signal, wherein PVZ represents position, velocity and acceleration; A fitness value calculation unit 23 is configured to calculate the fitness value of the particle swarm by using the acquired PVZ feedback signal; A parameter adjustment unit 24 is configured to dynamically adjust the parameters of the PID controller by using the HDPSO algorithm; A control signal output unit is configured to calculate and output a control signal by using the adjusted PID parameters; An update unit 25 is configured to update the state of the optoelectronic control system by using the output control signal; A judgment unit 26 is configured to judge whether any of the termination conditions is met, wherein the termination conditions include: the maximum number of iterations is reached; the fitness value converges; the target has been successfully tracked; there is a manual stop instruction; if yes, the process is ended; if no, the acquisition unit is triggered.

[0053] Further, the fitness function used to calculate the fitness value represents the weighted sum of the PVZ tracking error.

[0054] Further, the acquisition unit 22 is specifically configured to calculate the PVZ feedback signal of the target based on a constant acceleration model.

[0055] Further, the parameter adjusting unit 24 is specifically configured to update the individual optimal position and the global optimal position according to the fitness value of the particle; and update the speed and position of each particle according to the individual optimal position and the global optimal position, while introducing an acceleration coefficient in the position updating formula.

[0056] Based on the same inventive concept as disclosed above, the present disclosure also provides an electronic device, a block diagram of which is shown in Figure 3 The electronic device of the present disclosure comprises at least one processor and at least one memory electrically connected, wherein the memory is electrically connected with the processor, and the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method as described above.

[0057] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between the lines, and the indirect connection mode can also be applicable to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0058] Based on the same inventive concept, the present disclosure also provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0059] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for adjusting the parameters of a PID controller of a photoelectric tracking system, characterized in that: include: Initializing the photoelectric tracking system, including: initializing parameters of a PID controller, initializing a particle swarm, wherein the position of each particle in the particle swarm represents a set of parameters of the PID controller; Get PVZ feedback signal, where PVZ is position, velocity and acceleration; The fitness value of the particle swarm is calculated using the obtained PVZ feedback signal; The HDPSO algorithm is used to dynamically adjust the parameters of the PID controller, where the position of each particle represents a set of PID controller parameters; Calculate and output the control signal using the adjusted PID parameters; Use the output control signal to update the state of the photoelectric control system; Determine whether any of the termination conditions are met, where the termination conditions include: reaching the maximum number of iterations; fitness value convergence; the target has been successfully tracked; there is a manual stop command; If yes, then end; if no, then jump to the step of obtaining the PVZ feedback signal.

2. The method according to claim 1, characterized in that The fitness function used to calculate the fitness value represents the weighted sum of the PVZ tracking errors.

3. The method according to claim 1, characterized in that Get PVZ feedback signals, including: Get the target location; The PVZ feedback signal of the target is calculated based on the constant acceleration model.

4. The method according to claim 3, characterized in that The target's PVZ feedback signal is calculated based on the constant acceleration model, including: The constant acceleration model is used in combination with Kalman filtering to calculate the target's PVZ feedback signal.

5. The method according to claim 1, wherein The HDPSO algorithm is used to dynamically adjust the parameters of the PID controller, including: According to the fitness value of the particle, update the individual optimal position and the global optimal position; According to the individual optimal position and the global optimal position, the speed and position of each particle are updated, and the acceleration coefficient is introduced into the position update formula.

6. A PID parameter adjustment device for a photoelectric tracking system, characterized in that: include: An initialization unit is used to initialize the photoelectric tracking system, including: initializing the parameters of the PID controller and initializing the particle swarm, wherein the position of each particle in the particle swarm represents a set of parameters of the PID controller; An acquisition unit is used to obtain a PVZ feedback signal, where PVZ represents position, velocity, and acceleration; A fitness value calculation unit is used to calculate the fitness value of the particle swarm using the obtained PVZ feedback signal; A parameter adjustment unit, used to dynamically adjust the parameters of the PID controller using the HDPSO algorithm; A control signal output unit, used to calculate and output a control signal using the adjusted PID parameters; An updating unit, configured to update the state of the photoelectric control system using the output control signal; The judgment unit is used to judge whether any of the termination conditions are met, where the termination conditions include: reaching the maximum number of iterations; the fitness value converges; the target has been successfully tracked; there is a manual stop instruction; if so, the process ends; if not, the acquisition unit is triggered.

7. The device according to claim 6, characterized in that The acquisition unit is specifically used to obtain the target position and calculate the target's PVZ feedback signal based on the constant acceleration model.

8. The device according to claim 6, characterized in that The parameter adjustment unit is specifically used to update the individual optimal position and the global optimal position according to the fitness value of the particle; based on the individual optimal position and the global optimal position, the speed and position of each particle are updated, and the acceleration coefficient is introduced into the position update formula.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method according to any one of claims 1 to 5 when executing a program stored in a memory.

10. A computer storage medium, characterized in that The computer storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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