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CN-121979195-A - Control optimization method for homeland planning and measuring carrier based on group intelligent algorithm

CN121979195ACN 121979195 ACN121979195 ACN 121979195ACN-121979195-A

Abstract

The invention discloses a control optimization method of a national land planning measurement carrier based on a group intelligent algorithm, which belongs to the technical field of group intelligent control optimization and comprises the steps of constructing a differential surveying and mapping vehicle PID control system, introducing an improved kangaroo escape optimization algorithm module, setting motor rotating speed PID control parameters in the differential surveying and mapping vehicle motor rotating speed PID control system, and setting optimal control parameters as operating parameters of a motor rotating speed PID controller module in the differential surveying and mapping vehicle motor rotating speed PID control system. The invention focuses on improving the optimizing efficiency of PID control parameters of the motor speed of the differential surveying and mapping vehicle, is based on a group intelligent algorithm, introduces an improved kangaroo escape optimizing algorithm, systematically improves the optimizing efficiency of PID parameters of the motor speed of the differential surveying and mapping vehicle based on a plurality of improved strategies, realizes more accurate control of the motor speed of the differential surveying and mapping vehicle, and reduces the influence of the fluctuation of the speed on the quality of acquired data of a follow-up homeland planning and mapping sensor.

Inventors

  • CAI MING
  • XU SHIDONG
  • XU WEIWEI

Assignees

  • 聊城市城乡规划设计研究院

Dates

Publication Date
20260505
Application Date
20251218

Claims (10)

  1. 1. A control optimization method for a homeland planning and measuring carrier based on a group intelligent algorithm is characterized by comprising the following steps: S1, constructing a PID control system of a differential surveying and mapping vehicle, wherein the PID control system comprises a motor rotating speed error calculation module, a motor rotating speed PID controller module, an improved kangaroo escape optimization algorithm module, a motor rotating speed adjustment module and a motor rotating speed monitoring module; step S2, introducing an improved kangaroo escape optimization algorithm module, wherein the improved kangaroo escape optimization algorithm comprises the following improvement strategies: S21, reversely reconnaissance an initialization strategy, wherein in an algorithm initialization stage, an initial kangaroo survival community is selected preferentially according to a survival safety index value by calculating a shadow population survival position vector and combining the shadow population survival position vector with an initial random kangaroo population survival position vector; s22, tent type biological rhythm and rhythmic gait adjustment strategies are adopted, the tent type mapping is utilized to update the biological rhythm energy factors in the environment and biological rhythm parameter updating stage, and the rhythmic gait adjustment factors changing in cosine law are introduced; s23, a quantum instinct convergence mechanism updates a kangaroo individual survival position vector by simulating quantum potential field attraction in the stage of searching a safe area; s24, a bait putting strategy is adopted, and a kangaroo individual is guided to conduct fine exploration on a global safest area by generating a sparse mask vector in the stage of searching the safe area; S25, guiding a kangaroo individual to execute Z-shaped movement by using a rotation vector in a low physical energy escape stage so as to get rid of predator tracking; S26, a group interactive learning mechanism is combined with a differential evolution strategy in the low physical energy escape stage, and kangaroo individual survival position vectors are updated by using individual difference information inside kangaroo survival communities; step S27, a super long jump mechanism updates a kangaroo individual survival position vector by utilizing a heavy tail random step length generated by a Laiweider flight strategy in a high physical ability escape stage; step S28, a survival crisis response mechanism, and restarting an evolution process by executing shadow migration operation on weak individuals and executing chaotic heuristic operation on a global safest area position vector in an algorithm stagnation stage; S3, utilizing an improved kangaroo escape optimization algorithm module to set motor speed PID control parameters in a motor speed PID control system of the differential surveying and mapping vehicle, taking an integral criterion of motor speed errors as a survival safety index value, and outputting an optimal proportion coefficient through iterative optimization Integral coefficient Differential coefficient ; And S4, setting three optimal control parameters obtained by optimizing an improved kangaroo escape optimization algorithm module as operation parameters of a motor speed PID controller module in a motor speed PID control system of the differential surveying and mapping vehicle.
  2. 2. The method for optimizing control of a land planning and measuring vehicle based on a population intelligent algorithm as set forth in claim 1, wherein in step S3, an improved Kangaroo escape optimization algorithm module is utilized to set motor speed PID control parameters in a differential surveying and mapping vehicle motor speed PID control system, and the adaptability function for evaluating the advantages and disadvantages of the control parameters is utilized The mathematical expression of (2) is: , In the middle of The weight of the error is represented by the weight of the error, Indicating that the performance index is over-weighted, Representing the weight of the control energy, Representing the time multiplied by the absolute error integration value, A portion indicating that the overshoot exceeds a preset threshold, Indicating the portion of the adjustment time exceeding the preset threshold, Representing the control energy loss index.
  3. 3. The method for controlling and optimizing a homeland planning and measuring vehicle based on a population intelligent algorithm according to claim 1, wherein the reverse scout initialization strategy in step S21 comprises the following steps: Step S211, initializing biorhythmic energy factor Generating an initial random kangaroo population survival position vector in a survival territory for random numbers in interval (0, 1) At the same time, calculating shadow population survival position vector based on survival collar boundary The calculation formula is as follows: , In the middle of Represents the survival position vector of the shadow population, Representing a survival collar border lower bound vector, Represents the upper bound vector of the living collar border, Representing the survival position vector of the initial random kangaroo population; step S212, the survival position vector of the initial random kangaroo population is calculated Vector of survival position of shadow population Merging, namely calculating survival safety index values of all the individuals after merging, and sorting the victory and the defeat according to the survival safety index values; step S213, selecting the front with the optimal survival safety index value Individual members form an initial kangaroo viable colony wherein Representing population size.
  4. 4. The method for controlling and optimizing a homeland planning and measuring vehicle based on a population intelligent algorithm according to claim 1, wherein the tent biorhythm and rhythmic gait adjustment strategy in step S22 comprises the following steps: step S221, updating biological rhythm energy factor by tent type biological rhythm mapping When (when) Less than 0.5, will Updated to When (when) When the ratio is greater than or equal to 0.5, the mixture is Updated to ; Step S222, calculating rhythmic gait adjustment factor The calculation formula is as follows: , In the middle of A rhythmic gait adjustment factor representing the current life cycle, Indicating the number of times of the current life cycle, Representing the maximum number of life cycles; Step S223, utilizing rhythmic gait adjustment factor And (3) regulating the moving stride of the kangaroo in the follow-up bait throwing action and the Z-shaped maneuver avoidance.
  5. 5. The method for controlling and optimizing a homeland planning and measuring vehicle based on a population intelligent algorithm according to claim 1, wherein the quantum instinct convergence mechanism in step S23 comprises the following steps: Step S231, calculating the average survival position vector of the current kangaroo survival community The calculation formula is as follows: , In the middle of The mean-survival-position vector is represented, Represents the scale of the living community of kangaroo, Representing the first of the communities Survival position vectors of individual kangaroos; step S232, when searching the safety area, when the generated random probability When the preset condition is met, firstly calculating the weighted attraction point vector of the individual and global safest area The calculation formula is as follows: , In the middle of Representing the weighted suction point vector of the image, Representing the random vectors generated in interval 0 to 1, Representing a global most secure region location vector, Representing the current kangaroo individual survival position vector; Then updating the survival position vector of the Kangaroo individual according to the quantum potential field attraction rule, at the moment The formula of (2) is: , In the middle of Indicating the updated kangaroo individual survival position vector, Indicating that the addition or the subtraction is selected according to the comparison result of the random number generated independently and the preset threshold value 0.5, An average survival position vector representing the current kangaroo survival community, Representing a linearly decreasing coefficient of contraction and calculating the coefficient as , Representing the random number generated between intervals 0 to 1.
  6. 6. The method for optimizing control of a homeland planning and measuring vehicle according to claim 1, wherein the bait delivery strategy in step S24 comprises: when generating random probability Executing bait throwing behavior when the triggering condition of the quantum instinct convergence mechanism is not satisfied The formula of (2) is: , In the middle of Indicating the updated kangaroo individual survival position vector, Represents the rhythmic gait adjustment factor, Representing a random vector subject to a standard normal distribution, A binary sparse mask vector is represented in which the value of each dimension is determined to be 1 or 0 based on the result of a comparison of the random number to a preset threshold.
  7. 7. The method for controlling and optimizing a homeland planning and measuring vehicle based on a population intelligent algorithm as set forth in claim 1, wherein the zigzag maneuver avoidance maneuver in step S25 includes: physical stamina of Kangaroo individuals When the Z-shaped avoidance triggering condition is met and the Z-shaped avoidance triggering condition is low, the rotation vector is utilized to update the survival position vector of the Kangaroo individual, and at the moment The formula of (2) is: , In the middle of Indicating the updated kangaroo individual survival position vector, Indicating the random steering angle of the vehicle, The rotation vector is represented, and its calculation depends on the direction vector pointing to the global optimum and the unit vector orthogonal thereto.
  8. 8. The method for controlling and optimizing a homeland planning and measuring vehicle based on a population intelligent algorithm according to claim 1, wherein the population interaction learning mechanism in step S26 comprises the following steps: Step S261, when Kangaroo individual physical stamina level When the Z-shaped avoidance triggering condition is low and is not met, two different kangaroo individual survival position vectors are randomly selected from kangaroo survival communities And ; Step S262, based on the global most secure region position vector And updating the kangaroo individual survival position vector by the differential vector of the selected individual at this time The formula of (2) is: , In the middle of The differential scaling factor is represented and takes on the value of a random number within interval 0.5,0.8.
  9. 9. The method for optimizing control of a homeland planning and measuring vehicle according to claim 1, wherein the super long jump mechanism in step S27 comprises the following steps: Step S271, kangaroo individual physical stamina level When the method is higher, random step length vectors based on Lev distribution are generated by Mantegna algorithm The calculation formula is as follows: , In the middle of Representing a random step size vector of the sequence, And Represents a random variable subject to a normal distribution, Representing the Lewy flight parameter, wherein the value of the Lewy flight parameter is 1.5; Step S272, using random step vector Updating the survival position vector of Kangaroo individual at this time The formula of (2) is: , In the middle of Step size coefficient representing linear decay along with life cycle number and calculating formula 。
  10. 10. The method for optimizing control of a homeland planning and measuring vehicle according to claim 1, wherein the survival crisis response mechanism in step S28 comprises the following steps: Step S281, monitoring survival crisis counter Counter for survival crisis Reaching the threshold of survival crisis When the algorithm is judged to be in a stagnation state; s282, executing shadow migration operation, selecting the vulnerable individuals with the worst survival safety index value of 20% in the kangaroo survival community, and for each vulnerable individual, carrying out survival position vector Updating to reverse shadow solution The calculation formula is as follows: , In the middle of Representing a survival collar border lower bound vector, Representing a survival territory boundary upper bound vector; step S283, executing chaos heuristic operation, and carrying out the position vector of the global safest area Applying chaotic disturbance to generate a heuristic position vector The calculation formula is as follows: , In the middle of The heuristic position vector is represented as such, Representing a global most secure region location vector, Representing the magnitude of the chaotic disturbance decaying with the number of life cycles, Is shown in the interval A random number generated between the two, And Upper and lower bound vectors representing a living territory, respectively; Step S284, calculating a heuristic position vector If the index is better than the survival safety index of the worst individual in the kangaroo survival community, replacing the worst individual by a heuristic position vector, and then counting the survival crisis Reset to 0.

Description

Control optimization method for homeland planning and measuring carrier based on group intelligent algorithm Technical Field The invention belongs to the technical field of intelligent group control optimization, and particularly relates to a control optimization method for a homeland planning and measuring carrier based on a group intelligent algorithm. Background The differential surveying and mapping vehicle is an important measuring carrier in a homeland planning strategy, is used as a key carrier of a mobile measuring system and an instant positioning and map building technology, and has the core task of realizing high-precision track tracking and data acquisition in a complex and changeable unknown environment. In actual working conditions, the surveying and mapping vehicle faces nonlinear and time-varying interferences such as abrupt change of road friction coefficient, uneven weight distribution of vehicle-mounted equipment, mechanical transmission clearance and the like, the interferences can lead to dynamic fluctuation of the motor rotating speed, if the interferences cannot be restrained rapidly, the accumulated error of the odometer can be directly increased, even the diagram building distortion or navigation failure can be caused when the accumulated error is serious, so that PID control is introduced, but PID control effect is very dependent on setting of control parameters, parameter setting is basically adjusted depending on experience, and setting efficiency is lower. Disclosure of Invention In order to overcome the technical problems in the background art, the invention provides a control and optimization method for a homeland planning and measuring carrier based on a group intelligent algorithm, which aims at improving the optimizing efficiency of PID control parameters of the motor speed of a differential surveying and mapping vehicle, is based on the group intelligent algorithm, introduces an improved kangaroo escape optimizing algorithm, systematically improves the optimizing efficiency of PID parameters of the motor speed of the differential surveying and mapping vehicle based on a plurality of improving strategies, realizes more accurate control of the motor speed of the differential surveying and mapping vehicle, and reduces the influence of speed fluctuation on the quality of acquired data of a follow-up homeland planning and mapping sensor. The technical scheme of the invention is that the control and optimization method for the homeland planning and measuring carrier based on the group intelligent algorithm comprises the following steps: S1, constructing a PID control system of a differential surveying and mapping vehicle, wherein the PID control system comprises a motor rotating speed error calculation module, a motor rotating speed PID controller module, an improved kangaroo escape optimization algorithm module, a motor rotating speed adjustment module and a motor rotating speed monitoring module; step S2, introducing an improved kangaroo escape optimization algorithm module, wherein the improved kangaroo escape optimization algorithm comprises the following improvement strategies: S21, reversely reconnaissance an initialization strategy, wherein in an algorithm initialization stage, an initial kangaroo survival community is selected preferentially according to a survival safety index value by calculating a shadow population survival position vector and combining the shadow population survival position vector with an initial random kangaroo population survival position vector; s22, tent type biological rhythm and rhythmic gait adjustment strategies are adopted, the tent type mapping is utilized to update the biological rhythm energy factors in the environment and biological rhythm parameter updating stage, and the rhythmic gait adjustment factors changing in cosine law are introduced; s23, a quantum instinct convergence mechanism updates a kangaroo individual survival position vector by simulating quantum potential field attraction in the stage of searching a safe area; s24, a bait putting strategy is adopted, and a kangaroo individual is guided to conduct fine exploration on a global safest area by generating a sparse mask vector in the stage of searching the safe area; S25, guiding a kangaroo individual to execute Z-shaped movement by using a rotation vector in a low physical energy escape stage so as to get rid of predator tracking; S26, a group interactive learning mechanism is combined with a differential evolution strategy in the low physical energy escape stage, and kangaroo individual survival position vectors are updated by using individual difference information inside kangaroo survival communities; step S27, a super long jump mechanism updates a kangaroo individual survival position vector by utilizing a heavy tail random step length generated by a Laiweider flight strategy in a high physical ability escape stage; step S28, a survival crisis response mechanism, and r