Rotary table tracking control method for group targets

Abstract

The invention provides a group target-oriented turntable tracking control method, which belongs to the technical field of high-precision turntable control, is realized based on a turntable tracking system, and comprises a servo control module, a turntable and a configuration identification module, the configuration recognition module is used for calculating the miss distance of the main node unmanned aerial vehicle and transmitting an error between the miss distance and a tracking point expected value to the servo control module; and the servo control module calculates the motion speed of the rotary table and controls the rotary table to rotate by using a PI control algorithm according to the error, and the PI control algorithm comprises the following steps: according to the error value, performing segmented adjustment on a proportional parameter and an integral parameter of a rotary table speed ring PI controller. According to the invention, the segmented PI control algorithm is used for segmented adjustment, and the improved particle swarm optimization algorithm is used for optimizing and setting the proportional parameter and the integral parameter, so that the complexity of a manual trial and error method is eliminated, and the unmanned aerial vehicle at the main node of the group target can be stably tracked with the optimal performance under the condition of ensuring that the target is not lost.

Description

Technical Field

[0001] The invention belongs to the technical field of high-precision turntable control, and more specifically, relates to a turntable tracking control method oriented to group targets. Background Art

[0002] With the rapid development and application of large-scale UAV formation flight technology, anti-UAV swarm technology is also developing rapidly. In anti-UAV swarm technology, it is particularly important to identify the UAV swarm configuration and effectively strike the identified master node UAV. Before effectively striking the master node UAV, it is necessary to ensure that the master node UAV can be stably tracked. High-precision and stable tracking of the master node involves high-precision control of the turntable, which is often called follow-up control.

[0003] The control input of a follow-up control system (also known as a servo system) is a time function with an unknown change law. The system's task is to make the controlled quantity of the control object change according to the same law and keep the error with the input signal within a specified range. The servo system is generally a closed-loop control system with negative feedback. The error between the input signal and the feedback signal is used as the input of the controller. The control goal is to make the error approach zero as quickly as possible. As a typical servo system, the turntable is often used to track a moving target. The control quantity includes the position and speed of the turntable. In the tracking control of the turntable servo system, commonly used control methods include: PID control, which can achieve good results under low tracking performance indicators, but the selection of PID control parameters often relies on the engineering experience of the commissioning personnel and needs to be obtained through multiple trials; adaptive control, which can tolerate the uncertainty of the system model and improve the robustness of the system, but the control accuracy is greatly affected by the measurement accuracy; fuzzy control, which is a nonlinear system with strong robustness and anti-interference ability, but because it requires the differential signal of the error, it is highly sensitive to the input signal, and the slightest carelessness will lead to instability or uncontrollable situations; optimal control, also known as Bang-Bang control, can complete the control of the system in the fastest time or with the least energy, but this control has the phenomenon of high-frequency jitter.

[0004] In addition, for group target tracking, it is not only necessary to consider the turntable tracking performance, but also the reliable and accurate recognition of the main node by the recognition system. If the turntable's early tracking speed is too fast and the target movement between two consecutive frames is too large, the recognition system will think that there are two targets and will not transmit the miss distance to the servo control board, that is, the target is lost. Therefore, the parameters of the controller should also ensure that the turntable movement speed will not cause the target to be lost. Therefore, for the turntable for group target tracking, how to use the optimal control so that the turntable can stably track the target as quickly as possible and the tracking process will not cause the target to be lost is an urgent problem to be solved. Summary of the invention

[0005] The object of the present invention is to provide a turntable tracking control method for group targets, which can stably track the group target master node drone with optimal performance while ensuring that the target is not lost.

[0006] To achieve the above-mentioned purpose, the first aspect of the present invention provides a turntable tracking control method for group targets, wherein the control method is implemented based on a turntable tracking system, wherein the turntable tracking system includes a servo control module, a turntable and a configuration recognition module; the configuration recognition module is used to calculate the miss distance of the master node UAV, and transmit the error between the miss distance and the expected value of the tracking point to the servo control module, wherein the miss distance is the deviation between the turntable position and the target position; the servo control module calculates the movement speed of the turntable according to the error and uses a PI control algorithm to control the rotation of the turntable, and the turntable position is obtained after the turntable speed is integrated; the PI control algorithm includes the following steps:

[0007] According to the error value, the proportional parameter and the integral parameter of the turntable speed loop PI controller are adjusted in sections.

[0008] Furthermore, the segmented adjustment is performed in three stages:

[0009] According to the size of the error value, a threshold value A and a threshold value B are set, and the threshold value A is smaller than the threshold value B; when the error value is greater than the threshold value B, the first group of PI controller parameters K is used. P1 , K I1 When the error value is between threshold A and threshold B, the second set of PI controller parameters K is used P2 , K I2 ; When the error value is less than the threshold value A, the third set of PI controller parameters K is used P3 , K I3 ; The control rate and parameter adjustment formula of the controller are as follows:

[0010] u(t)=K P e(t)+K I ∫e(t)dt

[0011]

[0012] Wherein, e(t) is the error value, and u(t) is the output of the controller.

[0013] Furthermore, the PI control algorithm also includes the following steps: optimizing the proportional parameters and the integral parameters of each segment using a particle swarm algorithm to obtain the optimal proportional parameters and the optimal integral parameters of each segment.

[0014] Furthermore, the particle swarm algorithm is an improved particle swarm algorithm, and the improved particle swarm algorithm improves the inertia weight and the learning factor.

[0015] Furthermore, the improved inertia weight calculation formula is:

[0016]

[0017] The improved learning factor calculation formula is:

[0018]

[0019] In the formula, w min ,w max are the minimum and maximum values ​​of inertia weight respectively; c min ,c max are the minimum and maximum values ​​of the learning factor, η iter ,η max are the current number of iterations and the maximum number of iterations respectively.

[0020] Furthermore, in the improved particle swarm algorithm, the fitness function used is:

[0021]

[0022] Wherein, e(t) is the error value, t is the simulation time; when the target is lost, loseflag is 1, otherwise it is 0.

[0023] Furthermore, the optimization of the proportional parameter and the integral parameter of each segment by using the particle swarm algorithm comprises the following steps:

[0024] S1. Parameter initialization: determine the size of the particle swarm n and the maximum inertia weight w max and the minimum value w min , maximum number of iterations η max , the maximum and minimum values ​​of the learning factor c max ,c min The value of the minimum fitness f min , upper and lower bounds on particle positions and velocities;

[0025] S2, substituting the 6 proportion parameters and the integral parameters to be optimized in each segment as the positions of particles in the particle swarm algorithm into the particle swarm algorithm to perform an iterative optimization;

[0026] S3, substituting the parameters obtained by the iterative optimization into the simulation model for simulation, and calculating the fitness of each particle according to the fitness function;

[0027] S4, calculate the individual extreme value pbest and the global extreme value gbest of the particle swarm through the fitness minimum function;

[0028] S5, determine whether gbest satisfies greater than f min If it is satisfied, then go to step S6, if not, then jump to step S8; f min is the minimum fitness;

[0029] S6. Determine the current number of iterations η iter Is it greater than η? max If satisfied, proceed to step S8, otherwise proceed to step S7;

[0030] S7, according to the improved calculation formula of inertia weight and learning factor, update the inertia weight w, learning factors c1, c2, and calculate the position of the next generation of particle swarm according to the particle swarm algorithm, increase the number of iterations by 1, and return to step S2;

[0031] S8: The algorithm ends and gbest is output, that is, the optimal proportion parameter and optimal integral parameter of each segment are obtained.

[0032] A second aspect of the present invention provides an electronic device, comprising: at least one processor, and a memory and a communication interface communicatively connected to the at least one processor;

[0033] The memory stores instructions that can be executed by the at least one processor, and the at least one processor is used to execute the instructions in the memory so that when the instructions are executed, the electronic device executes any one of the methods described above.

[0034] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes any one of the methods described above.

[0035] Compared with the prior art, the present invention has the following technical effects:

[0036] A group target-oriented servo turntable tracking control method of the present invention utilizes a segmented PI control algorithm to segmentally adjust the proportional parameter and the integral parameter of the turntable speed loop PI controller, so as to track the target as quickly as possible without losing the target.

[0037] Furthermore, a servo turntable tracking control method for group targets of the present invention utilizes a particle swarm algorithm to optimize and adjust the proportional parameters and integral parameters of the turntable speed loop PI controller, eliminating the tediousness of manual trial and error, and can stably track the group target main node UAV with optimal performance while ensuring that the target is not lost.

[0038] Furthermore, a servo turntable tracking control method for group targets of the present invention utilizes an improved particle swarm algorithm to optimize and adjust the proportional parameters and integral parameters of the turntable velocity loop PI controller, which can accelerate the convergence speed of the algorithm during the optimization process, overcome the defect that the traditional particle swarm algorithm is prone to fall into the local optimum, obtain the global optimal solution, and improve the accuracy of the turntable tracking the group target main node drone.

[0039] Since an electronic device and a computer-readable storage medium of the present invention store a group-target servo turntable tracking control method of the present invention, they also have the advantages of the control method of the present invention, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 A schematic diagram of a group target oriented servo turntable tracking control method provided by an embodiment of the present invention;

[0042] Figure 2 An algorithm flow chart for optimizing the proportional parameters and integral parameters of each segment of the turntable speed loop PI controller using an improved particle swarm algorithm provided in an embodiment of the present invention;

[0043] Figure 3 A comparison diagram of the change process of the fitness value with the number of iterations under different control methods provided in Example 1 of the present invention;

[0044] Figure 4 A comparison diagram of the target position and the turntable position under different control methods provided in Example 1 of the present invention;

[0045] Figure 5 A comparison diagram of different tracking errors under different control methods provided in Example 1 of the present invention;

[0046] Figure 6 A comparison diagram of the optimal individual fitness value changing with the number of iterations under different control methods provided in Example 2 of the present invention;

[0047] Figure 7 This is a comparison diagram of the target position and the turntable position under different control methods provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0048] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0050] The terms "first" and "second" are used only for descriptive purposes to distinguish objects such as substances from each other, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. For example, without departing from the scope of the embodiments of the present invention, the first XX may also be referred to as the second XX, and similarly, the second XX may also be referred to as the first XX. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0051] In the field of servo control, the PID control algorithm is currently the most widely used control algorithm. PID control allows for more aggressive proportions and integrals, and has no overshoot. However, the disadvantage of PID control is that it increases the complexity of the system and amplifies the noise, and the differential mainly acts on the mid-frequency band of the system to improve the dynamic performance of the system. In the embodiment of the present invention, the turntable is mainly used for tracking drones, and the angular velocity of the turntable tracking caused by the movement of the drone is also low. Therefore, it is optimal to use PI control in the turntable tracking control method for group targets in the embodiment of the present invention.

[0052] In order to solve the problem of how to use optimal control to enable the turntable to stably track the target as quickly as possible without causing the target to be lost during the tracking process, an embodiment of the present invention provides a turntable tracking control method for group targets. The method is improved on the traditional PI control. First, to ensure that the target is not lost, the turntable speed cannot be too fast during the entire tracking process. Because the error is large in the early stage of tracking, a smaller proportional parameter should be selected. Because the error becomes very small in the later stage of tracking, a larger proportional parameter can be selected to ensure faster convergence of the system. In addition, in order to ensure the smooth switching of the proportional parameter, a buffer zone should be set, that is, in the middle stage of tracking, the proportional parameter should be the intermediate value of the proportional parameter in the early and late stages of tracking. In addition, the integral parameter should also take different values ​​before, during, and after tracking. Only in this way can the target be tracked as quickly as possible without losing the target.

[0053] Based on this, the embodiment of the present invention adopts segmented PI control, that is, different areas are set according to the size of the error, and the control parameters of different areas are also different. Although segmented PI can solve the problem of group target turntable tracking, the segmented PI control technology requires more parameters to be adjusted, and its control parameters will change from the original two to six. How to select the control parameters to achieve the best is also a big problem. If it is constantly tried and tested, the efficiency is low and repeated debugging is required, it will be extremely cumbersome. Therefore, in the embodiment of the present invention, a particle swarm algorithm is used to optimize the controller parameters. It is found that the algorithm converges slowly during the optimization process of the traditional particle swarm algorithm and is prone to fall into local optimality.

[0054] Therefore, further, the embodiment of the present invention also improves the traditional particle swarm algorithm and proposes an improved particle swarm algorithm. The improved particle swarm algorithm proposed in the embodiment of the present invention is used to adjust the controller parameters, which can well meet the system requirements and eliminate the tediousness of manual trial and error, thereby further improving the accuracy of turntable tracking control.

[0055] The embodiment of the present invention uses an improved particle swarm algorithm to intelligently adjust the control parameters of the segmented PI controller, and also verifies the effectiveness and correctness of the control method of the present invention through comparative experiments, that is, it can stably track the group target master node UAV with optimal performance while ensuring that the target is not lost.

[0056] The first aspect of the embodiment of the present invention provides a turntable tracking control method for group targets, which is implemented based on a turntable tracking system, and the turntable tracking system includes a servo control module, a turntable and a configuration recognition module. The configuration recognition module is used to calculate the miss amount of the master node UAV, and the error obtained by subtracting the miss amount from the tracking point expected value (the tracking point expected value is usually 0, that is, the center of the field of view; but sometimes in order to calibrate the coaxiality of the tracking center and the laser center, the tracking point expected value may not be 0) is transmitted to the servo control module, and the miss amount is the deviation between the turntable position and the target position, and the target position is the position of the master node UAV; the servo control module calculates the motion speed of the turntable (that is, the theoretical speed of the turntable) according to the error and using the PI control algorithm, and sends the calculated motion speed to the turntable to control the turntable rotation, and the turntable position is obtained after the turntable speed is integrated, and the turntable rotation makes the master node UAV always remain near the tracking point expected value.

[0057] In the embodiment of the present invention, the calculation of the turntable motion speed by the servo control module is the focus of the present invention, because in order to track the target as fast as possible without losing the target, the PI control algorithm includes the following steps:

[0058] According to the error between the miss distance and the expected value of the tracking point, the proportional parameter and the integral parameter of the turntable speed loop PI controller are adjusted in sections.

[0059] Specifically, in the embodiment of the present invention, the proportional parameter K of the turntable speed loop PI controller is P , integration parameter K I The adjustment is carried out in three stages, that is, two thresholds are set according to the size of the error. The two thresholds are set as threshold A and threshold B, and threshold A is less than threshold B. When the error is greater than threshold B, the first group of PI controller parameters K is used. P1 , K I1 ; When the error is between threshold A and threshold B, the second set of PI controller parameters K is used P2 , K I2 ; When the error is less than the threshold A, the third set of PI controller parameters K is used P3 , K I3 The control rate and controller parameter adjustment formula are as follows:

[0060] u(t)=K P e(t)+K I ∫e(t)dt

[0061]

[0062] Among them, e(t) is the error value signal of the system, that is, the error value between the miss distance and the expected value of the tracking point, e(t) is the input of the controller, and u(t) is the output of the controller.

[0063] In the implementation of the present invention, the setting of threshold A is to ensure the index requirements of tracking accuracy, and the value of threshold B is appropriately expanded on the basis of threshold A to ensure that the target is not lost during tracking. For example, the laser field of view is 0.008 rad, and to ensure that the tracked drone is always in the laser field of view, that is, the center to the edge is 0.004 rad, then in the embodiment of the present invention, 0.003 rad can be selected as threshold A and 0.008 rad can be selected as threshold B.

[0064] Among the three groups of PI controller parameters in the embodiment of the present invention, the first group of controller parameters is to ensure that the target is not lost; the second group of controller parameters is to ensure transition stability while the target is not lost; and the third group of controller parameters is to ensure tracking accuracy index requirements.

[0065] In order to facilitate the setting of controller parameters, the embodiment of the present invention uses the particle swarm algorithm to optimize the proportional parameters and integral parameters of each segment to obtain the optimal proportional parameters and optimal integral parameters of each segment. However, the embodiment of the present invention found that it is easy to fall into the local optimum after optimizing the three groups of controller parameters using the particle swarm algorithm. The particle swarm algorithm is then improved and the controller parameters are optimized using the improved particle swarm algorithm. The principle diagram is shown in FIG. Figure 1The particle swarm algorithm is first introduced below, and then the improvement of the particle swarm algorithm by the present invention is introduced.

[0066] Particle Swarm Optimization (PSO) is a swarm intelligence algorithm for solving optimization problems proposed by Eberhart and Kennedy in 1995 based on the study of bird flock foraging behavior. The position of each particle in PSO represents a solution to the optimization problem. The particle will calculate its own speed at the next moment based on the position pbest closest to the food reached during the algorithm optimization process and the position gbest closest to the food of all particles in the current iteration, thereby updating its position p. Assuming there are j particles in a certain area, the speed update formula and position update formula of the i-th particle in the k+1th iteration are as follows:

[0067]

[0068] Where: w is the inertia weight, which indicates the influence of the previous iteration speed on the current speed. In traditional PSO, this value is generally 0.9; c1, c2 are learning factors, usually 2; r1, r2 are random numbers between 0 and 1.

[0069] The embodiment of the present invention is an improved particle swarm optimization (Improved Particle Swarm Optimization, IPSO) proposed based on the particle swarm algorithm, which improves the inertia weight and learning factor in the particle swarm algorithm. In traditional PSO, the inertia weight is a constant value, but the value of the inertia weight has different effects in different stages of PSO search. PSO should take a larger inertia weight at the beginning of the iteration, and the rate of change of the inertia weight should also be relatively slow to ensure that the algorithm can fully achieve global search and avoid falling into local optimality. In the later stage, a smaller inertia weight should be taken, and the rate of change should be relatively fast, so that the algorithm can converge faster. In addition, for the learning factor, c1 represents the current particle's ability to learn the optimal value of its previous iteration process, referred to as self-learning ability. In the early stage of the iteration, in order to avoid falling into the local optimum, c1 should also take a larger value and a slower rate of change. In the later stage, it should take a smaller value and a faster rate of change to make the algorithm converge faster; c2 represents the current particle's ability to learn the optimal value of all particles in the current iteration process, referred to as social learning ability. Social learning ability and self-learning ability are complementary. In the early stage of the algorithm iteration, it should have weaker social ability, that is, c2 takes a smaller value and the value changes slowly to ensure the diversity of particles in the early stage of the iteration, so as to avoid the algorithm falling into the local optimum. In the later stage of the iteration, the particles should have stronger social learning ability, that is, c2 takes a larger value and the value changes faster to ensure the rapid convergence of the algorithm.

[0070] Based on the above analysis, the embodiment of the present invention changes the inertia weight and learning factor in the traditional particle swarm algorithm from a constant value to a real-time adjustment based on the characteristics of the spur line function, that is, the function value is large and changes slowly at the highest point, and the function value is small and changes quickly at the inflection point, so as to find a better solution for the optimization problem. The specific formula for adjusting the inertia weight and learning factor is as follows:

[0071]

[0072] c2(iter)=c min +c max -c1(iter)

[0073]

[0074] Where: w min ,w max are the minimum and maximum values ​​of inertia weight respectively; c min ,c max are the minimum and maximum values ​​of the learning factor respectively; η iter ,η max are the current number of iterations and the maximum number of iterations respectively.

[0075] In addition, in the improved particle swarm algorithm of the embodiment of the present invention, the position of each particle i represents a controller parameter, represents the parameter value of the i-th group of controllers in the K+1th iteration, Represents the change value of the parameters of the i-th group of controllers in the K+1-th iteration. represents the optimal parameter value of the i-th group of controller parameters in the first k iterations, represents the optimal set of parameter values ​​among all groups of controller parameters in the kth iteration. The size of the particle swarm n represents the number of groups of controller parameters.

[0076] Next, the controller parameters of the IPSO segmented PI are optimized so that the system can ensure that the target is not lost and has the best turntable tracking performance. The specific process is as follows:

[0077] First, a simulation model is established using simulation software, which can be any one of matlab, scilab, dSPACE, etc., and a target loss judgment module is added. The module input is the miss amount of the previous frame and the miss amount of the current frame, and the output is the target loss flag loseflag. If the target is lost, then loseflag is 1, otherwise it is 0. Based on this, an embodiment of the present invention also proposes a fitness function of a particle swarm algorithm, which includes two parts. The first part selects the ITAE value of the system, and the second part is loseflag. In this way, the tracking performance of the system can be optimized as much as possible under the premise of ensuring that the target is not lost. The specific formula of the fitness function is as follows:

[0078]

[0079] Where e(t) is the error value signal in the control system, and t is the simulation time.

[0080] The specific algorithm flow chart of IPSO optimized segmented PI controller is as follows: Figure 2 As shown, the specific steps of the algorithm are as follows:

[0081] S1. Parameter initialization: determine the size of the particle swarm n and the maximum inertia weight w max and the minimum value w min , maximum number of iterations η max , the maximum and minimum values ​​of the learning factor c max ,c min The value of, the minimum fitness f min , upper and lower bounds on particle positions and velocities;

[0082] S2, substituting the six to-be-optimized proportional parameters and integral parameters of each segment as the positions of particles in the particle swarm algorithm into the particle swarm algorithm to perform an iterative optimization search;

[0083] S3, substituting the parameters obtained by an iterative optimization into the simulation model for simulation, and calculating the fitness of each particle according to the fitness function;

[0084] S4, calculate the individual extreme value pbest and the global extreme value gbest of the particle swarm through the fitness minimum function;

[0085] S5, determine whether gbest satisfies greater than f min If it is satisfied, then go to step S6, if not, then jump to step S8; f min is the minimum fitness;

[0086] S6. Determine the current number of iterations η iter Is it greater than η? max If satisfied, proceed to step S8, otherwise proceed to step S7;

[0087] S7, according to the improved calculation formula of inertia weight and learning factor, update the inertia weight w, learning factors c1, c2, and calculate the position of the next generation of particle swarm according to the particle swarm algorithm, increase the number of iterations by 1, and return to step S2;

[0088] S8: The algorithm ends and gbest is output, that is, the optimal proportion parameter and optimal integral parameter of each segment are obtained.

[0089] The control method of the embodiment of the present invention adopts a segmented PI controller, and uses an improved particle swarm algorithm to intelligently adjust the three groups of parameters of the PI controller. The control method of the embodiment of the present invention not only solves the problem of tracking the main node drone of the group target, but also simplifies the adjustment of the controller parameters. The control method of the embodiment of the present invention not only saves the tediousness of manual adjustment of parameters, but also makes it easier to find the controller parameters with the best performance by using the control method of the embodiment of the present invention. In addition, the improved particle swarm algorithm proposed in the embodiment of the present invention can not only be applied to the intelligent adjustment of controller parameters, but also can be applied to optimization problems such as path planning, neural networks, and combinatorial optimization.

[0090] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, comprising: at least one processor, and a memory and a communication interface communicatively connected to the at least one processor;

[0091] The memory stores instructions that can be executed by at least one processor, and the at least one processor is used to execute the instructions in the memory so that when the instructions are executed, the electronic device executes the control method described above.

[0092] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the control method described above.

[0093] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on a computer or other programmable device to produce a computer-implemented process, so that the instructions executed on a computer or other programmable device provide steps for implementing the functions specified in the flow chart.

[0094] The effectiveness and correctness of the group-target-oriented turntable tracking control method of the embodiment of the present invention are verified in combination with specific embodiments below.

[0095] Example 1

[0096] Embodiment 1 of the present invention provides a servo turntable tracking control method for group targets. In order to verify the effectiveness of the method, a turntable simulation model is first established, and then two controllers are designed, one is a segmented PI controller based on an improved particle swarm algorithm (IPSO-PI), and the other is a segmented PI controller based on a particle swarm algorithm (PSO-PI). The control effects of IPSO-PI and PSO-PI controllers are compared through simulation experiments. It is found that IPSO is more capable of finding the optimal controller parameters than PSO, and IPSO-PI is more capable of improving the turntable tracking accuracy while ensuring that the target is not lost. The specific parameters are as follows:

[0097] 1. The turntable is equivalent to a delay link with a delay of 10ms, and the simulation time t is 10s.

[0098] 2. Six parameters K of the segmented PI controller P1 ,K P2 ,K P3 ,K I1 ,K I2 ,K I3 The upper and lower bounds of u are b =[300,300,300,100,100,100],l b =[0,0,0,0,0,0], thresholds A and B are 0.003 and 0.008 respectively.

[0099] 3. The size of the particle swarm is 20, the maximum number of iterations is 60, the maximum value of the inertia weight in the IPSO algorithm is 0.9, the minimum value is 0.4, the maximum value of the learning factor is 3, and the minimum value is 1. In the PSO algorithm, the inertia weight is 0.9, and the learning factor is 2.

[0100] 4. Since the turntable tracking performance must consider both the tracking performance of static targets and the tracking performance of moving targets, the target is set to simulate static targets within the first 5 seconds of the simulation and to simulate moving targets within the last 5 seconds. The target position tpos formula is as follows:

[0101]

[0102] Wherein, cpos represents the angle of rotation of the turntable required to move the edge of the visible light field of view to the center of the field of view, and cpos is taken to be equal to 1.52 degrees.

[0103] IPSO and PSO are used to optimize the controller parameters of the piecewise PI, and the changes of IPSO and PSO fitness with the number of iterations are shown as follows: Figure 3 As shown, according to the fitness function, the smaller the fitness function value (ie, fitness value), the better the performance of finding the parameters. Figure 3 It can be clearly seen that compared with PSO, IPSO algorithm has stronger search ability and can find more optimal solutions for optimization problems. The comparison of optimized parameters and minimum fitness minfit is shown in Table 1.

[0104] Table 1 Six parameters and minimum fitness obtained after IPSO and PSO optimization

[0105]

[0106] IPSO-PI and PSO-PI control the turntable to track the master node UAV. The comparison between the turntable position and the target position is shown in the figure below. Figure 4 As shown, the tracking error is Figure 5 As shown. Figure 4 , Figure 5 It can be concluded that compared with PSO-PI, the IPSO-PI proposed in the embodiment of the present invention has a much better tracking effect on the turntable and can achieve zero static error tracking of the target, which further illustrates the superiority of the control method of the embodiment of the present invention.

[0107] Example 2

[0108] When tracking the master node drone, if the target is not the master node drone, it is necessary to switch to the real master node drone. This is equivalent to a step signal. In order to test this capability, a step signal is set and the above IPSO-PI and PSO-PI are used to optimize the controller parameters. The parameters are consistent with Example 1. The optimized controller parameters and minimum fitness function are shown in Table 2.

[0109] Table 2 Six parameters and minimum fitness obtained after IPSO and PSO optimization

[0110]

[0111] Fitness function comparison chart Figure 6 As shown in the figure, the comparison between the target position and the turntable position is as follows Figure 7 As shown. Figure 6 , Figure 7 It can be seen that compared with PSO, IPSO can find a smaller fitness and the parameters found have the best performance. That is, compared with PSO-PI, IPSO-PI has better control performance and stable target switching.

[0112] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A turntable tracking control method for group targets, characterized in that: The control method is implemented based on a turntable tracking system, which includes a servo control module, a turntable and a configuration recognition module; the configuration recognition module is used to calculate the miss distance of the master node UAV, and transmit the error between the miss distance and the expected value of the tracking point to the servo control module, and the miss distance is the deviation between the turntable position and the target position; the servo control module calculates the movement speed of the turntable according to the error and controls the rotation of the turntable by using a PI control algorithm, and the turntable position is obtained after the turntable speed is integrated; the PI control algorithm includes the following steps: According to the error value, the proportional parameter and the integral parameter of the turntable speed loop PI controller are adjusted in sections.

2. A group-target-oriented turntable tracking control method as claimed in claim 1, characterized in that: The segmented regulation is carried out in three stages: threshold A and threshold B are set according to the size of the error value, and threshold A is smaller than threshold B; when the error value is larger than threshold B, the first group of PI controller parameters K is used. P1 , K I1 When the error value is between threshold A and threshold B, the second set of PI controller parameters K is used P2 , K I2 ; When the error value is less than the threshold value A, the third set of PI controller parameters K is used P3 , K I3 ; The control rate and parameter adjustment formula of the controller are as follows: u(t)=K P e(t)+K I ∫e(t)dt Wherein, e(t) is the error value and u(t) is the output of the controller.

3. A group-target-oriented turntable tracking control method as claimed in claim 1, characterized in that: The PI control algorithm also includes the following steps: optimizing the proportional parameters and the integral parameters of each segment using a particle swarm algorithm to obtain the optimal proportional parameters and the optimal integral parameters of each segment.

4. A group-target-oriented turntable tracking control method as claimed in claim 3, characterized in that: The particle swarm algorithm is an improved particle swarm algorithm, and the improved particle swarm algorithm improves the inertia weight and the learning factor.

5. A group-target-oriented turntable tracking control method as claimed in claim 4, characterized in that: The improved inertia weight calculation formula is: The improved learning factor calculation formula is: c2(iter)=c min +c max -c1(iter), In the formula, w min ,w max are the minimum and maximum values ​​of inertia weight respectively; c min ,c max are the minimum and maximum values ​​of the learning factor, η iter ,η max are the current number of iterations and the maximum number of iterations respectively.

6. A group-target-oriented turntable tracking control method as claimed in claim 5, characterized in that: In the improved particle swarm algorithm, the fitness function used is: Wherein, e(t) is the error value, t is the simulation time; when the target is lost, loseflag is 1, otherwise it is 0.

7. A group-target-oriented turntable tracking control method as claimed in claim 6, characterized in that: The optimization of the proportion parameter and the integral parameter of each segment by using the particle swarm algorithm comprises the following steps: S1. Parameter initialization: determine the size of the particle swarm n and the maximum inertia weight w max and the minimum value w min , maximum number of iterations η max , the maximum and minimum values ​​of the learning factor c max ,c min The value of, the minimum fitness f min , upper and lower bounds on particle positions and velocities; S2, substituting the 6 proportion parameters and the integral parameters to be optimized in each segment as the positions of particles in the particle swarm algorithm into the particle swarm algorithm to perform an iterative optimization; S3, substituting the parameters obtained by the iterative optimization into the simulation model for simulation, and calculating the fitness of each particle according to the fitness function; S4, calculate the individual extreme value pbest and the global extreme value gbest of the particle swarm through the fitness minimum function; S5, determine whether gbest satisfies greater than f min If it is satisfied, then go to step S6, if not, then jump to step S8; f min is the minimum fitness; S6. Determine the current number of iterations η iter Is it greater than η? max If satisfied, proceed to step S8, otherwise proceed to step S7; S7, according to the improved calculation formula of inertia weight and learning factor, update the inertia weight w, learning factors c1, c2, and calculate the position of the next generation of particle swarm according to the particle swarm algorithm, increase the number of iterations by 1, and return to step S2; S8: The algorithm ends and gbest is output, that is, the optimal proportion parameter and optimal integral parameter of each segment are obtained.

8. An electronic device, characterized in that: include: at least one processor, and a memory and a communication interface communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor is used to execute the instructions in the memory so that when executing the instructions, the electronic device executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the method according to any one of claims 1 to 7.

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