CN117369244A - Welding gun position control optimization method based on welding robot - Google Patents
Welding gun position control optimization method based on welding robot Download PDFInfo
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Abstract
The invention discloses a welding robot-based welding gun position control optimization method, which belongs to the technical field of PID control optimization and comprises the following specific steps: step one, designing a welding gun position control system of a welding robot, and converting the welding gun position control problem of the welding robot into a mathematical model to be optimized; step two, improving a standard cheetah optimization algorithm, and writing a test code through Matlab to verify the performance of the improved cheetah optimization algorithm, wherein the performance comprises an improved algorithm medium-term waiting strategy and an introduced self-adaptive weight coefficient; optimizing a PID controller of the welding gun position of the welding robot by using the improved Leopard optimization algorithm to obtain PID control parameters Kp, ki and Kd of the optimal welding gun position of the welding robot; inputting the obtained PID control parameters of the welding gun position of the optimal welding robot into an experimental simulation model built by Simulink, applying the parameters to the welding robot, and debugging to obtain the optimal welding gun position control effect of the welding robot.
Description
Technical Field
The invention belongs to the technical field of PID control optimization, and particularly relates to a welding gun position control optimization method based on a welding robot.
Background
The welding robot can greatly improve the production efficiency and the product quality, and reduce the labor cost and the influence of human factors on the production. With the continuous development of industrial automation technology, welding robots will play an increasingly important role in future industrial production. The welding robot mainly comprises two parts, namely a robot and welding equipment, wherein the robot consists of a robot body and a control system, and the control system is a key for determining the welding accuracy and stability of the welding robot.
Because the welding position is incorrect or problems occur when a welding gun is searched, the welding is inaccurate, the traditional welding robot is controlled by adopting a traditional PID controller, the parameter adjustment of the traditional PID controller is complex, the dependence on the parameter is strong, and if the characteristics of a system change or the system is disturbed, the PID control performance is influenced, so that the welding inaccuracy of the welding robot is caused.
The method is characterized in that a novel group intelligent optimization algorithm is proposed by the natural leopard hunting in 2022, the novel group intelligent optimization algorithm is realized by simulating 3 strategies of searching, sitting and the like and attacking of the leopard in the hunting process, the performance of the leopard optimization algorithm is tested for 30 functions in total on a classical IEEECEC2017 and a latest IEEECEC2022 test set, and compared with some intelligent optimization algorithms, the performance of the leopard optimization algorithm has superiority, but in the algorithm exploration stage, the novel group intelligent optimization algorithm is easy to sink into local optimum, and meanwhile in the whole algorithm hunting process, the local optimum is not jumped out, so that the optimal parameters cannot be found.
Disclosure of Invention
The invention aims at: by improving the leopard optimizing algorithm, the improved leopard optimizing algorithm is utilized to optimize three parameters of a traditional PID controller of the welding gun position of the welding robot, and control accuracy and control sensitivity of PID control of the welding gun position are improved, so that the problem that the welding gun of the welding robot is difficult to accurately control under complex conditions is solved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a welding gun position control optimization method based on a welding robot comprises the following specific steps:
step one, designing a welding gun position control system of the welding robot, and converting the welding gun position control problem of the welding robot into a mathematical model to be optimized.
Step two, improving a standard cheetah optimization algorithm, writing a test code through Matlab, and verifying the performance of the improved cheetah optimization algorithm; the improved standard cheetah optimization algorithm comprises an improved algorithm mid-term waiting strategy and an introduced adaptive weight coefficient, and is specifically as follows:
step1, firstly, utilizing an optimal position changing algorithm to wait for a strategy position updating formula in the middle period, then introducing a t distribution strategy, and the improved position updating formula is as follows:
in the method, in the process of the invention,for the (th) th dimension position of the (th) t+1th iteration of the (th) first leopard, trnd (it) is the t distribution strategy, +.>Is the current optimal position;
step2, then, introducing an adaptive weight coefficient into a position updating strategy in an algorithm attack stage, wherein the adaptive weight coefficient formula is as follows:
w＝w 0 +(w max -w min )×e -α·f(best)/f(worst) (2)
wherein w is the self-adaptive inertia weight coefficient of the current iteration, and w 0 Initial value of inertial weight coefficient, w min For minimum inertial weight coefficient, ω max F (word) is the current worst fitness value, and f (best) is the best fitness value; a is the attenuation rate of the inertia weight, adopts a form of linearly decreasing with the iteration number, and has the formula:
in the formula, T is the current iteration number, and T is the total iteration number.
And thirdly, optimizing a PID controller of the welding gun position of the welding robot by using the improved Leopard optimization algorithm to obtain PID control parameters Kp, ki and Kd of the optimal welding gun position of the welding robot.
Inputting the obtained PID control parameters Kp, ki and Kd of the welding gun position of the optimal welding robot into an experimental simulation model built by the Simulink, and applying the parameters to the welding robot to debug the welding gun to obtain the optimal welding gun position control effect of the welding robot.
Further, in the first step, the welding robot welding gun position control system comprises a welding gun position signal input unit, a position type PID controller unit, an improved leopard optimizing algorithm unit, an electric control controller unit, a welding robot motor unit and a welding robot welding gun angle position sensor acquisition unit; the welding gun position signal input unit inputs data as set welding gun target angle position data, a difference value e (t) between the welding gun target angle position data and a real-time angle position of a welding gun is used as input of a position type PID controller unit, the improved cheetah optimization algorithm unit acquires the difference value e (t) and obtains an optimal PID parameter value through algorithm iterative computation, the PID control parameter value obtained by optimizing the improved cheetah optimization algorithm is applied to the position type PID controller unit, the position type PID controller unit outputs a value u (t) to control an electric controller, and the electric controller controls a welding robot motor to rotate so as to realize welding gun adjustment positions of the welding robot.
In the first step, the welding gun position control problem of the welding robot is converted into a mathematical model to be optimized, and the mathematical model is an objective function of an improved leopard optimization algorithm.
Further, in the second step, the standard cheetah optimization algorithm is improved by combining the t distribution and the optimal position, so that the algorithm can better balance the global searching and the local searching capability in the searching process, in the initial stage of the algorithm, a larger t value can enable particles to have a larger speed step length, so that a better region is detected in the global range, and in the later stage of the algorithm, a smaller t value can ensure that the particles can perform fine searching around an extreme point, so that the algorithm has a larger probability of converging to the global optimal solution position; the combination of the optimal position strategy can better balance the global searching and local searching capabilities and better convergence performance.
Furthermore, in the second step, the adaptive weight coefficient is introduced into the position updating strategy in the attack stage of the algorithm, so that the algorithm can be prevented from sinking into local optimum to the greatest extent, and the optimizing precision of the algorithm is improved.
Further, in the third step, the improved leopard optimization algorithm is utilized to optimize the welding gun position PID controller of the welding robot, and optimal PID control parameters Kp, ki and Kd are obtained, which comprises the following specific steps:
s1, designing a Simulink simulation transfer function aiming at a welding robot work environment, wherein a transfer function model adopts a second-order function to describe a position adjustment process of a welding gun of the welding robot under a complex condition;
s2, giving an input signal of a Simulink simulation system, wherein the input signal is a welding gun target angle value of the welding robot;
s3, initializing a population scale N of an improved cheetah optimization algorithm, a problem dimension D, an algorithm search space upper bound ub, an algorithm search space lower bound lb, a maximum iteration number Max_iter, an initial position of the population and a current population leader fitness value; the algorithm search space upper bound ub and the algorithm search space lower bound lb are parameter ranges of a welding gun position PID controller of the welding robot, the initial position of the population is an initial value of a welding gun position PID controller parameter of the welding robot, the algorithm iterates, the process of updating the population position of the cheetah is a process of optimizing the welding gun position PID control and searching the optimal parameter by the algorithm, and each cheetah represents a welding gun position PID controller parameter solution;
s4, encoding PID control parameters Kp, ki and Kd of the welding gun position of the welding robot into a position solution for improving a cheetah optimization algorithm;
s5, designing an objective function for quantifying the performance of the control system, wherein the objective function adopts ITAE to calculate the fitness value, and the optimal fitness value of the iteration is reserved, and the formula of the objective function is as follows:
wherein J is an algorithm objective function value, e (t) is the deviation between the objective angle of the welding gun position of the welding robot and the real-time angle value of the welding gun position sensor of the welding robot, and t is the current iteration number;
s6, comparing the current iteration optimal fitness value with the last iteration optimal fitness value, reserving the minimum fitness value, and determining an optimal target object;
s7, introducing a strategy selection mechanism H, if the H is more than 0.5, executing a search strategy for improving a cheetah optimization algorithm, and updating the population position according to the following formula (3), namely, a parameter solution of a PID controller of the welding gun position of the welding robot;
in the method, in the process of the invention,the (j) th dimension position for the (t+1) th iteration of the (i) th head leopard>For the jth iteration of the ith leopard, the jth dimension position,/th dimension>Random numbers which are normally distributed in the j th dimension of the i-th leopard head are added with +.>Searching step length of the j-th dimension for the t-th iteration of the i-th head leopard;
s8, if H=0.5 is met, firstly, updating a cheetah population position updating formula by combining t distribution and an optimal position, and then executing a waiting strategy of an improved cheetah optimization algorithm, as shown in formula (1);
s9, if H <0.5 is met, firstly introducing an adaptive weight coefficient w, and then updating the population position according to the following formula (4);
；
wherein r is i，j For the ith leopard jth dimension steering factor,reflecting interactions between the first and second parts of the leopard or between the first and second parts of the leopard and the second part of the leopard;
s10, updating the positions of members of the cheetah population, checking boundary limit, evaluating new solutions, and self-adding the iteration times t;
s11, circularly executing S4-S10, judging whether the current iteration number reaches the maximum iteration number, if so, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to three parameters of a PID controller of the welding gun position.
Further, in the step S1, the transfer function is used to describe the relationship between the target angular position of the welding gun and the angular position of the PID control output of the welding gun position of the PID control system of the welding robot, so that the output of the PID controller of the welding gun position of the welding robot can better track the target angular position, and meanwhile, the steady-state error and the dynamic error of the system are reduced, and the performance and the robustness of the control system are improved, where the formula is:
where s is a complex frequency domain variable.
Further, in the step S3, an initial population of the leopard is generated, an improved leopard optimization algorithm is initialized, initial values of Kp, ki, kd parameters of a PID controller of the welding gun position of the welding robot are determined, and the leopard initialization position is described as follows:
X i，j ＝LB j +rand(UB j -LB j )，i＝1，2，3，......,N；j＝1，2，3，.....D；
wherein X is i，j UB for the j-th dimensional position of the i-th head leopard j 、LB j The random number is between 0 and 1, which are the upper limit value and the lower limit value of the j-th dimension search space respectively; n is the scale of the cheetah population and D is the dimension of the problem.
Further, in S5, e (t) is a deviation between a target angular position of the welding gun position of the welding robot and a real-time angular position of a sensor of the welding robot gun, and the output u (t) of the PID controller of the welding gun position is calculated by e (t); the formula is:
u(t)＝Kp·[e(t)-e(t-1)]+Ki·e(t)+Kd·[e(t)-2e(t-1)+e(t-2)]；
where u (t) is an angular position value output by a PID controller of the welding gun position of the welding robot.
Further, in S6, the optimal target object is a parameter value of a PID controller of the welding gun position in the optimizing process.
Further, in the step S7, a policy selection mechanism H, H is introduced as follows:
H＝|2·randn·e 2-2·t/T |；
in the formula, randn is a normal distribution random number, T is the current iteration number, and T is the total iteration number.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the invention provides a welding robot-based welding gun position control optimization method, which combines t distribution and an optimal position to improve a standard cheetah optimization algorithm, so that the improved cheetah optimization algorithm better balances global searching and local searching capabilities in the searching process, and a larger t value can enable particles to have a larger speed step length in the initial stage of the algorithm, so that a better region is detected in a global range; introducing a self-adaptive weight coefficient into a position updating strategy in an algorithm attack stage, avoiding the algorithm to be trapped into local optimum to the greatest extent, and improving the optimizing precision of the algorithm; the improved cheetah optimization algorithm is applied to welding gun position control of the welding robot, the setting and self-adapting capacity of parameters of the PID controller are improved, the controller of the welding robot is more sensitive under complex conditions, and the problems that the welding gun of the welding robot is difficult to accurately control and accurately weld under complex conditions and the welding robot is slow in response are effectively solved.
Drawings
Fig. 1 is a schematic step diagram of a welding gun position control optimization method based on a welding robot.
Fig. 2 is a block diagram of a welding gun position control system of the welding robot.
Fig. 3 is a simulation diagram of a welding robot gun position control system Simulink.
FIG. 4 is a flow chart of a PID controller for optimizing welding robot gun position using an improved leopard optimization algorithm.
Fig. 5 is a graph comparing fitness values of the improved leopard optimization algorithm with the standard leopard optimization algorithm and the particle swarm optimization algorithm, and the white whale optimization algorithm.
Fig. 6 is a graph comparing the effects of improved leopard optimization algorithm with standard leopard optimization algorithm and particle swarm optimization algorithm and white whale optimization algorithm to optimize welding robot gun position control.
Detailed Description
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. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention; 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.
Referring to fig. 1-6, the present invention provides a technical solution:
a welding gun position control optimization method based on a welding robot comprises the following specific steps of;
as shown in fig. 1, a welding gun position control optimization method based on a welding robot is divided into four steps.
Step one, designing a welding gun position control system of the welding robot, and converting the welding gun position control problem of the welding robot into a mathematical model to be optimized.
Step two, improving a standard cheetah optimization algorithm, writing a test code through Matlab, and verifying the performance of the improved cheetah optimization algorithm; the improved standard cheetah optimization algorithm comprises an improved algorithm mid-term waiting strategy and an introduced adaptive weight coefficient, and is specifically as follows:
step1, firstly, utilizing an optimal position changing algorithm to wait for a strategy position updating formula in the middle period, then introducing a t distribution strategy, and the improved position updating formula is as follows:
in the method, in the process of the invention,for the (th) th dimension position of the (th) t+1th iteration of the (th) first leopard, trnd (it) is the t distribution strategy, +.>Is the current optimal position;
step2, then, introducing an adaptive weight coefficient into a position updating strategy in an algorithm attack stage, wherein the adaptive weight coefficient formula is as follows:
w＝w 0 +(w max -w min )×e -α·f(best)/f(worst) (2)
wherein w is the self-adaptive inertia weight coefficient of the current iteration, and w 0 Initial value of inertial weight coefficient, w min Is the minimum inertia weight coefficient, w max F (word) is the current worst fitness value, and f (best) is the best fitness value; a is the attenuation rate of the inertia weight, adopts a form of linearly decreasing with the iteration number, and has the formula:
in the formula, T is the current iteration number, and T is the total iteration number.
And thirdly, optimizing a PID controller of the welding gun position of the welding robot by using the improved Leopard optimization algorithm to obtain PID control parameters Kp, ki and Kd of the optimal welding gun position of the welding robot.
Inputting the obtained PID control parameters Kp, ki and Kd of the welding gun position of the optimal welding robot into an experimental simulation model built by the Simulink, and applying the parameters to the welding robot to debug the welding gun to obtain the optimal welding gun position control effect of the welding robot.
Further, in the first step, the welding robot welding gun position control system comprises a welding gun position signal input unit, a position type PID controller unit, an improved leopard optimizing algorithm unit, an electric control controller unit, a welding robot motor unit and a welding robot welding gun angle position sensor acquisition unit; the welding gun position signal input unit inputs data as set welding gun target angle position data, a difference value e (t) between the welding gun target angle position data and a real-time angle position of a welding gun is used as input of a position type PID controller unit, the improved cheetah optimization algorithm unit acquires the difference value e (t) and obtains an optimal PID parameter value through algorithm iterative computation, the PID control parameter value obtained by optimizing the improved cheetah optimization algorithm is applied to the position type PID controller unit, the position type PID controller unit outputs a value u (t) to control an electric controller, and the electric controller controls a welding robot motor to rotate so as to realize welding gun adjustment positions of the welding robot.
In the first step, the welding gun position control problem of the welding robot is converted into a mathematical model to be optimized, and the mathematical model is an objective function of an improved leopard optimization algorithm.
Further, in the second step, the standard cheetah optimization algorithm is improved by combining the t distribution and the optimal position, so that the algorithm can better balance the global searching and the local searching capability in the searching process, in the initial stage of the algorithm, a larger t value can enable particles to have a larger speed step length, so that a better region is detected in the global range, and in the later stage of the algorithm, a smaller t value can ensure that the particles can perform fine searching around an extreme point, so that the algorithm has a larger probability of converging to the global optimal solution position; the combination of the optimal position strategy can better balance the global searching and local searching capabilities and better convergence performance.
Furthermore, in the second step, the adaptive weight coefficient is introduced into the position updating strategy in the attack stage of the algorithm, so that the algorithm can be prevented from sinking into local optimum to the greatest extent, and the optimizing precision of the algorithm is improved.
Further, in the third step, the improved leopard optimization algorithm is utilized to optimize the welding gun position PID controller of the welding robot, so as to obtain optimal PID control parameters Kp, ki, kd, as shown in fig. 4, and the specific steps are as follows:
s1, designing a Simulink simulation transfer function aiming at a welding robot work environment, wherein a transfer function model adopts a second-order function to describe a position adjustment process of a welding gun of the welding robot under a complex condition;
s2, giving an input signal of a Simulink simulation system, wherein the input signal is a welding gun target angle value of the welding robot;
s3, initializing a population scale N of an improved cheetah optimization algorithm, a problem dimension D, an algorithm search space upper bound ub, an algorithm search space lower bound lb, a maximum iteration number Max_iter, an initial position of the population and a current population leader fitness value; the algorithm search space upper bound ub and the algorithm search space lower bound lb are parameter ranges of a welding gun position PID controller of the welding robot, the initial position of the population is an initial value of a welding gun position PID controller parameter of the welding robot, the algorithm iterates, the process of updating the population position of the cheetah is a process of optimizing the welding gun position PID control and searching the optimal parameter by the algorithm, and each cheetah represents a welding gun position PID controller parameter solution;
s4, encoding PID control parameters Kp, ki and Kd of the welding gun position of the welding robot into a position solution for improving a cheetah optimization algorithm;
s5, designing an objective function for quantifying the performance of the control system, wherein the objective function adopts ITAE to calculate the fitness value, and the optimal fitness value of the iteration is reserved, and the formula of the objective function is as follows:
wherein J is an algorithm objective function value, e (t) is the deviation between the objective angle of the welding gun position of the welding robot and the real-time angle value of the welding gun position sensor of the welding robot, and t is the current iteration number;
s6, comparing the current iteration optimal fitness value with the last iteration optimal fitness value, reserving the minimum fitness value, and determining an optimal target object;
s7, introducing a strategy selection mechanism H, if the H is more than 0.5, executing a search strategy for improving a cheetah optimization algorithm, and updating the population position according to the following formula (3), namely, a parameter solution of a PID controller of the welding gun position of the welding robot;
in the method, in the process of the invention,the (j) th dimension position for the (t+1) th iteration of the (i) th head leopard>For the jth iteration of the ith leopard, the jth dimension position,/th dimension>Random numbers which are normally distributed in the j th dimension of the i-th leopard head are added with +.>Searching step length of the j-th dimension for the t-th iteration of the i-th head leopard;
s8, if H=0.5 is met, firstly, updating a cheetah population position updating formula by combining t distribution and an optimal position, and then executing a waiting strategy of an improved cheetah optimization algorithm, as shown in formula (1);
s9, if H <0.5 is met, firstly introducing an adaptive weight coefficient w, and then updating the population position according to the following formula (4);
；
wherein r is i，j For the ith leopard jth dimension steering factor,reflecting interactions between the first and second parts of the leopard or between the first and second parts of the leopard and the second part of the leopard;
s10, updating the positions of members of the cheetah population, checking boundary limit, evaluating new solutions, and self-adding the iteration times t;
s11, circularly executing S4-S10, judging whether the current iteration number reaches the maximum iteration number, if so, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to three parameters of a PID controller of the welding gun position.
Further, in the step S1, the transfer function is used to describe the relationship between the target angular position of the welding gun and the angular position of the PID control output of the welding gun position of the PID control system of the welding robot, so that the output of the PID controller of the welding gun position of the welding robot can better track the target angular position, and meanwhile, the steady-state error and the dynamic error of the system are reduced, and the performance and the robustness of the control system are improved, where the formula is:
where s is a complex frequency domain variable.
Further, in the step S3, an initial population of the leopard is generated, an improved leopard optimization algorithm is initialized, initial values of Kp, ki, kd parameters of a PID controller of the welding gun position of the welding robot are determined, and the leopard initialization position is described as follows:
X i，j ＝LB j +rand(UB j -LB j )，i＝1，2，3，......,N；j＝1，2，3，.....D；
wherein X is i，j UB for the j-th dimensional position of the i-th head leopard j 、LB j The random number is between 0 and 1, which are the upper limit value and the lower limit value of the j-th dimension search space respectively; n is the scale of the cheetah population and D is the dimension of the problem.
Further, in S5, e (t) is a deviation between a target angular position of the welding gun position of the welding robot and a real-time angular position of a sensor of the welding robot gun, and the output u (t) of the PID controller of the welding gun position is calculated by e (t); the formula is:
u(t)＝Kp·[e(t)-e(t-1)]+Ki·e(t)+Kd·[e(t)-2e(t-1)+e(t-2)]；
where u (t) is an angular position value output by a PID controller of the welding gun position of the welding robot.
Further, in S6, the optimal target object is a parameter value of a PID controller of the welding gun position in the optimizing process.
Further, in the step S7, a policy selection mechanism H, H is introduced as follows:
H＝|2·randn·e 2-2·t/T |；
in the formula, randn is a normal distribution random number, T is the current iteration number, and T is the total iteration number.
In order to verify the superiority of the welding gun position control optimization method based on the welding robot, the invention specifically performs performance comparison on an improved cheetah optimization algorithm, a standard cheetah optimization algorithm, a particle swarm optimization algorithm and a cheetah optimization algorithm through Matlab and Simulink, a Simulink simulation system is shown in fig. 3, in the welding gun position control of the welding robot, the performance of a PID controller determines the accuracy of a welding gun, and the performance of the algorithm determines the performance of the PID controller.
In Matlab, the population size is set to 50, the maximum iteration number is set to 500, w 0 0, running a program, optimizing by a standard cheetah optimization algorithm (CO)A fitness value of 0.049841, a Particle Swarm Optimization (PSO) fitness value of 0.0017416, a white whale optimization (BWO) fitness value of 0.55978, and an Improved Cheetah Optimization (ICO) fitness value of 8.3931e -38 The method comprises the steps of carrying out a first treatment on the surface of the It can be obviously found that the fitness value of the improved cheetah optimization algorithm is smaller than that of the other three algorithms, and the performance is better.
As shown in fig. 5, the improved cheetah optimization algorithm has higher optimization accuracy than the other three algorithms; the standard cheetah optimization algorithm, the white whale optimization algorithm and the particle swarm optimization algorithm are in optimal layout after 100 iterations, the improved cheetah optimization algorithm is not in local optimal all the time, and the optimizing speed is high.
FIG. 6 is a graph showing the effects of improving the position control of a welding gun of a welding robot by using a leopard optimizing algorithm, a standard leopard optimizing algorithm, a particle swarm optimizing algorithm and a white whale optimizing algorithm, wherein the graph shows that the heading control effect based on the white whale optimizing algorithm is the worst and the overshoot is the largest; and secondly, the course control based on the particle swarm optimization algorithm is inferior to the white whale optimization algorithm in overshoot, compared with the welding robot welding gun position control with improved cheetah optimization algorithm, the overshoot is minimum, the reaction speed is fastest, and the effect is best.
The welding gun position control optimization method based on the welding robot provided by the invention has superiority and innovativeness in welding robot welding gun position control.
Claims (2)
1. The welding gun position control optimization method based on the welding robot is characterized by comprising the following specific steps:
step one, designing a welding gun position control system of a welding robot, and converting the welding gun position control problem of the welding robot into a mathematical model to be optimized;
step two, improving a standard cheetah optimization algorithm, writing a test code through Matlab, and verifying the performance of the improved cheetah optimization algorithm; the improved standard cheetah optimization algorithm comprises an improved algorithm mid-term waiting strategy and an introduced adaptive weight coefficient, and is specifically as follows:
step1, firstly, utilizing an optimal position changing algorithm to wait for a strategy position updating formula in the middle period, then introducing a t distribution strategy, and the improved position updating formula is as follows:
in the method, in the process of the invention,for the (th) th dimension position of the (th) t+1th iteration of the (th) first leopard, trnd (it) is the t distribution strategy, +.>Is the current optimal position;
step2, then, introducing an adaptive weight coefficient into a position updating strategy in an algorithm attack stage, wherein the adaptive weight coefficient formula is as follows:
w＝w 0 +(w max -w min )×e -a·f(best)/f(worst) (2)；
wherein w is the self-adaptive inertia weight coefficient of the current iteration, and w 0 Initial value of inertial weight coefficient, w min Is the minimum inertia weight coefficient, w max F (word) is the current worst fitness value, and f (best) is the best fitness value; a is the attenuation rate of the inertia weight, adopts a form of linearly decreasing with the iteration number, and has the formula:
wherein T is the current iteration number, and T is the total iteration number;
optimizing a PID controller of the welding gun position of the welding robot by using the improved Leopard optimization algorithm to obtain PID control parameters Kp, ki and Kd of the optimal welding gun position of the welding robot;
inputting the obtained PID control parameters Kp, ki and Kd of the welding gun position of the optimal welding robot into an experimental simulation model built by the Simulink, and applying the parameters to the welding robot to debug the welding gun to obtain the optimal welding gun position control effect of the welding robot.
2. The welding robot-based gun position control optimization method according to claim 1, wherein the improved leopard optimization algorithm is used for optimizing the welding robot gun position PID controller, and the specific steps are as follows:
s1, designing a Simulink simulation transfer function aiming at a welding robot work environment, wherein a transfer function model adopts a second-order function to describe a position adjustment process of a welding gun of the welding robot under a complex condition;
s2, giving an input signal of a Simulink simulation system, wherein the input signal is a welding gun target angle of the welding robot;
s3, initializing a population scale N of an improved cheetah optimization algorithm, a problem dimension D, an algorithm search space upper bound ub, an algorithm search space lower bound lb, a maximum iteration number Max_iter, an initial position of the population and a current population leader fitness value; the algorithm search space upper bound ub and the algorithm search space lower bound lb are parameter ranges of a welding gun position PID controller of the welding robot, the initial position of the population is an initial value of a welding gun position PID controller parameter of the welding robot, the algorithm iterates, the process of updating the population position of the cheetah is a process of optimizing the welding gun position PID control and searching the optimal parameter by the algorithm, and each cheetah represents a welding gun position PID controller parameter solution;
s4, encoding PID control parameters Kp, ki and Kd of the welding gun position of the welding robot into a position solution for improving a cheetah optimization algorithm;
s5, designing an objective function for quantifying the performance of the control system, wherein the objective function adopts ITAE to calculate the fitness value, and the optimal fitness value of the iteration is reserved, and the formula of the objective function is as follows:
J＝∫ o ∞ t|e(t)dt；
wherein J is an algorithm objective function value, e (t) is the deviation between the objective angle of the welding gun position of the welding robot and the real-time angle value of the welding gun position sensor of the welding robot, and t is the current iteration number;
s6, comparing the current iteration optimal fitness value with the last iteration optimal fitness value, reserving the minimum fitness value, and determining an optimal target object;
s7, introducing a strategy selection mechanism H, if the H is more than 0.5, executing a search strategy for improving a cheetah optimization algorithm, and updating the population position according to the following formula (3), namely, a parameter solution of a PID controller of the welding gun position of the welding robot;
in the method, in the process of the invention,the (j) th dimension position for the (t+1) th iteration of the (i) th head leopard>For the jth iteration of the ith leopard, the jth dimension position,/th dimension>Random numbers which are normally distributed in the j th dimension of the i-th leopard head are added with +.>Searching step length of the j-th dimension for the t-th iteration of the i-th head leopard;
s8, if H=0.5 is met, firstly, updating a cheetah population position updating formula by combining t distribution and an optimal position, and then executing a waiting strategy of an improved cheetah optimization algorithm, as shown in formula (1);
s9, if H <0.5 is met, firstly introducing an adaptive weight coefficient w, and then updating the population position according to the following formula (4);
；
wherein r is i，j For the ith leopard jth dimension steering factor,reflecting interactions between the first and second parts of the leopard or between the first and second parts of the leopard and the second part of the leopard;
s10, updating the positions of members of the cheetah population, checking boundary limit, evaluating new solutions, and self-adding the iteration times t;
s11, circularly executing S4-S10, judging whether the current iteration number reaches the maximum iteration number, if so, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to three parameters of a PID controller of the welding gun position.
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