Self-adaptive control method based on large-inertia fan

Abstract

The invention discloses a self-adaptive control method based on a large-inertia fan, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: S1, constructing a large-inertia fan control system; s2, improving a mathematical model in a standard tornado optimization algorithm; s3, using an improved tornado optimization algorithm to optimize control parameters of a speed loop PID controller in the large-inertia fan control system, and obtaining a group of optimal PID control parameters Kp, Ki and Kd through optimization of the algorithm; and S4, applying the group of optimal PID control parameters obtained in the S3 to a speed loop PID controller in the large-inertia fan control system, and outputting a corresponding control quantity to realize speed control of the large-inertia fan control system. The speed loop PID controller in the large-inertia fan control system is optimized through the improved tornado optimization algorithm, the adaptive capacity of the PID controller is enhanced, the steady-state error in the fan operation process is effectively reduced, the control precision is improved, and the robustness of the large-inertia fan control system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of PID control optimization, and in particular relates to an adaptive control method based on a large inertia fan. Background Art

[0002] The core characteristics of large-inertia fans include high-speed inertia, large impeller or rotor mass and long radius. Huge inertia needs to be overcome during startup, and the acceleration time is long. However, large-inertia fans have high stability and reliability, and their high-strength materials and anti-corrosion technology can better adapt to harsh environments. Asynchronous motors are usually used for control, mainly concentrated in high-energy consumption and large-scale industrial scenarios. In the energy field, they are mainly used in offshore wind farms. In the industrial field, the steel, chemical, and power industries rely on large-inertia fans for flue gas treatment, material transportation and cooling. In terms of control, large-inertia fans have problems such as dynamic response lag, sensitive control parameters, and high control complexity, which put higher requirements on the performance of motor control. Future technological breakthroughs need to focus on intelligent control algorithms, lightweight materials, and predictive maintenance driven by digital twins to achieve a better balance between energy efficiency, life, and economy.

[0003] Asynchronous motor control usually adopts vector control FOC, the core of which is to achieve decoupling control of torque and flux through a dual closed-loop strategy of current and speed. The PID controller of the speed loop determines the dynamic response performance and steady-state accuracy of the system, especially in the scenario of complex loads such as large inertia fans, its parameter design and optimization are crucial. The PID controller is one of the most classic and widely used control algorithms in the field of industrial control. Its core idea is to dynamically adjust the system error through a combination of three links: proportional (Kp), integral (Ki), and differential (Kd). However, the traditional fixed parameter design faces the disadvantages of slow dynamic response, weak anti-interference ability, and low control accuracy in the scenario of large inertia fans. When faced with complex control environments or parameter changes due to equipment aging, traditional PID control is difficult to adapt to changes, which may lead to large control errors and unstable performance. By introducing intelligent optimization algorithms to adaptively adjust and optimize parameters, the adaptability and performance of the control system can be effectively improved.

[0004] The Tornado Optimization Algorithm (TOC) is a new type of physics-inspired intelligent optimization algorithm designed to solve global optimization and constrained optimization problems. The algorithm is inspired by the formation and development process of tornadoes, combining natural phenomena such as storm airflow and Coriolis force, and forming an optimized search behavior through the interaction between individuals. TOC has excellent performance in multiple engineering optimization problems. Compared with other optimization algorithms, it has better global optimization ability and stability. Summary of the invention

[0005] The present invention aims to solve the problems that traditional control methods are difficult to cope with the response lag and poor adaptability caused by the large inertia moment of the control system of a large inertia fan, as well as the problem that the manual parameter adjustment of the traditional PID controller is complicated and difficult to accurately optimize. For this reason, an adaptive control method based on a large inertia fan is proposed. The speed loop PID controller parameters in the large inertia fan control system are optimized by improving the tornado optimization algorithm. The method can enhance the adaptive ability of the system, adapt to changes in different working conditions, effectively reduce the steady-state error during the operation of the fan, and improve the control accuracy. Through the optimized optimal PID control parameters, the fan can quickly respond to the target speed command, shorten the adjustment time of the system, and accelerate the achievement of the set target speed, thereby improving the overall dynamic performance and control stability of the system.

[0006] An adaptive control method based on a large inertia fan, the specific steps are as follows.

[0007] S1. Construct a large-inertia fan control system, which includes: a speed loop PID controller, an improved tornado optimization algorithm, a q-axis current loop PID controller, a d-axis current loop PID controller, an IPARK transformation, a PARK transformation, a CLARK transformation, a SVPWM, a three-phase inverter, an AC asynchronous motor, a large-inertia fan, a flux observer, and a speed estimator.

[0008] S2. Improve the mathematical model in the standard tornado optimization algorithm, including two improvements, as follows: S21. A multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm-to-thunderstorm stage in the tornado optimization algorithm. The strategy is divided into two stages according to the algorithm's iterative process. In the early stage of the algorithm, the adaptive memory factors λ1, λ2 and the reverse learning strategy are integrated to update the population position. In the later stage of the algorithm, the adaptive inertia weight ψ(iter) and the adaptive Gaussian perturbation are integrated to update the population position. S22. A dynamic fitness value balancing strategy that integrates population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm. The strategy dynamically adjusts the size of the factor according to the population diversity and the fitness value changes of the current individual.

[0009] S3. Use the improved tornado optimization algorithm to optimize the control parameters of the speed loop PID controller in the large inertia fan control system, and obtain a set of optimal PID control parameters Kp, Ki, and Kd through algorithm optimization.

[0010] S4. Use a set of optimal PID control parameters obtained in S3 for the speed loop PID controller in the large inertia fan control system, output the corresponding control quantity, and realize the speed control of the large inertia fan control system.

[0011] Preferably, in said S1, the overall control process of the constructed large inertia fan control system is: performing error calculation between a given target speed and an actual speed obtained by a speed estimator to obtain a real-time error e(t), inputting the real-time error e(t) into a speed loop PID controller, optimizing the parameters of the PID controller by means of an improved tornado optimization algorithm, applying the optimized parameters to the speed loop PID controller, and giving a corresponding control quantity U(t) according to the speed error, taking the control quantity U(t) as a reference value of the q-axis current, and using a q-axis current loop PID controller and a d-axis current loop PID controller to respectively control the rotating target according to the q-axis current reference value and the d-axis current loop. The current of the dq axis in the stationary coordinate system is adjusted, and the corresponding voltage command of the dq axis is output. The dq axis voltage command is converted into the αβ axis voltage in the stationary coordinate system through IPARK transformation, and the αβ axis voltage is input into the SVPWM module. The switch of the three-phase inverter is controlled by SVPWM to output three-phase AC power to control the rotation of the AC asynchronous motor, and at the same time drive the rotation of the large inertia fan. The three-phase current of the motor is collected in real time, and the three-phase current of the motor is converted into the current of the αβ axis in the stationary coordinate system through CLARK transformation. The current of the αβ axis is converted to the dq axis in the rotating coordinate system through PARK transformation, and the current of the dq axis is fed back to the corresponding current loop PID controller. At the same time, the flux observer and speed estimator can obtain the motor's flux signal, electrical angle and real-time speed in real time. The speed loop does not directly control the motor's speed, but adjusts the motor's q-axis current, which determines the motor's electromagnetic torque. The speed change is indirectly achieved by adjusting the torque. The control of the large inertia fan is to overcome the fan's load torque by the motor output electromagnetic torque, so that the fan reaches the target speed. The mathematical model of the speed loop control is as follows: (1); In formula (1), J represents the moment of inertia of the system, K represents the gain coefficient, U(t) represents the control quantity as shown in formula (2), TL represents the load torque of the fan as shown in formula (3), B represents the damping coefficient, and ω represents the speed; (2); In formula (2), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; (3); In formula (3), Cm represents the torque coefficient of the fan, ρ represents the air density, A represents the swept area of ​​the fan blade, R represents the radius of the fan blade, ω represents the rotation speed, Bf represents the friction coefficient, M represents the mass of the fan blade, g represents the gravitational acceleration, rg represents the eccentric distance of the blade center of mass relative to the rotation axis, and Co represents the additional mechanical torque of the system.

[0012] Preferably, in S21, a multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm evolving into the thunderstorm stage in the tornado optimization algorithm, and the specific mathematical model after the improvement is: (4); In formula (4), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, iter represents the current iteration number, max_iter represents the maximum iteration number, and rand represents a random number between [0,1]. Indicates the position of the second-ranked individual in the population fitness value, represents the position of the individual with the best fitness value in the population, represents the reverse learning position of the current individual, , lb represents the lower limit of the search space, ub represents the upper limit of the search space, λ1, λ2, λ3 represent the adaptive memory factors, and the values ​​of λ1 and λ2 are , the value of λ3 is , ψ(iter) represents the adaptive inertia weight, , represents the adaptive Gaussian distribution, represents the adaptive standard deviation factor, and the calculation formula is shown in formula (5); (5); In formula (5), represents the maximum standard deviation, It represents the minimum standard deviation, iter represents the current number of iterations, and max_iter represents the maximum number of iterations. Indicates the threshold for judging falling into the local optimum. When the algorithm starts The initial value of is 0. When the absolute value of the difference between the current optimal position in the population and the optimal position in the previous iteration is less than 0.01, The value of is increased by one, when If the value of is greater than (max_iter / 10)+1, it means that the algorithm falls into a local optimal solution. Then the maximum standard deviation is used for Gaussian distribution perturbation. When the optimal position in the population varies beyond the minimum value of the variation, The value of is cleared to zero.

[0013] Preferably, a multi-stage adaptive search and disturbance enhancement strategy is combined with a variety of adaptive adjustment mechanisms. Through the segmented execution of two stages, it can ensure that the algorithm maintains a good global search capability in the early stage, and also ensure the local search capability of the algorithm in the later stage. By adding a reverse learning mechanism, the search range of the algorithm in the early stage can be improved, and the diversity of search individuals can be improved. In the second half of the algorithm iteration, an adaptive Gaussian distribution is added for disturbance, which can improve the adaptability of the algorithm. When the algorithm falls into a local optimal solution, the population update is adjusted through Gaussian disturbance, and then it quickly jumps out, thereby improving the algorithm's optimization accuracy.

[0014] Preferably, in S22, a dynamic fitness value balance strategy integrating population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm, and the specific mathematical model after the improvement is: (6); In formula (6), f represents the Lilioolis force factor, rand represents a random number between [0, 1], and β represents the disturbance amplitude factor, which ranges from [0.5, 2]. represents population diversity, and the calculation formula is shown in formula (7): represents the maximum diversity, lb represents the lower limit of the search space, ub represents the upper limit of the search space, and dim represents the problem dimension. represents the weight of individual fitness value change, and the calculation formula is shown in formula (8); (7); In formula (7), N represents the population size, y(iter) represents the position of the current individual, Indicates the position of the individual with the best fitness value in the population; (8); In formula (8), fy represents the fitness value of the current individual, fbest represents the fitness value of the best individual in the population, and fworst represents the worst fitness value in the population.

[0015] Preferably, a dynamic fitness value balancing strategy integrating population diversity combines the information of individual fitness value and population diversity, and improves the global search capability of the algorithm in the early stage to avoid premature convergence by dynamically adjusting the size of the Coriolis force factor. In the later stage, the search accuracy of the algorithm is improved and the convergence speed is accelerated. By adjusting the value of f, the change of storm speed is optimized, making the search process of the algorithm more stable and more adaptable.

[0016] Preferably, in S3, the control parameters of the speed loop PID controller in the large inertia fan control system are optimized using an improved tornado optimization algorithm, and the specific steps are: S31, initializing the parameters of the improved tornado optimization algorithm, the initialization parameters include: population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, search space lower limit lb, and generating individual positions in the initial population through the initialization parameters; S32. Establish a mapping relationship between the improved tornado optimization algorithm and the speed loop PID controller in the large inertia fan control system, convert the individual position change in the algorithm optimization process into the PID controller parameter tuning process, and calculate the fitness of the individual position according to the error value of the actual control effect of the control system. The specific mapping relationship is: the current individual position y(iter)=[y1, y2, y3], the PID control parameters are [Kp, Ki, Kd], the individual value y1 in the first dimension corresponds to Kp, the value y2 in the second dimension corresponds to Ki, and the value y3 in the third dimension corresponds to Kd. The position of the individual y(iter) is adjusted through the iterative update of the algorithm, so as to tune the parameters of the PID controller and finally obtain a set of optimal parameter combinations; S33, setting the fitness value function of the improved tornado optimization algorithm, calculating the fitness value of individuals in the initial population through the fitness value function, and sorting and selecting the top two individuals in fitness value and the individual position with the worst fitness value and the corresponding fitness value size. The specific fitness value function formula is as follows: (9); In formula (9), J represents the fitness value, T represents the total system operation time, and e(t) represents the real-time speed error value calculated by the given target speed and the real-time speed obtained by the speed estimator; S34, updating the positions of individuals in the population through the mathematical model of the improved tornado optimization algorithm, the specific steps are: Step 1. Calculate the storm speed V(iter) of the improved tornado optimization algorithm. The specific formula is: (10); In formula (10), V(iter+1) represents the updated storm velocity, η represents the contraction factor, τ represents the adaptive momentum, f represents the Coriolis force factor, and the calculation formula is shown in formula (6). c represents a range random number, Rl represents the radius of curvature of the storm track in the northern hemisphere, Rr represents the radius of curvature of the storm track in the southern hemisphere, CFl represents the Coriolis force in the northern hemisphere, CFr represents the Coriolis force in the southern hemisphere, and the calculation formula is shown in formula (11). rand represents a random number in [0, 1]; Step 2: The improved tornado optimization algorithm enters the evolution stage from storm to tornado and updates the individual positions. The specific formula is: (11); In formula (11), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, represents the individual position with the best fitness value in the population, α represents the evolution factor, represents the position of a randomly selected storm individual, V(iter) represents the storm speed, and the calculation formula is shown in formula (10); Step 3, the improved tornado optimization algorithm enters the stage where the storm evolves into a thunderstorm, and updates the individual positions. The specific formula is shown in formula (4); Step 4: The improved tornado optimization algorithm enters the stage where the thunderstorm evolves into a tornado and updates the individual position. The specific formula is: (12); In formula (12), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, represents the individual position with the best fitness value in the population, α represents the evolution factor, represents the position of a randomly selected storm individual; Step 5: The improved tornado optimization algorithm enters the random generation stage of the storm and updates the individual positions. The specific formula is: (13); In formula (12), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, ay represents the adaptability parameter, lb represents the lower limit of the search space, ub represents the upper limit of the search space, rand represents a random number in [0, 1], σ2 represents the sign change factor related to the update direction, and ||y(iter)- || represents the Euclidean distance between the current storm individual and the tornado individual, where the tornado individual is the position of the optimal individual in the population, and v represents an exponential parameter; Step 6, calculate the fitness of the updated individual, update the best position, the second position with the best fitness value, and the worst position in the population; S35, determine whether the current number of iterations has reached the maximum number of iterations max_iter, if not, continue to execute S34 to perform algorithm optimization, if reached, end the algorithm optimization, and output the optimal individual position in the population as the optimal solution.

[0017] By adopting the above technical scheme, the advantages of the present invention are: combining the multi-stage adaptive search and disturbance enhancement strategy, and the dynamic fitness value balance strategy integrating population diversity, the mathematical model of the tornado optimization algorithm is improved, and this improvement significantly improves the adaptability and optimization accuracy of the algorithm. The improved tornado optimization algorithm can more effectively cope with the application scenarios of large-inertia fans driven by AC asynchronous motors. In the large-inertia fan control system, the improved algorithm is used to optimize the parameters of the speed loop PID controller, which not only improves the response speed and stability of the speed loop, but also helps the fan to quickly reach the target speed and maintain stable operation, significantly reducing the impact of errors on the control system during the adjustment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of an adaptive control method based on a large inertia fan.

[0019] Figure 2 This is the model diagram of the large inertia fan control system.

[0020] Figure 3 Comparison chart of speed loop PID controller response of the standard tornado optimization algorithm and the improved tornado optimization algorithm.

[0021] Figure 4 Optimize the speed loop PID controller flow chart to improve the tornado optimization algorithm.

[0022] Figure 5 This is a comparison chart of the changes in fitness values ​​during the optimization process of the speed loop PID controller using the standard tornado optimization algorithm and the improved tornado optimization algorithm. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0024] The present invention provides a technical solution: an adaptive control method based on a large inertia fan, specifically comprising the following steps: Figure 1 shown.

[0025] S1. Construct a large inertia fan control system, the control system includes: a speed loop PID controller, an improved tornado optimization algorithm, a q-axis current loop PID controller, a d-axis current loop PID controller, an IPARK transformation, a PARK transformation, a CLARK transformation, a SVPWM, a three-phase inverter, an AC asynchronous motor, a large inertia fan, a flux observer, and a speed estimator, such as Figure 2 shown.

[0026] Furthermore, in the S1, the overall control process of the large inertia fan control system is constructed as follows: the error between the given target speed and the actual speed obtained by the speed estimator is calculated to obtain a real-time error e(t), the real-time error e(t) is input into the speed loop PID controller, the parameters of the PID controller are optimized by the improved tornado optimization algorithm, the optimized parameters are applied to the speed loop PID controller, and the corresponding control quantity U(t) is given according to the speed error, the control quantity U(t) is used as the reference value of the q-axis current, and the q-axis current loop PID controller and the d-axis current loop PID controller are used to respectively control the rotating target according to the q-axis current reference value and the d-axis current loop. The current of the dq axis in the stationary coordinate system is adjusted, and the corresponding voltage command of the dq axis is output. The dq axis voltage command is converted into the αβ axis voltage in the stationary coordinate system through IPARK transformation, and the αβ axis voltage is input into the SVPWM module. The switch of the three-phase inverter is controlled by SVPWM to output three-phase AC power to control the rotation of the AC asynchronous motor, and at the same time drive the rotation of the large inertia fan. The three-phase current of the motor is collected in real time, and the three-phase current of the motor is converted into the current of the αβ axis in the stationary coordinate system through CLARK transformation. The current of the αβ axis is converted to the dq axis in the rotating coordinate system through PARK transformation, and the current of the dq axis is fed back to the corresponding current loop PID controller. At the same time, the flux observer and speed estimator can obtain the motor's flux signal, electrical angle and real-time speed in real time. The speed loop does not directly control the motor's speed, but adjusts the motor's q-axis current, which determines the motor's electromagnetic torque. The speed change is indirectly achieved by adjusting the torque. The control of the large inertia fan is to overcome the fan's load torque by the motor output electromagnetic torque, so that the fan reaches the target speed. The mathematical model of the speed loop control is as follows: (1); In formula (1), J represents the moment of inertia of the system, K represents the gain coefficient, U(t) represents the control quantity as shown in formula (2), TL represents the load torque of the fan as shown in formula (3), B represents the damping coefficient, and ω represents the speed; (2); In formula (2), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; (3); In formula (3), Cm represents the torque coefficient of the fan, ρ represents the air density, A represents the swept area of ​​the fan blade, R represents the radius of the fan blade, ω represents the rotation speed, Bf represents the friction coefficient, M represents the mass of the fan blade, g represents the gravitational acceleration, rg represents the eccentric distance of the blade center of mass relative to the rotation axis, and Co represents the additional mechanical torque of the system.

[0027] S2. Improve the mathematical model in the standard tornado optimization algorithm, including two improvements, as follows: S21. A multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm-to-thunderstorm stage in the tornado optimization algorithm. The strategy is divided into two stages according to the algorithm's iterative process. In the early stage of the algorithm, the adaptive memory factors λ1, λ2 and the reverse learning strategy are integrated to update the population position. In the later stage of the algorithm, the adaptive inertia weight ψ(iter) and the adaptive Gaussian perturbation are integrated to update the population position. S22. A dynamic fitness value balancing strategy that integrates population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm. The strategy dynamically adjusts the size of the factor according to the population diversity and the fitness value changes of the current individual.

[0028] Furthermore, in S21, a multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm evolving into the thunderstorm stage in the tornado optimization algorithm. The specific mathematical model after the improvement is: (4); In formula (4), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, iter represents the current iteration number, max_iter represents the maximum iteration number, and rand represents a random number between [0,1]. Indicates the position of the second-ranked individual in the population fitness value, represents the position of the individual with the best fitness value in the population, represents the reverse learning position of the current individual, , lb represents the lower limit of the search space, ub represents the upper limit of the search space, λ1, λ2, λ3 represent the adaptive memory factors, and the values ​​of λ1 and λ2 are , the value of λ3 is , ψ(iter) represents the adaptive inertia weight, , represents the adaptive Gaussian distribution, represents the adaptive standard deviation factor, and the calculation formula is shown in formula (5); (5); In formula (5), represents the maximum standard deviation, It represents the minimum standard deviation, iter represents the current number of iterations, and max_iter represents the maximum number of iterations. Indicates the threshold for judging falling into the local optimum. When the algorithm starts The initial value of is 0. When the absolute value of the difference between the current optimal position in the population and the optimal position in the previous iteration is less than 0.01, The value of is increased by one, when If the value of is greater than (max_iter / 10)+1, it means that the algorithm falls into a local optimal solution. Then the maximum standard deviation is used for Gaussian distribution perturbation. When the optimal position in the population varies beyond the minimum value of the variation, The value of is cleared to zero.

[0029] Furthermore, in S22, a dynamic fitness value balance strategy integrating population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm. The specific mathematical model after the improvement is: (6); In formula (6), f represents the Lilioolis force factor, rand represents a random number between [0, 1], and β represents the disturbance amplitude factor, which ranges from [0.5, 2]. represents population diversity, and the calculation formula is shown in formula (7): represents the maximum diversity, lb represents the lower limit of the search space, ub represents the upper limit of the search space, and dim represents the problem dimension. represents the weight of individual fitness value change, and the calculation formula is shown in formula (8); (7); In formula (7), N represents the population size, y(iter) represents the position of the current individual, Indicates the position of the individual with the best fitness value in the population; (8); In formula (8), fy represents the fitness value of the current individual, fbest represents the fitness value of the best individual in the population, and fworst represents the worst fitness value in the population.

[0030] S3. Use the improved tornado optimization algorithm to optimize the control parameters of the speed loop PID controller in the large inertia fan control system, and obtain a set of optimal PID control parameters Kp, Ki, and Kd through algorithm optimization.

[0031] Furthermore, in S3, the control parameters of the speed loop PID controller in the large inertia fan control system are optimized using an improved tornado optimization algorithm, such as Figure 4 As shown, the specific steps are: S31, initializing the parameters of the improved tornado optimization algorithm, the initialization parameters include: population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, search space lower limit lb, and generating individual positions in the initial population through the initialization parameters; S32. Establish a mapping relationship between the improved tornado optimization algorithm and the speed loop PID controller in the large inertia fan control system, convert the individual position change in the algorithm optimization process into the PID controller parameter tuning process, and calculate the fitness of the individual position according to the error value of the actual control effect of the control system. The specific mapping relationship is: the current individual position y(iter)=[y1, y2, y3], the PID control parameters are [Kp, Ki, Kd], the individual value y1 in the first dimension corresponds to Kp, the value y2 in the second dimension corresponds to Ki, and the value y3 in the third dimension corresponds to Kd. The position of the individual y(iter) is adjusted through the iterative update of the algorithm, so as to tune the parameters of the PID controller and finally obtain a set of optimal parameter combinations; S33, setting the fitness value function of the improved tornado optimization algorithm, calculating the fitness value of individuals in the initial population through the fitness value function, and sorting and selecting the top two individuals in fitness value and the individual position with the worst fitness value and the corresponding fitness value size. The specific fitness value function formula is as follows: (9); In formula (9), J represents the fitness value, T represents the total system operation time, and e(t) represents the real-time speed error value calculated by the given target speed and the real-time speed obtained by the speed estimator; S34, updating the positions of individuals in the population through the mathematical model of the improved tornado optimization algorithm, the specific steps are: Step 1. Calculate the storm speed V(iter) of the improved tornado optimization algorithm. The specific formula is: (10); In formula (10), V(iter+1) represents the updated storm velocity, η represents the contraction factor, τ represents the adaptive momentum, f represents the Coriolis force factor, and the calculation formula is shown in formula (6). c represents a range random number, Rl represents the radius of curvature of the storm track in the northern hemisphere, Rr represents the radius of curvature of the storm track in the southern hemisphere, CFl represents the Coriolis force in the northern hemisphere, CFr represents the Coriolis force in the southern hemisphere, and the calculation formula is shown in formula (11). rand represents a random number in [0, 1]; Step 2: The improved tornado optimization algorithm enters the evolution stage from storm to tornado and updates the individual positions. The specific formula is: (11); In formula (11), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, represents the individual position with the best fitness value in the population, α represents the evolution factor, represents the position of a randomly selected storm individual, V(iter) represents the storm speed, and the calculation formula is shown in formula (10); Step 3, the improved tornado optimization algorithm enters the stage where the storm evolves into a thunderstorm, and updates the individual positions. The specific formula is shown in formula (4); Step 4: The improved tornado optimization algorithm enters the stage where the thunderstorm evolves into a tornado and updates the individual position. The specific formula is: (12); In formula (12), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, represents the individual position with the best fitness value in the population, α represents the evolution factor, represents the position of a randomly selected storm individual; Step 5: The improved tornado optimization algorithm enters the random generation stage of the storm and updates the individual positions. The specific formula is: (13); In formula (12), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, ay represents the adaptability parameter, lb represents the lower limit of the search space, ub represents the upper limit of the search space, rand represents a random number in [0, 1], σ2 represents the sign change factor related to the update direction, and ||y(iter)- || represents the Euclidean distance between the current storm individual and the tornado individual, where the tornado individual is the position of the optimal individual in the population, and v represents an exponential parameter; Step 6, calculate the fitness of the updated individual, update the best position, the second position with the best fitness value, and the worst position in the population; S35, determine whether the current number of iterations has reached the maximum number of iterations max_iter, if not, continue to execute S34 to perform algorithm optimization, if reached, end the algorithm optimization, and output the optimal individual position in the population as the optimal solution.

[0032] S4. Use a set of optimal PID control parameters obtained in S3 for the speed loop PID controller in the large inertia fan control system, output the corresponding control quantity, and realize the speed control of the large inertia fan control system.

[0033] Furthermore, in order to verify that the present invention has certain advantages, Matlab and Simulink are used to carry out simulation experiments. First, the mathematical model formula of the standard tornado optimization algorithm is improved by Matlab, and the improved algorithm is defined as function ITOC (N, max_iter, lb, ub, dim, fobj). The algorithm before improvement is defined as TOC (N, max_iter, lb, ub, dim, fobj), where N represents the population size, max_iter represents the maximum number of iterations, lb represents the lower limit of the search space, ub represents the upper limit of the search space, dim represents the problem dimension, and fobj represents the fitness value calculation function. fobj is a code program designed according to the fitness value formula in formula (9), and the mathematical model of the speed loop control is Laplace transformed and converted into the corresponding controlled object function. , s represents the variable in the complex frequency domain, U(s) represents the form of the control quantity U(s) in the complex frequency domain, set the initial parameters of the experiment: the moment of inertia of the system J=0.4, the gain coefficient K=20, the damping coefficient B=0.03, the torque coefficient of the fan Cm=0.1, the air density ρ=1.225, the swept area of ​​the fan blade A=6.28, the radius of the fan blade R=2, the friction coefficient Bf=2, the mass of the fan blade M=80, the gravity acceleration g=9.81, the eccentric distance rg=0.07, the additional mechanical torque Co=10 of the system, and simulate the actual operation state of the fan through the Simulink design simulation model program. Initialize the parameters in the main program, the population size N=100, the maximum number of iterations max_iter=20, the search space range [lb, ub]=[0.001, 100], the problem dimension dim=3, Run the algorithm functions ITOC(N, max_iter, lb, ub, dim, fobj) and TOC(N, max_iter, lb, ub, dim, fobj) before and after improvement. Each time the algorithm optimization is performed, the parameters are passed into the simulation model program for operation, and the actual speed output of the model is returned. The fitness value of the parameter is calculated by the fitness value function as the evaluation criterion for algorithm optimization, and the result of each iteration is retained. The optimal control parameters found by the improved algorithm are Kp=0.08979, Ki=4.3005, Kd=4.4905, and the optimal parameters found by the algorithm before improvement are Kp=0.28795, Ki=1.2567, Kd=89.9962. The fitness value changes during the algorithm optimization process and the PID response curve of the control system are visualized to obtain Figure 3 and Figure 5 .

[0034] Furthermore, Figure 3 This is a comparison chart of the speed loop PID controller response optimized by the standard tornado optimization algorithm and the improved tornado optimization algorithm. The target value is set to 1 unit. From the figure, it can be seen that the response curve of the speed loop PID controller optimized by the improved tornado optimization algorithm is more stable, the steady-state error is smaller, and it stabilizes near the target value in a shorter time. Figure 5 This is a comparison chart of the fitness value changes during the optimization process of the speed loop PID controller of the standard tornado optimization algorithm and the improved tornado optimization algorithm. It can be seen from the figure that the improved tornado optimization algorithm reaches the optimal solution with fewer iterations, and the fitness value of the optimal solution finally found is smaller and the accuracy is higher. The optimal fitness value found by the standard tornado optimization algorithm is 11.1169, and the optimal fitness value found by the improved tornado optimization algorithm is 11.1058.

Claims

1. An adaptive control method based on a large inertia fan, characterized in that: The specific steps are as follows: S1. Construct a large inertia fan control system, the control system comprising: a speed loop PID controller, an improved tornado optimization algorithm, a q-axis current loop PID controller, a d-axis current loop PID controller, an IPARK transformation, a PARK transformation, a CLARK transformation, a SVPWM, a three-phase inverter, an AC asynchronous motor, a large inertia fan, a flux observer, and a speed estimator; S2. Improve the mathematical model in the standard tornado optimization algorithm, including two improvements, as follows: S21. A multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm-to-thunderstorm stage in the tornado optimization algorithm. The strategy is divided into two stages according to the algorithm's iterative process. In the early stage of the algorithm, the adaptive memory factor and reverse learning strategy are integrated to update the population position. In the later stage of the algorithm, the adaptive inertia weight and adaptive Gaussian perturbation are integrated to update the population position. S22. A dynamic fitness value balancing strategy integrating population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm. The strategy dynamically adjusts the size of the factor according to the population diversity and the fitness value change of the current individual; S3. Use the improved tornado optimization algorithm to optimize the control parameters of the speed loop PID controller in the large inertia fan control system, and obtain a set of optimal PID control parameters Kp, Ki, and Kd through algorithm optimization; S4. Use a set of optimal PID control parameters obtained in S3 for the speed loop PID controller in the large inertia fan control system, output the corresponding control quantity, and realize the speed control of the large inertia fan control system.

2. The adaptive control method based on a large inertia fan according to claim 1, characterized in that: In the above S1, the overall control process of the constructed large inertia fan control system is as follows: the error between the given target speed and the actual speed obtained by the speed estimator is calculated to obtain a real-time error e(t), the real-time error e(t) is input into the speed loop PID controller, the parameters of the PID controller are optimized by the improved tornado optimization algorithm, the optimized parameters are applied to the speed loop PID controller, and the corresponding control quantity U(t) is given according to the speed error, the control quantity U(t) is used as the reference value of the q-axis current, and the q-axis current loop PID controller and the d-axis current loop PID controller are used to respectively adjust the rotating coordinate system according to the q-axis current reference value and the d-axis current loop. The current of the dq axis is adjusted, and the corresponding voltage command of the dq axis is output. The dq axis voltage command is converted into the αβ axis voltage in the stationary coordinate system through IPARK transformation, and the αβ axis voltage is input into the SVPWM module. The switch of the three-phase inverter is controlled by SVPWM to output three-phase AC power to control the rotation of the AC asynchronous motor, and at the same time drive the rotation of the large inertia fan. The three-phase current of the motor is collected in real time, and the three-phase current of the motor is converted into the current of the αβ axis in the stationary coordinate system through CLARK transformation. The current of the αβ axis is converted to the dq axis in the rotating coordinate system through PARK transformation, and the current of the dq axis is fed back to the corresponding current loop PID controller. At the same time, the flux observer and speed estimator can obtain the motor's flux signal, electrical angle and real-time speed in real time. The speed loop does not directly control the motor's speed, but adjusts the motor's q-axis current, which determines the motor's electromagnetic torque. The speed change is indirectly achieved by adjusting the torque. The control of the large inertia fan is to overcome the fan's load torque by the motor output electromagnetic torque, so that the fan reaches the target speed. The mathematical model of the speed loop control is as follows: (1)； In formula (1), J represents the moment of inertia of the system, K represents the gain coefficient, U(t) represents the control quantity, TL represents the load torque of the fan, B represents the damping coefficient, and ω represents the speed.

3. The adaptive control method based on a large inertia fan according to claim 2, characterized in that: In S21, a multi-stage adaptive search and disturbance enhancement strategy is used to improve the mathematical model of the storm evolving into the thunderstorm stage in the tornado optimization algorithm. The specific mathematical model after the improvement is: (4)； In formula (4), y(iter+1) represents the position of the updated individual, y(iter) represents the position of the current individual, iter represents the current iteration number, max_iter represents the maximum iteration number, and rand represents a random number between [0,1]. Indicates the position of the second-ranked individual in the population fitness value, represents the position of the individual with the best fitness value in the population, represents the reverse learning position of the current individual, , lb represents the lower limit of the search space, ub represents the upper limit of the search space, λ1, λ2, λ3 represent the adaptive memory factors, and the values ​​of λ1 and λ2 are , the value of λ3 is , ψ(iter) represents the adaptive inertia weight, , represents the adaptive Gaussian distribution, represents the adaptive standard deviation factor, and the calculation formula is shown in formula (5); (5)； In formula (5), represents the maximum standard deviation, It represents the minimum standard deviation, iter represents the current number of iterations, and max_iter represents the maximum number of iterations. Indicates the threshold for judging falling into the local optimum. When the algorithm starts The initial value of is 0. When the absolute value of the difference between the current optimal position in the population and the optimal position in the previous iteration is less than 0.01, The value of is increased by one, when If the value of is greater than (max_iter / 10)+1, it means that the algorithm falls into a local optimal solution. Then the maximum standard deviation is used for Gaussian distribution perturbation. When the optimal position in the population varies beyond the minimum value of the variation, The value of is cleared to zero.

4. The adaptive control method based on a large inertia fan according to claim 3 is characterized in that: In S22, a dynamic fitness value balance strategy integrating population diversity is used to improve the Coriolis force factor f of the storm speed in the tornado optimization algorithm. The specific mathematical model after the improvement is: (6)； In formula (6), f represents the Lilioolis force factor, rand represents a random number between [0, 1], and β represents the disturbance amplitude factor, which ranges from [0.5, 2]. represents population diversity, and the calculation formula is shown in formula (7): represents the maximum diversity, lb represents the lower limit of the search space, ub represents the upper limit of the search space, and dim represents the problem dimension. represents the weight of individual fitness value change, and the calculation formula is shown in formula (8); (7)； In formula (7), N represents the population size, y(iter) represents the position of the current individual, Indicates the position of the individual with the best fitness value in the population; (8)； In formula (8), fy represents the fitness value of the current individual, fbest represents the fitness value of the best individual in the population, and fworst represents the worst fitness value in the population.

5. The adaptive control method based on a large inertia fan according to claim 3 is characterized in that: In S3, the control parameters of the speed loop PID controller in the large inertia fan control system are optimized using the improved tornado optimization algorithm, and the specific steps are as follows: S31, initializing the parameters of the improved tornado optimization algorithm, the initialization parameters include: population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, search space lower limit lb, and generating individual positions in the initial population through the initialization parameters; S32. Establish a mapping relationship between the improved tornado optimization algorithm and the speed loop PID controller in the large inertia fan control system, convert the individual position change in the algorithm optimization process into the PID controller parameter tuning process, and calculate the fitness of the individual position according to the error value of the actual control effect of the control system. The specific mapping relationship is: the current individual position y(iter)=[y1, y2, y3], the PID control parameters are [Kp, Ki, Kd], the individual value y1 in the first dimension corresponds to Kp, the value y2 in the second dimension corresponds to Ki, and the value y3 in the third dimension corresponds to Kd. The position of the individual y(iter) is adjusted through the iterative update of the algorithm, so as to tune the parameters of the PID controller and finally obtain a set of optimal parameter combinations; S33, setting the fitness value function of the improved tornado optimization algorithm, calculating the fitness value of individuals in the initial population through the fitness value function, and sorting and selecting the top two individuals in fitness value and the individual position with the worst fitness value and the corresponding fitness value size. The specific fitness value function formula is as follows: (9)； In formula (9), J represents the fitness value, T represents the total system operation time, and e(t) represents the real-time speed error value calculated by the given target speed and the real-time speed obtained by the speed estimator; S34, updating the positions of individuals in the population through the mathematical model of the improved tornado optimization algorithm; S35, determine whether the current number of iterations has reached the maximum number of iterations max_iter, if not, continue to execute S34 to perform algorithm optimization, if reached, end the algorithm optimization, and output the optimal individual position in the population as the optimal solution.

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