CN-121828304-B - Hydraulic control optimization method for intelligent steel bar trolley of tunnel

CN121828304BCN 121828304 BCN121828304 BCN 121828304BCN-121828304-B

Abstract

The invention discloses a hydraulic control optimization method for an intelligent steel bar trolley of a tunnel, which belongs to the technical field of control optimization and comprises the steps of constructing a PID control system of a hydraulic proportional control valve of the intelligent steel bar trolley of the tunnel, introducing an improved freshwater snail optimization algorithm, setting PID control parameters of the hydraulic proportional control valve, mapping the obtained three optimal control parameters to a PID controller of the hydraulic proportional control valve of the intelligent steel bar trolley of the tunnel, and optimizing the adjustment control quality of the hydraulic proportional control valve. The invention is based on an improved freshwater snail optimization algorithm, relatively optimizes the dynamic response quality of the hydraulic proportional control system, can relatively effectively inhibit the overshoot of the system while greatly shortening the adjustment time, enhances the steady-state margin and the closed-loop robustness of the system, and provides relatively reliable control guarantee for improving the positioning precision, the operation stability and the construction safety of the intelligent steel bar trolley of the tunnel.

Inventors

Gao Shuojie
GONG XIAOWEI
ZHANG JINWANG
LI HONGHONG
LI YONG
ZHANG YAO
LI HONGLING
MA TENGFEI
WANG SHAOWEI
LI CHAO
SUN KE
WANG ZHILI
ZHANG LI
Du Yilian
GAO ZHONGTAO
BI SHIBO
QIU YIHUAN

Assignees

中铁十四局集团第三工程有限公司
中铁十四局集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260313

Claims (10)

1. A hydraulic control optimization method for an intelligent steel bar trolley for a tunnel is characterized by comprising the following steps of: S1, constructing a PID control system of a hydraulic proportional control valve of the intelligent tunnel reinforcement trolley; S2, introducing an improved freshwater snail optimization algorithm, wherein the specific improvement strategy is as follows: S21, a chaotic flow field ecological level distribution and reverse landscape observation enhancement strategy introduced in an initialization stage is adopted, so that the distribution uniformity and the search space coverage rate of an initial population are enhanced; s22, buoyancy adjustment exploration strategies based on heavy trailing random migration introduced in a position updating stage of an explorer subgroup, and the capability of an algorithm to jump out of a local optimal habitat is enhanced by simulating strong kick characteristics and dynamic buoyancy disturbance of the Lewy flight; S23, an adaptive gene variation and information cross development strategy introduced in the evolution cycle of a developer subgroup is used for improving the convergence speed and the control parameter refinement degree of an algorithm in a local search stage by dynamically adjusting variation factors and cross probabilities; s24, compensating self-adaptive displacement of individuals in the dense area by a free step displacement correction strategy triggered by collision and introduced in a collision avoidance stage after updating the individual positions, and preventing searching stagnation caused by excessive population aggregation; S25, carrying out fine sampling on a local search space by a golden section law-driven accurate foraging strategy introduced in the later stage of algorithm evolution, and improving the final steady-state precision of PID parameter setting; S26, a deep survival stagnation monitoring and chaotic disturbance restarting strategy introduced in a stagnation judging stage of the main cycle is adopted, so that the global exploration potential of the algorithm at the end of convergence is enhanced; S3, optimizing PID control parameters of the hydraulic proportional control valve of the steel bar trolley by using an improved freshwater snail optimization algorithm; and S4, setting the three optimal control parameters obtained by optimizing as parameters of a PID controller of the hydraulic proportional control valve of the steel bar trolley.
2. The hydraulic control optimization method for the intelligent steel bar trolley for the tunnel according to claim 1, wherein in the PID control system of the hydraulic proportional control valve of the intelligent steel bar trolley for the tunnel constructed in the step S1, an improved fresh water snail optimization algorithm module is utilized to compare internal parameters of a PID controller module of the hydraulic proportional control valve 、、 Performing off-line optimization to obtain an optimal control parameter combination, mapping the optimal control parameter combination to a PID controller module of the hydraulic proportional control valve, collecting an actual measurement value of the hydraulic proportional control valve in real time through a hydraulic proportional control valve monitoring module, transmitting the actual measurement value to a hydraulic proportional control valve error calculation module, and obtaining an error signal by comparing and calculating a preset target value and the target value by the hydraulic proportional control valve error calculation module Subsequently the error signal Inputting the error signal into a PID controller module of the hydraulic proportional control valve, and optimizing parameters by the PID controller module of the hydraulic proportional control valve according to the error signal Calculating the control quantity in real time And will control the amount And the hydraulic proportional control valve is sent to a hydraulic proportional control valve adjusting module to drive the hydraulic proportional control valve to complete the action.
3. The hydraulic control optimization method for the intelligent reinforcement trolley for the tunnel according to claim 1, wherein the chaotic flow field ecological niche distribution and reverse landscape observation enhancement strategy in the step S21 comprises the following steps: step S211, generating a chaotic variable sequence with uniform distribution by using a Logistic chaotic mapping iterative formula; , In the middle of Representing the next generation of chaotic variables after the iteration, Representing the current generation of chaotic variable, The value range of the chaotic mapping control parameter is [0,4]; Step S212, mapping the chaotic variable sequence to a search space of PID parameters of the hydraulic proportional control valve by using a mapping formula to generate an initial freshwater snail individual position; , In the middle of Representing the generated initial individual position vector of the fresh water snail, Representing the search space lower bound vector, Representing the current generation of chaotic variable, Indicating the PID parameter search space upper bound vector of the hydraulic proportional control valve; S213, performing reverse landscape observation, generating corresponding reverse landscape position vectors, and selecting the front from the initial position and the reverse position according to the ascending order of fitness function values The positions form an initial evolution population; , In the middle of Representing the generated reverse landscape position vector, Representing the search space lower bound vector, Representing the vector of the upper bound of the search space, Representing the individual position vector of the initial fresh water snail.
4. The hydraulic control optimization method for the intelligent reinforcement trolley for the tunnel according to claim 1, wherein the buoyancy adjustment exploration strategy based on heavy trailing random migration in the step S22 comprises the following steps: step S221, utilizing the Lewy flight index Random vector subject to standard normal distribution Calculating the migration step length of heavy trailing machine ; , In the middle of Represents the heavy trailing random migration Leyveromyces step size, Representing a random vector subject to a standard normal distribution, Representing a random vector subject to a standard normal distribution, Representing the lewy flight index; step S222, according to the current evolution period times And maximum evolution period times Calculating dynamic buoyancy coefficient And combining random numbers Branch decision logic for calculating sine and cosine water flow fluctuation factor ; , In the middle of Representing the buoyancy coefficient of the current evolution period, Representing a random number within a [0,1] closed interval, Represents an exponential function based on natural logarithms, Represents the coefficient of the attenuation rate of the buoyancy, Representing the number of current evolution cycles, Representing a maximum total number of evolution periods; , In the middle of Representing the sine and cosine water flow fluctuation factors, The representation of a sinusoidal function is given, Representing a cosine function of the sign of the signal, The circumference ratio is indicated as such, Represents a globally optimal habitat location vector, Represents the historic optimal position vector of the individual freshwater snails, Representing a two-norm distance operation, Representing the vector-mean calculation function, Representing the vector of the upper bound of the search space, Representing a search space lower bound vector; Step S223, utilizing a position update formula to realize global position evolution of the explorator subgroup; , In the middle of Representing the position vector of the fresh water snail corresponding to the explorer after updating, Represents the current global optimum perch position vector, Representing the buoyancy coefficient of the current evolution period, Represents the heavy trailing random migration Leyveromyces step size, Representing the seeker position vector prior to the update, Representing the water flow fluctuation factor, Representing the historic optimal position vector of the individual fresh water snails.
5. The hydraulic control optimization method for the intelligent steel bar trolley for the tunnel according to claim 1, wherein the adaptive genetic variation and information crossover development strategy in the step S23 comprises the following steps: Step S231, monitoring random number Generating a variation factor within a preset closed interval when the variation factor meets a preset update probability threshold value Probability of crossover ; Step S232, generating a variation vector guided by the global optimal position by using a formula ; , In the middle of Represents the variance vector of the subgroup of the developer, Represents the current global optimum perch position vector, The expression of the variation factor is given, Representing a first random individual position vector within a subgroup of developers, Representing a second random individual position vector within the developer subgroup; Step S233, executing information crossing operation according to the random number and the crossing probability Determining individual positions of developers And (3) realizing feature recombination.
6. The hydraulic control optimization method for a tunnel intelligent reinforcement trolley according to claim 1, wherein the collision triggered free-step displacement correction strategy in step S24 comprises the steps of: s241, calculating Euclidean distance between fresh water snail individuals, when the Euclidean distance is smaller than the collision judgment threshold value Triggering a free-step mechanism; step S242, calculating free-step factor by using distance deviation ; , In the middle of The free-step factor generated is represented by, Representing the vector-mean calculation function, Represents a globally optimal habitat location vector, Representing the two-norm distance of the current freshwater snail position from the global optimum habitat position, Represents the population scale of the freshwater snails, Representing the updated fresh water snail preliminary position vector; step S243, performing a displacement correction formula to realize secondary dispersion of population distribution; , In the middle of Representing the position vector of the updated individual fresh water snails, Representing the free-step factor generated.
7. The hydraulic control optimization method for intelligent reinforcement trolley in tunnel according to claim 1, wherein the golden section law-driven accurate foraging strategy in step S25 comprises the steps of: step S251, according to golden section number Calculating feature sampling points And (3) with ; , ; In the middle of A first sample point is indicated and is indicated, A second sample point is indicated and is indicated, The circumference ratio is indicated as such, Representing the golden section number; step S252, utilizing golden sine formula to make global optimal solution Nearby generation refining locations ; , In the middle of Representing the resulting refined position vector of the object, Represents a globally optimal habitat location vector, The absolute value operation is represented by a function, The representation of a sinusoidal function is given, Representing random numbers within the interval 0,2 pi, Represents a random number within the interval 0, pi, As a first sampling point of the sample, As a second sampling point of the sample, Representing the position vector of the randomly extracted freshwater snail individuals in the population; Step S253, executing logic with smaller fitness function value, and updating the refining position with smaller fitness function value into a new global optimal position.
8. The hydraulic control optimization method for the intelligent reinforcement trolley for the tunnel according to claim 1, wherein the deep survival stagnation monitoring and chaotic disturbance restarting strategy in the step S26 comprises the following steps: step S261, monitoring the fitness function value of the global optimal position, if continuous If the cycle is not lifted, judging that the survival is stagnated; Step S262, ascending order is carried out according to the fitness function value, and fresh water snail individuals with preset proportion at the tail of the population ranking are locked; step S263, performing chaotic disturbance resetting on the target individual position by using a restarting formula; , In the middle of Representing the individual position vector of the freshwater snail after reset, Represents the current global optimum perch position vector, Representing a preset disturbance amplitude coefficient, Representing the random number within the [0,1] closed interval.
9. The hydraulic control optimization method for the intelligent steel bar trolley for the tunnel according to claim 1, further comprising an annular ecological boundary mapping step between the steps S24 and S25, specifically comprising: correcting the individual position of the freshwater snail exceeding the search space [ L, U ] by using modular operation; , In the middle of Representing the position vector of the updated individual fresh water snails, Representing a component-by-component modulo operation, Representing the search space lower bound vector, The PID parameter search space upper bound vector of the hydraulic proportional control valve is represented.
10. The hydraulic control optimization method for the intelligent reinforcement trolley for the tunnel according to claim 1, wherein the execution step of the modified freshwater snail optimization algorithm in the step S3 comprises the steps of: Step A, an initialization stage, namely generating initial population positions of freshwater snails in a PID parameter search space based on a logic mapping sequence, generating corresponding reverse populations by utilizing a reverse landscape observation strategy, and constructing an initial evolution population by selecting a principle that the fitness function value is smaller and better; Step B, a dynamic grouping stage, according to the current evolution period times Dynamically calculating seeker proportions Dividing the population into a explorator subgroup for executing global search and a developer subgroup for executing local development; Step C, in the evolution stage of the explorer, the individual positions of the explorer subgroup are updated by utilizing the Laiwei flight step length and combining the dynamic buoyancy coefficient and the sine and cosine water flow fluctuation factor, so that the global diffusion capacity of the algorithm is enhanced; Step D, in the evolution stage of the developer, performing self-adaptive mutation and information intersection operation on a subgroup of the developer, determining a recombination mode of component characteristics according to random probability, and improving the local search precision of an algorithm; Step E, in the correction and refining stage, after the individual position is updated, free step displacement correction triggered by collision, annular ecological boundary mapping and accurate foraging operation based on golden section law are sequentially executed, and local refinement sampling is carried out on the current global optimal position; And F, in the stagnation monitoring and restarting stage, monitoring the change of the fitness function value of the global optimal solution, and if the survival stagnation criterion is met, executing chaotic disturbance resetting on part of low-concentration individuals until the maximum evolution period is reached, and outputting an optimal PID parameter combination.

Description

Hydraulic control optimization method for intelligent steel bar trolley of tunnel Technical Field The invention belongs to the technical field of hydraulic control optimization, and particularly relates to a hydraulic control optimization method for an intelligent steel bar trolley for a tunnel. Background The intelligent steel bar trolley for the tunnel is used as key mechanical equipment in underground engineering construction and bears the working tasks of grabbing, transferring, positioning, auxiliary installation and the like, wherein a hydraulic proportional control valve is used as a core control element of a hydraulic system of the equipment, the hydraulic proportional control valve can precisely drive actuating components such as a mechanical arm, a lifting mechanism, a telescopic oil cylinder and the like, the performance advantages and disadvantages of the hydraulic proportional control system directly influence the action stability, the positioning precision and the working safety of the trolley under the complex tunnel working condition, are important bases for guaranteeing the tunnel construction quality, in actual engineering application, a control scheme aiming at the hydraulic proportional control valve adopts a traditional proportional integral differential control strategy at present, the controller parameter is set by means of a critical proportional integral differential method, an empirical trial method and the like, however, the tunnel construction environment faces complex working conditions such as abrupt load change, temperature rise influence, oil pollution and the like, the hydraulic system has the characteristics of serious nonlinearity, time variability, delay and the like, the problems of large mechanism oscillation, slow response speed and the like of the equipment possibly caused by the limitations such as complicated debugging process, the limited control precision and the like, and the problems of the prior control technology are solved, the intelligent control valve is improved in the current scheme. Disclosure of Invention In order to overcome the technical problems in the prior art, the invention provides the hydraulic control optimization method for the intelligent reinforcement trolley for the tunnel, which is based on an improved freshwater snail optimization algorithm, relatively optimizes the dynamic response quality of a hydraulic proportional control system, can relatively effectively inhibit system overshoot while greatly shortening the adjustment time, enhances the steady state margin and closed loop robustness of the system, and provides relatively reliable control guarantee for improving the positioning precision, the operation stability and the construction safety of the intelligent reinforcement trolley for the tunnel. The technical scheme of the invention is that the hydraulic control optimization method for the intelligent reinforced bar trolley for the tunnel comprises the following steps: S1, constructing a PID control system of a hydraulic proportional control valve of the intelligent tunnel reinforcement trolley; S2, introducing an improved freshwater snail optimization algorithm, wherein the specific improvement strategy is as follows: S21, a chaotic flow field ecological level distribution and reverse landscape observation enhancement strategy introduced in an initialization stage is adopted, so that the distribution uniformity and the search space coverage rate of an initial population are enhanced; s22, buoyancy adjustment exploration strategies based on heavy trailing random migration introduced in a position updating stage of an explorer subgroup, and the capability of an algorithm to jump out of a local optimal habitat is enhanced by simulating strong kick characteristics and dynamic buoyancy disturbance of the Lewy flight; S23, an adaptive gene variation and information cross development strategy introduced in an evolution cycle of a developer subgroup is used for improving the convergence speed and the control parameter definition of an algorithm in a local search stage by dynamically adjusting variation factors and cross probabilities; S24, compensating the self-adaptive displacement of the individuals in the dense area by a free-step displacement correction strategy triggered by collision and introduced in a collision avoidance stage after the individual position is updated, so as to prevent searching stagnation caused by excessive aggregation of the population; s25, carrying out fine sampling on a local search space by a golden section law-driven accurate foraging strategy introduced in the later stage of algorithm evolution, and improving the final steady-state precision of PID parameter setting; S26, a deep survival stagnation monitoring and chaotic disturbance restarting strategy introduced in a stagnation judging stage of a main cycle is adopted, so that the global exploration potential of an algorithm at the end of convergence is enha