Dynamic self-adaptive control method for mechanical arm holder

Abstract

The invention discloses a dynamic self-adaptive control method for a mechanical arm holder, which belongs to the technical field of PID (Proportion Integration Differentiation) control optimization, and specifically comprises the following steps: step 1, constructing a mechanical arm holder control system; step 2, improving an enzyme action optimization algorithm; step 3, obtaining an error between the current position and the target position of the mechanical arm holder, calculating an angle error of the holder needing to be adjusted, and optimizing control parameters of a PID controller module of the mechanical arm holder control system through an improved enzyme action optimization algorithm to obtain a group of optimal control parameters; step 4, inputting a group of optimal control parameters obtained in the step 3 into a PID controller, and adjusting the mechanical arm holder to reach a target angle through the control quantity output by the PID controller; a PID controller in the mechanical arm holder control system is optimized through an improved enzyme action optimization algorithm, and the adaptive capacity and the control precision of the mechanical arm holder control system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of PID control optimization, and particularly relates to a dynamic adaptive control method for a robotic arm pan-tilt head. Background Art

[0002] With the rapid development of intelligent manufacturing, robotics technology and unmanned systems, robotic arm and pan-tilt head systems have been widely used in many fields such as industrial automation, aerospace, inspection and detection, medical surgery, service robots, etc. As a multi-degree-of-freedom actuator, the robotic arm has flexible trajectory control capabilities; the pan-tilt head system is widely used in visual perception and attitude adjustment tasks, and is often used to carry devices such as cameras and sensors for target tracking and environmental monitoring. Traditional pan-tilt head control methods mostly rely on PID control strategies with fixed parameters and are difficult to adapt to dynamic environmental changes, such as load disturbances, robotic arm joint vibrations, changes in the operating path, etc., which bring attitude deviation and jitter problems. In complex application scenarios, the pan-tilt head needs to perform real-time adjustment according to the end state of the robotic arm, its own load characteristics and target feedback information, which puts higher requirements on the response speed, robustness and adaptive ability of the control system.

[0003] The PID control strategy adjusts the system control output through the weighted combination of proportional, integral and derivative parts, and is widely used in industrial process control. Due to its simple structure, convenient implementation and reliable performance, the PID controller has been widely used in the field of engineering control. However, the traditional PID control strategy also has some disadvantages. Firstly, its performance highly depends on the setting of proportional, integral and derivative coefficients, and these parameters need to be manually adjusted under different working conditions, lacking adaptive ability. Secondly, for complex non-linear systems or in the case of external disturbances, the fixed-parameter PID controller cannot respond to system changes in real time and is prone to control instability. By combining intelligent optimization algorithms with PID control technology to automatically optimize PID parameters, this method can effectively solve the problem of empirical dependence in PID parameter selection and improve the accuracy and robustness of the controller.

[0004] The Enzyme Action Optimization Algorithm (EAO) is a novel bio-inspired optimization algorithm that mimics the adaptive mechanism of enzymes in biological systems. The EAO draws on this mechanism, regards candidate solutions as "substrates", and interacts with the "enzyme", that is, the current best solution. The algorithm dynamically adjusts the exploration and exploitation strategies according to the observed performance, balancing the exploration of different regions of the solution space and the refined search of promising regions. The EAO incorporates random elements and adaptive factors, similar to biological processes, thus avoiding premature convergence and enhancing the diversity of solutions, making it particularly suitable for complex, high-dimensional and multimodal optimization problems. Summary of the Invention

[0005] The object of the present invention is to improve the enzyme action optimization algorithm, use the improved enzyme action optimization algorithm to optimize the control parameters of the PID controller in the robotic arm pan-tilt control system. The improved enzyme action optimization algorithm has better performance and stronger adaptability during the optimization process, can better adapt to the pan-tilt control scenario, can find the optimal solution with fewer iteration times. At the same time, the angle of the robotic arm pan-tilt control system is controlled by the optimized PID controller, which improves the adaptive ability of the system and enhances the control accuracy of the system. In complex control scenarios, the pan-tilt can also quickly respond and rotate to the target angle, facilitating the grasping work of the robotic arm, effectively solving the problem of poor adaptability caused by the fixed parameters of the PID controller, and thus improving the robustness of the robotic arm pan-tilt control system.

[0006] To achieve the above object, the present invention adopts the following technical solutions.

[0007] A dynamic adaptive control method for a robotic arm pan-tilt, the specific steps are as follows.

[0008] Step 1: Construct a robotic arm pan-tilt control system, the control system includes: a position acquisition module, an angle calculation module, a PID controller module, an improved enzyme action optimization algorithm module, a pan-tilt attitude adjustment module, and a pan-tilt real-time position detection module.

[0009] Step 2: Improve the enzyme action optimization algorithm, the specific improvements include: S1: Update the adaptive factor AF of the enzyme action optimization algorithm through an adaptive adjustment mechanism based on local gradient. This strategy combines the local gradient information of individuals in the population and the change of search space diversity entropy, and real-time feedback and adjusts the size of the adaptive factor AF of the algorithm during the optimization process; S2: Improve the mathematical model of the position update of the second substrate in the enzyme action optimization algorithm through an orbital perturbation-induced position update strategy. This strategy starts from the non-Euclidean orbital path evolution and control perturbation field generation mechanism, simulates the dynamic path of the substrate through the conformational channel change of the enzyme, and maps it to the transition trajectory of the individual position path in the algorithm. The individual position is updated by generating a non-dynamic enzyme potential field factor U(iter) and a micro-perturbation factor T(iter).

[0010] Step 3: Obtain the error between the current position and the target position of the robotic arm pan-tilt, calculate the angle error that the pan-tilt needs to adjust, and optimize the control parameters of the PID controller module of the robotic arm pan-tilt control system through the improved enzyme action optimization algorithm to obtain an optimal set of control parameters.

[0011] Step 4: Input a set of optimal control parameters obtained in Step 3 into the PID controller, and adjust the robotic arm pan-tilt to the target angle through the control quantity output by the PID controller.

[0012] Preferably, in the robotic arm pan-tilt control system constructed in Step 1, the position acquisition module acquires the current position information and target position information of the pan-tilt. The angle calculation module calculates the angle error e(t) that the pan-tilt needs to adjust through the current position and target position acquired by the position acquisition module. Through the angle error e(t), the PID controller module uses the improved enzyme optimization algorithm module to optimize the control parameters, and inputs the optimized control quantity U(t) into the pan-tilt attitude adjustment module. The pan-tilt attitude adjustment module adjusts the angle of the pan-tilt by driving the motor of the pan-tilt to make it reach the target position. During the control process, the real-time position detection module of the pan-tilt updates the position of the pan-tilt in real time and feeds it back to the position acquisition module for real-time adjustment. The mathematical model of the control quantity U(t) is shown in Equation (1), and the mathematical model of the pan-tilt attitude adjustment module is shown in Equation (2); (1); In Equation (1), U(t) represents the control quantity, and e(t) represents the angle error. 、 、 respectively represent the proportional gain parameter, integral gain parameter, and derivative gain parameter obtained by optimizing the PID controller through the improved enzyme optimization algorithm; (2); In Equation (2), J represents the moment of inertia of the pan-tilt, Km represents the torque constant of the motor, θ(t) represents the rotation angle of the pan-tilt, and U(t) represents the control quantity as shown in Equation (1).

[0013] Preferably, in S1, an adaptive adjustment mechanism based on local gradient is used to update the adaptive factor AF of the enzyme optimization algorithm. First, the local gradient XT is calculated. The local gradient is determined by the difference between the current individual's position and the optimal position in the population and the fitness values of the current individual's position and the optimal position. Then, the entropy change of the search space is introduced, that is, the distribution entropy H of the current individual is calculated to evaluate the diversity of the current solution. Finally, based on the feedback of the local gradient and entropy change, the value of the adaptive factor AF is dynamically adjusted. If the values of the local gradient and distribution entropy H increase, it means that the range of the current solution space is wide, and at this time, the value of the adaptive factor AF is increased. If the values of the local gradient and distribution entropy H decrease, it means local convergence and reduced diversity, and the value of the adaptive factor AF is decreased. The specific update formula of AF is shown in Equation (3): (3); In Equation (3), AF represents the adaptive factor, r1 and r2 represent the adjustment factors, the value of r1 is 0.4, the value of r2 is 0.6, XT represents the local gradient, , f represents the fitness value of the current individual position, represents the fitness value of the optimal individual in the population, X(iter) represents the current individual position, represents the optimal individual position, H represents the distribution entropy, and the specific calculation formula is shown in Equation (4); (4); In Equation (4), N represents the number of population individuals, ω represents the distribution weight value, which is a random number between [0,1], exp() represents the exponential calculation function, X(iter) represents the current individual position, represents the optimal individual position, h0 represents the initial bandwidth, the value is 0.5, r represents the adjustment coefficient, the value is 1.2, represents the value of the distribution entropy in the previous iteration, represents the maximum value of the distribution entropy. The algorithm optimizes the three parameters of the PID controller and sets the maximum value of the distribution entropy to the problem dimension value 3.

[0014] Preferably, the innovation of an adaptive adjustment mechanism based on local gradient lies in combining local gradient information and search space diversity, and being able to adjust the balance between algorithm exploration and exploitation according to real-time feedback during the optimization process. This adaptive adjustment mechanism can more intelligently respond to local changes in complex control problems, avoid the algorithm falling into premature convergence or local optimal solutions, improve the robustness and global optimization ability of the algorithm, thereby improving the accuracy of pan-tilt control and reducing the adverse effects caused by the accumulation of dynamic errors during the angle adjustment process.

[0015] Preferably, in S2, a mathematical model for updating the position of the second substrate in the enzyme action optimization algorithm is improved through an orbital perturbation-induced position update strategy. The search space is regarded as a non-Euclidean orbit, and each individual follows the propagation of the local energy orbit in the space. Instead of directly updating the individual position, a non-dynamic enzyme potential field factor U(iter) and a perturbation factor T(iter) are constructed, and the individual moves along the perturbed dynamic transition path in the perturbed orbit. The non-dynamic enzyme potential field factor U(iter) and the perturbation factor T(iter) are shown in Equations (5) and (6): (5); In Equation (5), N represents the number of population individuals, rand represents a random number between [0, 1], f represents the fitness value of the current individual position, exp() represents the exponential calculation function, iter represents the current iteration number, X(iter) represents the current individual position, X(iter - 1) represents the position of the individual at the previous iteration, σ(iter) represents the expansion scale at the iteration stage, σ(iter)=iter / max_iter, and max_iter represents the maximum number of iterations; (6); In Equation (6), K represents the number of disturbance terms, taking the value of 5, exp() represents the exponential calculation function, iter represents the current iteration number, fk represents the frequency of the disturbance term, with the value range of [1, 3], Xk represents the inner product of the current individual position vector and the average position vector of individuals in the population, and d represents the disturbance direction, taking a random number between [-1, 1]; Then, set the step size regulation factor y for updating the individual position, y = ymax / 1 + log(1 + iter), where ymax represents the maximum step size, taking the value of (ub - lb) / 2, ub represents the upper limit of the search space, lb represents the lower limit of the search space. As the number of iterations increases, the value of y decays. Combining all the mathematical models constructed by the above strategy, update the position of the individual. The specific update formula is shown in Equation (7): (7); In Equation (7), X(iter + 1) represents the updated individual position, X(iter) represents the current individual position, y represents the step size regulation factor, U(iter) represents the non-dynamic enzyme potential field factor, and T(iter) represents the perturbation factor.

[0016] Preferably, an orbital perturbation-induced position update strategy realizes the trajectory evolution control of the individual position during the optimization process. This strategy abandons the traditional direct position update method that relies on mutation or difference, and instead introduces an energy orbit transfer mechanism. By constructing a non-linear perturbation trajectory, it guides the individual to achieve a smooth transition, thus effectively enhancing the global optimization ability and convergence stability of the algorithm. The control parameters optimized based on this update mechanism can significantly improve problems such as response hysteresis and overshoot in the traditional PID controller in a high non-linear and multi-perturbation environment, and further enhance the dynamic stability and environmental adaptability of the pan-tilt control system.

[0017] Preferably, in step three, the control parameters of the PID controller module of the robotic arm pan-tilt control system are optimized by an improved enzyme action optimization algorithm. The specific steps are as follows: Step 1: Initialize the parameters of the improved enzyme action optimization algorithm, including the number of population individuals N, the problem dimension dim, the maximum number of iterations max_iter, the upper bound ub of the search space, and the lower bound lb of the search space; Step 2: Map the improved enzyme action optimization algorithm to the PID controller module in the constructed robotic arm pan-tilt control system. Specifically, combine the optimization process of the improved enzyme action optimization algorithm with the parameter tuning of the PID controller. The problem dimension dim of the improved enzyme action optimization algorithm is 3, and the position of each individual contains three dimensions corresponding to the three parameters Kp, Ki, and Kd of the PID controller. , 、 、 Denote the components of the individual position vector in the three dimensions. Input the parameters obtained by each algorithm optimization into the PID controller and return the real-time error value e(t) of the control. Calculate the fitness value of each group of parameters through the fitness value function as the optimization index; Step 3: Set the fitness value function of the improved enzyme action optimization algorithm, calculate the fitness values of the individuals in the initial population through the fitness value function, and sort according to the fitness value size. Select the position of the optimal individual. The specific fitness value function is: (8); In Equation (8), J represents the fitness value, e(t) represents the real-time error, and T represents the system running time; Step 4: Update the individual positions in the population through the mathematical model of the improved enzyme action optimization algorithm, and sort the updated population according to the fitness value. The specific mathematical model is: Step 41: First, update the adaptive factor AF of the improved enzyme action optimization algorithm. The specific formula is shown in Equation (3); Step 42: Update the position of the first substrate of the individual. The specific update formula is shown in Equation (9): (9); In Equation (9), X(iter + 1) represents the updated individual position, X(iter) represents the current individual position, represents the optimal individual position, ρ represents a random number between [0, 1], and AF represents the adaptive factor. The specific formula is shown in Equation (3); Step 43: Update the position of the second substrate of the individual. The specific update formula is shown in Equation (7); Step 44: Select the individual with a better fitness value after the update of the two substrates as the final updated position; Step 5: Determine whether the current iteration count has reached the maximum iteration count. If not, return to Step 4 for optimization. If so, complete the algorithm optimization and output the optimal solution.

[0018] By adopting the above technical solution, the advantages of the invention are as follows: The mathematical model for optimizing the enzyme action optimization algorithm is improved, and the improved enzyme action optimization algorithm is applied to the optimization process of the PID controller parameters in the robotic arm pan-tilt control system. By introducing a new optimization mechanism, the improved algorithm is more superior in terms of search efficiency, solution quality, and adaptability to different control requirements. It can more effectively adapt to the dynamic characteristics of the pan-tilt control system. During the optimization process, the improved algorithm can converge to high-quality solutions at a faster speed, significantly shortening the iteration time. With the optimized PID controller, the pan-tilt attitude angle can be accurately adjusted. The system shows stronger adaptive ability and angle control accuracy in a changing environment, ensuring that the pan-tilt can quickly respond to control commands and complete the alignment with the target direction in a short time, thereby improving the efficiency and accuracy of the robotic arm's grasping task. Compared with the traditional PID control method with fixed parameters, this method effectively overcomes problems such as poor parameter adaptability and response lag, and significantly enhances the robustness and reliability of the pan-tilt control system in complex application scenarios. Description of the Drawings

[0019] Figure 1 It is a flowchart of a dynamic adaptive control method for a robotic arm pan-tilt.

[0020] Figure 2 It is a model diagram for constructing a robotic arm pan-tilt control system.

[0021] Figure 3 It is a comparison chart of the fitness value changes during the optimization process between the improved enzyme action optimization algorithm and the original enzyme action optimization algorithm.

[0022] Figure 4 It is a comparison chart of the responses of the improved enzyme action optimization algorithm and the original enzyme action optimization algorithm for optimizing the PID controller in the robotic arm pan-tilt control system. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0024] The present invention provides a technical solution: A dynamic adaptive control method for a robotic arm pan-tilt, which specifically includes the following steps, as shown in Figure 1.

[0025] Step 1. Construct a robotic arm pan-tilt control system. The control system includes: a position acquisition module, an angle calculation module, a PID controller module, an improved enzyme action optimization algorithm module, a pan-tilt attitude adjustment module, and a pan-tilt real-time position detection module, as Figure 2 shown.

[0026] Furthermore, for the robotic arm pan-tilt control system constructed in Step 1, the position acquisition module acquires the current position information and target position information of the pan-tilt. The angle calculation module calculates the angle error e(t) that the pan-tilt needs to adjust based on the current position and target position obtained by the position acquisition module. Based on the angle error e(t), the PID controller module uses the improved enzyme action optimization algorithm module to optimize the control parameters, and inputs the optimized control quantity U(t) into the pan-tilt attitude adjustment module. The pan-tilt attitude adjustment module adjusts the angle of the pan-tilt by driving the motor of the pan-tilt to make it reach the target position. During the control process, the pan-tilt real-time position detection module updates the position of the pan-tilt in real time and feeds it back to the position acquisition module for real-time adjustment. The mathematical model of the control quantity U(t) is shown in Equation (1), and the mathematical model of the pan-tilt attitude adjustment module is shown in Equation (2); (1); In Equation (1), U(t) represents the control quantity, and e(t) represents the angle error. , , respectively represent the proportional gain parameter, integral gain parameter, and derivative gain parameter obtained by optimizing the PID controller through the improved enzyme action optimization algorithm; (2); In Equation (2), J represents the moment of inertia of the pan-tilt, Km represents the torque constant of the motor, θ(t) represents the rotation angle of the pan-tilt, and U(t) represents the control quantity as shown in Equation (1).

[0027] Step 2. Improve the enzyme action optimization algorithm. The specific improvements include: S1. Update the adaptive factor AF of the enzyme action optimization algorithm through an adaptive adjustment mechanism based on local gradients. This strategy combines the local gradient information of individuals in the population and the change in the diversity entropy of the search space, and provides real-time feedback and adjusts the size of the adaptive factor AF during the optimization process; S2. Improve the mathematical model for updating the position of the second substrate in the enzyme action optimization algorithm through an orbital perturbation-induced position update strategy. This strategy starts from the non-Euclidean orbital path evolution and the control perturbation field generation mechanism, simulates the dynamic path of the substrate by the conformational channel change of the enzyme, and maps it to the transition trajectory of the individual position path in the algorithm. Update the individual position by generating the non-dynamic enzyme potential field factor U(iter) and the micro-perturbation factor T(iter).

[0028] Furthermore, in S1, update the adaptive factor AF of the enzyme action optimization algorithm through an adaptive adjustment mechanism based on local gradient. First, calculate the local gradient XT, which is determined by the difference between the position of the current individual relative to the optimal position in the population and the fitness values of the current individual position and the optimal position. Then, introduce the entropy change of the search space, that is, evaluate the diversity of the current solution by calculating the distribution entropy H of the current individual. Finally, based on the feedback of the local gradient and the entropy change, dynamically adjust the value of the adaptive factor AF. If the values of the local gradient and the distribution entropy H increase, it means that the range of the current solution space is wide, and at this time, increase the value of the adaptive factor AF. If the values of the local gradient and the distribution entropy H decrease, it means local convergence and reduced diversity, then decrease the value of the adaptive factor AF. The specific update formula of AF is shown in Equation (3): (3); In Equation (3), AF represents the adaptive factor, r1 and r2 represent the adjustment factors, r1 takes the value of 0.4, r2 takes the value of 0.6, XT represents the local gradient, , f represents the fitness value of the current individual position, represents the fitness value of the optimal individual in the population, X(iter) represents the current individual position, represents the optimal individual position, H represents the distribution entropy, and the specific calculation formula is shown in Equation (4); (4); In Equation (4), N represents the number of population individuals, ω represents the distribution weight value, which is a random number between [0,1], exp() represents the exponential calculation function, X(iter) represents the current individual position, represents the optimal individual position, h0 represents the initial bandwidth, which takes the value of 0.5, r represents the adjustment coefficient, which takes the value of 1.2, represents the value of the distribution entropy in the previous iteration, represents the maximum value of the distribution entropy. The algorithm optimizes the three parameters of the PID controller, and sets the maximum value of the distribution entropy to the problem dimension value 3.

[0029] Further, in S2, a mathematical model for updating the position of the second substrate in the enzyme action optimization algorithm is improved through an orbital perturbation-induced position update strategy. The search space is regarded as a non-Euclidean orbit, and each individual follows the propagation of the local energy orbit in the space. Instead of directly updating the individual position, a non-dynamic enzyme potential field factor U(iter) and a perturbation factor T(iter) are constructed. The individual moves along the perturbation dynamic transition path in the perturbed orbit. The non-dynamic enzyme potential field factor U(iter) and the perturbation factor T(iter) are shown in Equations (5) and (6): (5); In Equation (5), N represents the number of population individuals, rand represents a random number between [0,1], f represents the fitness value of the current individual position, exp() represents the exponential calculation function, iter represents the current iteration number, X(iter) represents the current individual position, X(iter - 1) represents the position of the individual at the previous iteration number, σ(iter) represents the expansion scale at the iteration stage, σ(iter)=iter / max_iter, and max_iter represents the maximum number of iterations; (6); In Equation (6), K represents the number of perturbation terms, taking the value of 5, exp() represents the exponential calculation function, iter represents the current iteration number, fk represents the frequency of the perturbation term, with the value range of [1,3], Xk represents the inner product of the current individual position vector and the average position vector of the individuals in the population, and d represents the perturbation direction, taking a random number between [-1,1]; Then, a step size regulation factor y for updating the individual position is set, y = ymax / 1 + log(1 + iter), where ymax represents the maximum step size, taking the value of (ub - lb) / 2, ub represents the upper limit of the search space, and lb represents the lower limit of the search space. As the number of iterations increases, the value of y decays. Combining all the mathematical models constructed by the above strategy, the position of the individual is updated. The specific update formula is shown in Equation (7): (7); In Equation (7), X(iter + 1) represents the updated individual position, X(iter) represents the current individual position, y represents the step size regulation factor, U(iter) represents the non-dynamic enzyme potential field factor, and T(iter) represents the perturbation factor.

[0030] Step 3: Obtain the error between the current position and the target position of the robotic arm pan-tilt, calculate the angle error that the pan-tilt needs to adjust, and optimize the control parameters of the PID controller module of the robotic arm pan-tilt control system through the improved enzyme action optimization algorithm to obtain an optimal set of control parameters.

[0031] Furthermore, in step 3, the control parameters of the PID controller module of the robotic arm pan-tilt control system are optimized by an improved enzyme action optimization algorithm. The specific steps are as follows: step1. Initialize the parameters of the improved enzyme action optimization algorithm, including the number of population individuals N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit of the search space ub, and the lower limit of the search space lb; step2. Map the improved enzyme action optimization algorithm to the PID controller module in the constructed robotic arm pan-tilt control system. Specifically, combine the optimization process of the improved enzyme action optimization algorithm with the parameter tuning of the PID controller. The problem dimension dim of the improved enzyme action optimization algorithm is 3, and the position of each individual contains three dimensions corresponding to the three parameters Kp, Ki, and Kd of the PID controller. , , , represent the components of the individual position vector in three dimensions. Input the parameters obtained by each algorithm optimization into the PID controller and return the real-time error value e(t) of the control. Calculate the fitness value of each group of parameters through the fitness value function as the optimization index; step3. Set the fitness value function of the improved enzyme action optimization algorithm, calculate the fitness value of the individuals in the initial population through the fitness value function, and sort according to the fitness value size, and select the position of the optimal individual. The specific fitness value function is: (8); In formula (8), J represents the fitness value, e(t) represents the real-time error, and T represents the system operation time; step4. Update the individual positions in the population through the mathematical model of the improved enzyme action optimization algorithm, and sort the updated population according to the fitness value. The specific mathematical model is: step41. First, update the adaptive factor AF of the improved enzyme action optimization algorithm. The specific formula is shown in formula (3); step42. Update the position of the first substrate of the individual. The specific update formula is shown in formula (9): (9); In formula (9), X(iter + 1) represents the updated individual position, X(iter) represents the current individual position, represents the optimal individual position, ρ represents a random number between [0, 1], and AF represents the adaptive factor. The specific formula is shown in formula (3); step43. Update the position of the second substrate of the individual. The specific update formula is shown in Equation (7). step44. Select the individual with a better fitness value after the update of the two substrates as the final updated position. step5. Determine whether the current iteration count has reached the maximum iteration count. If not, return to step4 to perform optimization. If so, complete the algorithm optimization and output the optimal solution.

[0032] Step Four: Input a set of optimal control parameters obtained in Step Three into the PID controller, and adjust the robotic arm gimbal to the target angle through the control quantity output by the PID controller.

[0033] Furthermore, to verify the advantages of the present invention, experiments are conducted using MATLAB and Simulink. Through MATLAB, the above two improvement strategies are used to improve the mathematical model of the original enzyme action optimization algorithm, and the code implementation of the mathematical module of the gimbal attitude adjustment module. The specific code of the gimbal attitude adjustment module is as follows: function theta = simulate_gimbal_pid(Kp, Ki, Kd,t) % Kp, Ki, Kd - PID controller parameters % theta - Response of gimbal angle over time % t - Simulation time J = 0.01; % Moment of inertia of the gimbal Km = 1.2; % Motor gain ref = 1; % Desired angle (unit: rad) % Initialize variables n = length(t); e = zeros(1, n); % Error u = zeros(1, n); % Control quantity U(t) theta = zeros(1, n); % Angle theta(t) dtheta = zeros(1, n); % Angular velocity dθ / dt integral = 0; % Initialize the integral term for i = 2:n % Current error e(i) = ref - theta(i-1); % Integral term integral = integral + e(i) * (t(i) - t(i-1)); % Differential term derivative = (e(i) - e(i-1)) / (t(i) - t(i-1)); % PID controller output U(t) u(i) = Kp * e(i) + Ki * integral + Kd * derivative; % System dynamics formula: J * θ'' = Km * U(t) ddtheta = (Km * u(i)) / J; % Update angular velocity and angle (numerical integration) dtheta(i) = dtheta(i-1) + ddtheta * (t(i) - t(i-1)); theta(i) = theta(i-1) + dtheta(i) * (t(i) - t(i-1)); end end。

[0034] Furthermore, a simulation model is built using Simulink, including: a position acquisition module, an angle calculation module, a PID controller module, an improved enzyme action optimization algorithm module, a gimbal attitude adjustment module, and a gimbal real-time position detection module. The improved enzyme action optimization algorithm module calls the code of the improved enzyme action optimization algorithm in MATLAB, and the gimbal attitude adjustment module calls the simulate_gimbal_pid(Kp, Ki, Kd,t) function to simulate the operation of the system, and the controlled field of the system is set to a second-order Laplace transfer function, specifically , where s represents the variable in the complex frequency domain. In the main function, the initial parameters of the original algorithm and the improved algorithm are set. The maximum number of iterations max_iter is set to 20, the problem dimension dim is set to 3, the population size is set to 100, the upper limit of the search space ub is set to 100, and the lower limit lb is set to 0.001. The codes of the original algorithm and the improved algorithm are called in turn, and a set of optimized parameters obtained by optimization are input into the simulation model for control. The control error is returned for fitness value calculation. The algorithms are sorted according to the size of the fitness value, and a set of optimal control parameters are obtained under the specified number of iterations to control the robotic arm gimbal control system. Figures 3 - 4They are respectively the comparison chart of the fitness value changes during the optimization process of the improved enzyme action optimization algorithm and the original enzyme action optimization algorithm, and the comparison chart of the response of the PID controller in the robotic arm pan-tilt control system optimized by the improved enzyme action optimization algorithm and the original enzyme action optimization algorithm.

[0035] Furthermore, from Figure 3 it can be seen that the fitness value changes more frequently during the optimization process of the improved enzyme action optimization algorithm, indicating that the algorithm has a stronger adjustment ability. When falling into a local optimal solution, it can jump out and continue to optimize in a very short number of iterations, and the fitness value of the finally obtained solution is smaller, and the optimization accuracy is better. The control target value is set to 1 unit. From Figure 4 it can be seen that the response curve of the PID controller in the robotic arm pan-tilt control system optimized by the original enzyme action optimization algorithm has a large overshoot and many oscillations near the target value. The response curve of the PID controller in the robotic arm pan-tilt control system optimized by the improved enzyme action optimization algorithm has better stability, smaller overshoot, and can quickly stabilize near the target value. In the actual control scenario, the robotic arm pan-tilt optimized by the improved enzyme action optimization algorithm can quickly and stably adjust to the target position to grasp the object, with stronger adaptability and robustness.

Claims

1. A dynamic adaptive control method for a robotic arm pan-tilt, characterized in that The specific steps are as follows: Step 1: Construct a robotic arm pan-tilt control system, which includes a position acquisition module, an angle calculation module, a PID controller module, an improved enzyme action optimization algorithm module, a pan-tilt attitude adjustment module, and a pan-tilt real-time position detection module; Step 2: Improve the enzyme action optimization algorithm. The specific improvements include: S1: Update the adaptive factor AF of the enzyme action optimization algorithm through an adaptive adjustment mechanism based on local gradients. This strategy combines the local gradient information of individuals in the population and the change in search space diversity entropy, and adjusts the size of the adaptive factor AF in the optimization process of the algorithm in real-time; S2: Improve the mathematical model of the position update of the second substrate in the enzyme action optimization algorithm through an orbital perturbation-induced position update strategy. This strategy starts from the non-Euclidean orbital path evolution and control perturbation field generation mechanism, simulates the dynamic path of the substrate through conformational channel changes of the enzyme, and maps it to the transition trajectory of the individual position path in the algorithm. The individual position is updated by generating a non-dynamic enzyme potential field factor U(iter) and a micro-perturbation factor T(iter); Step 3: Obtain the error between the current position and the target position of the robotic arm pan-tilt, calculate the angle error that the pan-tilt needs to adjust, and optimize the control parameters of the PID controller module of the robotic arm pan-tilt control system through the improved enzyme action optimization algorithm to obtain an optimal set of control parameters; Step 4: Input the optimal set of control parameters obtained in Step 3 into the PID controller, and adjust the robotic arm pan-tilt to the target angle through the control quantity output by the PID controller.

2. The dynamic adaptive control method for a robotic arm pan-tilt according to claim 1, characterized in that, In the robotic arm pan-tilt control system constructed in Step 1, the position acquisition module acquires the current position information and target position information of the pan-tilt. The angle calculation module calculates the angle error e(t) that the pan-tilt needs to adjust based on the current position and the target position acquired by the position acquisition module. Through the angle error e(t), the PID controller module uses the improved enzyme action optimization algorithm module to optimize the control parameters, and inputs the optimized control quantity U(t) into the pan-tilt attitude adjustment module. The pan-tilt attitude adjustment module adjusts the angle of the pan-tilt by driving the motor of the pan-tilt to make it reach the target position. During the control process, the pan-tilt real-time position detection module updates the position of the pan-tilt in real-time and feeds it back to the position acquisition module for real-time adjustment.

3. The dynamic adaptive control method for a robotic arm pan-tilt according to claim 2, characterized in that, In S1, an adaptive factor AF of the enzymatic action optimization algorithm is updated through an adaptive adjustment mechanism based on local gradient. First, the local gradient XT is calculated. The local gradient is determined by the difference between the position of the current individual relative to the optimal position in the population and the fitness values of the current individual position and the optimal position. Then, the entropy change of the search space is introduced, that is, the diversity of the current solution is evaluated by calculating the distribution entropy H of the current individual. Finally, based on the feedback of the local gradient and entropy change, the value of the adaptive factor AF is dynamically adjusted. If the values of the local gradient and the distribution entropy H increase, it indicates that the range of the current solution space is wide, and at this time, the value of the adaptive factor AF is increased. If the values of the local gradient and the distribution entropy H decrease, it indicates local convergence and reduced diversity, and then the value of the adaptive factor AF is decreased. The specific update formula of AF is shown in Equation (3): (3)； In Equation (3), AF represents the adaptive factor, r1 and r2 represent the adjustment factors, the value of r1 is 0.4, the value of r2 is 0.6, XT represents the local gradient, , f represents the fitness value of the current individual position, represents the fitness value of the optimal individual in the population, X(iter) represents the current individual position, represents the optimal individual position, H represents the distribution entropy. The specific calculation formula is shown in Equation (4); (4)； In Equation (4), N represents the number of population individuals, ω represents the distribution weight value, which is a random number between [0, 1], exp() represents the exponential calculation function, X(iter) represents the current individual position, represents the optimal individual position, h0 represents the initial bandwidth, with a value of 0.5, r represents the adjustment coefficient, with a value of 1.2, represents the value of the distribution entropy in the previous iteration, represents the maximum value of the distribution entropy. The algorithm optimizes the three parameters of the PID controller and sets the maximum value of the distribution entropy to the problem dimension value 3.

4. The dynamic adaptive control method for a robotic arm pan-tilt according to claim 3, wherein In S2, a mathematical model for updating the position of the second substrate in the enzymatic action optimization algorithm is improved through an orbital perturbation-induced position update strategy. The search space is regarded as a non-Euclidean orbit, and each individual propagates along the local energy orbit in the space. Instead of directly updating the individual position, a non-dynamic enzyme potential field factor U(iter) and a perturbation factor T(iter) are constructed. The individual moves along the perturbation dynamic transition path in the perturbed orbit. The non-dynamic enzyme potential field factor U(iter) and the perturbation factor T(iter) are shown in Equations (5) and (6): (5)； In Equation (5), N represents the number of population individuals, rand represents a random number between [0,1], f represents the fitness value of the current individual position, exp() represents the exponential calculation function, iter represents the current iteration number, X(iter) represents the current individual position, X(iter - 1) represents the position of the individual at the previous iteration number, σ(iter) represents the expansion scale at the iteration stage, σ(iter)=iter / max_iter, and max_iter represents the maximum iteration number; (6)； In Equation (6), K represents the number of perturbation terms, with a value of 5, exp() represents the exponential calculation function, iter represents the current iteration number, fk represents the frequency of the perturbation term, with a value range of [1,3], Xk represents the inner product of the current individual position vector and the average position vector of the individuals in the population, and d represents the perturbation direction, with a value of a random number between [-1,1]; Then, the step size regulation factor y is set, y = ymax / 1 + log(1 + iter), where ymax represents the maximum step size, with a value of (ub - lb) / 2, ub represents the upper limit of the search space, and lb represents the lower limit of the search space. As the iteration number increases, the value of y decays. Combining all the mathematical models constructed by this strategy, the position of the individual is updated. The specific update formula is shown in Equation (7): (7)； In Equation (7), X(iter + 1) represents the updated individual position, X(iter) represents the current individual position, y represents the step size regulation factor, U(iter) represents the non-dynamic enzyme potential field factor, and T(iter) represents the perturbation factor.

5. The dynamic adaptive control method for a robotic arm pan-tilt according to claim 4, wherein In step 3, the control parameters of the PID controller module of the robotic arm pan-tilt control system are optimized by an improved enzyme action optimization algorithm. The specific steps are as follows: step1. Initialize the parameters of the improved enzyme action optimization algorithm, including the number of population individuals N, the problem dimension dim, the maximum number of iterations max_iter, the upper bound ub of the search space, and the lower bound lb of the search space; Step 2: Map the improved enzyme action optimization algorithm to the PID controller module in the constructed robotic arm pan-tilt control system. Specifically, combine the optimization process of the improved enzyme action optimization algorithm with the parameter tuning of the PID controller. The problem dimension dim of the improved enzyme action optimization algorithm is 3, and the position of each individual contains three dimensions corresponding to the three parameters Kp, Ki, and Kd of the PID controller. , , , represent the components of the individual position vector in the three dimensions. Input the parameters obtained by each algorithm optimization into the PID controller and return the real-time error value e(t) of the control. Calculate the fitness value of each group of parameters through the fitness value function as the index for optimization. step3. Set the fitness value function of the improved enzyme action optimization algorithm, calculate the fitness values of the individuals in the initial population through the fitness value function, and sort them according to the fitness value size, and select the position of the optimal individual. The specific fitness value function is: (8)； In Equation (8), J represents the fitness value, e(t) represents the real-time error, and T represents the system running time; step4. Update the positions of the individuals in the population through the mathematical model of the improved enzyme action optimization algorithm, and sort the updated population according to the fitness value; step5. Determine whether the current number of iterations has reached the maximum number of iterations. If not, return to step4 for optimization. If so, complete the algorithm optimization and output the optimal solution.

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