CN118393901A - Air pump control optimization method for tree climbing robot - Google Patents
Air pump control optimization method for tree climbing robot Download PDFInfo
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Abstract
The invention discloses an air pump control optimization method for a tree climbing robot, which relates to the field of PID control and comprises the following steps: establishing an air pump control mathematical model of the tree climbing robot and an air pump control system of the tree climbing robot, wherein the control system comprises an air pump control unit and an execution unit, and according to the relationship of air pressure, tree diameter and climbing speed, an artificial original optimization algorithm is improved, and the improved artificial original optimization algorithm is utilized to optimize an air pump PID closed-loop control algorithm of the tree climbing robot; collecting real-time pressure data P (t) between a moment arm of the tree climbing robot and the tree by using a pressure sensor, and calculating an error value e (t) of a target pressure value Pa between the moment arm of the robot and the tree and the real-time pressure data P (t); and calculating an air pump PID control output value u (t) according to the e (t) and parameters Kp, ki and Kd of the PID controller, converting the u (t) into a control signal of the air pump, improving the air pump adjusting gas output precision, and reaching a target air pressure value.
Description
Technical Field
The invention belongs to the technical field of air pump PID control, and particularly relates to an air pump control optimization method for a tree climbing robot.
Background
The pneumatic tree climbing robot combines soft materials, bionic structures and the like, so that the motion adaptability and the attachment reliability of the rigid robot are greatly improved, the robot has the capability of performing crawling operation in unstructured environments such as forests, rod-shaped object equipment and the like, and the pneumatic tree climbing robot has wide application prospects in the fields of forests high-altitude investigation, resource exploration, major disaster rescue and the like; however, when the existing tree climbing robot controls air pressure to realize stable climbing and moving, the problems of low control precision, high energy consumption and the like exist, so how to optimize air pressure control to improve the working efficiency and stability of the tree climbing robot is a technical problem to be solved.
The pneumatic tree climbing robot mainly outputs air pressure to control four force arms of the robot through an air pump, and the main difficulty is that: ① The arm joint of the pneumatic tree climbing robot is mainly made of elastic materials, and the deformation of the arm joint is extremely easily influenced by external load, so that the self-adaption is poor; ② The nonlinear control of the pneumatic tree climbing robot makes dynamic modeling extremely difficult, so that the control and positioning effects of the pneumatic tree climbing robot are poor; ③ Passive deformation of the flexible material when in contact with the grasping target also makes shape detection particularly difficult.
In the actual operation process of the pneumatic tree climbing robot, the air pump needs to be accurately controlled to maintain stable air pressure, and the arm control of the tree climbing robot is realized through the air pressure, so that stable climbing of the tree climbing robot is ensured; the PID controller is widely applied to various tree climbing robot controls due to the simplicity, easiness in realization and good control effect, however, optimization of PID control parameters is always a challenge, and the accuracy of the parameters influences the tree climbing robot arm control.
The artificial pro-optimization Algorithm (APO) is a novel bionic meta-heuristic algorithm, the design inspiration of which is derived from protozoa in nature, and simulates the foraging, dormancy and propagation behaviors of the protozoa. The global search phase of the algorithm includes autotrophic mode and heterotrophic behavior; the algorithm local development stage comprises a sleep mode and a propagation behavior; the artificial raw optimization Algorithm (APO), while excellent in solving engineering optimization problems, has some drawbacks such as: the defects of difficult parameter adjustment and limited adaptability to large-scale problems affect the convergence accuracy and optimizing efficiency of the algorithm.
Disclosure of Invention
The invention aims at: aiming at the problems of the air pump control robot arm of the tree climbing robot in the background technology, the invention optimizes the PID closed-loop algorithm of the air pump control of the tree climbing robot by utilizing the improved manual original optimization algorithm, and provides the robustness of the PID closed-loop algorithm of the air pump, thereby improving the control precision and the response speed of the arm of the robot, and solving the problem of low working efficiency caused by poor sensitivity and self-adaptation capability of the arm of the tree climbing robot in the working process.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the utility model provides an air pump control optimization method for climbing robot, includes climbing robot's air pump control system, utilizes pressure sensor to gather the pressure data between climbing robot arm of force and the trees to adjust the output atmospheric pressure of air pump through the PID controller, improve robot arm of force implementation crookedness's accuracy and robustness, the specific step is.
S1, establishing an air pump control mathematical model of the tree climbing robot according to the relation of air pressure, tree diameters and climbing speeds.
S2, establishing an air pump control system of the tree climbing robot, wherein the control system comprises an air pump control unit and an execution unit.
S3, improving an artificial original optimization algorithm, optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using the improved artificial original optimization algorithm, and improving the artificial original optimization algorithm, wherein the specific steps are as follows:
s31, improving the scale factors of dormancy and propagation of artificial original population The population scale of the local development stage of the artificial original optimization algorithm is adjusted, and the algorithm position updating strategies of the local development stage and the global searching stage are adjusted;
S32, improving foraging factors of the artificial original optimization algorithm by introducing a dynamic adjustment mechanism based on the diversity of the artificial original population and the change of the optimal solution of the artificial original position ；
S33, utilizing improved foraging factorsThe position updating mathematical model of the global searching stage of the artificial original optimization algorithm is improved;
S34, providing a uniform statistical search initialization method, uniformly initializing the positions of the artificial original population, and optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using an improved artificial original optimization algorithm;
s4, acquiring real-time pressure data P (t) between the arm of the tree climbing robot and the tree by using a pressure sensor, and calculating an error value e (t) of the target pressure value Pa between the arm of the tree climbing robot and the tree and the real-time pressure data P (t).
S5, calculating an air pump PID control output value u (t) according to the error value e (t) and parameters Kp, ki and Kd of the PID controller, converting the output value u (t) into a control signal of the air pump, enabling the air pump to adjust gas output, and controlling a robot arm.
More specifically, the air pump control mathematical model of the tree climbing robot is as follows:
（1）；
In the formula (1), Y (t) is the variation degree of a moment arm of the tree climbing robot, m is the mass (kg) of the robot, g is the gravity acceleration (9.81 m/s; mu is the friction coefficient), W (t) is the tree diameter of different time periods, d (t) is the contact width of the moment arm of the robot and the tree in different time periods, k v is the speed, t is the time unit of seconds, and u (t) is the PID control output value of the air pump.
More specifically, the air pump control system of the tree climbing robot comprises an air pump control unit and an execution unit, wherein the air pump control unit comprises an air pump controller, the air pump controller adopts a PID closed-loop control algorithm, a real-time pressure data difference e (t) between a moment arm of the tree climbing robot and a tree is input into the air pump controller, and the real-time pressure data difference e (t) is calculated by a formulaCalculating an air pump PID control output value u (T) of the tree climbing robot, inputting the air pump PID control output value u (T) into an air pump control mathematical model of the tree climbing robot, namely a formula (1), so that the degree of change of an arm of the tree climbing robot is changed, meanwhile, a pressure sensor collects real-time pressure data P (T) between the arm of the tree climbing robot and a tree, returns to calculate a real-time pressure data difference e (T) between the arm of the tree climbing robot and the tree, and realizes closed-loop control of the tree climbing robot until T reaches a control maximum time T, and an execution unit is a permanent magnet motor for providing the arm of the tree climbing robot with a gas driving robot.
More specifically, the scaling factor of dormancy and propagation of artificial primitive populations of primitive artificial primitive optimization algorithmsThe method and the device determine the proportion of the number of individuals sleeping and propagating in the artificial original population to the total population number, are simply and randomly determined and cannot respond to the change of the population state in time, dynamically adjust the proportion of sleeping and propagating based on the population diversity and the fitness change rate, adjust the population scale of the sleeping and propagating stage of the artificial original optimization algorithm, enable the algorithm to be more flexible, better balance exploration and development, and improve the optimization efficiency and effect; wherein, the population diversity is measured by calculating the distance between artificial original individuals, and the improved artificial original population dormancy and propagation scale factorsThe mathematical model is:
(2)；
In the formula (2), the amino acid sequence of the compound, The improved scaling factor for dormancy and propagation of artificial original populations for the ith iteration,Ranking the fitness values of the ith individual in the population, N being the maximum scale of the artificial source,For the diversity of the current population for the ith iteration,Is the diversity of the initial population. And adjusting the overall dormancy and propagation proportion by using the population diversity index, and avoiding premature convergence to a local optimal solution.
More specifically, the foraging factor of the artificial original optimization algorithm is improved by introducing a dynamic adjustment mechanism based on the diversity of the artificial original population and the change of the adaptability of the artificial original positionImproved foraging factorThe mathematical model is:
（3）；
In the formula (3), the amino acid sequence of the compound, For the improved foraging factor of the ith iteration,For the improved minimum value of foraging factors, the value is 0,The value of the improved foraging factor is 2; For the current number of iterations, For the maximum number of iterations to be performed,Is a dynamic adjustment mechanism.
More specifically, dynamic adjustment mechanismBased on the artificial original population entropy change rate and the optimal solution change rate, the mathematical model is:
（4）；
In the formula (4), the amino acid sequence of the compound, For the entropy value of the position of the artificial original population in the ith iteration of (item+1),For the entropy value of the artificial original population position of the ith iteration,For the entropy value of the initial artificial original population position,For the optimal solution fitness value of the iterative population of the ith time,For the previous population optimal solution fitness value,The value is 0.001, wherein the calculation formula of the entropy value of the artificial original group position is as follows: wherein N is the maximum population size, The probability of the ith artificial primitive is obtained through normalization of the position fitness value.
More specifically, compared with the original foraging factors, the improved dynamic adjustment foraging factors introduce the relation between the population entropy value and the optimal solution change, so that the adjustment of the foraging factors is more complex and diversified; using improved foraging factorsThe method improves the position updating mathematical model of the global searching stage of the artificial original optimization algorithm, so that the algorithm can dynamically adapt to the change in the searching process, the searching is more effectively carried out in the early stage, the developing is carried out in the later stage, the convergence process is accelerated, and the adaptability of the tree climbing robot in the complex environment is improved.
More specifically, improved foraging factors are utilizedThe position updating mathematical model of the global searching stage of the improved artificial original optimization algorithm is as follows:
（5）；
In the formula (5), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive,For the ith iteration randomThe position of the artificial origin is changed,For the number of pairs of artificial original neighbors,Is a foraging mapping vector with the size of 1X dim, the value in the vector is 1,As a weight for the autotrophic mode,For the weight of the heterotrophic mode, rand is a random number within 0 to 1,Randomly selected artificial home positions in the k-th pairing neighborhood, where k is greater than i,Randomly selecting artificial original positions in the k pairing neighborhood, wherein k is less than i; the i-k h artificial home position selected for the k-th pairing neighbor, An i+k-th artificial home location selected for the k-th pairing neighbor; For demarcation parameters for autotrophic and heterotrophic behavior, An improved foraging factor for the ith iteration.
More specifically, when an improved artificial original optimization algorithm is utilized to optimize an air pump PID closed-loop control algorithm of the tree climbing robot, an objective function is required to be designed to guide the optimization of the algorithm to the air pump PID closed-loop control algorithm, the air pump control of the tree climbing robot mainly considers the air pump output value of the tree climbing robot through control, so that the precision of the variation degree of the arm of the tree climbing robot is improved, the radius difference between the radius of the adjusted arm and the radius of the tree is as small as possible, and the tree climbing robot works more stably, so that an objective function is designed by taking the error value e (t) of the objective pressure value Pa and the real-time pressure data P (t) between the arm of the tree climbing robot and the tree and the variation degree Y (t) of the arm of the tree climbing robot into consideration, and the objective functionThe mathematical model is:
（6）；
in the formula (6), T is the control maximum time, The weight of the variation Y (t) of the arm of the tree climbing robot is that of the arm of the tree climbing robot.
More specifically, an improved artificial original optimization algorithm is utilized to optimize an air pump PID closed-loop control algorithm of the tree climbing robot, and the improved artificial original optimization algorithm is utilized to control a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd of the air pump PID closed-loop control algorithm of the tree climbing robot, wherein the specific steps are as follows:
setting an artificial original scale N of an improved artificial original optimization algorithm, a maximum iteration number Max_iter, a problem dimension dim, an upper bound UB and a lower bound LB;
step two, uniformly initializing the artificial original population by using a uniform statistical search initialization method according to a formula (7), so that the artificial original individuals cover the search space of the whole improved artificial original optimization algorithm; upper and lower limits LB on the search space of the improved artificial original optimization algorithm;
（7）；
In the formula (7), the amino acid sequence of the compound, The initialized position of the j-th dimension of the i-th artificial original individual is obtained, wherein,;，Is the lower limit position of the original artificial individual,Is the upper limit position of the original artificial individual,For the global optimal artificial original body position of the last iteration, rand (0, 1) is a random number in 0 to 1;
Thirdly, establishing three-dimensional mapping between the position of an artificial original body in the search space and a vector formed by Kp, ki and Kd of an air pump PID closed-loop control algorithm of the tree climbing robot;
Calculating the position fitness value of each artificial original individual in the artificial original population of the ith iteration by utilizing the objective function, and taking the position of the artificial original individual corresponding to the smaller fitness value in the population as the optimal position of the current iteration;
Establishing a population position updating mathematical model of the improved artificial original optimization algorithm in a global searching stage and a local development stage, and updating the positions of artificial original individuals, wherein the method comprises the following specific steps of:
S351, calculating the scale factors of dormancy and propagation of the artificial original population improved by the ith iteration by using the formula (2) The size is set asIf the ith individual of the current iteration is within the population range, the ith individual location update performs step S352; otherwise, step S353 is executed;
S352, establishing an artificial original body position updating mathematical model in an algorithm local convergence stage according to a formula (8) according to dormancy and propagation behaviors of the model artificial original body;
（8）；
in the formula (8), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive, rand is a random number within 0 to 1,Boundary parameters for dormancy and propagation whenWhen the number of the propagation positions is larger than the rand, executing the dormant position updating mathematical model of the artificial origin, otherwise, executing the propagation position updating mathematical model of the artificial origin;
s353, searching behavior of the model artificial source through improved foraging factors The method comprises the steps that (1) a position updating mathematical model of a global searching stage of an improved manual original optimization algorithm is adopted, namely, a formula (5) builds a manual original individual position mathematical model of the algorithm global searching stage;
Step six, calculating the position fitness value of each individual in the artificial original population after the position updating by using the objective function again, and taking the individual position corresponding to the currently obtained minimum fitness value as the global optimal artificial original individual position;
Step seven, executing item=item+1, judging whether the current iteration number item satisfies item=max_item, if so, outputting and analyzing the artificial original body position corresponding to the global minimum fitness value into Kp, ki and Kd parameter values of an air pump PID closed-loop control algorithm of the climbing robot, otherwise, executing step two, and uniformly initializing the artificial original population by using a uniform statistics search initialization method according to a formula (7).
Compared with the prior art, the invention has the beneficial effects that: the dormancy and propagation proportion of the artificial original optimization algorithm is dynamically adjusted based on population diversity and fitness change rate, so that the algorithm is more flexible, the exploration and development stages can be better balanced, and the optimization efficiency and effect of the algorithm are improved; the improved manual original optimization algorithm is used for optimizing the PID closed-loop algorithm controlled by the air pump of the tree climbing robot, so that the control precision and the response speed of the arm of the robot are obviously improved, and the problem of low working efficiency caused by poor sensitivity and self-adaptation capability of the arm of the robot is solved; meanwhile, the output of the air pump is accurately controlled, so that the air pressure is kept stable, the climbing robot is ensured to stably climb in unstructured environments such as forests and rod-shaped devices, and the adaptability and the reliability of the climbing robot are enhanced.
Drawings
FIG. 1 is a diagram of the steps of a method for optimizing the control of an air pump of a tree climbing robot using an improved artificial original optimization algorithm;
FIG. 2 is a flow chart of a method for optimizing the air pump PID closed-loop control algorithm of the tree climbing robot by using the improved manual original optimization algorithm;
FIG. 3 is a graph comparing the results of Kp, ki and Kd setting of the improved artificial original optimization algorithm and the standard artificial original optimization algorithm on the air pump PID closed-loop control algorithm of the tree climbing robot;
FIG. 4 is a graph showing the comparison of the fitness function change of the air pump PID closed-loop control algorithm of the climbing robot by using the improved artificial original optimization algorithm and the standard artificial original optimization algorithm;
Fig. 5 is a comparison diagram of the effects of different control methods for controlling the air pump of the tree climbing robot.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
The invention provides a technical scheme that: the utility model provides an air pump control optimization method for climbing robot, includes climbing robot's air pump control system, utilizes pressure sensor to gather the pressure data between climbing robot arm of force and the trees to adjust the output atmospheric pressure of air pump through the PID controller, improve robot arm and implement accuracy and the robustness of degree of variation, first as shown in figure 1, climbing robot's air pump control specific step is.
S1, establishing an air pump control mathematical model of the tree climbing robot according to the relation of air pressure, tree diameters and climbing speeds.
Further, the air pump control mathematical model of the tree climbing robot is as follows:
（1）；
In the formula (1), Y (t) is the variation degree of a moment arm of the tree climbing robot, m is the mass (kg) of the robot, g is the gravity acceleration (9.81 m/s; mu is the friction coefficient), W (t) is the tree diameter of different time periods, d (t) is the contact width of the moment arm of the robot and the tree in different time periods, k v is the speed, t is the time unit of seconds, and u (t) is the PID control output value of the air pump.
S2, establishing an air pump control system of the tree climbing robot, wherein the control system comprises an air pump control unit and an execution unit.
Further, the air pump control unit and the execution unit of the air pump control system of the tree climbing robot, wherein the air pump control unit comprises an air pump controller, the air pump controller adopts a PID closed-loop control algorithm, the real-time pressure data difference e (t) between the arm of the tree climbing robot and the tree is input into the air pump controller, and the real-time pressure data difference e (t) passes through the formulaCalculating an air pump PID control output value u (T) of the tree climbing robot, inputting the air pump PID control output value u (T) into an air pump control mathematical model of the tree climbing robot, namely a formula (1), so that the degree of change of an arm of the tree climbing robot is changed, meanwhile, a pressure sensor collects real-time pressure data P (T) between the arm of the tree climbing robot and a tree, returns to calculate a real-time pressure data difference e (T) between the arm of the tree climbing robot and the tree, and realizes closed-loop control of the tree climbing robot until T reaches a control maximum time T, and an execution unit is a permanent magnet motor for providing the arm of the tree climbing robot with a gas driving robot.
S3, improving an artificial original optimization algorithm, and optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using the improved artificial original optimization algorithm, wherein the specific steps are S31 to S34.
S31, improving the scale factors of dormancy and propagation of artificial original populationThe population scale of the local development stage of the artificial original optimization algorithm is adjusted, and the algorithm position updating strategies of the local development stage and the global searching stage are adjusted; furthermore, the invention dynamically adjusts the proportion of dormancy and reproduction based on the diversity of the population and the change rate of the adaptability, adjusts the population scale of the sleep and reproduction stage of the artificial original optimization algorithm, ensures that the algorithm is more flexible, can better balance exploration and development, and improves the optimization efficiency and effect; wherein, the population diversity is measured by calculating the distance between artificial original individuals, and the improved artificial original population dormancy and propagation scale factorsThe mathematical model is:
(2)；
In the formula (2), the amino acid sequence of the compound, The improved scaling factor for dormancy and propagation of artificial original populations for the ith iteration,Ranking the fitness values of the ith individual in the population, N being the maximum scale of the artificial source,For the diversity of the current population for the ith iteration,Is the diversity of the initial population. And adjusting the overall dormancy and propagation proportion by using the population diversity index, and avoiding premature convergence to a local optimal solution.
S32, improving foraging factors of the artificial original optimization algorithm by introducing a dynamic adjustment mechanism based on the diversity of the artificial original population and the change of the optimal solution of the artificial original position; Improved foraging factorThe mathematical model is:
（3）；
In the formula (3), the amino acid sequence of the compound, For the improved foraging factor of the ith iteration,For the improved minimum value of foraging factors, the value is 0,The value of the improved foraging factor is 2; For the current number of iterations, For the maximum number of iterations to be performed,Is a dynamic adjustment mechanism.
Further, dynamic adjustment mechanismBased on the artificial original population entropy change rate and the optimal solution change rate, the mathematical model is:
（4）；
In the formula (4), the amino acid sequence of the compound, For the entropy value of the position of the artificial original population in the ith iteration of (item+1),For the entropy value of the artificial original population position of the ith iteration,For the entropy value of the initial artificial original population position,For the optimal solution fitness value of the iterative population of the ith time,For the previous population optimal solution fitness value,The value is 0.001, wherein the calculation formula of the entropy value of the artificial original group position is as follows: wherein N is the maximum population size, The probability of the ith artificial primitive is obtained through normalization of the position fitness value.
S33, utilizing improved foraging factorsThe position updating mathematical model of the global searching stage of the artificial original optimization algorithm is improved; the position updating mathematical model of the global searching stage of the improved artificial original optimization algorithm is as follows:
（5）；
In the formula (5), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive,For the ith iteration randomThe position of the artificial origin is changed,For the number of pairs of artificial original neighbors,Is a foraging mapping vector with the size of 1X dim, the value in the vector is 1,As a weight for the autotrophic mode,For the weight of the heterotrophic mode, rand is a random number within 0 to 1,Randomly selected artificial home positions in the k-th pairing neighborhood, where k is greater than i,Randomly selecting artificial original positions in the k pairing neighborhood, wherein k is less than i; the i-k h artificial home position selected for the k-th pairing neighbor, An i+k-th artificial home location selected for the k-th pairing neighbor; For demarcation parameters for autotrophic and heterotrophic behavior, An improved foraging factor for the ith iteration.
S34, providing a uniform statistical search initialization method, uniformly initializing the positions of an artificial original population, optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using an improved artificial original optimization algorithm, and optimizing a proportional coefficient Kp, an integral coefficient Ki and a differential coefficient Kd of the air pump PID closed-loop control algorithm of the tree climbing robot by using the improved artificial original optimization algorithm, wherein the specific steps are as shown in fig. 2:
setting an artificial original scale N of an improved artificial original optimization algorithm, a maximum iteration number Max_iter, a problem dimension dim, an upper bound UB and a lower bound LB;
step two, uniformly initializing the artificial original population by using a uniform statistical search initialization method according to a formula (7), so that the artificial original individuals cover the search space of the whole improved artificial original optimization algorithm; upper and lower limits LB on the search space of the improved artificial original optimization algorithm;
（7）；
In the formula (7), the amino acid sequence of the compound, The initialized position of the j-th dimension of the i-th artificial original individual is obtained, wherein,;，Is the lower limit position of the original artificial individual,Is the upper limit position of the original artificial individual,For the global optimal artificial original body position of the last iteration, rand (0, 1) is a random number in 0 to 1;
Thirdly, establishing three-dimensional mapping between the position of an artificial original body in the search space and a vector formed by Kp, ki and Kd of an air pump PID closed-loop control algorithm of the tree climbing robot;
Calculating the position fitness value of each artificial original individual in the artificial original population of the ith iteration by utilizing the objective function, and taking the position of the artificial original individual corresponding to the smaller fitness value in the population as the optimal position of the current iteration;
Establishing a population position updating mathematical model of the improved artificial original optimization algorithm in a global searching stage and a local development stage, and updating the positions of artificial original individuals, wherein the method comprises the following specific steps of:
S351, calculating the scale factors of dormancy and propagation of the artificial original population improved by the ith iteration by using the formula (2) The size is set asIf the ith individual of the current iteration is within the population range, the ith individual location update performs step S352; otherwise, step S353 is executed;
S352, establishing an artificial original body position updating mathematical model in an algorithm local convergence stage according to a formula (8) according to dormancy and propagation behaviors of the model artificial original body;
（8）；
in the formula (8), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive, rand is a random number within 0 to 1,Boundary parameters for dormancy and propagation whenWhen the number of the propagation positions is larger than the rand, executing the dormant position updating mathematical model of the artificial origin, otherwise, executing the propagation position updating mathematical model of the artificial origin;
s353, searching behavior of the model artificial source through improved foraging factors The method comprises the steps that (1) a position updating mathematical model of a global searching stage of an improved manual original optimization algorithm is adopted, namely, a formula (5) builds a manual original individual position mathematical model of the algorithm global searching stage;
Step six, calculating the position fitness value of each individual in the artificial original population after the position updating by using the objective function again, and taking the individual position corresponding to the currently obtained minimum fitness value as the global optimal artificial original individual position;
Step seven, executing item=item+1, judging whether the current iteration number item satisfies item=max_item, if so, outputting and analyzing the artificial original body position corresponding to the global minimum fitness value into Kp, ki and Kd parameter values of an air pump PID closed-loop control algorithm of the climbing robot, otherwise, executing step two, and uniformly initializing the artificial original population by using a uniform statistics search initialization method according to a formula (7).
Furthermore, when the improved artificial original optimization algorithm is utilized to optimize the air pump PID closed-loop control algorithm of the tree climbing robot, an objective function is required to be designed to guide the optimization of the algorithm to the air pump PID closed-loop control algorithm, the air pump control of the tree climbing robot mainly considers the air pump output value of the tree climbing robot through control, so that the precision of the variation degree of the arm of the tree climbing robot is improved, the radius difference between the radius of the adjusted arm and the radius of the tree is as small as possible, and the tree climbing robot works more stably, and therefore, an objective function is designed by taking the error value e (t) of the objective pressure value Pa and the real-time pressure data P (t) between the arm of the tree climbing robot and the variation degree Y (t) of the arm of the tree climbing robot into consideration, and the objective functionThe mathematical model is:
（6）；
in the formula (6), T is the control maximum time, The weight of the variation Y (t) of the arm of the tree climbing robot is that of the arm of the tree climbing robot.
S4, acquiring real-time pressure data P (t) between the arm of the tree climbing robot and the tree by using a pressure sensor, and calculating an error value e (t) of the target pressure value Pa between the arm of the tree climbing robot and the tree and the real-time pressure data P (t).
S5, calculating an air pump PID control output value u (t) according to the error value e (t) and parameters Kp, ki and Kd of the PID controller, converting the output value u (t) into a control signal of the air pump, enabling the air pump to adjust gas output, forming a target air pressure value, and controlling a robot arm.
Further, designing codes of an improved artificial original optimization algorithm (FAPO) and a standard artificial original optimization Algorithm (APO) in Matlab, setting an artificial original population size N=40, a maximum iteration number Max_iter=60, a problem dimension dim=3, an upper bound UB= [60,100,15] and a lower bound LB= [0, 0]; using objective functionsThe air pump control system of the tree climbing robot builds an air pump control simulation model of the tree climbing robot in the Simulink, wherein the simulation model comprises an air pump PID control parameter input module, an objective function module and an air pump control mathematical model frequency domain expression module of the tree climbing robot, namely a controlled function module; inputting parameters of an optimal PID closed-loop control algorithm into an air pump PID control parameter input module, building an objective function module according to a formula (6), designing an air pump control mathematical model frequency domain expression of the tree climbing robot into a second-order complex frequency domain form, wherein the mathematical model is as follows:
where s is a complex frequency domain parameter.
Further, matlab and Siulink system simulation is run to obtain optimal Kp, ki and Kd parameter values of an air pump PID closed-loop control algorithm of the tree-climbing robot, which are obtained along with the increase of iteration times, as shown in fig. 3, and a change comparison graph of optimal fitness values of each iteration in the process of setting air pump PID closed-loop control algorithm control parameters of the tree-climbing robot by an improved artificial original optimization algorithm (FAPO) and a standard artificial original optimization Algorithm (APO), as shown in fig. 4, and finally, as shown in fig. 5, different control methods are output at Siumlink for comparing air pump control effects of the tree-climbing robot.
Further, the optimal Kp, ki and Kd parameter values of the air pump PID closed-loop control algorithm of the tree climbing robot are obtained after 60 iterations, as shown in fig. 3, the improved artificial original optimization algorithm (FAPO) adjusts the optimal kp=4.36, ki=1.02 and kd=1.31 obtained by the air pump PID closed-loop control algorithm of the tree climbing robot; the standard manual original optimization Algorithm (APO) adjusts the optimal kp=8.41, ki=1.02 and kd=2.75 obtained by the air pump PID closed-loop control algorithm of the tree climbing robot.
Furthermore, the adaptability value reflects the performance of the algorithm on the adjustment of the control parameters of the air pump PID closed-loop control algorithm of the tree climbing robot, and the smaller the adaptability value is, the better the performance on the adjustment of the control parameters of the air pump PID closed-loop control algorithm of the tree climbing robot is, and the greater the adaptability value is, the worse the performance is; as shown in fig. 4, the tuning fitness value of the improved artificial prime optimization algorithm (FAPO) is smaller at the initial stage of iteration compared with the tuning fitness value of the standard artificial prime optimization Algorithm (APO), which indicates that the improved artificial prime optimization algorithm (FAPO) has better performance for the control parameter tuning of the air pump PID closed loop control algorithm of the climbing robot, and the tuning fitness value of the improved artificial prime optimization algorithm (FAPO) decreases faster with the increase of the iteration number 10, which indicates that the tuning speed is faster, and finally, the tuning fitness value of the improved artificial prime optimization algorithm (FAPO) is smaller than the tuning fitness value of the standard artificial prime optimization Algorithm (APO) in the whole iterative tuning process, which indicates that the tuning accuracy of the improved artificial prime optimization algorithm (FAPO) is higher, and the performance of the improved artificial prime optimization algorithm (FAPO) is better.
Further, as shown in FIG. 5, the comparison of the effect performance of the air pump of the APO-PID algorithm and the FAPO-PID algorithm in controlling the tree climbing robot is shown; setting the target air pressure to be 2 unit pressures, wherein the rising time of the FAPO-PID method is shorter than that of the APO-PID method, which means that the FAPO-PID method can reach the target air pressure set value faster; meanwhile, as can be seen from the graph, the overshoot of the FAPO-PID is smaller than that of the APO-PID, the overshoot hardly exists, when the air pump output air pressure value of the FAPO-PID control tree climbing robot reaches a target value in about 2 seconds, the air pressure formed by the air pump output air under the FAPO-PID method is 1.8, the overshoot is 0.2, the stability of the FAPO-PID algorithm is higher, the fluctuation in the adjusting process is less, and finally, when the same steady-state value 2 units of air pressure value is treeled, the steady-state error of the FAPO-PID algorithm is smaller, so that the method provided by the invention can reach the set target value more accurately.
Claims (7)
1. The air pump control optimization method for the tree climbing robot is characterized by comprising the following specific steps of:
S1, establishing an air pump control mathematical model of the tree climbing robot according to the relation of air pressure, tree diameters and climbing speeds;
s2, establishing an air pump control system of the tree climbing robot, wherein the control system comprises an air pump control unit and an execution unit;
s3, improving an artificial original optimization algorithm, and optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using the improved artificial original optimization algorithm, wherein the specific steps are as follows:
s31, improving the scale factors of dormancy and propagation of artificial original population The population scale of the local development stage of the artificial original optimization algorithm is adjusted, and the algorithm position updating strategies of the local development stage and the global searching stage are adjusted;
S32, improving foraging factors of the artificial original optimization algorithm by introducing a dynamic adjustment mechanism based on the diversity of the artificial original population and the change of the optimal solution of the artificial original position ；
S33, utilizing improved foraging factorsThe position updating mathematical model of the global searching stage of the artificial original optimization algorithm is improved;
S34, providing a uniform statistical search initialization method, uniformly initializing the positions of the artificial original population, and optimizing an air pump PID closed-loop control algorithm of the tree climbing robot by using an improved artificial original optimization algorithm;
S4, acquiring real-time pressure data P (t) between a moment arm of the tree climbing robot and the tree by using a pressure sensor, and calculating an error value e (t) of a target pressure value Pa between the moment arm of the tree climbing robot and the tree and the real-time pressure data P (t);
S5, calculating an air pump PID control output value u (t) according to the error value e (t) and parameters Kp, ki and Kd of the PID controller, converting the output value u (t) into a control signal of the air pump, enabling the air pump to adjust gas output, and controlling a robot arm.
2. The optimization method for controlling the air pump of the tree climbing robot according to claim 1, wherein the mathematical model of the air pump control of the tree climbing robot in step S1 is:
（1）；
in the formula (1), Y (t) is the variation degree of a moment arm of the tree climbing robot, m is the mass of the robot, g is the gravity acceleration, mu is the friction coefficient, W (t) is the tree diameter of different time periods, d (t) is the contact width of the moment arm of the robot and the tree in different time periods, k v is the speed, t is the time unit of seconds, and u (t) is the PID control output value of the air pump.
3. The optimization method for air pump control of a tree climbing robot according to claim 2, wherein the air pump control unit and the execution unit of the air pump control system of the tree climbing robot in step S2 comprise an air pump controller, the air pump controller adopts a PID closed-loop control algorithm, a real-time pressure data difference e (t) between a moment arm of the tree climbing robot and a tree is input into the air pump controller, and the real-time pressure data difference e (t) is calculated by a formulaCalculating an air pump PID control output value u (T) of the tree climbing robot, inputting the air pump PID control output value u (T) into an air pump control mathematical model of the tree climbing robot, namely a formula (1), so that the degree of change of a moment arm of the tree climbing robot is changed, meanwhile, a pressure sensor collects real-time pressure data P (T) between the moment arm of the tree climbing robot and a tree, and returns to calculate a real-time pressure data difference e (T) between the moment arm of the tree climbing robot and the tree until T reaches a control maximum time T, so that closed-loop control of the tree climbing robot is realized; the execution unit is a permanent magnet motor and provides a gas-driven robot arm of force for the tree climbing robot.
4. A method for optimizing air pump control for a tree climbing robot according to claim 3, wherein the step S31 is improved in scaling factor of dormancy and propagation of artificial parent populationThe mathematical model is:
(2)；
In the formula (2), the amino acid sequence of the compound, The improved scaling factor for dormancy and propagation of artificial original populations for the ith iteration,Ranking the fitness values of the ith individual in the population, N being the maximum scale of the artificial source,For the diversity of the current population for the ith iteration,For the diversity of the initial population, the overall dormancy and propagation proportion is adjusted by using the population diversity index, so that the premature convergence to a local optimal solution is avoided.
5. The method for optimizing air pump control for a tree climbing robot according to claim 4, wherein said step S32 improves foraging factorThe mathematical model is:
（3）；
In the formula (3), the amino acid sequence of the compound, For the improved foraging factor of the ith iteration,For the improved minimum value of foraging factors, the value is 0,The value of the improved foraging factor is 2; For the current number of iterations, For the maximum number of iterations to be performed,In order for the dynamic adjustment mechanism to be implemented,Based on the artificial original population entropy change rate and the optimal solution change rate, the mathematical model is:
（4）；
In the formula (4), the amino acid sequence of the compound, For the entropy value of the position of the artificial original population in the ith iteration of (item+1),For the entropy value of the artificial original population position of the ith iteration,For the entropy value of the initial artificial original population position,For the optimal solution fitness value of the iterative population of the ith time,For the previous population optimal solution fitness value,The value is 0.001, wherein the calculation formula of the entropy value of the artificial original group position is as follows: wherein N is the maximum population size, The probability of the ith artificial primitive is obtained through normalization of the position fitness value.
6. The optimization method for controlling the air pump of the tree climbing robot according to claim 5, wherein the location update mathematical model of the global search phase of the improved artificial original optimization algorithm in step S33 is:
（5）；
In the formula (5), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive,For the ith iteration randomThe position of the artificial origin is changed,For the number of pairs of artificial original neighbors,Is a foraging mapping vector with the size of 1X dim, the value in the vector is 1,As a weight for the autotrophic mode,For the weight of the heterotrophic mode, rand is a random number within 0 to 1,Randomly selected artificial home positions in the k-th pairing neighborhood, where k is greater than i,Randomly selecting artificial original positions in the k pairing neighborhood, wherein k is less than i; the i-k h artificial home position selected for the k-th pairing neighbor, An i+k-th artificial home location selected for the k-th pairing neighbor; For demarcation parameters for autotrophic and heterotrophic behavior, An improved foraging factor for the ith iteration.
7. The air pump control optimization method for the tree climbing robot according to any one of claims 1 to 6, wherein the air pump PID closed-loop control algorithm of the tree climbing robot is optimized by using an improved artificial original optimization algorithm, and the proportional coefficient Kp, the integral coefficient Ki and the differential coefficient Kd of the air pump PID closed-loop control algorithm of the tree climbing robot are optimized by the improved artificial original optimization algorithm, which comprises the following specific steps:
setting an artificial original scale N of an improved artificial original optimization algorithm, a maximum iteration number Max_iter, a problem dimension dim, an upper bound UB and a lower bound LB;
step two, uniformly initializing the artificial original population by using a uniform statistical search initialization method according to a formula (7), so that the artificial original individuals cover the search space of the whole improved artificial original optimization algorithm; upper and lower limits LB on the search space of the improved artificial original optimization algorithm;
（7）；
In the formula (7), the amino acid sequence of the compound, The initialized position of the j-th dimension of the i-th artificial original individual is obtained, wherein,;，Is the lower limit position of the original artificial individual,Is the upper limit position of the original artificial individual,For the global optimal artificial original body position of the last iteration, rand (0, 1) is a random number in 0 to 1;
Thirdly, establishing three-dimensional mapping between the position of an artificial original body in the search space and a vector formed by Kp, ki and Kd of an air pump PID closed-loop control algorithm of the tree climbing robot;
Calculating the position fitness value of each artificial original individual in the artificial original population of the ith iteration by utilizing the objective function, and taking the position of the artificial original individual corresponding to the smaller fitness value in the population as the optimal position of the current iteration;
Establishing a population position updating mathematical model of the improved artificial original optimization algorithm in a global searching stage and a local development stage, and updating the positions of artificial original individuals, wherein the method comprises the following specific steps of:
S351, calculating the scale factors of dormancy and propagation of the artificial original population improved by the ith iteration by using the formula (2) The size is set asIf the ith individual of the current iteration is within the population range, the ith individual location update performs step S352; otherwise, step S353 is executed;
S352, establishing an artificial original body position updating mathematical model in an algorithm local convergence stage according to a formula (8) according to dormancy and propagation behaviors of the model artificial original body;
（8）；
in the formula (8), the amino acid sequence of the compound, For the new position of the ith artificial origin,For the ith iteration the location of the ith artificial primitive, rand is a random number within 0 to 1,Boundary parameters for dormancy and propagation whenWhen the number of the propagation positions is larger than the rand, executing the dormant position updating mathematical model of the artificial origin, otherwise, executing the propagation position updating mathematical model of the artificial origin;
s353, searching behavior of the model artificial source through improved foraging factors The method comprises the steps that (1) a position updating mathematical model of a global searching stage of an improved manual original optimization algorithm is adopted, namely, a formula (5) builds a manual original individual position mathematical model of the algorithm global searching stage;
Step six, calculating the position fitness value of each individual in the artificial original population after the position updating by using the objective function again, and taking the individual position corresponding to the currently obtained minimum fitness value as the global optimal artificial original individual position;
Step seven, executing item=item+1, judging whether the current iteration number item satisfies item=max_item, if so, outputting and analyzing the artificial original body position corresponding to the global minimum fitness value into Kp, ki and Kd parameter values of an air pump PID closed-loop control algorithm of the climbing robot, otherwise, executing step two, and uniformly initializing the artificial original population by using a uniform statistics search initialization method according to a formula (7).
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