Course control method for electric power inspection robot

Abstract

The invention discloses a course control method for an electric power inspection robot, and belongs to the technical field of PID control optimization, and the method comprises the specific steps: S1, constructing a course control system for the electric power inspection robot; s2, improving a black-wing plinary algorithm, wherein a specific improved strategy is as follows: S21, improving an attack behavior stage in the black-wing plinary algorithm by using a self-adaptive elite disturbance strategy; s22, using a dynamic step migration strategy to improve a migration behavior stage in the improved black-wing plinuary algorithm; s3, optimizing control parameters of a PID (Proportion Integration Differentiation) controller module in the course control system of the electric power inspection robot by utilizing an improved black-wing plinary algorithm; s4, inputting a group of optimal parameters obtained in S3 into a PID controller module of a course control system of the electric power inspection robot; the improved black-wing plinary algorithm is applied to a PID controller module in the course control system of the electric power inspection robot, so that the stability and the adaptive capacity of the 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 heading control method for a power inspection robot. Background Art

[0002] Power inspection robots are widely used in scenarios such as substations, cable tunnels, and high-voltage lines. Their core task is to replace humans to complete complex, dangerous, or repetitive inspection tasks. To complete these tasks, the robot must have good heading control capabilities to ensure smooth travel along the preset path during the inspection, avoid deviation or collision, and be able to make adaptive adjustments according to real-time obstacles or environmental changes. Usually, a PID controller is used to control the heading angle.

[0003] A PID controller is a feedback controller widely used in industrial control systems. It adjusts the output of the system by calculating the control quantities of three parts: proportional (P), integral (I), and derivative (D) to achieve the desired goal. Specifically, the proportional part is adjusted according to the current error magnitude, the integral part processes the accumulation of errors to eliminate the steady-state error, and the derivative part predicts the trend of error change to improve the dynamic characteristics of the system response. By reasonably adjusting the three parameters, the PID controller can achieve precise control of the system and is widely used in fields such as temperature control, heading control, and speed control.

[0004] The Black Kite Algorithm (BKA) is a swarm intelligence optimization algorithm inspired by the predation behavior of black kites in nature. During the predation process, black kites exhibit behaviors such as hovering and rapid diving. BKA simulates this hunting process and searches the solution space through two main behaviors: attack behavior and migration behavior. The attack behavior refines the search through local exploration, while the migration behavior promotes the population towards the optimal solution through global guidance. This algorithm has strong global search capabilities and adaptability, can effectively avoid falling into local optimal solutions, and is widely used in the solution of optimization problems. Summary of the Invention

[0005] The purpose of the present invention is to improve the Black Kite Algorithm (BKA) to better adapt to the application scenario of the heading control of a power inspection robot. The improved BKA has stronger global optimization capabilities and dynamic adjustment performance, can more accurately optimize the PID controller parameters in the control system of the power inspection robot. The optimized PID controller can more accurately adjust the heading of the robot in a complex power environment, quickly respond to path changes, significantly reduce the heading error and path deviation, improve the heading stability of the robot during obstacle avoidance and turning. In addition, the optimization process can also reduce unnecessary steering adjustments, reduce energy consumption, and extend the service life of the equipment, thereby achieving high-precision, strong robustness, and low-energy consumption heading control to meet the requirements of high intelligence and high stability for modern power grid inspection tasks.

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

[0007] A method for controlling the heading of a power inspection robot, the specific steps are as follows.

[0008] S1. Construct a heading control system for the power inspection robot, and the control system includes: an inspection instruction module, a heading calculation module, a PID controller module, an improved black-winged kite algorithm module, a heading control module, and a heading detection module.

[0009] S2. Improve the black-winged kite algorithm, and the specific improvement strategy is: S21. Use an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm. This strategy guides the individual position by introducing an elite individual and performs linear perturbation through the change of the adaptive factor with the number of iterations; S22. Use a dynamic step migration strategy to improve the mathematical model in the migration behavior stage of the improved black-winged kite algorithm. This strategy dynamically adjusts the step of individual movement according to the change of the fitness value of the current individual, so as to update the position of individuals in the population.

[0010] S3. Use the improved black-winged kite algorithm to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot, and obtain the optimal set of Kp, Ki, and Kd parameter values through the optimization of the improved algorithm.

[0011] S4. Input the optimal set of Kp, Ki, and Kd parameters obtained in S3 into the PID controller module of the heading control system of the power inspection robot to optimize the heading control of the robot.

[0012] Preferably, in S1, for the constructed heading control system of the power inspection robot, the relationship between modules is as follows: when the power inspection robot needs to change its heading, it will send a specified heading command through the inspection instruction module, and transmit the heading command to the heading calculation module to calculate the corresponding target heading angle to be changed. The target heading angle is subjected to error calculation with the actual heading angle detected by the heading detection module to obtain the real-time error e(t). Then e(t) is transmitted to the PID controller module and its parameters are optimized through the improved black-winged kite algorithm module, and the optimized control quantity u(t) is output. The control quantity u(t) is input into the heading control module to control the heading of the power inspection robot. The expression of u(t) is shown in Equation (1). The heading control module is simplified to a second-order inertial system, and the specific differential equation is shown in Equation (2). And Equation (2) is subjected to Laplace transform to generate a second-order transfer function to simulate the actual operation state as shown in Equation (3); In Equation (1), \(K_p\) represents the proportional gain, \(K_i\) represents the integral gain, \(K_d\) represents the derivative gain, and \(e(t)\) represents the real-time error; In Equation (2), \(\theta(t)\) represents the current heading angle of the robot, \(\zeta\) represents the damping ratio, \(\omega\) n represents the natural frequency, and \(u(t)\) represents the control quantity as shown in Equation (1); In Equation (3), \(G(s)\) represents the transfer function, and \(s\) represents the complex variable of the Laplace transform.

[0013] Preferably, in the step S21, an adaptive elite perturbation strategy is used to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm. The specific improved mathematical model is: In Equation (4), \(y(iter + 1)\) represents the updated individual position, \(y(iter)\) represents the current individual position, \(\alpha\) represents the linear attenuation factor, iter represents the current iteration number, max_iter represents the maximum iteration number, \(n\) represents the perturbation intensity factor, \(n = 0.05\cdot e\) -2(iter / max_iter) , \(X\) elite represents the elite position, that is, the optimal position in the population, \(p\) represents the control behavior switching probability factor, with a value of 0.5, and \(r\) represents a random number between \([0, 1]\).

[0014] Preferably, an adaptive elite perturbation strategy can make the update of individuals quickly approach the vicinity of the optimal solution by introducing the guidance of the elite position, thereby accelerating the algorithm to find the global optimal solution. By adaptively adjusting the weight of the elite individuals through the linear attenuation factor, it can better adapt to the optimization process of the algorithm. The size of the linear attenuation factor at different iteration numbers can better balance the global search and local search capabilities of the algorithm.

[0015] Preferably, in the step S22, a dynamic step-size migration strategy is used to improve the mathematical model in the migration behavior stage of the improved black-winged kite algorithm. The specific improved mathematical model is: In Equation (5), \(y(iter + 1)\) represents the updated individual position, \(y(iter)\) represents the current individual position, \(y\) best represents the optimal position in the population, \(C(0, 1)\) represents a random perturbation following the Cauchy distribution, \(F\) represents the fitness value of the current individual, \(F_{best}\) represents the fitness value of the optimal position in the population, and \(o(iter)\) represents the dynamic step-size factor, and its calculation formula is as shown in Equation (6); In Equation (6), σ max represents the maximum step size, with a value of 0.9, F represents the fitness value of the current individual, Fbest represents the fitness value of the optimal position in the population, and λ represents the step size decay rate factor, with a value of 0.7.

[0016] Preferably, after introducing the dynamic step size migration strategy, the black-winged kite algorithm can adaptively adjust the step size between global search and local development, ensuring that individuals with poor fitness values have sufficient exploration space, while individuals with good fitness values can accelerate convergence. Through the step size decay mechanism, the adaptability and convergence speed of the algorithm are further enhanced, thereby improving the overall optimization effect.

[0017] Preferably, in S3, the improved black-winged kite algorithm is used to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot. The specific steps are as follows: S31. Initialize the parameters of the improved black-winged kite algorithm, including the population size 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. Initialize the population position through the initial parameters of the algorithm. S32. Establish a mapping relationship between the parameters of the improved black-winged kite algorithm and the PID controller parameters in the heading control system of the power inspection robot. The three dimensions of the individual position vector y(iter) of the algorithm correspond to the proportional coefficient Kp, the integral coefficient Ki, and the differential coefficient Kd of the PID controller respectively. Specifically, the individual position vector is y(iter) = [y1(iter), y2(iter), y3(iter)], and [y1(iter), y2(iter), y3(iter)] = [Kp, Ki, Kd]. Through the iterative update of the optimization algorithm, the position of the individual in the search space is continuously adjusted, so as to optimize the PID parameters to achieve the optimal response of the performance index, and finally obtain the optimal parameter combination of the PID controller. S33. Set the fitness value function of the improved black-winged kite algorithm. Calculate the fitness values of the initial population individuals through the set fitness value function, sort according to the size of the fitness values, and select the optimal position y of the population best , and the specific fitness value function is: In Equation (7), J represents the fitness value, e(t) represents the real-time error value, and T represents the running time of the system. S34. Update the positions of the individuals in the population through the mathematical model of the improved black-winged kite algorithm, and update the optimal position in the population in real time. The mathematical model of the improved black-winged kite algorithm is specifically: Step 1: The algorithm enters the attack behavior stage to update the individual position, and the specific mathematical model is shown in Equation (4). Step 2: The algorithm enters the migration behavior stage to update the individual position, and the specific mathematical model is shown in Equation (5). S35: Determine whether the current iteration number has reached the maximum iteration number. If it has reached, complete the optimization and output the optimal solution; otherwise, continue to execute S34 for optimization.

[0018] By adopting the above technical solution, the beneficial effects of the present invention are as follows: The black-winged kite algorithm is improved through an adaptive elite perturbation strategy and a dynamic step-size migration strategy, which improves the adaptability of the algorithm in the optimization process, enhances the optimization accuracy and speed of the algorithm. Applying the improved black-winged kite algorithm to the PID controller module in the heading control system of the power inspection robot can improve the response speed of the system. The control obtained through optimization can quickly respond to the heading command of the robot to adjust the angle and reduce the dynamic error in the adjustment process. Description of the Drawings

[0019] Figure 1 It is a flowchart of a heading control method for a power inspection robot.

[0020] Figure 2 It is a model diagram of a heading control system for a power inspection robot.

[0021] Figure 3 It is a comparison diagram of the change of fitness values in the optimization process between the improved black-winged kite algorithm and the standard black-winged kite algorithm.

[0022] Figure 4 It is a comparison diagram of the effects of the improved black-winged kite algorithm and the improved black-winged kite algorithm for optimizing the PID controller. Detailed Embodiment

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 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 heading control method for a power inspection robot, specifically including the following steps, as Figure 1 shown.

[0025] S1: Construct a heading control system for a power inspection robot. The control system includes: an inspection instruction module, a heading calculation module, a PID controller module, an improved black-winged kite algorithm module, a heading control module, and a heading detection module, asFigure 2 as shown

[0026] Preferably, in the above S1, for the constructed heading control system of the power inspection robot, the relationship between modules is as follows: When the power inspection robot needs to change its heading, it will send a specified heading command through the inspection instruction module, and input the heading command into the heading calculation module to calculate the corresponding target heading angle to be changed. The target heading angle is used to calculate the error with the actual heading angle detected by the heading detection module to obtain the real-time error e(t). Then e(t) is input into the PID controller module and its parameters are optimized by the improved black-winged kite algorithm module, and the optimized control quantity u(t) is output. The control quantity u(t) is input into the heading control module to control the heading of the power inspection robot. The expression of u(t) is shown in Equation (1). The heading control module is simplified to a second-order inertial system, and the specific differential equation is shown in Equation (2). Then Equation (2) is Laplace-transformed to generate a second-order transfer function to simulate the actual operating state as shown in Equation (3); In Equation (1), Kp represents the proportional gain, Ki represents the integral gain, Kd represents the derivative gain, and e(t) represents the real-time error; In Equation (2), θ(t) represents the current heading angle of the robot, ζ represents the damping ratio, ω n represents the natural frequency, and u(t) represents the control quantity as shown in Equation (1); In Equation (3), G(s) represents the transfer function, and s represents the complex variable of the Laplace transform.

[0027] S2. Improved black-winged kite algorithm, and the specific improvement strategy is as follows: S21. Use an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm. This strategy guides the individual position by introducing an elite individual and performs linear perturbation through an adaptive factor that changes with the number of iterations; S22. Use a dynamic step-size migration strategy to improve the mathematical model in the migration behavior stage of the improved black-winged kite algorithm. This strategy dynamically adjusts the step size of individual movement according to the change of the fitness value of the current individual, so as to update the positions of individuals in the population.

[0028] Preferably, in the above S21, use an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm. The specific improved mathematical model is as follows: In Equation (4), y(iter + 1) represents the updated individual position, y(iter) represents the current individual position, α represents the linear attenuation factor, iter represents the current iteration number, max_iter represents the maximum iteration number, n represents the perturbation intensity factor, n = 0.05·e -2(iter / max_iter) , X elite represents the elite position, that is, the optimal position in the population, p represents the control behavior switching probability factor, with a value of 0.5, and r represents a random number between [0, 1].

[0029] Preferably, in S22, a dynamic step - size migration strategy is used to improve the mathematical model in the migration behavior stage of the improved black - winged kite algorithm. The specific improved mathematical model is: In Equation (5), y(iter + 1) represents the updated individual position, y(iter) represents the current individual position, y best represents the optimal position in the population, C(0, 1) represents a random perturbation following the Cauchy distribution, F represents the fitness value of the current individual, Fbest represents the fitness value of the optimal position in the population, and σ(iter) represents the dynamic step - size factor, and its calculation formula is as shown in Equation (6); In Equation (6), σ max represents the maximum step - size, with a value of 0.9, F represents the fitness value of the current individual, Fbest represents the fitness value of the optimal position in the population, and λ represents the step - size attenuation speed factor, with a value of 0.7.

[0030] S3. Use the improved black - winged kite algorithm to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot, and obtain the optimal set of Kp, Ki, and Kd parameter values through the optimization of the improved algorithm.

[0031] Preferably, in S3, using the improved black - winged kite algorithm to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot, the specific steps are: S31. Initialize the parameters of the improved black - winged kite algorithm, including the population size N, the problem dimension dim, the maximum iteration number max_iter, the upper limit ub and the lower limit lb of the search space, and initialize the population position through the initial parameters of the algorithm; S32. Establish a mapping relationship between the parameters of the improved black-winged kite algorithm and the PID controller parameters in the heading control system of the power inspection robot. Corresponding the three dimensions of the individual position vector y(iter) of the algorithm to the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller respectively. Specifically, the individual position vector is y(iter) = [y1(iter), y2(iter), y3(iter)], and [y1(iter), y2(iter), y3(iter)] = [Kp, Ki, Kd]. Through the iterative update of the optimization algorithm, the position of the individual in the search space is continuously adjusted, thereby optimizing the PID parameters to achieve the optimal response of the performance index, and finally obtaining the optimal parameter combination of the PID controller. S33. Set the fitness value function of the improved black-winged kite algorithm. Calculate the fitness values of the individuals in the initial population through the set fitness value function, sort them according to the size of the fitness values, and select the optimal position y of the population. best Specifically, the function of the fitness value is: In Equation (7), J represents the fitness value, e(t) represents the real-time error value, and T represents the running time of the system. S34. Update the positions of the individuals in the population through the mathematical model of the improved black-winged kite algorithm, and update the optimal position in the population in real time. The mathematical model of the improved black-winged kite algorithm is specifically as follows: Step 1. When the algorithm enters the attack behavior stage, update the individual positions. The specific mathematical model is shown in Equation (4). Step 2. When the algorithm enters the migration behavior stage, update the individual positions. The specific mathematical model is shown in Equation (5). S35. Judge whether the current iteration number has reached the maximum iteration number. If it has reached, complete the optimization and output the optimal solution. Otherwise, continue to execute S34 for optimization.

[0032] S4. Input the set of optimal Kp, Ki, and Kd parameters obtained in S3 into the PID controller module of the power inspection robot heading control system to optimize the robot heading control.

[0033] Furthermore, in order to verify the beneficial effects of the present invention, simulation experiments are carried out through Matlab and Simulink. First, Matlab is used to improve the mathematical model of the Black-winged Kite algorithm. The specific improvements are as follows: using an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the Black-winged Kite algorithm and using a dynamic step-size migration strategy to improve the mathematical model in the migration behavior stage of the improved Black-winged Kite algorithm, and initializing the parameters of the improved algorithm, including the population size of 100, the problem dimension set to 3, the maximum number of iterations initially set to 20 times, the upper limit of the search space set to 50, and the lower limit set to 0.001. Use Simulink to build a simulation system to simulate the operation of the inspection robot's heading control. According to the actual scenario, the transfer function of the simulation system is set as: s represents the complex variable of the Laplace transform. Run the algorithms before and after improvement in the main program for optimization comparison. The experimental results are as Figure 3 shown in the figure.

[0034] Figure 3 Figure is a comparison chart of the change of fitness values in the optimization process between the improved Black-winged Kite algorithm and the standard Black-winged Kite algorithm. It can be seen from the figure that the improved Black-winged Kite algorithm has better adaptability in the optimization process, can jump out of the local optimum in a shorter time, and the fitness value of the optimized solution is lower and the accuracy is higher. Figure 4 Figure is a comparison chart of the effects of the improved Black-winged Kite algorithm and the improved Black-winged Kite algorithm optimized PID controller. It can be seen from the figure that the response curve of the PID controller optimized by the improved Black-winged Kite algorithm has better stability, smaller oscillation amplitude, smaller steady-state error in the tuning process, and better robustness.

Claims

1. A heading control method for a power inspection robot, characterized in that, The specific steps are as follows: S1. Construct a heading control system for the power inspection robot. The control system includes: an inspection instruction module, a heading calculation module, a PID controller module, an improved black-winged kite algorithm module, a heading control module, and a heading detection module; S2. Improve the black-winged kite algorithm. The specific improvement strategy is: S21. Use an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm. This strategy guides the individual position by introducing an elite individual and performs linear perturbation through the change of the adaptive factor with the number of iterations; S22. Use a dynamic step-size migration strategy to improve the mathematical model in the migration behavior stage of the improved black-winged kite algorithm. This strategy dynamically adjusts the step size of individual movement according to the change of the fitness value of the current individual, so as to update the positions of individuals in the population; S3. Use the improved black-winged kite algorithm to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot, and obtain an optimal set of Kp, Ki, and Kd parameter values through the optimization of the improved algorithm; S4. Input the optimal set of Kp, Ki, and Kd parameters obtained in S3 into the PID controller module of the heading control system of the power inspection robot to optimize the heading control of the robot.

2. The heading control method of a power inspection robot according to claim 1, wherein In S1, for the constructed heading control system of the power inspection robot, the relationship between modules is as follows: When the power inspection robot needs to change its heading, it sends a specified heading command through the inspection instruction module, and passes the heading command into the heading calculation module to calculate the corresponding target heading angle to be changed. The target heading angle and the actual heading angle detected by the heading detection module are used to calculate the real-time error e(t). Then e(t) is passed into the PID controller module and its parameters are optimized by the improved black-winged kite algorithm module, and the optimized control quantity u(t) is output. The control quantity u(t) is input into the heading control module to control the heading of the power inspection robot.

3. A heading control method for a power inspection robot according to claim 2, characterized in that, In S21, when using an adaptive elite perturbation strategy to improve the mathematical model in the attack behavior stage of the black-winged kite algorithm, the specific improved mathematical model is: In Equation (4), y(iter + 1) represents the updated individual position, y(iter) represents the current individual position, α represents the linear attenuation factor, iter represents the current iteration number, max_iter represents the maximum iteration number, n represents the perturbation intensity factor, n = 0.05·e -2(iter / max_iter) , x elite represents the elite position, that is, the optimal position in the population, p represents the control behavior switching probability factor, with a value of 0.5, and r represents a random number between [0, 1].

4. A method for controlling the heading of a power inspection robot according to claim 3, characterized in that, In S22, when using a dynamic step-size migration strategy to improve the mathematical model in the migration behavior stage of the improved black-winged kite algorithm, the specific improved mathematical model is: In Equation (5), y(iter + 1) represents the updated individual position, y(iter) represents the current individual position, and y best represents the optimal position in the population, C(0, 1) represents a random perturbation following the Cauchy distribution, F represents the fitness value of the current individual, Fbest represents the fitness value of the optimal position in the population, and σ(iter) represents the dynamic step size factor, and its calculation formula is shown in Equation (6); In formula (6), σ max represents the maximum step size, with a value of 0.9, F represents the fitness value of the current individual, Fbest represents the fitness value of the optimal position in the population, and λ represents the step size decay rate factor, with a value of 0.

7.

5. A heading control method for a power inspection robot according to claim 4, characterized in that, In S3, when using the improved black-winged kite algorithm to optimize the control parameters of the PID controller module in the heading control system of the power inspection robot, the specific steps are: S31. Initialize the parameters of the improved black-winged kite algorithm, including the population size N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub and the lower limit lb of the search space, and initialize the population position through the initial parameters of the algorithm; S32. Establish a mapping relationship between the parameters of the improved black-winged kite algorithm and the parameters of the PID controller in the heading control system of the power inspection robot. The three dimensions of the individual position vector y(iter) of the algorithm respectively correspond to the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller. Specifically: the individual position vector is y(iter) = [y1(iter), y2(iter), y3(iter)], and [y1(iter), y2(iter), y3(iter)] = [Kp, Ki, Kd]. Through the iterative update of the optimization algorithm, the position of the individual in the search space is continuously adjusted, so as to optimize the PID parameters to achieve the optimal response of the performance index, and finally obtain the optimal parameter combination of the PID controller; S33. Set the fitness value function of the improved black-winged kite algorithm, calculate the fitness values of the individuals in the initial population through the set fitness value function, sort them according to the magnitudes of the fitness values, and select the optimal position y of the population best , and the specific function of the fitness value is as follows: In formula (7), J represents the fitness value, e(t) represents the real-time error value, and T represents the running time of the system; S34. Update the positions of the individuals in the population through the mathematical model of the improved black-winged kite algorithm, and update the optimal position in the population in real time. The mathematical model of the improved black-winged kite algorithm is specifically as follows: Step 1. When the algorithm enters the attack behavior stage, update the individual position. The specific mathematical model is shown in formula (4); Step 2. When the algorithm enters the migration behavior stage, update the individual position. The specific mathematical model is shown in formula (5); S35. Judge whether the current iteration number has reached the maximum iteration number. If it has reached, complete the optimization and output the optimal solution. Otherwise, continue to execute S34 for optimization.

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