CN117891162B - Control method and system of telescopic support leg of pipeline robot and pipeline robot - Google Patents
Control method and system of telescopic support leg of pipeline robot and pipeline robot Download PDFInfo
- Publication number
- CN117891162B CN117891162B CN202410289090.4A CN202410289090A CN117891162B CN 117891162 B CN117891162 B CN 117891162B CN 202410289090 A CN202410289090 A CN 202410289090A CN 117891162 B CN117891162 B CN 117891162B
- Authority
- CN
- China
- Prior art keywords
- mantis
- telescopic
- pipeline robot
- random number
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 241000258241 Mantis Species 0.000 claims abstract description 193
- 238000010845 search algorithm Methods 0.000 claims abstract description 54
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 37
- 238000004088 simulation Methods 0.000 claims abstract description 17
- 238000013178 mathematical model Methods 0.000 claims description 33
- 238000011161 development Methods 0.000 claims description 28
- 230000033001 locomotion Effects 0.000 claims description 19
- 230000007246 mechanism Effects 0.000 claims description 19
- 238000006243 chemical reaction Methods 0.000 claims description 11
- 238000005315 distribution function Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 230000010354 integration Effects 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000002789 length control Methods 0.000 abstract description 6
- 230000006399 behavior Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 238000005457 optimization Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 230000004044 response Effects 0.000 description 6
- 238000011217 control strategy Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 230000010355 oscillation Effects 0.000 description 4
- 239000003381 stabilizer Substances 0.000 description 4
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000010006 flight Effects 0.000 description 2
- 238000005295 random walk Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000004634 feeding behavior Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 210000003128 head Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000000284 resting effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a control method and a control system for a telescopic support leg of a pipeline robot and the pipeline robot, wherein the control method comprises the following steps: calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot; setting PID parameters of the pipeline robot according to the difference value of the telescopic length and the improved mantis searching algorithm, and determining the optimal PID parameters of the pipeline robot; inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model, and outputting the telescopic control quantity of the telescopic support leg so as to enable the pipeline robot to adjust the telescopic support leg according to the telescopic control quantity. In the mode, the optimal PID parameters of the pipeline robot are determined by improving the mantis search algorithm, and the telescopic control quantity of the telescopic support legs is determined according to the optimal PID parameters, so that the accuracy of telescopic length control of the telescopic support legs of the pipeline robot is improved, and the accuracy and efficiency of operation of the pipeline robot are improved.
Description
Technical Field
The invention relates to the technical field of pipeline robot control, in particular to a control method and system for telescopic support legs of a pipeline robot and the pipeline robot.
Background
The pipeline robot is a special robot capable of entering the interior of a pipeline to detect, maintain and clean, at present, the pipeline robot mainly uses the diameter of a pipeline capable of being self-adapted, and the self-adapted telescopic support leg is an important component of the pipeline robot and mainly aims at supporting and adjusting the posture and the height of the robot in the interior of the pipeline. The self-adaptive telescopic support can be automatically adjusted according to the shape and the size of the inside of the pipeline, so that the robot always keeps the correct posture and the correct height, and the accuracy and the efficiency of operation are ensured.
The self-adaptive telescopic support of the pipeline robot is controlled by a motor, the telescopic length is controlled through the rotating rudder angle of the motor shaft, the telescopic length is accurately controlled according to the internal size of the pipeline, and the accuracy and the efficiency of operation are ensured. The current self-adaptive telescopic support of the pipeline robot mainly adopts a PID control method, and the PID control method can accurately control the telescopic length of the telescopic support leg of the pipeline robot under the interference-free condition, but in a complex pipeline, the current PID control method cannot ensure the accuracy of the telescopic length control of the telescopic support leg of the pipeline robot, and cannot further ensure the accuracy and efficiency of the operation of the pipeline robot.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method and a system for controlling a telescopic leg of a pipe robot, and a pipe robot, so as to improve accuracy of controlling a telescopic length of the telescopic leg of the pipe robot, and further improve accuracy and efficiency of operation of the pipe robot.
In a first aspect, an embodiment of the present invention provides a method for controlling a telescopic leg of a pipeline robot, including: calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot; setting PID parameters of the pipeline robot according to the difference value of the telescopic length and the improved mantis searching algorithm, and determining the optimal PID parameters of the pipeline robot; inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model, and outputting the telescopic control quantity of the telescopic support leg so as to enable the pipeline robot to adjust the telescopic support leg according to the telescopic control quantity.
Further, before the step of calculating the difference between the target telescopic length and the telescopic length of the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic leg of the pipeline robot, the method further includes: acquiring a real-time rotating rudder angle of a motor of the pipeline robot; calculating the real-time telescopic length according to the real-time rotary rudder angle and a preset conversion mathematical model; converting the mathematical model into:
; wherein/> For real-time telescoping length,/>For the total number of polynomials set in advance,/>For the current item number,/>For the preset conversion coefficient,/>The rudder angle is rotated in real time.
Further, the improved mantis search algorithm is set by the following method: adjusting the distance factor of a standard mantis search algorithm through a random distribution function to obtain an adjusted distance factor; the adjusted distance factor is: ; wherein/> In order to adjust the distance factor after the adjustment,Is a random number between two values of 0 and 1,/>For the current iteration number of standard mantis search algorithm,/>Is the total iteration number of the standard mantis search algorithm,/>Is a preset speed factor,/>As a random distribution function,/>Is the lower limit of random number,/>Is the upper limit of random number,/>Is the number of random numbers,/>Is the dimension of the random number; acquiring a current optimal fitness value and a current worst fitness value of a standard mantis search algorithm, and adjusting an optimizing mechanism of the standard mantis search algorithm according to a preset circulation system optimizer, the current optimal fitness value and the current worst fitness value to obtain an adjusted optimizing mechanism; the adjusted optimizing mechanism is as follows: /(I); Wherein/>For the best position updated by mantis population,/>For/>Individual mantis positions/>, relative to individual mantis positionsThe control parameters of the movement are set to be the same,For mantis individual position/>Relative to mantis individual position/>Control parameters during movement,/>、/>And/>For the position of the mantis with the smallest first three names in the current population fitness,/>For the fitness weight coefficient,/>For/>Fitness value of individual positions of mantis,/>For the current worst fitness value,/>For the current optimal fitness value,/>Is the maximum scale of mantis population.
Further, the step of determining the optimal PID parameter of the pipeline robot according to the difference value of the telescopic length and the PID parameter of the improved mantis search algorithm comprises the following steps: s1: acquiring an absolute error integral index and a time error integral performance index corresponding to a PID controller of the pipeline robot; s2: generating an objective function corresponding to the PID controller based on the telescopic length difference value, the absolute error integral index and the time error integral performance index; s3: initializing and improving parameters of a mantis searching algorithm and initial positions of each mantis in a mantis population; wherein, each PID parameter corresponds to an individual position of a mantis; the parameters of the improved mantis search algorithm comprise the maximum iteration times; s4: constructing a position updating mathematical model of the mantis population, updating the initial position of each mantis according to the position updating mathematical model, and obtaining the updated individual position of each mantis; s5: calculating the fitness value of each updated individual position of the mantis according to the objective function, and adding 1 to the iteration times; the initial value of the iteration number is 0; s6: determining the minimum fitness value of the fitness values as the current minimum fitness value, and judging whether the current minimum fitness value is smaller than the optimal minimum fitness value or not; s7: if the current minimum fitness value is smaller than the optimal minimum fitness value, updating the optimal minimum fitness value according to the current minimum fitness value; s8: repeating the steps S4-S7 until the iteration number is equal to the maximum iteration number; s9: and decoding the individual position of each mantis corresponding to the optimal minimum fitness value to obtain the optimal PID parameter of the pipeline robot.
Further, the objective function is: ; wherein/> For the first preset weight coefficient,For the second preset weight coefficient,/>For the difference of the telescopic length,/>The time from the target telescopic length to the real-time telescopic length of the telescopic support leg is obtained.
Further, parameters for improving the mantis search algorithm comprise a circulation control factor and a development factor; constructing a position updating mathematical model of the mantis population, and updating the individual position of each mantis according to the position updating mathematical model, wherein the step comprises the following steps: acquiring a circulation control factor and a preset random disturbance, and judging whether the circulation control factor is larger than or equal to the random disturbance; if yes, updating the initial position of each mantis according to a preset first global exploration model to obtain a first updated individual position; if not, updating the individual position of each mantis according to a preset second global exploration model to obtain a first updated individual position; acquiring a second random number and a fourth random number, and judging whether the fourth random number is larger than the second random number or not; if yes, updating a first updated individual position according to a preset first local development model to obtain an updated individual position of each mantis; if not, updating the first updated individual position according to a preset second local development model to obtain a second updated individual position; judging whether the development factor is larger than a fourth random number; if the development factor is larger than the fourth random number, updating the second updated individual position according to an optimizing mechanism of the improved mantis searching algorithm to obtain updated individual positions of each mantis; if the development factor is smaller than or equal to the fourth random number, determining the second updated individual position as the updated individual position of each mantis.
Further, the telescopic control amount includes a target telescopic length of the telescopic support leg and a target rotating rudder angle of a motor of the pipeline robot.
In a second aspect, an embodiment of the present invention provides a control system for a telescopic leg of a pipe robot, including: the telescopic length acquisition module is used for calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot; the optimal PID parameter calculation module is used for setting PID parameters of the pipeline robot according to the telescopic length difference value and the improved mantis search algorithm, and determining the optimal PID parameters of the pipeline robot; the telescopic control quantity output module is used for inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model and outputting the telescopic control quantity of the telescopic support leg so that the pipeline robot can adjust the telescopic support leg according to the telescopic control quantity.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, and a processor, where the memory stores a computer program executable on the processor, and where the processor implements a method as described above when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a pipeline robot, including a robot body, a camera, a telescopic leg, a motor, and wheels; the control system of the telescopic support leg of the pipeline robot is also included; the control system controls the telescopic support legs to move in a telescopic way through a motor; the telescopic support leg is used for connecting the robot body and the wheels.
The embodiment of the invention provides a control method and a system for a telescopic support leg of a pipeline robot and the pipeline robot, wherein the control method comprises the following steps: calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot; setting PID parameters of the pipeline robot according to the difference value of the telescopic length and the improved mantis searching algorithm, and determining the optimal PID parameters of the pipeline robot; inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model, and outputting the telescopic control quantity of the telescopic support leg so as to enable the pipeline robot to adjust the telescopic support leg according to the telescopic control quantity. In the mode, the optimal PID parameters of the pipeline robot are determined by improving the mantis search algorithm, and the telescopic control quantity of the telescopic support legs of the pipeline robot is determined according to the optimal PID parameters and the telescopic support leg control simulation model of the pipeline robot, so that the telescopic support legs of the pipeline robot are adjusted according to the telescopic control quantity by the pipeline robot, the accuracy of telescopic length control of the telescopic support legs of the pipeline robot is improved, and the accuracy and efficiency of operation of the pipeline robot are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a control method of a telescopic leg of a pipeline robot according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps for determining optimal PID parameters for a pipe robot according to an embodiment of the invention;
FIG. 3 is a step diagram of updating individual positions of each mantis according to the position updating mathematical model according to the first embodiment of the present invention;
FIG. 4 is a graph showing the variation of PID control parameters of the tuning pipeline robot according to the MSA algorithm and SMSA algorithm provided in the first embodiment of the present invention;
FIG. 5 is a graph showing the comparison of fitness values of the MSA algorithm and SMSA algorithm according to the first embodiment of the present invention;
FIG. 6 is a graph showing the comparison of control performance of a self-adaptive telescopic support of a pipeline robot under the control of a common PID, MSA-PID and SMSA-PID according to an embodiment of the present invention;
fig. 7 is a motor rudder angle effect diagram of a pipeline robot controlling expansion and contraction of an expansion support leg according to the first embodiment of the invention;
Fig. 8 is a schematic diagram of a control system of a telescopic leg of a pipeline robot according to a second embodiment of the present invention;
fig. 9 is a schematic structural diagram of a pipeline robot according to a third embodiment of the present invention.
Icon: 1-a telescopic length acquisition module; 2-an optimal PID parameter calculation module; 3-a telescopic control quantity output module; 4-robot body; 5-a camera; 6-telescoping support legs; 7-a motor; 8-a control system; 9-wheels.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to facilitate understanding of the present embodiment, the following describes embodiments of the present invention in detail.
Embodiment one:
fig. 1 is a flowchart of a control method of a telescopic leg of a pipeline robot according to an embodiment of the present invention.
Referring to fig. 1, a control method of a telescopic leg of a pipe robot includes:
step S101, calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot.
Here, the pipe robot includes a robot body, a camera, a telescopic leg, and a motor controlling telescopic movement of the telescopic leg. The target telescopic length of the telescopic support leg is determined after the distance between the pipeline in the advancing direction and the telescopic support leg is shot by the camera.
Further, before the step of step S101, the method further includes:
and acquiring a real-time rotating rudder angle of a motor of the pipeline robot.
Here, the flexible length of the flexible stabilizer blade of pipeline robot influences the robot gesture in the pipeline, and flexible stabilizer blade just can guarantee the motion of pipeline robot in the pipeline according to pipeline internal diameter size self-adaptation change, and the flexible motion of flexible stabilizer blade passes through motor rotation regulation, and the motor rotation rudder angle is bigger, and the flexible range of flexible stabilizer blade is also bigger.
Calculating the real-time telescopic length according to the real-time rotary rudder angle and a preset conversion mathematical model; the conversion mathematical model is shown in formula (1):
（1）
Wherein, For real-time telescoping length,/>For the total number of polynomials set in advance,/>For the current item number,/>For the preset/>Conversion coefficient of term,/>The rudder angle is rotated in real time.
Here, polynomial total term numberThe scale of the prey is equal to that of the mantis in the improved mantis search algorithm, and the prey is preset according to the actual situation.
Conversion coefficientThe intelligent robot is obtained by testing according to relevant parameters of the intelligent robot in advance.
Before the target telescopic length and the real-time telescopic length of the telescopic support leg are obtained, simulation test is carried out in advance according to relevant parameters of the intelligent robot, and the rotating rudder angle of the motor of the pipeline robot and response data of the telescopic support leg are obtained. The response data includes telescoping performance of the telescoping leg and sensitivity performance during telescoping of the telescoping leg. The real-time telescopic length can be determined according to the real-time rotary rudder angle through the conversion mathematical model of the formula (1).
The method comprises the following steps that a response type motion mathematical model corresponding to a telescopic support leg of a pipeline robot is preset, and a Laplace transformation formula corresponding to the preset response type motion mathematical model is shown as a formula (2):
（2）
Wherein, Is a responsive motion mathematical model of a telescopic support leg,/>Is the course angle of the motor,/>Is rudder angle of motor,/>For Laplace transform quantity,/>For the telescoping performance of telescoping legs,/>The sensitivity performance in the telescopic process of the telescopic support legs is achieved.
And/>Are all estimated in advance according to the related parameters of the intelligent robot,/>May be 0.3,/>May be 0.4.
And constructing a disturbance mathematical model controlled by the telescopic support legs of the pipeline robot according to the response type motion mathematical model of the telescopic support legs of the pipeline robot, wherein the disturbance mathematical model adopts a second-order oscillation link, as shown in a formula (3).
（3）
Wherein,The disturbance mathematical model is controlled by the telescopic support leg obtained according to the second-level oscillation link.
And S102, setting PID parameters of the pipeline robot according to the telescopic length difference value and the improved mantis search algorithm, and determining the optimal PID parameters of the pipeline robot.
Here, the standard mantis search Algorithm (MANTIS SEARCH Algorithm, MSA) is a natural inspired optimization Algorithm that mimics the unique hunting behavior and occasional homogeneous feeding behavior observed in mantis.
In one embodiment, the improved mantis search algorithm is set by the following method:
Adjusting the distance factor of a standard mantis search algorithm through a random distribution function to obtain an adjusted distance factor; the adjusted distance factor is the distance factor corresponding to the improved mantis search algorithm.
The adjusted distance factor is shown in formula (4):
（4）
Wherein, For the adjusted distance factor,/>Is a random number between two values of 0 and 1,/>For the current iteration number of standard mantis search algorithm,/>Is the total iteration number of the standard mantis search algorithm,/>As a result of the preset speed factor,As a random distribution function,/>Is the lower limit of random number,/>Is the upper limit of random number,/>Is the number of random numbers,/>Is the dimension of the random number.
Here, the random number is automatically generated by a preset system.Can be set to-2,/>Can be set to 2,/>Can be set to 1,/>May be set to 1.
Distance factor of long-distance search prey strategy of global exploration stage of standard mantis search algorithm by combining random distribution function improvementBy improved distance factor/>Control algorithm search step size/>The accuracy and efficiency of a mantis algorithm long-distance hunting strategy are improved.
Acquiring a current optimal fitness value and a current worst fitness value of a standard mantis search algorithm, and adjusting an optimizing mechanism of the standard mantis search algorithm according to a preset circulation system optimizer, the current optimal fitness value and the current worst fitness value to obtain an adjusted optimizing mechanism; here, the adjusted optimizing mechanism is the optimizing mechanism corresponding to the improved mantis searching algorithm.
The adjusted optimizing mechanism is shown in a formula (5):
（5）
Wherein, For the best position updated by mantis population,/>For/>Individual mantis positions/>, relative to individual mantis positionsControl parameters during movement,/>For mantis individual position/>Relative to mantis individual position/>Control parameters during movement,/>、/>And/>For the position of the mantis with the smallest first three names in the current population fitness,/>In order to adapt the weight coefficient of the degree,For/>Fitness value of individual positions of mantis,/>For the current worst fitness value,/>For the current optimal fitness value,/>Is the maximum scale of mantis population.
Here, the optimizing mechanism of the mantis searching algorithm is improved, firstly, a circulatory system optimizer is fused, then, the optimal fitness value and the worst fitness value are introduced, and the optimizing mechanism of the mantis searching algorithm is improved.
When the standard mantis search algorithm is improved, the random distribution function is introduced to provide a random search direction for the algorithm, so that the mantis search algorithm is facilitated to jump out of a local optimal solution, the global search capability is further enhanced, in the optimization process, a balance exists between the solution for exploring a new search area and the known good solution, and the sine and cosine scheduling strategy can help the algorithm to find a better balance between exploration and development, so that the global search capability is improved.
In one embodiment, referring to fig. 2, the step of step S102 includes:
Step S1: and acquiring an absolute error integral index and a time error integral performance index corresponding to the PID controller of the pipeline robot.
Here, the absolute error integration index (Integral of Absolute Error, IAE) and the Time error integration performance index (itie) are two methods of evaluating the performance of the control system, which measure the performance of the PID controller by the cumulative effect of the quantization error. The absolute error integral index and the time error integral performance index are obtained through a pre-simulation experiment.
Step S2: and generating an objective function corresponding to the PID controller based on the telescopic length difference value, the absolute error integration index and the time error integration performance index.
Here, the mathematical expression of the absolute error integration index is as shown in formula (6):
（6）
Wherein, Is an absolute error integral index,/>To determine the difference between the desired value and the actual output value at time t, in this embodiment time t may be equal to the current number of iterations,/>Is the difference in telescoping length.
The mathematical expression of the time error integral performance index is shown in formula (7):
（7）
Wherein, The performance index is integrated for the time error.
The objective function is shown in equation (8):
（8）
Wherein, For the first preset weight coefficient,/>For the second preset weight coefficient,/>For the difference of the telescopic length,/>The time from the target telescopic length to the real-time telescopic length of the telescopic support leg is obtained.
Step S3: initializing and improving parameters of a mantis searching algorithm and initial positions of each mantis in a mantis population; wherein, each PID parameter corresponds to an individual position of a mantis; the parameters of the improved mantis search algorithm comprise the maximum iteration times.
Here, the parameters of the improved mantis search algorithm include the maximum iteration numberMaximum population size/>Development factor/>(I.e. the exchange probability between the exploration and development phases of the mantis search algorithm), the cyclic control factor/>Upper bound/>, algorithm optimizationAnd lower bound/>Question dimension/>Prey scale/>, mantis。
PID parameters include Kp (proportional coefficient, proportional Gain), ki (integral coefficient, INTEGRAL GAIN) and Kd (derivative coefficient, DERIVATIVE GAIN).
Initializing parameters for improving a mantis searching algorithm and initial positions of individuals of a mantis population, and encoding Kp, ki and Kd of a PID controller of the pipeline robot as a solution set to be the positions of the individuals of the mantis population; each mantis represents a candidate solution of the Kp, ki, kd solution set of a pipeline robot PID controller in the mantis search algorithm.
Step S4: and constructing a position updating mathematical model of the mantis population, updating the initial position of each mantis according to the position updating mathematical model, and obtaining the updated individual position of each mantis.
Here, the improved mantis search algorithm realizes population position update through three main stages of position update mathematical models, namely pipeline robot PID controller parameter update, wherein the first stage is mantis population initialization, which is responsible for randomly distributing mantis individuals in an optimized search space; the second stage is a global exploration stage, which simulates the behavior of mantis in searching for prey, and mainly provides more solutions for the convergence stage; the third stage is a convergence stage, which simulates the attack behavior of mantis, and mainly improves the optimizing precision.
In one embodiment, step S4 constructs a location update mathematical model of the mantis population, and updates the individual location of each mantis according to the location update mathematical model, including:
And acquiring a circulation control factor and a preset random disturbance, and judging whether the circulation control factor is larger than or equal to the random disturbance.
If so, updating the initial position of each mantis according to a preset first global exploration model to obtain a first updated individual position.
Here, a first global exploration model as in equation (9) is built by integrating Levy flights and normal distributions to simulate mantis looking for prey behavior far from them.
（9）
Wherein,For/>Individual mantis/>Location of the next iteration,/>For/>Position of current iteration of individual mantis, i.e./>Numerical value generated for Levy flight strategy,/>Random number generated for normal distribution,/>、/>、/>For randomly selected individual positions in the respective current population,/>Is the first random number in interval [0,1 ]/>Is the second random number with interval of [0,1 ]/>A random number of 0 or 1. The random numbers are all automatically generated by a PID controller in the pipeline robot in the running process.
Levy flight is a random walk model that is used to describe an atypical class of random paths that involve successive small-step random walks and occasional long-distance jumps. Levy flight is characterized by steps that follow a heavy-tail probability distribution, typically a Levy stable distribution, meaning that the steps are likely to be much larger than the average steps, and such long distance "flight" steps have a non-zero probability in the step distribution. In the optimization algorithm, levy flights are used to simulate search strategies to efficiently explore in the global search space while avoiding premature trapping into locally optimal solutions. This strategy helps the algorithm jump out of local optimization and explore widely in the search space.
If not, updating the individual position of each mantis according to a preset second global exploration model to obtain a first updated individual position.
Here, a second global exploration model, shown in equation (10), is built up to simulate the behavior of a mantis resting on its own site, waiting for the prey to reach the distance to be attacked and relying on the environment around the eye scan of the head.
（10）
Wherein,For the optimal position of the current iteration,/>And/>Upper and lower bounds optimized for mantis algorithm,/>For the improved nonlinear distance factor,/>Is the sixth random number with interval of [0,1 ]/>A seventh random number in the interval of [0,1],Eighth random number in interval [0,1 ]/>Ninth random number in interval [0,1 ]/>Is the tenth random number with interval of [0,1 ].
And acquiring the second random number and the fourth random number, and judging whether the fourth random number is larger than the second random number.
Here, the second random number and the fourth random number are randomly generated during the operation of the system.
If so, updating a first updated individual position according to a preset first local development model to obtain updated individual positions of each mantis.
Here, when the first local development model of the formula (11) is a mathematical model established by simulating the behavior of mantis preying on prey objects, changing the direction to attack again when the mantis fails to prey, and randomly selecting the directions of two mantis from the population to change the attack direction.
（11）
Wherein,Is the twelfth random number with interval of [0,1 ].
If not, updating the first updated individual position according to a preset second local development model to obtain a second updated individual position; judging whether the development factor is larger than a fourth random number; if the development factor is larger than the fourth random number, updating the second updated individual position according to an optimizing mechanism of the improved mantis searching algorithm to obtain updated individual positions of each mantis; if the development factor is smaller than or equal to the fourth random number, determining the second updated individual position as the updated individual position of each mantis.
Here, the second locally developed model, as in equation (12), is a mathematical model that simulates the behavior of a mantis predatory prey success.
（12）
Here the number of the elements is the number,Is the position of the latest prey,/>For the first proposed speed factor,/>In order to control the speed of mantis hunting,Is the distance between the current mantis and the prey.
If the development factor is larger than the fourth random number, the improved mantis searching algorithm is optimized and converged by utilizing a position updating strategy after the fusion circulation system optimizer, and the mantis population position is updated by referring to a formula (5), so that the updated individual position of each mantis is obtained.
Specifically, referring to FIG. 3, a global exploration phase of the improved mantis search algorithm is entered.
And judging whether H is larger than rand.
If so, simulating the behavior of mantis to find the prey far away from the mantis, and updating mantis population positions by using a formula (9), namely updating PID parameters of the pipeline robot.
If not, simulating the behavior that the mantis does not move at the existing position, waiting for the prey to reach the attacked distance and scanning the surrounding environment by eyes, and updating the mantis population position by using a formula (10), namely updating the PID parameters of the pipeline robot.
Entering a local development stage of an improved mantis search algorithm.
And judging whether the fourth random number is larger than the second random number.
If so, randomly selecting two mantis directions from the population to change the attack direction, and updating mantis population positions by using a formula (11), namely updating PID parameters of the pipeline robot.
If not, simulating the behavior of the mantis successfully hunting, and updating the mantis population position by using a formula (12), namely updating the PID parameters of the pipeline robot.
And judging whether the development factor is larger than a fourth random number.
If yes, updating the mantis population position by using the formula (5), namely updating the PID parameters of the pipeline robot.
Step S5: calculating the fitness value of each updated individual position of the mantis according to the objective function, and adding 1 to the iteration times; the initial value of the iteration number is 0.
Here, the current iteration number t=t+1 is performed, where the initial value of the current iteration number t is 0.
Step S6: and determining the minimum fitness value of the fitness values as the current minimum fitness value, and judging whether the current minimum fitness value is smaller than the optimal minimum fitness value or not.
Step S7: and if the current minimum fitness value is smaller than the optimal minimum fitness value, updating the optimal minimum fitness value according to the current minimum fitness value.
Here, the minimum fitness value of the current iteration is to beMinimum fitness value/>, with last iterationAnd comparing, and reserving the positions of the mantis individuals with smaller fitness values.
Step S8: repeating steps S4-S7 until the iteration number is equal to the maximum iteration number.
Step S9: and decoding the individual position of each mantis corresponding to the optimal minimum fitness value to obtain the optimal PID parameter of the pipeline robot.
Here, decoding the individual position of each mantis corresponding to the optimal minimum fitness value to obtain a space vector solution set of Kp, ki and Kd of the PID controller, namely the optimal PID parameter.
And step S103, inputting the optimal PID parameters into a pre-built simulation model for controlling the telescopic support legs of the pipeline robot, and outputting the telescopic control quantity of the telescopic support legs so that the pipeline robot can adjust the telescopic support legs according to the telescopic control quantity.
Here, the pipe robot telescoping leg control simulation model is built in advance in a Simulink (visual simulation tool) according to the conversion mathematical model of formula (1), the response type motion mathematical model of formula (2), and the disturbance mathematical model of formula (3).
The optimal PID parameters are input into a pre-built pipeline robot telescopic support leg control simulation model, and the telescopic control quantity of the telescopic support leg can be output. The telescopic control quantity comprises a target telescopic length of the telescopic support leg and a target rotary rudder angle of the pipeline robot motor.
The pipeline robot adjusts a motor of the pipeline robot according to the target rotating rudder angle so that telescopic support legs of the pipeline robot are attached to the pipeline.
Specifically, in one embodiment, a maximum number of iterations is designed=30, Maximum population size/>=50, Development factor=0.4, The exchange probability between the mantis search algorithm exploration and development stages is set to 0.5, the cyclic control factor/>=0.5, Upper bound/>, algorithm optimizationFor [300,300,300], the algorithm optimizes the lower bound/>Is [0, 0], problem dimension/>Prey scale/>, mantis =3=50。
SMSA and MSA set pipe robot as shown in fig. 4, the PID parameters set by SMSA are kp= 241.12, ki=0.00, and kd=21.35, respectively. PID parameters set by MSA are kp= 150.12, ki= 270.35 and kd=17.68, respectively.
As can be seen from fig. 5, the fitness decreases with increasing iteration number, the objective functionThe smaller the error of PID control is, the shorter the adjustment time is, and the better the control effect is; compared with the MSA algorithm, the fitness of SMSA is reduced more rapidly and is maintained at a lower level in the subsequent iteration, and the change of the fitness value is smoother and stable, which means that the SMSA algorithm not only finds a good solution more rapidly, but also shows better stability and robustness in the process of searching the optimal solution; from the aspect of fitness, the performance of SMSA algorithm is better than that of MSA algorithm, SMSA algorithm not only finds better solution in the early stage of iteration, but also keeps lower fitness value in the whole optimization process, which shows that it explores the knowledge space more effectively and finds the best PID control parameters.
Comparing the effects of the three control strategies in fig. 6 on the performance of the telescopic support PID controller of the pipeline robot, it can be obviously found that the overshoot is the largest when the pipeline robot adopts the common PID to control the telescopic support to stretch and retract, and the pipeline robot has larger oscillation before reaching a steady state; the overshoot and oscillation are obviously reduced when the two telescopic brackets are telescopic under MSA-PID control, and as an improved version of MSA, the SMSA-PID control strategy shows better performance in terms of reducing overshoot, and SMSA-PID is optimized in terms of control self-adaptability and predictability, so that the controller can more effectively resist system disturbance, quickly reach the target telescopic length, and simultaneously keep stable.
As shown in fig. 7, in order to control the telescopic length of the telescopic support leg, the effect diagram of the motor rudder angle is shown, and compared with the MSA-PID control strategy, the motor rudder angle is smoother under the SMSA-PID control strategy, and the telescopic length of the telescopic support leg is changed due to the small rudder angle change, so that the SMSA-PID control strategy is better in performance.
The embodiment of the invention provides a control method and a system for a telescopic support leg of a pipeline robot and the pipeline robot, wherein the control method comprises the following steps: calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot; setting PID parameters of the pipeline robot according to the difference value of the telescopic length and the improved mantis searching algorithm, and determining the optimal PID parameters of the pipeline robot; inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model, and outputting the telescopic control quantity of the telescopic support leg so as to enable the pipeline robot to adjust the telescopic support leg according to the telescopic control quantity. In the mode, the optimal PID parameters of the pipeline robot are determined by improving the mantis search algorithm, and the telescopic control quantity of the telescopic support legs of the pipeline robot is determined according to the optimal PID parameters and the telescopic support leg control simulation model of the pipeline robot, so that the telescopic support legs of the pipeline robot are adjusted according to the telescopic control quantity by the pipeline robot, the accuracy of telescopic length control of the telescopic support legs of the pipeline robot is improved, and the accuracy and efficiency of operation of the pipeline robot are improved.
Embodiment two:
Fig. 8 is a schematic diagram of a control system of a telescopic leg of a pipeline robot according to a second embodiment of the present invention.
Referring to fig. 8, a control system of a telescopic leg of a pipe robot includes:
the telescopic length acquisition module 1 is used for calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot.
And the optimal PID parameter calculation module 2 is used for setting the PID parameters of the pipeline robot according to the telescopic length difference value and the improved mantis search algorithm, and determining the optimal PID parameters of the pipeline robot.
And the telescopic control quantity output module 3 is used for inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model and outputting the telescopic control quantity of the telescopic support leg so as to enable the pipeline robot to adjust the telescopic support leg according to the telescopic control quantity.
The embodiment of the invention provides a control system for telescopic support legs of a pipeline robot, which is characterized in that an optimal PID parameter of the pipeline robot is determined by improving a mantis search algorithm, and the telescopic control quantity of the telescopic support legs of the pipeline robot is determined according to the optimal PID parameter and a simulation model for controlling the telescopic support legs of the pipeline robot, so that the telescopic support legs of the pipeline robot are adjusted according to the telescopic control quantity by the pipeline robot, the accuracy of telescopic length control of the telescopic support legs of the pipeline robot is improved, and the accuracy and efficiency of operation of the pipeline robot are improved.
Embodiment III:
fig. 9 is a schematic structural diagram of a pipeline robot according to a third embodiment of the present invention.
Referring to fig. 9, the pipe robot includes a robot body 4, a camera 5, telescopic legs 6, a motor 7, and wheels 9; the control system 8 of the telescopic support leg of the pipeline robot is also included; the control system 8 controls the telescopic support legs 6 to move in a telescopic way through the motor 7; the telescopic leg 6 is used to connect the robot body 4 and the wheels 9.
The control system 8 is deployed in the robot body 4; the number of the telescopic support legs 6 can be three, one end of the telescopic support legs is connected with the robot body 4, and the other end of the telescopic support legs is connected with the wheels 9. Wheels 9 are used for the pipe robot to move inside the pipe.
The telescopic length of the telescopic support leg of the pipeline robot influences the gesture of the robot in the pipeline, the telescopic support leg can be adaptively changed according to the inner diameter size of the pipeline, the motion of the pipeline robot in the pipeline can be guaranteed, the telescopic motion of the telescopic support leg is adjusted through motor rotation, and the larger the rotating rudder angle of the motor is, the larger the telescopic amplitude of the telescopic support leg is.
The embodiment of the invention provides a pipeline robot, in the mode, the motion of the pipeline robot in a pipeline is ensured through the self-adaptive change of the telescopic support legs according to the inner diameter size of the pipeline, so that the accuracy of the telescopic length control of the telescopic support legs of the pipeline robot is improved, and the accuracy and the efficiency of the operation of the pipeline robot are further improved.
The embodiment of the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the control method of the telescopic support leg of the pipeline robot provided by the embodiment when executing the computer program.
The computer program product provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to perform the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment and will not be described herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
In addition, in the description of embodiments of the present invention, unless explicitly stated and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A control method of a telescopic leg of a pipe robot, comprising:
Calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot;
setting PID parameters of the pipeline robot according to the telescopic length difference value and the improved mantis search algorithm, and determining optimal PID parameters of the pipeline robot;
Inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model, and outputting the telescopic control quantity of the telescopic support leg so that the pipeline robot can adjust the telescopic support leg according to the telescopic control quantity;
The improved mantis search algorithm is set by the following method:
Adjusting the distance factor of a standard mantis search algorithm through a random distribution function to obtain an adjusted distance factor; the adjusted distance factor is:
Wherein, As said adjusted distance factor,/>Is a random number between two values of 0 and 1,/>For the current iteration number of the standard mantis search algorithm,/>For the total iteration number of the standard mantis search algorithm,/>Is a preset speed factor,/>As a random distribution function,/>Is the lower limit of random number,/>Is the upper limit of the random number,/>For the number of random numbers,/>Is the dimension of the random number;
acquiring a current optimal fitness value and a current worst fitness value of the standard mantis search algorithm, and adjusting an optimizing mechanism of the standard mantis search algorithm according to a preset circulation system optimizer, the current optimal fitness value and the current worst fitness value to obtain an adjusted optimizing mechanism; the adjusted optimizing mechanism is as follows:
Wherein, For the best position updated by mantis population,/>For/>Individual mantis positions/>, relative to individual mantis positionsControl parameters during movement,/>For mantis individual position/>Relative to mantis individual position/>Control parameters during movement,/>、/>And/>For the position of the mantis with the smallest first three names in the current population fitness,/>For the fitness weight coefficient,/>For/>Fitness value of individual positions of mantis,/>For the current worst fitness value,/>For the current optimal fitness value,/>For mantis population maximum scale,/>For/>And the current iteration position of the individual mantis.
2. The method for controlling the telescopic leg of the pipe robot according to claim 1, wherein the method further comprises, before the step of calculating the telescopic length difference between the target telescopic length and the real-time telescopic length, based on the target telescopic length and the real-time telescopic length of the telescopic leg of the pipe robot:
Acquiring a real-time rotating rudder angle of a motor of the pipeline robot;
calculating the real-time telescopic length according to the real-time rotary rudder angle and a preset conversion mathematical model; the conversion mathematical model is as follows:
Wherein, For the real-time telescoping length,/>The total polynomial number is equal to the prey scale of the mantis in the improved mantis searching algorithmFor the current item number,/>For the preset/>Conversion coefficient of term,/>And rotating the rudder angle in real time.
3. The method for controlling the telescopic leg of the pipeline robot according to claim 1, wherein the step of setting PID parameters of the pipeline robot according to the telescopic length difference and the modified mantis search algorithm, and determining the optimal PID parameters of the pipeline robot comprises:
s1: acquiring an absolute error integral index and a time error integral performance index corresponding to a PID controller of the pipeline robot;
S2: generating an objective function corresponding to the PID controller based on the telescopic length difference value, the absolute error integration index and the time error integration performance index;
S3: initializing parameters of the improved mantis searching algorithm and the initial position of each mantis in the mantis population; wherein each PID parameter corresponds to an individual position of one mantis; the parameters of the improved mantis search algorithm comprise the maximum iteration times;
S4: constructing a position updating mathematical model of the mantis population, updating the initial position of each mantis according to the position updating mathematical model, and obtaining the individual position of each mantis after updating;
s5: calculating the fitness value of the individual position of each updated mantis according to the objective function, and adding 1 to the iteration times; the initial value of the iteration times is 0;
S6: determining the minimum fitness value of the fitness values as the current minimum fitness value, and judging whether the current minimum fitness value is smaller than the optimal minimum fitness value or not;
S7: if the current minimum fitness value is smaller than the optimal minimum fitness value, updating the optimal minimum fitness value according to the current minimum fitness value;
s8: repeating the steps S4-S7 until the iteration number is equal to the maximum iteration number;
S9: and decoding the individual position of each mantis corresponding to the optimal minimum fitness value to obtain the optimal PID parameter of the pipeline robot.
4. A control method of a telescopic foot of a pipe robot according to claim 3, wherein the objective function is:
Wherein, For the first preset weight coefficient,/>For the second preset weight coefficient,/>For the difference of the telescopic length,/>The time from the target telescoping length to the real-time telescoping length for the telescoping leg.
5. The control method of telescopic support legs of the pipeline robot according to claim 3, wherein the parameters of the improved mantis search algorithm comprise a circulation control factor and a development factor;
the step of constructing a position updating mathematical model of the mantis population, and updating the individual position of each mantis according to the position updating mathematical model comprises the following steps:
Acquiring the circulation control factor and a preset random disturbance, and judging whether the circulation control factor is larger than or equal to the random disturbance;
If yes, updating the initial position of each mantis according to a preset first global exploration model to obtain a first updated individual position;
if not, updating the individual position of each mantis according to a preset second global exploration model to obtain the first updated individual position;
acquiring a second random number and a fourth random number, and judging whether the fourth random number is larger than the second random number or not;
If so, updating the first updated individual position according to a preset first local development model to obtain updated individual positions of the mantis;
If not, updating the first updated individual position according to a preset second local development model to obtain a second updated individual position; judging whether the development factor is larger than the fourth random number; if the development factor is larger than the fourth random number, updating the second updated individual position according to an optimizing mechanism of the improved mantis searching algorithm to obtain updated individual positions of each mantis; if the development factor is smaller than or equal to the fourth random number, determining the second updated individual position as the updated individual position of each mantis;
wherein the first global survey model is as follows:
Wherein, For/>Individual mantis/>Location of the next iteration,/>For/>Position of current iteration of individual mantis, i.e./>For values generated based on Levy flight strategy,/>For random numbers generated based on normal distribution,/>、/>、/>For randomly selected individual positions in the respective current population,/>Is the first random number in interval [0,1 ]/>Is the second random number with interval of [0,1 ]/>A random number of 0 or 1;
The second global survey model is as follows:
Wherein, For the optimal position of the current iteration,/>And/>Upper and lower bounds optimized for mantis algorithm,/>For the improved nonlinear distance factor,/>Is the sixth random number with interval of [0,1 ]/>Is the seventh random number in interval [0,1 ]/>Eighth random number in interval [0,1 ]/>Ninth random number in interval [0,1 ]/>A tenth random number in the interval of [0,1 ];
the first local development model is as follows:
Wherein, A twelfth random number with interval of [0,1 ];
The second local development model is as follows:
Here the number of the elements is the number, Is the position of the latest prey,/>For the first proposed speed factor,/>To control the mantis hunting speed,/>Is the distance between the current mantis and the prey.
6. The method of controlling the telescopic leg of the pipe robot according to claim 1, wherein the telescopic control amount includes a target telescopic length of the telescopic leg and a target rudder angle of a motor of the pipe robot.
7. A control system for a telescoping leg of a pipe robot, comprising:
the telescopic length acquisition module is used for calculating a telescopic length difference value between the target telescopic length and the real-time telescopic length based on the target telescopic length and the real-time telescopic length of the telescopic support leg of the pipeline robot;
the optimal PID parameter calculation module is used for setting PID parameters of the pipeline robot according to the telescopic length difference value and the improved mantis search algorithm, and determining the optimal PID parameters of the pipeline robot;
The telescopic control quantity output module is used for inputting the optimal PID parameters into a pre-built pipeline robot telescopic support leg control simulation model and outputting the telescopic control quantity of the telescopic support leg so that the pipeline robot can adjust the telescopic support leg according to the telescopic control quantity;
The improved mantis search algorithm is set by the following method:
Adjusting the distance factor of a standard mantis search algorithm through a random distribution function to obtain an adjusted distance factor; the adjusted distance factor is:
Wherein, As said adjusted distance factor,/>Is a random number between two values of 0 and 1,/>For the current iteration number of the standard mantis search algorithm,/>For the total iteration number of the standard mantis search algorithm,/>Is a preset speed factor,/>As a random distribution function,/>Is the lower limit of random number,/>Is the upper limit of the random number,/>For the number of random numbers,/>Is the dimension of the random number;
acquiring a current optimal fitness value and a current worst fitness value of the standard mantis search algorithm, and adjusting an optimizing mechanism of the standard mantis search algorithm according to a preset circulation system optimizer, the current optimal fitness value and the current worst fitness value to obtain an adjusted optimizing mechanism; the adjusted optimizing mechanism is as follows:
Wherein, For the best position updated by mantis population,/>For/>Individual mantis positions/>, relative to individual mantis positionsControl parameters during movement,/>For mantis individual position/>Relative to mantis individual position/>Control parameters during movement,/>、/>And/>For the position of the mantis with the smallest first three names in the current population fitness,/>For the fitness weight coefficient,/>For/>Fitness value of individual positions of mantis,/>For the current worst fitness value,/>For the current optimal fitness value,/>The mantis population is the largest scale; /(I)For/>And the current iteration position of the individual mantis.
8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and wherein the processor, when executing the computer program, implements the method for controlling the telescopic leg of the pipe robot according to any one of claims 1-6.
9. The pipeline robot is characterized by comprising a robot body, a camera, telescopic support legs, a motor and wheels; a control system for telescopic support legs of a pipe robot according to claim 7; the control system controls the telescopic support legs to move in a telescopic mode through the motor; the telescopic support legs are used for connecting the robot body and the wheels.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410289090.4A CN117891162B (en) | 2024-03-14 | 2024-03-14 | Control method and system of telescopic support leg of pipeline robot and pipeline robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410289090.4A CN117891162B (en) | 2024-03-14 | 2024-03-14 | Control method and system of telescopic support leg of pipeline robot and pipeline robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117891162A CN117891162A (en) | 2024-04-16 |
| CN117891162B true CN117891162B (en) | 2024-06-07 |
Family
ID=90642794
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410289090.4A Active CN117891162B (en) | 2024-03-14 | 2024-03-14 | Control method and system of telescopic support leg of pipeline robot and pipeline robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117891162B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106439387A (en) * | 2016-12-07 | 2017-02-22 | 中国计量大学 | Pipeline robot capable of self-adapting to pipe diameter |
| CN111928841A (en) * | 2020-09-15 | 2020-11-13 | 天津瀚海蓝帆海洋科技有限公司 | Modular pipeline surveying underwater robot |
| CN216201603U (en) * | 2021-10-28 | 2022-04-05 | 崔鹏鹏 | Pipeline inspection robot convenient to change camera for municipal road construction |
| CN115126962A (en) * | 2022-06-13 | 2022-09-30 | 燕山大学 | Bionic unpowered pipeline robot and control method |
| WO2023121627A2 (en) * | 2021-12-23 | 2023-06-29 | Jazari̇ Dynami̇cs Mekatroni̇k Ve Yazilim Anoni̇m Şi̇rketi̇ | In-pipe panoramic inspection system |
| CN116892664A (en) * | 2023-08-18 | 2023-10-17 | 北京信息科技大学 | Gas-electricity hybrid drive pipeline detection robot |
| CN117369244A (en) * | 2023-11-08 | 2024-01-09 | 重庆衍数自动化设备有限公司 | Welding gun position control optimization method based on welding robot |
| CN117539144A (en) * | 2023-12-11 | 2024-02-09 | 常州大学 | PID control method and system based on improved hunter prey algorithm |
-
2024
- 2024-03-14 CN CN202410289090.4A patent/CN117891162B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106439387A (en) * | 2016-12-07 | 2017-02-22 | 中国计量大学 | Pipeline robot capable of self-adapting to pipe diameter |
| CN111928841A (en) * | 2020-09-15 | 2020-11-13 | 天津瀚海蓝帆海洋科技有限公司 | Modular pipeline surveying underwater robot |
| CN216201603U (en) * | 2021-10-28 | 2022-04-05 | 崔鹏鹏 | Pipeline inspection robot convenient to change camera for municipal road construction |
| WO2023121627A2 (en) * | 2021-12-23 | 2023-06-29 | Jazari̇ Dynami̇cs Mekatroni̇k Ve Yazilim Anoni̇m Şi̇rketi̇ | In-pipe panoramic inspection system |
| CN115126962A (en) * | 2022-06-13 | 2022-09-30 | 燕山大学 | Bionic unpowered pipeline robot and control method |
| CN116892664A (en) * | 2023-08-18 | 2023-10-17 | 北京信息科技大学 | Gas-electricity hybrid drive pipeline detection robot |
| CN117369244A (en) * | 2023-11-08 | 2024-01-09 | 重庆衍数自动化设备有限公司 | Welding gun position control optimization method based on welding robot |
| CN117539144A (en) * | 2023-12-11 | 2024-02-09 | 常州大学 | PID control method and system based on improved hunter prey algorithm |
Non-Patent Citations (3)
| Title |
|---|
| A Novel Mantis Search Algorithm for Economic Dispatch in Combined Heat and Power Systems;Moustafa G, Alnami H, Hakmi S H, et al.;IEEE Access;20240131;全文 * |
| Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems;Abdel-Basset M, Mohamed R, Zidan M, et al;Computer Methods in Applied Mechanics and Engineering;20231231;全文 * |
| 仿生算法在地下水模型反演中的应用现状与展望;刘国东;戴振学;邢冰;王焱;孟玉川;李俊;;水文地质工程地质;20160115(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117891162A (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11132606B2 (en) | Reinforcement learning to train a character using disparate target animation data | |
| Vecerik et al. | Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards | |
| KR102457974B1 (en) | Method and apparatus for searching new material | |
| JP6513015B2 (en) | Method for controlling machine operation, and control system for repetitively controlling machine operation | |
| JP5059939B2 (en) | Character simulation method and system | |
| US11366433B2 (en) | Reinforcement learning method and device | |
| CN116610025B (en) | PID controller optimization method based on improved meta heuristic algorithm | |
| CN110462531A (en) | For controlling the model predictive control system and method for machine operation | |
| US20210174909A1 (en) | Generative machine learning models for predicting functional protein sequences | |
| CN113093779B (en) | Robot motion control method and system based on deep reinforcement learning | |
| JP2010287027A (en) | Information processor, information processing method and program | |
| JP5549112B2 (en) | PID adjustment device and PID adjustment program | |
| CN112930541A (en) | Determining a control strategy by minimizing delusional effects | |
| CN117891162B (en) | Control method and system of telescopic support leg of pipeline robot and pipeline robot | |
| Nygaard et al. | Multi-objective evolution of fast and stable gaits on a physical quadruped robotic platform | |
| Bossens et al. | Learning behaviour-performance maps with meta-evolution | |
| Ertel | Reinforcement learning | |
| CN108549928B (en) | Continuous movement-based visual tracking method and device under deep reinforcement learning guidance | |
| CN117733863A (en) | Robot motion control method, device, equipment, robot and storage medium | |
| CN113341696A (en) | Intelligent setting method for attitude control parameters of carrier rocket | |
| KR102580138B1 (en) | Character motion generating method for moving to target position and computer apparatus | |
| WO2020121494A1 (en) | Arithmetic device, action determination method, and non-transitory computer-readable medium storing control program | |
| CN115577755A (en) | Robot posture correction method, apparatus, computer device, and storage medium | |
| KR20200066740A (en) | Randomized reinforcement learning for control of complex systems | |
| CN117103241A (en) | Robot attitude control method, apparatus, computer device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |