An automatic grasping control method for hot melt washers of an automatic laying trolley

Abstract

The present invention provides an automatic grasping control method for hot-melt washers of an automatic laying trolley, which relates to the field of automatic control and includes: S1. Centered on the set position of the manipulator, the position of the hot-melt washer is obtained through image acquisition. When the two-dimensional coordinates of the position of the hot-melt washer are the same as the two-dimensional coordinates of the set position of the manipulator, the control method is started by the manipulator control system; S2. Calculate the vertical height value of the real-time position of the manipulator from the position of the hot-melt washer; calculate the current error height between the current descending height and the target descending height of the manipulator according to the vertical height value; S3. Feed back and input the current error height into the manipulator control system; S4. The manipulator control system outputs a control signal based on the current error height, precisely controls the position of the manipulator through the control signal, and returns to execute S2 until the target descending height is reached, realizing the automatic grasping control of the hot-melt washer. The performance of the system is significantly improved, and the efficiency and robustness of the manipulator control system are improved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control, and particularly to an automatic grasping control method for hot-melt washers of an automatic laying trolley. Background Art

[0002] In tunnel construction, the hanging of waterproof sheets is of utmost importance in lining construction. In the field of railway tunnels, generally, the geotextile is first hung, and the geotextile is fixed to the initial support surface of the tunnel through hot-melt washers, and then the waterproof sheet is hung on the geotextile through hot-melt washers; the hot-melt washers are grasped by a manipulator. During the automatic grasping process of the hot-melt washers, the manipulator performs position control through a control system. Its main goal is to ensure that the manipulator can accurately grasp when the hot-melt washer reaches below. When the hot-melt washer approaches the manipulator, the control system generates excessive acceleration, deceleration or unstable movement, resulting in inaccurate position (height) of the manipulator, increasing the failure probability of grasping, affecting the production efficiency of the product, causing the current hot-melt washer not to be grasped in time, and subsequent product backlog.

[0003] The Magnificent Frigatebird Optimization (MFO) algorithm is a nature-inspired metaheuristic algorithm. The update model of the MFO algorithm simulates the two-stage behavior of the magnificent frigatebird: the search stage and the prey deprivation stage, which represent the global search and local exploitation processes in the MFO algorithm respectively; in the position update mathematical model of the MFO algorithm during the search stage, it simulates the behavior of the magnificent frigatebird attacking and pecking other birds to achieve global search. The process of finding the optimal solution relies too much on random numbers and lacks effective guidance for the target solution space, resulting in excessive scattering in the early search stage and being difficult to quickly locate potential areas. The local exploitation stage mainly simulates the behavior of the magnificent frigatebird quickly diving and accurately grasping prey during the predation process. However, the position update mathematical model in this stage relies too much on the global optimal solution, leading to being trapped in the local optimum and being unable to effectively balance exploitation and search, affecting the optimization accuracy and speed of the solution. Summary of the Invention

[0004] Based on the problems existing in the above prior art, the present invention proposes an automatic grasping control method for hot-melt washers of an automatic laying trolley, which uses an improved magnificent frigatebird optimization algorithm to optimize the controller of the automatic grasping device, solves the problem of excessive acceleration, deceleration or unstable movement generated by the control system, and improves the grasping accuracy of the automatic grasping device for hot-melt washers.

[0005] To solve the problems in the background art, the specific solution of an automatic grasping control method for a hot-melt washer of an automatic laying trolley according to the present invention is as follows. The automatic grasping device for the hot-melt washer of the present invention includes a DC motor and a manipulator. The manipulator is lowered to a set height through a controller based on the PID control algorithm, that is, the position control of the manipulator. The specific steps are as follows:

[0006] S1. Centered on the set position of the manipulator, the position of the hot-melt washer is obtained through image acquisition. When the two-dimensional coordinates of the position of the hot-melt washer are the same as the two-dimensional coordinates of the set position of the manipulator, the control method of the manipulator control system is started; the control method includes S2 to S4;

[0007] S2. Calculate the vertical height value of the real-time position of the manipulator from the position of the hot-melt washer ; obtain the current descending height of the manipulator through the vertical height value and the current error height of the target descending height ; ;

[0008] S3. Feed back and input the current error height into the manipulator control system. The manipulator control system is an adaptive-adjusted PID controller. Specifically: the proportional, integral, and differential coefficients of the position PID controller are tuned through an improved frigatebird optimization algorithm to obtain the optimal control coefficients for the current manipulator control, and the optimal control coefficients are used for the position PID controller to obtain an adaptive-adjusted PID controller;

[0009] S4. The manipulator control system outputs a control signal based on the current error height , and through the control signal , accurately control the position of the manipulator, return to execute S2 until the target descending height is reached, and realize the automatic grasping control of the hot-melt washer.

[0010] Furthermore, by combining image acquisition and the position of the manipulator, a spatial coordinate can be established to accurately describe the spatial relationship between the hot-melt washer and the manipulator. The core idea of the coordinate model is to use the position of the manipulator as the center of the two-dimensional coordinate, and obtain the position of the hot-melt washer in space through image acquisition. When the hot-melt washer is directly below the manipulator, it is fed back to the manipulator control system in real time to ensure that the manipulator control system can adjust the movement of the manipulator according to the position of the hot-melt washer, so as to achieve fast and high-precision grasping. Among them, the height h of the set position of the manipulator is also the target descending height for the manipulator to grasp the hot-melt washer; the current descending height of the manipulator is calculated through the vertical height value , and the mathematical model is: .

[0011] Further, by feeding the current error height back to the manipulator control system as the input of the manipulator control system, the output of the manipulator control system is a control signal , where the control signal is input into the manipulator height adjustment mathematical model. The manipulator height adjustment mathematical model is established by simulating the relationship between the descending height of the manipulator and the control signal. The control signal drives the direct current motor to rotate the telescopic rod to push the manipulator down. Specifically, the control signal will drive the direct current motor to generate speed and torque, and control the movement of the telescopic rod through the transmission device, so that the manipulator can quickly and accurately fall to the position of the hot melt washer, avoiding failure to grasp due to insufficient descending height or passing through the central hole of the hot melt washer and being unable to grasp due to excessive descending height.

[0012] Further, the present invention describes the dynamic relationship between the control signal and the descending height of the manipulator, and establishes a second-order linear differential equation with constant coefficients according to the dynamic model of the mechanical system as the manipulator height adjustment mathematical model as follows:

[0013] ;

[0014] In the formula, is the mass of the telescopic rod, is the damping coefficient; is the current descending height of the manipulator, is the linear displacement of the telescopic rod when the motor rotates one circle, is the motor constant, is the control signal; in the formula, the second-order part represents the inertial effect. According to Newton's second law, the product of mass and acceleration is equal to the external force of the system, and this term affects the acceleration and deceleration of the telescopic rod. The first-order part represents the friction force of the system, and the damping coefficient determines the attenuation speed of the system movement; the right side of the equal sign in the formula is the control signal term, and the control signal drives the motor to generate speed, thereby affecting the movement of the telescopic rod.

[0015] Further, the manipulator control system is improved and optimized. The manipulator control system includes a height error processing module, a position PID controller module, a control signal output module, and a feedback input module; among them, the control accuracy of the manipulator control system is mainly determined by the position PID controller module. The position PID controller adopts an incremental PID control algorithm, and the proportional coefficient , integral coefficient , and differential coefficient of the position PID control algorithm are tuned by the improved frigatebird optimization algorithm to obtain the optimal proportional coefficient, integral coefficient, and differential coefficient, so that the position PID controller reaches the best control performance and realizes the improvement of the manipulator control system.

[0016] Furthermore, the position update model in the search stage of the existing MFO algorithm introduces a random number r, making the optimization agent of the MFO algorithm have strong randomness when selecting the target solution. At the same time, it only simulates a single attack and pecking behavior and lacks the modeling of other complex behaviors of frigatebirds, which limits the flexibility and diversity of the MFO algorithm. First, the present invention designs and introduces a fluctuation driving mechanism. By adjusting the fluctuation amplitude and frequency, the position update of the existing MFO algorithm has controlled randomness instead of completely relying on random numbers. Second, the present invention introduces diverse behavior patterns in the search stage, simulates the behavior of frigatebirds gliding in the air, glides along the current optimal solution direction, and adjusts the position of the agent individuals.

[0017] Furthermore, the mathematical model for updating the position of the agent individuals in the improved frigatebird optimization algorithm is divided into two stages, namely simulating the gliding behavior and attack behavior of frigatebirds, specifically: introducing a fluctuation driving mechanism, and updating the individual position with controlled randomness by adjusting the fluctuation amplitude and frequency. Second, introducing diverse behavior patterns in the global search stage, simulating the behavior of frigatebirds gliding in the air, gliding along the current optimal solution direction, and adjusting the position of the agent individuals. The mathematical model for updating the position in the global search stage of the improved frigatebird optimization algorithm is:

[0018] ;

[0019] In the formula, is the j - th dimension value of the position of the i - th agent individual at the (t + 1)-th iteration, is the j - th dimension value of the position of the i - th agent individual at the t - th iteration, is the phase shift, randomly taking values in [0, 2π], is the j - th dimension value of the position of the optimal agent individual at the t - th iteration, is the j - th dimension value of the position of the current global optimal agent individual, is the adaptive switching parameter value of the i - th agent individual, and rand is a random number within 0 to 1.

[0020] Furthermore, by introducing an adaptive switching parameter to switch the mathematical model for updating the position of the agent individuals, the agent individuals execute the corresponding mathematical model for updating the position in the global search stage to update the position. The adaptive switching parameter dynamically judges the position change of the agent individuals from the perspective of spatial distribution. The mathematical model is:

[0021] ;

[0022] Wherein, and are respectively the j - th dimension values of the maximum position and the minimum position at the t - th iteration. The design method is: determining the central position , ; Calculate the normalized distance between the i-th agent individual and the population center position , , The adaptive switching parameter is defined based on the distance of the agent individual as: , where the center position In the design, N is the total scale of agent individuals.

[0023] Further, in the design of the adaptive switching parameter, when the agent individual is far from the population center position, it is more inclined to perform gliding behavior for large-scale global search; when the agent individual is close to the population center position, it is more inclined to perform attack behavior for refined local development; by calculating the distance between each agent individual and the population center position and using this distance to control the switching between gliding and attack behaviors, it can avoid falling into local optimum and improve the global search efficiency at the same time.

[0024] Further, a vortex adjustment mechanism is proposed to simulate the behavior of agent individuals bypassing local optimum solutions in the solution space, which is realized by adjusting the search path and search intensity. The weight parameter γ is combined with the fitness change trend of the current agent individual, so as to realize a more flexible local development adjustment strategy. The method designed in the present invention can ensure that the vortex adjustment mechanism is inclined to large-scale exploration in the early stage and gradually turns to refined local development in the later stage. The local development mathematical model is:

[0025] ;

[0026] In the formula, is the local optimum position at the t-th iteration, that is, the local optimum solution at the t-th iteration, is the j-th dimensional value of the position of the i-th agent individual at the (t + 1)-th iteration, is the j-th dimensional value of the position of the i-th agent individual at the t-th iteration, is the maximum number of iterations, is the j-th dimensional value of the position of the current global optimum agent individual; γ controls the rotation direction of the individual towards the local optimum solution, and the mathematical model is:

[0027] ;

[0028] In the formula, is the weight parameter of the i-th agent individual, is the fitness value of the position of the current global optimum agent individual, is the fitness value of the position of the i-th agent individual at the t-th iteration, is the minimum value, and the value is 10^-6.

[0029] Further, apply the improved frigatebird optimization algorithm to the proportional coefficient of the position-type PID control algorithm 、Integral coefficient and differential coefficient tuning, it is necessary to establish a mapping relationship between the agent individual position and the solution, that is, the agent individual position and the proportional coefficient of the positional PID control algorithm 、Integral coefficient and differential coefficient establish a mapping. Specifically: encode the proportional coefficient 、Integral coefficient and differential coefficient into a set, each set is a solution, the set is three-dimensional, and the total scale N of the agent individuals is denoted as the number of sets. The mathematical model is: \(\left [ {{X}_{i,1},{X}_{i,2},{X}_{i,3}} \right ]=\left [ {{k}_{p},{k}_{i},{k}_{d}} \right ]_{i}\) , where \(i = 1,2,3,\cdots,N\).

[0030] Furthermore, the specific steps for tuning the proportional, integral, and differential coefficients of the positional PID controller by the improved frigatebird optimization algorithm are as follows:

[0031] S301. Set the maximum number of iterations \(T\), the total scale \(N\) of agent individuals, the dimension \(D\) of the solution, the maximum value \(ub\) and the minimum value \(lb\) of the solution of the improved frigatebird optimization algorithm, and update the position of each agent individual in the agent individual population by the random initialization method;

[0032] S302. Calculate the fitness value of the position of the \(i\)-th agent individual in the \(t\)-th iteration, and record the position of the agent individual corresponding to the minimum fitness value in the \(t\)-th iteration as the optimal agent individual position in the \(t\)-th iteration ;

[0033] S303. Update the agent individual position by using the mathematical model of position update in the global search stage of the improved frigatebird optimization algorithm. Specifically, for each agent individual, calculate its corresponding adaptive switching parameter , if , then simulate the attack behavior of the frigatebird to update the agent individual position, otherwise simulate the gliding behavior of the frigatebird to update the agent individual position;

[0034] S304. Calculate the fitness value of the agent individual position updated in S303. For the \(i\)-th agent individual, compare its fitness value in the \(t\)-th iteration with its fitness value in the \((t - 1)\)-th iteration, and take the position corresponding to the smaller fitness value of the two as the new position of the agent individual;

[0035] S305. Calculate the weight parameter of the i-th agent individual based on the updated agent individual position in S304, and update the agent individual position using the mathematical model of position update in the local development stage of the improved frigate optimization algorithm. , and update the agent individual position using the mathematical model of position update in the local development stage of the improved frigate optimization algorithm.

[0036] S306. Calculate the fitness value of the agent individual position updated in S305. For the i-th agent individual, compare the fitness value at the t-th iteration with the fitness value at the (t - 1)-th iteration, and use the position corresponding to the smaller fitness value as the new position of the agent individual.

[0037] S307. Determine whether the current iteration number is less than the maximum iteration number. If so, increment the current iteration number by one and return to execute S303; otherwise, use the position of the agent individual corresponding to the smallest fitness value among the N agent individuals, and resolve it into the optimal proportional coefficient, integral coefficient, and differential coefficient.

[0038] Furthermore, use the optimal proportional coefficient, integral coefficient, and differential coefficient in the position PID controller of the manipulator control system to obtain an adaptively adjusted PID controller. The mathematical model of the position PID controller is as follows:

[0039] ;

[0040] In the formula, is the control signal, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the current error height.

[0041] Furthermore, the fitness value of the agent individual position is calculated through a fitness function, and the fitness function is as follows:

[0042] ;

[0043] In the formula, is the j-th dimension value of the position of the i-th agent individual at the t-th iteration, is the maximum value of the control signal, is the target value of the control signal; the part measures the error between the dynamic response of the manipulator control system and the target descent height, and the part measures the overshoot of the manipulator control system, reducing the amplitude of the system response exceeding the target to avoid excessive oscillation and instability; among them, the target descent height is input into the mathematical model of manipulator height adjustment to obtain the target value of the control signal .

[0044] Beneficial effects and innovations of the present invention compared with the prior art: The present invention proposes an improved frigatebird optimization algorithm, which enhances the search ability of the MFO algorithm by introducing a fluctuation drive mechanism and diverse behavioral patterns; through an adaptive switching parameter mechanism, it dynamically judges the position changes of agent individuals from the perspective of spatial distribution and optimizes the position update model. This enables the algorithm to adjust the search strategy according to the position changes of agent individuals, avoid over-reliance on random numbers, and overcome the problems of excessive scattering in the early search stage and being trapped in local optimal solutions in the local development stage of the traditional MFO algorithm; at the same time, the present invention designs a vortex adjustment mechanism to simulate the behavior of agent individuals bypassing local optimal solutions and dynamically adjust the search path and search intensity. This makes the algorithm tend to large-scale exploration in the global search stage and gradually turn to refined local development in the local development stage, thus effectively improving the search accuracy and speed; through the above improvements, the present invention significantly enhances the performance of the hot melt gasket automatic grasping control system, including improving control accuracy, reducing unsteady motion, accelerating the grasping process, optimizing the parameters of the PID controller, and enhancing the efficiency and robustness of the manipulator control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of an automatic grasping control method for the hot melt gasket of an automatic laying trolley provided by this application;

[0046] Figure 2 It is a flowchart for tuning the proportional, integral, and derivative coefficients of the position PID controller by the improved frigatebird optimization algorithm;

[0047] Figure 3 It is a comparison diagram of the fitness value changes during the coefficient optimization process of the position-type PID control algorithm for the manipulator control system between the improved frigatebird optimization algorithm and the frigatebird optimization algorithm;

[0048] Figure 4 It is a comparison diagram of the changes in the proportional coefficient value of the position-type PID control algorithm for the manipulator control system;

[0049] Figure 5 It is a comparison diagram of the changes in the integral coefficient value of the position-type PID control algorithm for the manipulator control system;

[0050] Figure 6 It is a comparison diagram of the changes in the derivative coefficient value of the position-type PID control algorithm for the manipulator control system;

[0051] Figure 7 It is a comparison diagram of the control signal output accuracy of the manipulator control system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] The automatic grasping device for the hot-melt washer of the present invention includes a DC motor and a manipulator. The manipulator and the telescopic rod are integrated. A controller based on the position PID control algorithm controls the DC motor to lower the manipulator to a set height, that is, to control the position of the manipulator. The specific steps are as Figure 1 shown, including S1 to S4.

[0054] S1. Taking the set position of the manipulator as the center, the position of the hot-melt washer is obtained through image acquisition. When the two-dimensional coordinates of the position of the hot-melt washer are the same as the two-dimensional coordinates of the set position of the manipulator, the control method of the manipulator control system is started; the control method includes S2 to S4.

[0055] First of all, the automatic grasping device for the hot-melt washer consists of a DC motor, a telescopic rod, and a manipulator, and the accessory is a camera; the rotation of the DC motor controls the telescopic action of the telescopic rod, thereby controlling the telescopic of the manipulator. The manipulator will automatically open during the descending process of the telescopic rod; the entire device and the camera are installed directly above the end of the hot-melt washer production line. The mechanical scheme of the entire device is simply built according to the existing technology. The main focus of the present invention is to achieve precise automatic grasping of the hot-melt washer, that is, to control the descending height of the manipulator to ensure successful grasping.

[0056] Secondly, taking the set position of the manipulator as the center, only the x-axis and y-axis of the plane are used for this position, and the coordinates of this position are set as (0, 0). Then, the image of the hot-melt washer collected by the camera is processed to extract the center position of the hot-melt washer. In the experiment of the present invention, the contour of the hot-melt washer is extracted through the edge detection algorithm (Canny algorithm), the geometric shape of the hot-melt washer is identified, and its center is determined. During the implementation process, a tolerance range with a radius of 3 cm is set. When the difference between the two-dimensional coordinates of the center of the hot-melt washer and the two-dimensional coordinates of the set position of the manipulator is less than this tolerance, it is considered that the positions match, and then the control method of the manipulator control system is started for automatic grasping.

[0057] S2. Calculate the vertical height value of the real-time position of the manipulator from the position of the hot-melt washer ; obtain the current descending height of the manipulator through the vertical height value and the current error height from the target descending height .

[0058] First, the height of the set position of the manipulator is set to 50 cm. This height is determined according to the telescopic length of the telescopic rod, that is, the height between the initial position of the manipulator and the hot melt washer is 50 cm. The height distance between the manipulator and the position of the hot melt washer during the descending process of the manipulator is calculated by the infrared sensor and denoted as , and is used to calculate the current descending height of the manipulator, and then the error height is calculated .

[0059] S3. Feed the current error height back into the manipulator control system. The manipulator control system is an adaptive PID controller. Specifically: the proportional, integral, and differential coefficients of the position PID controller are tuned through an improved frigatebird optimization algorithm to obtain the optimal control coefficients for the current manipulator control, and the optimal control coefficients are used for the position PID controller to obtain an adaptive PID controller

[0060] First, improve the frigatebird optimization algorithm, including the mathematical model for updating the position of the surrogate individuals in the global search stage and the local development stage of the frigatebird optimization algorithm. The mathematical model for updating the global search position of the surrogate individuals in the improved frigatebird optimization algorithm is divided into two stages. Specifically: introduce a fluctuation drive mechanism, and update the individual position in a controlled random manner by adjusting the fluctuation amplitude and frequency, rather than relying entirely on random numbers; second, introduce diverse behavior patterns in the global search stage, simulate the behavior of frigatebirds gliding in the air, glide along the current optimal solution direction, and adjust the position of the surrogate individuals; by introducing an adaptive switching parameter switch the mathematical model for updating the position of the surrogate individuals, so that the surrogate individuals execute the corresponding mathematical model for updating the position. The mathematical model for updating the position in the global search stage of the improved frigatebird optimization algorithm is:

[0061] ;

[0062] In the formula, is the j-th dimension value of the position of the i-th surrogate individual at the (t + 1)-th iteration, is the j-th dimension value of the position of the i-th surrogate individual at the t-th iteration, is the phase shift, randomly taking values in [0, 2π], is the j-th dimension value of the position of the optimal surrogate individual at the t-th iteration, is the j-th dimension value of the position of the current global optimal surrogate individual, is the value of the adaptive switching parameter of the i-th surrogate individual, and rand is a random number between 0 and 1; among them, the adaptive switching parameter dynamically judges the position change of the surrogate individuals from the perspective of spatial distribution, and the mathematical model is:

[0063] ;

[0064] Among them, and are respectively the j - dimensional values of the maximum position and the minimum position in the t - th iteration. The design method is as follows: Determine the central position , ; Calculate the normalized distance between the i - th agent individual and the population central position, . The adaptive switching parameter is defined based on the distance of the agent individual as: , where the central position In the design, N is the total scale of the agent individuals.

[0065] Secondly, improve the mathematical model of the agent individual position update in the local exploitation stage of the frigatebird optimization algorithm, and propose a vortex adjustment mechanism to simulate the behavior of the agent individual bypassing the local optimal solution in the solution space. It is achieved by adjusting the search path and search intensity. Combine the weight parameter γ with the fitness change trend of the current agent individual to improve the mathematical model of the position update in the local exploitation stage of the frigatebird optimization algorithm. Specifically:

[0066] ;

[0067] In the formula, is the local optimal position in the t - th iteration, that is, the local optimal solution in the t - th iteration, is the j - dimensional value of the position of the i - th agent individual in the (t + 1) - th iteration, is the j - dimensional value of the position of the i - th agent individual in the t - th iteration, is the maximum number of iterations, is the j - dimensional value of the position of the current global optimal agent individual; γ controls the rotation direction of the individual towards the local optimal solution, and the mathematical model is:

[0068] ;

[0069] In the formula, is the weight parameter of the i - th agent individual, is the fitness value of the current global optimal agent individual position, is the fitness value of the position of the i - th agent individual in the t - th iteration, is a minimum value, with a value of 10^(-6).

[0070] Finally, apply the improved frigatebird optimization algorithm to tune the proportional coefficient , integral coefficient and differential coefficient of the position - type PID control algorithm. It is necessary to establish a mapping relationship between the agent individual position and the solution, that is, the agent individual position The proportional coefficient of the positional PID control algorithm , integral coefficient and differential coefficient Establish a mapping, specifically: map the proportional coefficient , integral coefficient and differential coefficient Encode into a set, each set is a solution, the set is three-dimensional, and the total scale N of the agent individuals is recorded as the number of sets. The mathematical model is: \(\left [ {{X}_{i,1},{X}_{i,2},{X}_{i,3}} \right ]=\left [ {{k}_{p},{k}_{i},{k}_{d}} \right ]_{i}\) , where \(i = 1,2,3,\cdots,N\). The specific steps for tuning the proportional, integral, and differential coefficients of the position PID controller using the improved frigatebird optimization algorithm are as Figure 2 shown, including:

[0071] S301. Set the maximum number of iterations \(T\), the total scale \(N\) of the agent individuals, the dimension \(D\) of the solution, the maximum value \(ub\) and the minimum value \(lb\) of the solution. Update the position of each agent individual in the agent individual population through a random initialization method;

[0072] S302. Calculate the fitness value of the position of the \(i\)-th agent individual at the \(t\)-th iteration, and record the position of the agent individual corresponding to the minimum fitness value at the \(t\)-th iteration as the optimal agent individual position at the \(t\)-th iteration ; The fitness value of the agent individual position is calculated through a fitness function, and the fitness function is:

[0073] ;

[0074] In the formula, is the \(j\)-th dimension value of the position of the \(i\)-th agent individual at the \(t\)-th iteration, is the maximum value of the control signal, is the target value of the control signal;

[0075] S303. Update the agent individual position using the global search phase position update mathematical model of the improved frigatebird optimization algorithm. Specifically, for each agent individual, calculate its corresponding adaptive switching parameter , if , then simulate the frigatebird attack behavior to update the agent individual position, otherwise simulate the frigatebird gliding behavior to update the agent individual position;

[0076] S304. Calculate the fitness value of the updated proxy individual position in S303. For the i-th proxy individual, compare the fitness value at the t-th iteration with the fitness value at the (t - 1)-th iteration, and use the position corresponding to the smaller fitness value of the two as the new position of the proxy individual;

[0077] S305. Based on the proxy individual position updated in S304, calculate the weight parameter of the i-th proxy individual , and update the proxy individual position using the mathematical model for position update in the local exploitation stage of the improved frigatebird optimization algorithm;

[0078] S306. Calculate the fitness value of the updated proxy individual position in S305. For the i-th proxy individual, compare the fitness value at the t-th iteration with the fitness value at the (t - 1)-th iteration, and use the position corresponding to the smaller fitness value of the two as the new position of the proxy individual;

[0079] S307. Whether the current iteration number is less than the maximum iteration number. If so, increment the current iteration number by one and return to execute S303; otherwise, for the proxy individual position corresponding to the minimum fitness value among the N proxy individuals, resolve it into the optimal proportional coefficient, integral coefficient, and differential coefficient.

[0080] Specifically, use the optimal proportional coefficient, integral coefficient, and differential coefficient for the position PID controller of the manipulator control system to obtain an adaptively adjusted PID controller, where the mathematical model of the position PID controller is:

[0081] ;

[0082] In the formula, is the control signal, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the current error height.

[0083] S4. The manipulator control system outputs the control signal based on the current error height , and precisely controls the position of the manipulator through the control signal , then returns to execute S2 until the target descent height is reached, achieving the automated grasping control of the hot-melt washer.

[0084] Specifically, the control signal is input into the mathematical model for manipulator height adjustment. The mathematical model for manipulator height adjustment is established by simulating the relationship between the descent height of the manipulator and the control signal. The control signal The telescopic rod is pushed down by controlling the rotation of the DC motor to drive the manipulator. Specifically, the control signal will drive the DC motor to generate speed and torque, and control the movement of the telescopic rod through the transmission device, so that the manipulator quickly and accurately descends to the position of the hot melt gasket. Among them, a second-order linear differential equation with constant coefficients is established according to the dynamic model of the mechanical system, and the mathematical model for adjusting the height of the manipulator is designed as follows:

[0085] ;

[0086] In the formula, is the mass of the telescopic rod, is the damping coefficient; is the current descending height of the manipulator, is the linear displacement of the telescopic rod when the motor rotates one circle, is the motor constant, is the control signal.

[0087] More specifically, in Matlab, the program design of the solution of the present invention is carried out, and the model simulation of the method of the present invention is carried out to verify the feasibility of the method of the present invention for the automatic grasping control of the hot melt gasket. At the same time, the standard frigatebird optimization algorithm (MFO) is introduced to optimize the position PID controller of the manipulator control system, and the beneficial effect of the method of the present invention for the automatic grasping control of the hot melt gasket is verified. The program design includes the above entire design scheme, and outputs the results of the optimal proportional coefficient, integral coefficient and differential coefficient, the comparison result of the fitness change and the control signal output accuracy of the manipulator control system, as Figures 3 to 5 shown.

[0088] More specifically, first, set the maximum number of iterations T = 100, the total scale of the agent individuals N = 30, the dimension of the solution D = 3, the maximum value of the solution ub = [50, 50, 50], and the minimum value lb = [0.001, 0.001, 0.001], and complete the design of the standard frigatebird optimization algorithm (MFO) and the improved frigatebird optimization algorithm (PMFO) respectively. Among them, in the improved frigatebird optimization algorithm, the adaptive switching parameter and the weight parameter of the i-th agent individual are uniformly set to the initial value of 0.5, and the range of adaptive update is [0, 1]; then, design the mathematical model for adjusting the height of the manipulator, set the mass m = 0.5 of the telescopic rod, the damping coefficient b = 5, the motor constant = 0.1, the linear displacement L = 50 of the telescopic rod when the motor rotates one circle, the initial value of the output signal is 0, and the current descending height of the manipulator is known to be 50, then the target value of the output signal is 5; finally, in the main program Main function, complete the tuning of the proportional, integral and differential coefficients of the position PID controller by the standard frigatebird optimization algorithm (MFO) and the improved frigatebird optimization algorithm (PMFO).

[0089] More specifically, as Figure 3 shown, the fitness value decreases rapidly in the initial stage, starts to stabilize and no longer change at the 18th iteration, and the final fitness value is 13.665648, indicating that the standard frigatebird optimization algorithm falls into a local optimal solution during the optimization process of the position PID controller of the manipulator control system, and the proportional coefficient, integral coefficient, and differential coefficient cannot be further optimized; the fitness value of the improved frigatebird optimization algorithm decreases significantly faster than the standard MFO, briefly falls into a local optimum at the 5th iteration, then quickly jumps out and continues to search for the optimum, approaches stability at the 21st iteration, and at the same time the final fitness value is significantly smaller than the standard MFO, indicating that its convergence and global search ability are stronger.

[0090] More specifically, as Figures 4 to 6 shown, they are the optimization change diagrams of the proportional coefficient, integral coefficient, and differential coefficient respectively, corresponding to the Figure 3 fitness diagram. The standard frigatebird optimization algorithm (MFO) obtains a local sub-optimal solution at the 18th iteration, with the corresponding proportional coefficient being 16.1336, the integral coefficient being 0.729175, and the differential coefficient being 7.67486; the improved frigatebird optimization algorithm obtains the global optimal solution at the 21st iteration, with the corresponding proportional coefficient being 13.5188, the integral coefficient being 0.413488, and the differential coefficient being 0.493425.

[0091] More specifically, as Figure 7 shown, for the compared PID controller, that is, the position PID controller of the manipulator control system by the standard frigatebird optimization algorithm (MFO), the output signal obviously has a large overshoot, there is an obvious overshoot phenomenon in the control signal before reaching the target value, and an obvious oscillation phenomenon appears, manifested as the amplitude of the control signal fluctuating near the target value; the large overshoot indicates that the controller adjusts too violently to the target value, and the oscillation prolongs the adjustment time of the control system, making the manipulator control system reach stability later, and the response in the steady state stage is not smooth enough, which easily leads to the instability of the manipulator control system; the output signal of the self-adaptive adjusted PID controller of the present invention has almost no overshoot, the control signal reaches the target value quickly and smoothly, without obvious oscillation phenomenon, manifested as a stable convergence to the target value, and the steady state control signal is smoother, indicating that the output signal of the controller is more stable.

Claims

1. An automatic grasping control method for the hot-melt washers of an automatic laying trolley, characterized in that, The automatic grasping device for the hot-melt washer includes a DC motor and a manipulator, where the manipulator and the telescopic rod are integrated. The specific steps are as follows: S1. Taking the set position of the manipulator as the center, the position of the hot-melt washer is obtained through image acquisition. When the two-dimensional coordinates of the position of the hot-melt washer are the same as the two-dimensional coordinates of the set position of the manipulator, the control system of the manipulator starts the control method. The control method includes S2 to S4. S2. Calculate the vertical height value of the real-time position of the manipulator from the position of the hot-melt washer ; Through the said Obtain the current descending height of the manipulator And the target descending height Of the current error height ; S3. The current error height feedback is input into the control system of the manipulator. The control system is an adaptive-adjusted PID controller. Specifically, the proportional, integral, and differential coefficients of the position PID controller are tuned through an improved frigatebird optimization algorithm to obtain the optimal control coefficients for the current manipulator control, and the optimal control coefficients are used for the position PID controller to obtain an adaptive-adjusted PID controller. The mathematical model for updating the position of the agent individual in the improved frigatebird optimization algorithm is divided into two stages, namely, simulating the gliding behavior and attacking behavior of the frigatebird. Specifically, a fluctuation driving mechanism is introduced, and the individual position is updated in a controlled random manner by adjusting the fluctuation amplitude and frequency. Secondly, in the global search stage, a diverse behavior pattern is introduced to simulate the behavior of the frigatebird gliding in the air, gliding along the current optimal solution direction to adjust the position of the agent individual. The mathematical model for updating the position in the global search stage of the improved frigatebird optimization algorithm is: ； wherein, is the j-th dimensional value of the position of the i-th agent individual at the (t + 1)-th iteration, is the j-th dimensional value of the position of the i-th agent individual at the t-th iteration, is the phase offset, randomly taking values in [0, 2π], is the j-th dimensional value of the position of the optimal agent individual at the t-th iteration, is the j-th dimensional value of the position of the current global optimal agent individual, is the adaptive switching parameter value of the i-th agent individual, and rand is a random number within 0 to 1; Secondly, a vortex adjustment mechanism is proposed to simulate the behavior of the agent individual bypassing the local optimal solution in the solution space, which is realized by adjusting the search path and search intensity. The weight parameter γ is combined with the change trend of the fitness of the current agent individual to improve the mathematical model for updating the position in the local development stage of the frigatebird optimization algorithm. Specifically: ； In the formula, is the local optimal position at the t-th iteration, that is, the local optimal solution at the t-th iteration, is the j-th dimensional value of the position of the i-th agent individual at the (t + 1)-th iteration, is the j-th dimensional value of the position of the i-th agent individual at the t-th iteration, is the maximum number of iterations, is the j-th dimensional value of the position of the current globally optimal agent individual; γ controls the rotation direction of the individual towards the local optimal solution, and the mathematical model is: ； wherein, is the weight parameter of the i-th agent individual, is the fitness value of the current globally optimal agent individual position, is the fitness value of the i-th agent individual position at the t-th iteration, is the minimum value, with a value of 10-6; S4. The manipulator control system outputs a control signal based on the current error height to precisely control the position of the manipulator through the control signal , and returns to execute S2 until the target descent height is reached , thus realizing the automatic precise grasping control of the hot-melt washer.​ 2. The automatic grasping control method for the hot-melt gasket of the automatic laying trolley according to claim 1, characterized in that By introducing an adaptive switching parameter The mathematical model for updating the position of the agent individual is switched, so that the agent individual executes the corresponding mathematical model for updating the position during the global search phase. The adaptive switching parameter dynamically judges the position change of the agent individual from the perspective of spatial distribution. The mathematical model is as follows: ； wherein, and are respectively the j - dimensional values of the maximum position and the minimum position in the t - th iteration. Among them, the design method is as follows: determine the central position of the surrogate individual population in the t - th iteration, ; calculate the normalized distance between the i - th surrogate individual and the central position of the population, and the adaptive switching parameter is defined based on the distance of the surrogate individual as: where the central position in the design, N is the total scale of the surrogate individuals.

3. The automatic grasping control method of the hot melt gasket of the automatic laying trolley according to claim 2, characterized in that, The specific steps for tuning the proportional, integral, and differential coefficients of the position PID controller through the improved frigatebird optimization algorithm are as follows: S301. Set the maximum number of iterations T, the total scale N of the agent individuals, the dimension D of the solution, the maximum value ub and the minimum value lb of the solution of the improved frigatebird optimization algorithm, and update the position of each agent individual in the agent individual population through a random initialization method. S302. Calculate the fitness value of the position of the $i$-th agent individual at the $t$-th iteration, and record the position of the agent individual corresponding to the minimum fitness value at the $t$-th iteration as the optimal agent individual position at the $t$-th iteration. ; where the fitness function is: ； In the formula, is the j-th dimensional value of the position of the i-th agent individual at the t-th iteration, is the maximum value of the control signal, is the target value of the control signal; through part measures the error between the dynamic response of the manipulator control system and the target descent height, and through part measures the overshoot of the manipulator control system, reducing the amplitude of the system response exceeding the target to avoid excessive oscillation and instability; among them, by inputting the target descent height into the mathematical model of the manipulator height adjustment to obtain the target value of the control signal; S303. Update the position of the agent individual using the mathematical model for position update in the global search phase of the improved frigatebird optimization algorithm. Specifically, for each agent individual, calculate its corresponding adaptive switching parameter , if , then update the position of the agent individual by simulating the attack behavior of the frigatebird; otherwise, update the position of the agent individual by simulating the gliding behavior of the frigatebird. S304. Calculate the fitness value of the position of the agent individual updated in S303. For the i-th agent individual, compare the fitness value at the t-th iteration with the fitness value at the (t - 1)-th iteration, and take the position corresponding to the smaller fitness value of the two as the new position of the agent individual. S305. Calculate the weight parameter of the i-th agent individual based on the updated agent individual position in S304 , and update the agent individual position using the mathematical model for position update in the local development stage of the improved frigatebird optimization algorithm S306. Calculate the fitness value of the position of the agent individual updated in S305. For the i-th agent individual, compare the fitness value at the t-th iteration with the fitness value at the (t - 1)-th iteration, and take the position corresponding to the smaller fitness value of the two as the new position of the agent individual. S307. Whether the current number of iterations is less than the maximum number of iterations. If so, increment the current number of iterations by one and return to execute S303; otherwise, take the position of the agent individual corresponding to the minimum fitness value among the N agent individuals, and resolve it into the optimal proportional coefficient, integral coefficient, and differential coefficient.

4. The automatic grasping control method for the hot melt washer of an automatic laying trolley according to claim 3, characterized in that The optimal proportional coefficient, integral coefficient, and differential coefficient are used for the manipulator control system to output a control signal. The control signal is input into the manipulator height adjustment mathematical model. The manipulator height adjustment mathematical model is established by simulating the relationship between the descending height of the manipulator and the control signal. The control signal controls the rotation of a DC motor to push a telescopic rod to lower the manipulator. Specifically, the control signal will drive the DC motor to generate speed and torque, and control the movement of the telescopic rod through a transmission device, so that the manipulator quickly and accurately descends to the position of the hot melt washer. Among them, a second-order constant coefficient linear differential equation is established according to the mechanical system dynamics model, and the manipulator height adjustment mathematical model is designed as: ； Wherein, is the mass of the telescopic rod, is the damping coefficient; is the current descending height of the manipulator, is the linear displacement of the telescopic rod when the motor rotates one circle, is the motor constant, is the control signal.

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