CN117434829B - Aircraft main engine wheel fan PID control method based on improved Jin Chai algorithm - Google Patents
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Abstract
The invention discloses an aircraft main engine wheel fan PID control method based on an improved Jin Chai algorithm, which belongs to the technical field of PID control optimization and comprises the following specific steps: step one, establishing a simulation model of an aircraft main engine wheel fan control system by using Simulink; step two, establishing a standard jackal algorithm mathematical model by using Matlab; step three, improving Jin Chai algorithm, including initializing population based on quasi-reflection learning mechanism, introducing a decreasing formula of adaptive parameter improvement prey energy E 1, and adding a position updating formula of t-distribution variation disturbance improvement algorithm; optimizing three parameters of a speed PID controller of the aircraft host machine wheel fan control system by improving Jin Chai algorithm, and finally transmitting the parameters to the speed PID controller to realize optimal robust control of the aircraft host machine wheel fan control system; the control precision and the response speed of the control system are improved while the control system of the main wheel fan of the aircraft has high stability.
Description
Technical Field
The invention belongs to the technical field of PID control optimization, and particularly relates to an aircraft host machine wheel fan PID control method based on an improved Jin Chai algorithm.
Background
In abroad, many airlines can add brake cooling fans to the hub of the main landing gear of the aircraft, such as large airlines of air passenger airbus A320, air passenger bus A330 and the like; in order to ensure the safety of the aircraft and passengers, aircraft designers often perform a "worst-case aircraft landing braking test" to verify whether the aircraft braking system meets the design requirements; the large aircraft project research in China is relatively late in comparison with foreign starting, so that the research on the main aircraft engine fan is relatively less; it has now been found that an aircraft brake cooling fan is added between the hubs of the transport-20 conveyor.
PID control is a control method that is classical and widely used in engineering control systems. The control of the stability, the precision and the response speed of the system is realized by adjusting the output of the controller based on three parts of the proportion, the integral and the differential of the error; the device has strong stability and simple structure, is widely applied to the field of industrial control, such as flow, pressure, temperature and other continuously-changing physical quantities, and generally adopts a closed-loop control mode; the control system of the aircraft main wheel fan usually operates under different load and rotation speed conditions, which has higher requirements on the control system; the conventional PID control method has the problem of limited performance when facing to the complexity, dynamic characteristics and variable working conditions of the aircraft main engine wheel fan control system, so that the control method with more intelligentization and stronger adaptability is needed to cope with the problem, and the control requirement of the aircraft main engine wheel fan control system on high precision, stability and quick response is better met.
Jin Chai the optimization algorithm (GJO) is an optimization algorithm based on natural Jin Chai foraging behavior heuristics; jin Chai is a social animal, hunting is performed in groups, and the hunting process is full of collaboration and competition; jin Chai the optimization algorithm simulates the behavior strategy of the jackfruit when finding food; the algorithm searches the optimal solution of the problem by establishing a collaboration mechanism among jackfruit individuals; carrying out iteration on Jin Chai individuals in a search space, and carrying out cooperation and competition among the individuals according to the self fitness value and neighborhood information; such algorithms focus on multiple locally optimal solutions in the search space to improve the comprehensiveness of the search.
Disclosure of Invention
The invention aims at: the PID control method for the aircraft main engine wheel fan based on the improved Jin Chai optimization algorithm is provided, so that the aircraft main engine wheel fan control system has high stability, and meanwhile, the control precision and response speed of the control system are greatly improved; by improving Jin Chai an optimization algorithm, the problem that the Jin Chai algorithm is unstable in convergence speed and easy to fall into a local optimal solution is solved, so that the parameter setting process of a speed PID controller of an aircraft main engine wheel fan control system is accelerated, and the anti-interference capability and response capability of the aircraft main engine wheel fan control system are enhanced.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The aircraft host machine wheel fan PID control method based on the improved Jin Chai optimization algorithm is characterized in that parameters of a speed PID controller of an aircraft host machine wheel fan control system are optimized through the improved Jin Chai algorithm, and response speed and robustness of the aircraft host machine wheel fan control system are improved.
Step one, a simulation model of an aircraft main engine wheel fan control system is established by using Simulink.
The simulation model of the aircraft brake cooling fan control system in the first step comprises the following steps: the system comprises a deviation calculation module, an improved Jin Chai algorithm module, a speed PID controller module, a current PID controller module, an SVPWM control module, an alternating current asynchronous motor module, an inverter module, a current detection module and a position and speed detection module. The error calculation module is used for calculating error values of the target speed and the actual speed and calculating error values of the target current value and the actual current value; the improved Jin Chai algorithm module is used for setting three parameters of the speed PID controller and transmitting the optimal PID parameters after algorithm iteration into the speed PID controller module; the speed PID controller module collects the speed error value and outputs current control quantity to the current PID controller through PID adjustment; the current PID controller module adjusts and outputs voltage control quantity to the SVPWM control module through PID; the SVPWM control module is used for outputting three pairs of complementary PWM signals to control the inverter module to output three-phase voltages Va, va and Vb; the current detection module is used for detecting the actual three-phase current value of the alternating current asynchronous motor; the position and speed detection module is used for detecting the rotation angular speed of the motor and the flux linkage position of the rotor.
And secondly, establishing a standard jackal algorithm mathematical model by using Matlab.
And thirdly, improving Jin Chai an algorithm, namely initializing a population based on a quasi-reflection learning mechanism, introducing a decreasing formula of adaptive parameter improvement prey energy E 1, and adding a position updating formula of a t-distribution variation disturbance improvement algorithm.
The specific improvement part of the improvement Jin Chai algorithm in the third step is three.
The first place, in the algorithm initialization population stage, the position Jin Chai is initialized based on the quasi-reflection learning mechanism, and the quasi-reflection learning formula is as follows:
Mj＝(Lb+Ub)/2；
wherein, the position fit () of the individual after initializing the population by comparing the sizes of fit (Y i) and fit (M) is determined as the fitness function, fit (Y i) is the fitness value of the current gold jackal individual, fit (M) is the fitness value of the individual at the M position, Y i,j is the value of the j-th dimension of the i-th individual in the population, i=1, …, n, n is the population number, j=1, …, dim, dim is the individual dimension, rand is the random number between [0,1], M j is the intermediate value of Ub and Lb on the j-th dimension of the individual, ub and Lb are the upper and lower bounds of the gold jackal position.
Secondly, introducing a decrementing formula of the self-adaptive parameter improved prey energy E 1, expanding the optimizing range of the algorithm, enhancing the fishing speed in the early stage of the algorithm, and the energy decrementing formula after improvement is as follows:
Where c 1＝1.5,c2 =1, t is the current iteration number, t max is the set maximum iteration number, Y 1 (t) is the updated position of the equation Jin Chai, Y male (t) is the best position of the equation Jin Chai in the previous iteration, p is the adaptive parameter related to the fitness, and E 1 is non-linearly reduced from 1.5 to 0.
Thirdly, improving a position updating formula of the jackal in the population by utilizing t-distribution disturbance variation, increasing the diversity of solutions, avoiding the algorithm from falling into local optimum, wherein the new position updating formula is as follows:
Wherein t is the current iteration number, Y (t+1) is the updated Jin Chai position, Y 1 (t) is the position of the common key Jin Chai in the current iteration process, Y 2 (t) is the position of the mother gold jackal in the current iteration process, Y k (t) is the position of one gold jackal randomly selected in the population, and t_disturb () is the t-distribution disturbance variation function.
And step four, optimizing three parameters of a speed PID controller of a simulation model of the aircraft main engine wheel fan control system through an improved Jin Chai algorithm, and finally transmitting the parameters to the speed PID controller to realize optimal robust control of the aircraft main engine wheel fan control system.
The speed PID controller of the simulation model of the aircraft main engine wheel fan control system in the fourth step comprises the following steps: the system comprises an expected speed, a deviation calculation module, a speed PID controller module, an output control quantity, an execution unit module, a controlled object module and an output speed; the speed PID controller obtains a deviation value e (t) between the expected speed and the actual speed through a deviation calculation module, calculates by combining Kp, ki and Kd parameters calculated by an improved Jin Chai algorithm, outputs a control quantity u (t) to an execution unit module, sends a command to a controlled object, controls the actual speed output by the controlled object to reach the expected speed in a short time, and stabilizes around the expected speed.
The optimizing the PID controller through the improved Jin Chai algorithm in the fourth step is specifically performed.
S1, selecting an adjusting range of PID parameters as an upper bound Ub and a lower bound Lb of the jackal position, and selecting a proper objective function, wherein an objective function formula is as follows:
Wherein e (t) is the difference between the actual output and the target rotation speed in the PID control system, t is a time variable, and fit is an fitness function value.
S2, initializing the jackal population parameters including the number n of male and female jackal individuals Jin Chai and the number dim of population individuals in the population, setting the maximum number t max of population iterations, and initializing the positions of the population individuals based on a quasi-reflection learning mechanism.
S3, calculating the fitness value of the jackal position in the population according to the objective function, and selecting an individual with the smallest fitness value as the population optimal common Jin Chai, and an individual with suboptimal fitness value as the population optimal jackal.
S4, determining a position updating formula of the jackfruit algorithm according to the value of E, wherein when the value of E is more than 1, the algorithm is in a hunting stage, and the position updating formula is as follows:
E＝E1*E0； (2)
In the formula (1), Y 1 (t) is the position of the common key Jin Chai in the current iteration process, Y 2 (t) is the position of the mother gold jackal in the current iteration process, Y male (t) and Y female (t) respectively represent the positions of the optimal common key Jin Chai and the mother gold jackal in the t-th iteration process, prey (t) represents a row vector of the position of the Prey, rl is a random number based on the Rhin flight, E represents the energy change when the Prey escapes, E 1 in the formula (2) represents the decreasing Prey energy, and E 0 represents the initial Prey energy.
S5, increasing the diversity of solutions in the population by using the Rhin flight, wherein the Rhin flight formula is as follows:
rl＝0.05×LF(y)； (3)
In the formula (3), rl is a random number based on the Rhin flight, LF (y) is a Rhin flight function, and in the formulas (4) and (4), μ and v are random numbers in the (0, 1) interval, and β is a constant value, and is usually 1.5.
S6, when the absolute E is less than 1, and the algorithm is in a prey attack stage, the jackal is rapidly close to the prey and attacks the prey, and then the position updating formula is as follows:
the meaning of each parameter and function in the formula is the same as S4.
S7, summing the positions of the optimal common holses Jin Chai and the optimal mother jackal according to an average equal division principle to obtain a middle value as the optimal position of the jackal individuals of the population, and improving a position updating formula of the jackal in the population by using t-distribution disturbance variation, wherein the position updating formula is as follows:
wherein each parameter and function are as same as the above.
And S8, judging whether the population reaches the maximum iteration times, if so, outputting an optimal solution to a speed PID controller to optimize PID parameters, otherwise, returning to S3 to continue optimizing.
And S9, giving the optimal solution to Kp, ki and Kd, and transmitting the optimal solution into a simulation model of the aircraft host wheel fan control system for simulation to obtain a model output result under the condition of optimal data.
In summary, by adopting the technical scheme, the invention has the beneficial effects that:
According to the invention, the anti-interference capability and response speed of the aircraft host machine wheel fan control system are improved by improving the Jin Chai algorithm to optimize the PID controller, the problem that the standard jackal algorithm is easy to be trapped in a local minimum extremum in the later iteration stage is solved by improving the Jin Chai algorithm, the convergence speed of the algorithm in the earlier stage is accelerated, the optimizing precision of the algorithm in the later stage is increased, and the optimal selection of three parameters Kp, ki and Kd in the PID control process is found, so that the aircraft host machine wheel fan can still stably and efficiently run in a complex working environment facing high temperature and high pressure.
Drawings
FIG. 1 is a flow chart for optimizing a PID controller by improving Jin Chai algorithm.
FIG. 2 is a block diagram of an aircraft main wheel fan control system.
FIG. 3 is a diagram of a model of an improved Jin Chai algorithm optimized speed PID controller.
FIG. 4 is a graph comparing the fitness of the modified Jin Chai algorithm with that of the standard jackal algorithm.
FIG. 5 is a graph showing the comparison of the response of the modified Jin Chai algorithm to the optimized PID of the standard jackal algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-5, the present invention provides a technical solution:
The aircraft host machine wheel fan PID control method based on the improved Jin Chai optimization algorithm is characterized in that parameters of a speed PID controller of an aircraft host machine wheel fan control system are optimized through the improved Jin Chai algorithm, and response speed and robustness of the aircraft host machine wheel fan control system are improved.
Step one, as shown in fig. 2, a simulation model of an aircraft main engine wheel fan control system is built by using Simulink.
The simulation model of the aircraft main engine wheel fan control system in the first step comprises the following steps: the system comprises a deviation calculation module, an improved Jin Chai algorithm module, a speed PID controller module, a current PID controller module, an SVPWM control module, an alternating current asynchronous motor module, an inverter module, a current detection module and a position and speed detection module; the error calculation module is used for calculating error values of the target speed and the actual speed and calculating error values of the target current value and the actual current value; the improved Jin Chai algorithm module is used for setting three parameters of the speed PID controller and transmitting the optimal PID parameters after algorithm iteration into the speed PID controller module; the speed PID controller module collects the speed error value and outputs current control quantity through PID adjustment; the current PID controller module adjusts the output voltage control quantity through PID; the SVPWM control module is used for outputting three pairs of complementary PWM signals to control the inverter module to output three-phase voltages Va, va and Vb; the current detection module is used for detecting the actual three-phase current value of the alternating current asynchronous motor; the position and speed detection module is used for detecting the rotation angular speed of the motor and the flux linkage position of the rotor.
And secondly, establishing a standard jackal algorithm mathematical model by using Matlab.
And thirdly, improving Jin Chai an algorithm, namely initializing a population based on a quasi-reflection learning mechanism, introducing a decreasing formula of adaptive parameter improvement prey energy E 1, and adding a position updating formula of a t-distribution variation disturbance improvement algorithm.
The specific improvement part of the improvement Jin Chai algorithm in the third step is three.
The first place, in the algorithm initialization population stage, the position Jin Chai is initialized based on the quasi-reflection learning mechanism, and the quasi-reflection learning formula is as follows:
Mj＝(Lb+Ub)/2；
Wherein, by comparing the sizes of fit (Y i) and fit (M), the position fit () of the individual after initializing the population by quasi-reflection learning is determined as the fitness function, fit (Y i) is the fitness value of the current gold jackal individual, fit (M) is the fitness value of the individual at the M position, Y i,j is the value of the j-th dimension of the i-th individual in the population, i=1, the.
Secondly, introducing a decremental formula of the self-adaptive parameter improved prey energy E 1, expanding the optimizing range of the algorithm, enhancing the earlier convergence speed of the algorithm, and the energy decremental formula after improvement is as follows:
Where c 1＝1.5,c2 =1, t is the current iteration number, t max is the set maximum iteration number, Y 1 (t) is the updated position of the equation Jin Chai, Y male (t) is the best position of the equation Jin Chai in the previous iteration, p is the adaptive parameter related to the fitness, and E 1 is non-linearly reduced from 1.5 to 0.
Thirdly, improving a position updating formula of the jackal in the population by utilizing t-distribution disturbance variation, increasing the diversity of solutions, avoiding the algorithm from falling into local optimum, wherein the new position updating formula is as follows:
Wherein t is the iteration number, Y (t+1) is the updated Jin Chai position, Y 1 (t) is the position of the common key Jin Chai in the current iteration process, Y 2 (t) is the position of the mother gold jackal in the current iteration process, Y k (t) is the position of one gold jackal randomly selected in the population, and t_disturb () is the t-distribution disturbance variation function.
And step four, optimizing three parameters of a speed PID controller of a simulation model of the aircraft main engine wheel fan control system through an improved Jin Chai algorithm, and finally transmitting the parameters to the speed PID controller to realize optimal robust control of the aircraft main engine wheel fan control system.
In the fourth step, as shown in fig. 3, the speed PID controller of the simulation model of the aircraft main wheel fan control system includes: the system comprises an expected speed, a deviation calculation module, a PID controller module, an output control quantity, an execution unit module, a controlled object module and an output speed; the speed PID controller obtains a deviation value e (t) between a desired speed ref and an actual speed through a deviation calculation module, calculates by combining Kp, ki and Kd parameters calculated by an improved Jin Chai algorithm, outputs a control quantity u (t) to an execution unit module, sends a command to a controlled object, controls the controlled object to output the actual speed to reach the desired speed in a short time, and stabilizes around the desired speed.
The optimization of the PID controller by the modified Jin Chai algorithm in step four is specifically as follows, as shown in fig. 1.
S1, selecting an adjusting range of PID parameters as an upper bound Ub and a lower bound Lb of the jackal position, and selecting a proper objective function, wherein an objective function formula is as follows:
Wherein e (t) is the difference between the actual output and the target rotation speed in the PID control system, t is a time variable, and fit is an fitness function value.
S2, initializing the jackal population parameters, including the number n of male and female jackal individuals Jin Chai and the number dim of population individuals, setting dim to 3, setting the maximum number t max of population iterations to 10, and initializing the positions of the population individuals based on a quasi-reflection learning mechanism.
S3, calculating the fitness value of the jackal position in the population according to the objective function, and selecting an individual with the smallest fitness value as the population optimal common Jin Chai, and an individual with suboptimal fitness value as the population optimal jackal.
S4, determining a position updating formula of the jackfruit algorithm according to the value of E, wherein when the value of E is more than 1, the algorithm is in a hunting stage, and the position updating formula is as follows:
E＝E1*E0； (2)
In the formula (1), Y 1 (t) is the position of the common key Jin Chai in the current iteration process, Y 2 (t) is the position of the mother gold jackal in the current iteration process, Y male (t) and Y female (t) respectively represent the positions of the optimal common key Jin Chai and the mother gold jackal in the t-th iteration process, t is the current iteration times of the population, prey (t) represents a row vector of the position of the game, rl is a random number based on the Rhin flight, E represents energy change when the game escapes, E 1 in the formula (2) represents the energy of the game which is reduced, and E 0 represents the energy of the original game.
S5, increasing the diversity of solutions in the population by using the Rhin flight, wherein the Rhin flight formula is as follows:
rl＝0.05*LF(y)； (3)
LF(y)＝0.01×(μ×σ)/(|v(1/β)|)； (4)
In the formula (3), rl is a random number based on the Rhin flight, LF (y) is a Rhin flight function, and in the formulas (4) and (4), μ and v are random numbers in the (0, 1) interval, and β is a constant value, and is usually 1.5.
S6, when the absolute E is less than 1, and the algorithm is in a prey attack stage, the jackal is rapidly close to the prey and attacks the prey, and then the position updating formula is as follows:
the meaning of each parameter and function in the formula is the same as S4.
S7, summing the positions of the optimal common holses Jin Chai and the optimal jackal according to an average equal division principle to obtain a middle value, and using the middle value as the optimal position of the jackal individuals in the population, and improving a position updating formula of the jackal in the population by using t-distribution disturbance variation, wherein the position updating formula is as follows:
wherein each parameter and function are as same as the above.
And S8, judging whether the population reaches the maximum iteration times, if so, outputting an optimal solution to a speed PID controller to optimize PID parameters, otherwise, returning to S3 to continue optimizing.
And S9, giving the optimal solution to Kp, ki and Kd, and transmitting the optimal solution into a simulation model of the aircraft host wheel fan control system for simulation to obtain a model output result under the condition of optimal data.
The magnitude of fitness is generally used to evaluate the quality of solutions in a population, and solutions with smaller fitness values are generally better than solutions with greater fitness; from the comparison of the fitness of the standard jackal optimization algorithm and the modified Jin Chai optimization algorithm in fig. 4, it can be seen that the modified Jin Chai optimization algorithm can obviously see the faster convergence speed in the early stage of algorithm iteration; within the same iteration times, the standard Jin Chai algorithm is 17.9174, the modified Jin Chai algorithm is 14.1084, and the adaptability value of the modified Jin Chai algorithm is smaller, which indicates that the modified Jin Chai algorithm can jump out the local optimal solution in the later iteration period to find a better solution.
Analysis of FIG. 5 shows that the overshoot of the PID control system based on the improved Jin Chai optimization algorithm is obviously smaller than that of the PID control system based on the standard jackal optimization algorithm, and the balance state can be achieved in a shorter time, which means that in the nonlinear system, the PID control system based on the improved Jin Chai optimization algorithm has better control performance and stronger robustness than that of the PID control system based on the standard jackal optimization algorithm.
Claims (4)
1. The aircraft host wheel fan PID control method based on the improved Jin Chai algorithm is characterized in that the speed PID controller parameters of the aircraft host wheel fan control system are optimized through the improved Jin Chai algorithm, the response speed and the robustness of the aircraft host wheel fan control system are improved, and the method specifically comprises the following steps:
step one, establishing a simulation model of an aircraft main engine wheel fan control system;
Step two, establishing a standard jackal algorithm mathematical model;
step three, improving Jin Chai algorithm;
the improved Jin Chai algorithm is divided into three parts:
S1, in an algorithm initialization population stage, initializing Jin Chai positions based on a quasi-reflection learning mechanism, wherein a quasi-reflection learning formula is as follows:
Mj＝(Lb+Ub)/2；
Wherein, comparing the size of fit (Y i) and fit (M), determining the position fit () of the individual after initializing the population by quasi-reflection learning as a fitness function, fit (Y i) as the fitness value of the current golden jackal individual, fit (M) as the fitness value of the individual at M position, Y i,j as the value of the j-th dimension of the i-th individual in the population, i=1, the number of the population individuals, n, n as the number of the population, j=1, the number of the dim, dim as the individual dimension, rand as the random number between [0,1], M j as the intermediate value of Ub and Lb on the j-th dimension of the individual, ub and Lb as the upper and lower bounds of the golden jackal position;
S2, introducing a decremental formula of the self-adaptive parameter improved prey energy E 1, expanding the optimizing range of an algorithm, wherein the energy decremental formula after improvement is as follows:
Wherein c 1＝1.5,c2 =1, t is the current iteration number, t max is the maximum iteration number, Y 1 (t) is the updated position of the equation Jin Chai, Y male (t) is the best position of the equation Jin Chai in the previous iteration, p is an adaptive parameter related to the fitness, and E 1 is nonlinearly reduced from 1.5 to 0;
S3, improving a position updating formula of the jackal in the population by utilizing t-distribution disturbance variation, increasing the diversity of solutions, and avoiding the algorithm from falling into local optimum, wherein the new position updating formula is as follows:
wherein t is the current iteration number, Y (t+1) is the updated Jin Chai position, Y 1 (t) is the common Jin Chai position in the current iteration, Y 2 (t) is the mother gold jackal position in the current iteration, Y k (t) is the randomly selected gold jackal position, and t_disturb () is a t-distribution disturbance variation function;
and fourthly, optimizing parameters of a speed PID controller of a simulation model of the aircraft host wheel fan control system by improving Jin Chai algorithm, and finally transmitting the parameters to the speed PID controller to realize optimal robust control of the aircraft host wheel fan control system.
2. The method for PID control of aircraft main wheel fan based on the modified Jin Chai algorithm as claimed in claim 1, wherein in the first step, a simulation model of the aircraft main wheel fan control system is built by using Simulink, comprising: the system comprises a deviation calculation module, an improved Jin Chai algorithm module, a speed PID controller module, a current PID controller module, an SVPWM control module, an alternating current asynchronous motor module, an inverter module, a current detection module and a position and speed detection module; the error calculation module is used for calculating error values of the target speed and the actual speed and calculating error values of the target current value and the actual current value; the improved Jin Chai algorithm module is used for setting three parameters of the speed PID controller and transmitting the optimal PID parameters after algorithm iteration into the speed PID controller module; the speed PID controller module collects the speed error value and outputs current control quantity to the current PID controller through PID adjustment; the current PID controller module adjusts and outputs voltage control quantity to the SVPWM control module through PID; the SVPWM control module is used for outputting three pairs of complementary PWM signals to control the inverter module to output three-phase voltages Va, va and Vb; the current detection module is used for detecting the actual three-phase current value of the alternating current asynchronous motor; the position and speed detection module is used for detecting the rotation angular speed of the motor and the flux linkage position of the rotor.
3. The method for PID control of a main wheel fan of an aircraft based on the modified Jin Chai algorithm according to claim 1, wherein in the fourth step, the speed PID controller of the simulation model of the main wheel fan control system of an aircraft comprises: the system comprises an expected speed, a deviation calculation module, a speed PID controller module, an output control quantity, an execution unit module, a controlled object module and an output speed; the speed PID controller obtains a deviation value e (t) between a desired speed ref and an actual speed through a deviation calculation module, calculates by combining Kp, ki and Kd parameters calculated by an improved Jin Chai algorithm, outputs a control quantity u (t) to an execution unit module, sends a command to a controlled object, controls the controlled object to output the actual speed to reach the desired speed in a short time, and stabilizes around the desired speed.
4. The aircraft main wheel fan PID control method based on the improved Jin Chai algorithm according to claim 1, wherein in the fourth step, the optimization of the PID controller by the improved Jin Chai algorithm comprises the following specific steps:
s1, selecting an adjusting range of PID parameters as an upper bound Ub and a lower bound Lb of the jackal position, and selecting a proper objective function, wherein an objective function formula is as follows:
fit＝∫0 ∞t|e(t)|dt；
Wherein e (t) is the difference between the actual rotating speed and the target rotating speed in the PID control system, t is a time variable, and fit is a target function value;
s2, initializing population parameters, including the number n of Male and Jin Chaijia female jackal in the population, the population individual dimension dim, the maximum number t max of population iteration, and initializing the population individual positions based on a quasi-reflection learning mechanism;
S3, calculating fitness values of the jackal positions in the population according to the objective function, and selecting individuals with optimal fitness values as population optimal common Jin Chai, wherein individuals with suboptimal fitness values are population optimal jackal;
S4, determining a position updating formula of the jackfruit algorithm according to the value of E, wherein when the value of E is equal to or more than 1, the algorithm is in a hunting stage, and the position updating formula is as follows:
E＝E1*E0；(2)
Y 1 (t) in the formula (1) is the position of the common lead Jin Chai in the current iteration, Y 2 (t) is the position of the jackal of the mother gold in the current iteration, Y male (t) and Y female (t) respectively represent the positions of the optimal common lead Jin Chai and the jackal of the mother gold in the t-th iteration, prey (t) represents a row vector of the position of the game, rl is a random vector based on the Rhin flight, E represents the energy change when the game escapes, E 1 in the formula (2) represents the decreasing game energy, and E 0 represents the initial game energy;
S5, increasing the diversity of solutions by using the Rhin flight, wherein the Rhin flight formula is as follows:
rl＝0.05*LF(y)；(3)
In the formula (3), rl is a random vector based on the Rhin flight, LF (y) is a Rhin flight function, mu and v in the formula (4) and the formula (5) are random numbers in the (0, 1) interval, and beta is a constant value, and the value is 1.5;
s6, when the algorithm is in the stage of attacking the prey when the I E I <1, the jackal is rapidly close to the prey and attacks the prey, and the position updating formula is as follows:
the meaning of each parameter and function in the formula is the same as S4;
s7, summing the positions of the optimal common holses Jin Chai and the optimal mother jackal according to an average equal division principle to obtain a middle value as the optimal position of the jackal individuals of the population, and improving a position updating formula of the jackal in the population by using t-distribution disturbance variation, wherein the position updating formula is as follows:
the meaning of each parameter and function in the formula is the same as that of the function;
S8, judging whether the population reaches the maximum iteration times, if so, outputting an optimal solution to a speed PID controller, otherwise, returning to S3 to continue optimizing;
And S9, giving the optimal solution to Kp, ki and Kd, and transmitting the optimal solution into a simulation model of the aircraft host wheel fan control system to obtain a model output result under the condition of optimal data.
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