CN117092905A - Optimal robust control method based on improved aircraft brake cooling fan - Google Patents
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Abstract
The invention discloses an optimal robust control method based on an improved airplane brake cooling fan, which belongs to the technical field of PID control optimization and comprises the following specific steps: firstly, building a simulation model of an aircraft brake cooling fan control system through Simulink; step two, writing a standard optical microscope optimization algorithm through Matlab, and constructing a speed PID controller model by using Simulink; step three, improving a standard optical microscope optimization algorithm, introducing a population position updating formula of an objective lens stage and an eyepiece stage which are improved by a self-adaptive proportional amplification coefficient, accelerating the convergence speed of an algorithm exploration stage, and avoiding the algorithm development stage from falling into local optimum; optimizing the speed PID controller parameters by utilizing an improved optical microscope optimization algorithm, and assigning the obtained optimal parameters to the speed PID controller parameters of the control system simulation model; stability and robustness of the aircraft brake cooling fan control system is enhanced.
Description
Technical Field
The invention belongs to the technical field of PID control optimization, and particularly relates to an optimal robust control method based on an improved aircraft brake cooling fan.
Background
The aircraft brake cooling fan refers to a radiator fan mounted in the hub of an aircraft tire; the maximum take-off weight of many large airliners at present is over 3000 tons, and the required runway length is about 3000 meters; allowing the aircraft to land safely on a limited runway requires a powerful set of deceleration systems; after years of research, airlines find that although the novel carbon brake has the advantages of long service life and large friction force, the abrasion to the carbon brake is most serious when the novel carbon brake is braked in a medium-temperature area of 80-250 ℃, and in order to ensure the safety of passengers and reduce the cost of an airplane, a part of large civil aviation can be provided with a cooling fan in the hub of the airplane tire; the control system is a key for designing the aircraft brake cooling fan, and the advantages and disadvantages of the control system can directly influence the efficiency and the safety and the reliability of the aircraft brake cooling fan, and further can influence the efficiency and the safety of the aircraft brake.
In engineering, a control system of the motor mainly adopts double closed-loop control of a speed loop and a current loop; PID control achieves accurate control of a control system by adjusting three parameters, namely a proportional coefficient (Kp), an integral coefficient (Ki) and a differential coefficient (Kd); the PID control has the main advantages of high response speed, high reliability and strong robustness, and is suitable for various industrial control processes; the traditional PID control mainly relies on experience to adjust parameters, so that a large amount of manpower and material resources are needed, and in practical engineering application, a plurality of control systems are complex in structure and have high nonlinearity and time-varying uncertainty, and the traditional PID control is difficult to obtain good effects; for these cases, many scholars have proposed various improved methods such as optimizing PID parameters, fuzzy PID control, and adaptive PID control using particle swarm algorithm; but some algorithms are difficult to apply directly due to the nonlinearity and complexity of the control system.
The Optical Microscopy Algorithm (OMA) is a meta-heuristic algorithm proposed in the present year, which simulates the process of magnifying an object by using an optical microscope: (1) magnifying the object using an objective lens; (2) performing a magnification process on the object using an eyepiece; after Matlab is used for mathematical modeling, the OMA result is compared with the results of 9-element heuristic algorithms, and the results show that the OMA performance is better and the calculation time is shorter; compared with other meta heuristic algorithms, the algorithm also has the advantages of less control parameters and easy realization; however, as in the general meta-heuristic algorithm, the optical microscope algorithm has a problem that the convergence speed is slow in the exploration phase and the development phase is easy to fall into local optimum.
Disclosure of Invention
The invention aims at: the method solves the problems that the convergence rate of an optical microscope algorithm is low in an exploration stage and the development stage is easy to fall into local optimum, and meanwhile, a new improved PID control method is provided for solving the problem that a traditional PID control method is difficult to achieve a good control effect on a multivariable nonlinear alternating current asynchronous motor control system, and the stability and the robustness of an aircraft brake cooling fan control system are enhanced by optimizing speed loop PID control parameters through the improved optical microscope algorithm.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the optimal robust control method based on the improved aircraft brake cooling fan is characterized in that parameters of a traditional PID controller are optimized by improving an optical microscope optimization algorithm, and finally the parameters are transmitted into a speed PID controller, and the optimal robust control method specifically comprises the following steps of.
Step one, building a simulation model of an aircraft brake cooling fan control system through Simulink.
Simulating an optical microscope working principle, writing a standard optical microscope algorithm through Matlab, mathematically modeling the optical microscope, building a speed PID controller model, and calculating the speed PID controller by collecting an error value between a set rotating speed and an actual rotating speed and combining an optimal PID parameter calculated by the optical microscope algorithm, so that the rotating speed of the motor can quickly reach the set rotating speed in a short time and be stabilized near the set rotating speed.
For standard optical microscopyImproved mirror optimization algorithm, and self-adaptive proportional amplification coefficient is introducedM r Objective lens and eyepiece position update formula for improved algorithm, and proportional amplification factorM r The mathematical formula of (a) is:
；
in the method, in the process of the invention,M r in order to adapt the scaling factor to be high,m r to take the value of 0,1]The random vector between the two,fit(best)for the current iteration the optimal individual fitness value,fit(t)fitness value for the current individual.
Optimizing the PID parameters of the speed loop by utilizing an improved optical microscope algorithm, and iterating for a plurality of times to obtain three parameters of the optimal PID controller, wherein the method comprises the following specific steps of:
s1, constructing a dynamic objective function, wherein expected values of the objective function and function values calculated in each time step are changed along with time, and the formula of the objective function is as follows:
；
in the method, in the process of the invention,xis a decision variable, here set to 0.5;tis a variable of the time of day,Ais an amplitude parameter, here set to 1; sin%t) Is a sine function with an independent variable t, and represents periodic change of time; the desired value (target value) of this function is changed at each time step, consisting ofA×sin(t) Determining; the real-time value is the function value calculated at each time step.
S2, initializing a population for improving an optical microscope optimization algorithm, wherein the population comprises an initial population individual position and population related parameters, and the related parameters comprise population overall scaleNPDimension of problemDim、Search space upper and lower boundsub,lb]And maximum number of iterationsMax_iterThe individual position formula is initialized as follows:
；
in the method, in the process of the invention, for the j-th dimension variable of individual i, +.>Andthe lower bound and the upper bound of the j-th dimensional variable,ris the value of [0,1 ]]Random numbers in between.
S3, taking the value range of the PID controller parameters as the search space of the algorithm, and randomly selecting a group of PID controller parameter initialization population in the [ lb, ub ] range.
S4, calculating the current fitness value of all individuals of the target population through a fitness function, enabling the current fitness value to be incrementally ordered and stored in an array, marking an array index, and recording the optimal fitness value of the current populationfit ness 。
S5, in the iteration process, in the stage of debugging the objective lens, introducing an adaptive scaling factor M r Improving a population individual position updating formula, updating individual positions, and calculating an updated fitness value of the target object;
（1）；
in the method, in the process of the invention,after the objective lens is debugged, the firstiThe location of the individual; />Is the firsttOptimal individual position of the generation population; />Is the firstiThe location of the individual;M r is an adaptive scaling factor.
S6, calculating through an objective functionAccording to a greedy selected policy, will +.>And (3) comparing the fitness value of the (c) with the individual optimal fitness value stored in the previous iteration, reserving the individual position with the smaller fitness value as the latest individual position, and updating the individual optimal fitness value.
And S7, comparing the fitness value of the latest position of the current individual with the optimal fitness value of the population according to a greedy selection strategy, keeping the value with smaller fitness as the optimal fitness value of the population, and updating the optimal individual of the population.
S8, randomly selecting an individual in the population in the eyepiece debugging stageThe space calculation formula is shown as formula (1), if +.>Then->Designing a position updating formula of population individuals as formula (2), generating new magnification, and calculating the fitness value of the modified target object;
（1）；
（2）；
in the method, in the process of the invention,is an adaptive scaling factor.
S9, obtainingAnd then greedy selection is carried out again to obtain the next generation population individuals.
And S10, judging whether the maximum iteration times are reached, if so, optimizing and stopping outputting the optimal parameters, otherwise, returning to S5 and continuing optimizing.
And S11, assigning the data of the optimal solution to Kp, ki and Kd, and transmitting the data into the controlled object for simulation to obtain a model output result under the condition of the optimal data.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
according to the invention, a self-adaptive proportional amplification factor is introduced to improve a proportional amplification formula of an objective lens and an eyepiece on the basis of a standard optical microscope algorithm, so that the problems that the convergence speed is low in an optical microscope algorithm exploration stage and local optimization is easy to fall into a development stage are solved, and meanwhile, aiming at the problem that a traditional PID control method is difficult to achieve a good control effect on a multivariable nonlinear aircraft cooling fan control system, a novel improved PID control method is provided, PID control parameters are optimized by utilizing the improved optical microscope algorithm, and the stability and robustness of the aircraft cooling fan control system are enhanced.
Drawings
FIG. 1 is a flow chart for improving the optimization of PID controllers by an optical microscopy algorithm.
FIG. 2 is a diagram of a model of a PID controller based on a modified optical microscopy algorithm.
FIG. 3 is a graph of improved light microscopy versus standard light microscopy for optimal individual fitness values.
Fig. 4 is a simulation model diagram of an aircraft cooling fan control system constructed by Simulink.
FIG. 5 is a graph comparing the effect of PID optimization by the improved light microscopy algorithm versus the standard light microscopy 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 optimal robust control method based on the improved aircraft brake cooling fan is characterized in that parameters of a traditional PID controller are optimized by improving an optical microscope optimization algorithm, and finally the parameters are transmitted into a speed PID controller model, and the optimal robust control method specifically comprises the following steps of.
Step one, building a simulation model of an aircraft brake cooling fan control system through Simulink.
Simulating an optical microscope working principle, writing a standard optical microscope algorithm through Matlab, carrying out mathematical modeling on the standard optical microscope algorithm, building a speed PID controller model, calculating the optimal PID parameter calculated by the speed PID controller through collecting an error value e (t) between a set rotating speed and an actual rotating speed and combining the optical microscope algorithm, outputting a control quantity u (t) to a current PID controller, modulating the current PID controller through an inverter module, controlling the motor to rotate, feeding back the actual rotating speed y (t) and the actual current, forming a double-closed-loop PID control system, controlling the motor to reach the set rotating speed in a short time, and stabilizing the motor near the set rotating speed.
Step three, improving the optimization algorithm of the standard optical microscope, and introducing the self-adaptive proportional amplification coefficientM r Objective lens and eyepiece position update formula for improved algorithm, and proportional amplification factorM r The mathematical formula of (a) is:
；
in the method, in the process of the invention,M r in order to adapt the scaling factor to be high,m r to take the value of 0,1]The random vector between the two,fit(best)optimal individual adaptation for current iterationThe value of the degree of compliance,fit(t)fitness value for the current individual.
Step four, as shown in fig. 1, optimizing the speed loop PID parameters by using an improved optical microscope algorithm, and performing multiple iterations to obtain three parameters of the optimal PID controller, wherein the specific steps are as follows:
s1, constructing a dynamic objective function, wherein expected values of the objective function and function values calculated in each time step are changed along with time, and the formula of the objective function is as follows:
；
in the method, in the process of the invention,xis a decision variable, here set to 0.5;tis a variable of the time of day,Ais an amplitude parameter, here set to 1; sin%t) Is a sine function with an independent variable t, and represents periodic change of time; the desired value (target value) of this function is changed at each time step, consisting ofA×sin(t) Determining; the real-time value is the function value calculated at each time step.
S2, initializing a population for improving an optical microscope optimization algorithm, wherein the population comprises an initial population individual position and population related parameters, and the related parameters comprise population overall scaleNPDimension of problemDim、Search space upper and lower boundsub,lb]And maximum number of iterationsMax_iterThe individual position formula is initialized as follows:
；
in the method, in the process of the invention, for the j-th dimension variable of individual i, +.>Andthe lower bound and the upper bound of the j-th dimensional variable,ris the value of [0,1 ]]Random numbers in between.
S3, taking the value range of the PID controller parameters as the search space of the algorithm, randomly selecting a group of PID controller parameter initialization populations in the [ lb, ub ] range by taking the value range of the PID controller parameters as the search space of the algorithm.
S4, calculating the current fitness value of all individuals of the target population through a fitness function, enabling the current fitness value to be incrementally ordered and stored in an array, marking an array index, and recording the optimal fitness value of the current populationfit ness 。
S5, in the iteration process, in the stage of debugging the objective lens, introducing an adaptive scaling factor M r Improving a population individual position updating formula, updating individual positions, and calculating an updated fitness value of the target object;
（1）；
in the method, in the process of the invention,after the objective lens is debugged, the firstiThe location of the individual; />Is the firsttOptimal individual position of the generation population; />Is the firstiThe location of the individual;M r is an adaptive scaling factor.
S6, calculating through an objective functionIs to be ∈>Comparing the fitness value of (2) with the individual optimal fitness value saved in the previous iteration, and keeping the individual position with smaller fitness value as the latest individual positionAnd updating the individual optimal fitness value.
And S7, comparing the fitness value of the latest position of the current individual with the optimal fitness value of the population, keeping the value with smaller fitness as the optimal fitness value of the population, and updating the optimal individual of the population.
S8, randomly selecting an individual in the population in the eyepiece debugging stageThe space calculation formula is shown as formula (1), if +.>Then->Designing a position updating formula of population individuals as formula (2), generating new magnification, and calculating the fitness value of the modified target object;
（1）；
（2）；
in the method, in the process of the invention,is an adaptive scaling factor.
S9, obtainingAnd then greedy selection is carried out again to obtain the next generation population individuals.
And S10, judging whether the maximum iteration times are reached, if so, optimizing and stopping outputting the optimal parameters, otherwise, returning to S5 and continuing optimizing.
S11, as shown in FIG. 2, the optimal solution data are assigned to Kp, ki and Kd of a speed PID controller through an improved optical microscope algorithm, the speed error e (t) is controlled, the control quantity u (t) is output to a current PID controller, the motor is controlled to rotate through modulation of an inverter module, the actual rotating speed y (t) and the actual current of the motor are fed back to form a double-closed-loop PID control system, and the motor rotating speed stably and rapidly reaches the target speed through multiple iterations, and a simulation result is output.
In order to verify the superiority of the improved optical microscope algorithm after optimizing the PID controller, the Matlab and Simulink are utilized to simulate the PID control system, and the experimental verification of the design method is completed by comparing the Matlab and Simulink with the standard optical microscope algorithm.
FIG. 3 is a graph comparing the optimal fitness value of a standard Optical Microscopy Algorithm (OMA) with the optimal fitness value of an improved optical microscopy algorithm, wherein in the early stage of the algorithm, the convergence speed of the improved optical microscopy algorithm is faster, and the solution of an individual in the later stage is more close to-0.75 of the optimal solution of the objective function; the improved optical microscope algorithm is faster and more stable than the standard optical microscope algorithm, so that the optimal solution is found; the performance of the improved optical microscopy algorithm is superior to that of the standard optical microscopy algorithm.
FIG. 4 is a schematic diagram of an aircraft brake cooling fan control system using Simulink simulation, including a Clark conversion module, a Park conversion module, an IPark conversion module, a speed PID controller module, two current PID controller modules, a SVPWM module, a flux linkage and position estimation module, a signal acquisition module, an inverter module, and an AC asynchronous motor module; the control system operates as follows.
Step one, the control system applies a forced rotation torque to the motor in an initial state, six bridge arms of the three-phase half-bridge inverter are in a non-conductive state at the moment, and the motor starts to rotate due to the given forced torque and outputs the torqueThree-phase stator current->And rotational speed->。
Step two, the signal acquisition module acquires torqueThree-phase stator current->And rotational speed->Three signals, three-phase stator current +.>Outputting to a Clark conversion module; the three-phase rotation coordinates of the stator current are converted into two-phase static coordinates through Clark conversion, and the three-phase stator current is +.>Conversion to->Shaft current and->Shaft current->And->Respectively represent +.>And->Symbol (S)>Shaft and->The axes are two coordinate axes of two-phase static coordinates, and are mutually orthogonal; output->Shaft current and->The axis current is fed to a Park conversion module, the two-phase stationary coordinate is converted into two-phase synchronous rotation coordinate through Park conversion, the d axis and the q axis are two coordinate axes of the two-phase synchronous rotation coordinate, and the two coordinate axes are mutually orthogonal, wherein synchronization means that the two coordinate axes synchronously rotate and are always orthogonal; output d-axis current +.>And q-axis current>。
Step three, d-axis current is conductedQ-axis current->And rotational speed->Inputting to a flux linkage and position estimation module, and calculating to obtain angle +.>And flux linkage position->，/>Refers to the angle through which the rotor rotates during one iteration, < >>Refers to the flux linkage position of the rotor passing through in one iteration process, < >>Representing mathematical +.>Symbol (S)>Representing mathematical +.>A symbol; in the first iteration of the system, the motor is in a static state in the initial state, and the motor is in a static state in the initial state>The angle is zero, and in the following iteration, the motor is in a rotating state, and the flux linkage and the position estimation module obtain +.>The angle needs to be input into the Park conversion module and the IPark conversion module to participate in calculation.
Step four, setting a target rotating speed of the control system, wherein the rotating speed is 1000rpm, and the rotating speed is obtained by the signal acquisition moduleIn rad/s, it is necessary to provide a total of +.>Converting the unit into rpm, it is necessary to add +.>Multiplying by parameter->Here +.>Refers to mathematical +.>Sign, rotation speed after rotation speed conversion +.>The actual rotation speed is rpm; will->Inputting the control quantity to a speed PID controller optimized based on an improved optical microscope algorithm, regulating the output control quantity through the speed PID controller, and inputting the control quantity to a PID controller of q-axis current to form a double closed-loop PID control subsystem; inputting the q-axis current regulated by the q-axis current PID controller into an IPark conversion module; IPark transformation is a process of transforming two-phase synchronous rotational coordinates into two-phase stationary coordinates.
Step five, giving a target current to the control systemThe value here is 10A, the d-axis current obtained by Park conversion module is +.>And the d-axis current is input to a d-axis current PID controller, and the regulated d-axis current is output to an IPark conversion module.
Step six, obtaining the product through calculation of an IPark conversion moduleShaft voltage and->Shaft voltage, will->Shaft voltage and->The shaft voltage is input into an SVPWM module, three pairs of complementary PWM waves are generated through signal processing and are input into a three-phase half-bridge inverter to control the conduction of six bridge arms, and g is used for representing the multiplexed PWM waves; the inverter modulates the direct current bus voltage Vdc input by the direct current power supply into three-phase alternating current voltage A, B, C, the three-phase alternating current voltage A, B, C is input into the motor, a rotating magnetic field is formed inside the motor, the motor rotor generates induction current by cutting the rotating magnetic field, and the induction current forms a magnetic field to drive the motor rotor to rotate.
Step seven, in the aircraft brake cooling fan control system, given iterationThe time step of the process is 1e-05s, the time step is required to be discretized in a control system related to electric power, and after multiple iterations, the control system controls a motor to quickly reach the target rotating speed of the system in a short time and stably rotate according to the target rotating speed; torque is appliedMagnetic linkage position->Rotational speed->Three-phase stator current->Respectively inputting the signals into an oscilloscope for observation.
Analysis of fig. 5 shows that the overshoot of the PID control system based on the improved optical microscope algorithm is significantly lower than that of the PID control system based on the standard optical microscope algorithm and the conventional PID control system, which can indicate that the PID control system based on the improved optical microscope algorithm has better control performance and stronger robustness than the PID control system based on the standard optical microscope algorithm and the conventional PID control system in the nonlinear system.
Claims (4)
1. The optimal robust control method based on the improved aircraft brake cooling fan is characterized in that parameters of a traditional PID controller are optimized by improving an optical microscope optimization algorithm, and finally the parameters are transmitted into a speed PID controller, and the method comprises the following specific steps of:
firstly, building a simulation model of an aircraft brake cooling fan control system through Simulink;
simulating an optical microscope working principle, programming a standard optical microscope optimization algorithm through Matlab, carrying out mathematical modeling on the optical microscope, and constructing a speed PID controller model by using Simulink;
step three, improving the optimization algorithm of the standard optical microscope, and introducing the self-adaptive proportional amplification coefficientM r Improving algorithmsObjective lens and eyepiece position update formula, scaling factorM r The mathematical formula of (a) is:
；
in the method, in the process of the invention,M r in order to adapt the scaling factor to be high,m r to take the value of 0,1]The random vector between the two,fit(best)for the current iteration the optimal individual fitness value,fit(t)the fitness value of the current individual;
optimizing parameters of the PID controller by utilizing an improved optical microscope optimization algorithm, and obtaining three optimal parameters of the PID controller through multiple iterations, wherein the method comprises the following specific steps:
s1, constructing a dynamic objective function, wherein expected values of the objective function and function values calculated in each time step are changed along with time, and the formula of the objective function is as follows:
；
in the method, in the process of the invention,xis a decision variable, here set to 0.5;tis a variable of the time of day,Ais an amplitude parameter, here set to 1, sin #t) Is a sinusoidal function of variable t representing a periodic variation in time, the expected value of this function being varied at each time step, consisting ofA×sin(t) Determining; the real-time value is the function value calculated in each time step;
s2, initializing a population for improving an optical microscope optimization algorithm, wherein the population comprises an initial population individual position and population related parameters, and the related parameters comprise population overall scaleNPDimension of problemDim、Search space upper and lower boundsub, lb]And maximum number of iterationsMax_iterThe individual position formula is initialized as follows:
；
in the method, in the process of the invention, for the j-th dimension variable of individual i, +.>And->The lower bound and the upper bound of the j-th dimensional variable,ris of value of [0,1 ]]Random numbers within;
s3, taking the value range of the PID controller parameters as the search space of the algorithm, and randomly selecting a group of PID controller parameter initialization populations in the [ lb, ub ] range;
s4, calculating the current fitness value of all individuals of the target population through a fitness function, enabling the current fitness value to be incrementally ordered and stored in an array, marking an array index, and recording the optimal fitness value of the current populationfit ness ；
S5, in the iteration process, in the stage of debugging the objective lens, introducing an adaptive scaling factor M r Improving a population individual position updating formula, updating individual positions, and calculating an updated fitness value of the target object;
（1）；
in the method, in the process of the invention,after the objective lens is debugged, the firstiThe location of the individual; />Is the firsttOptimal individual position of the generation population; />Is the firstiThe location of the individual;M r is an adaptive proportional amplification coefficient;
s6, calculating through an objective functionIs selected in greedy strategy>Comparing the fitness value of the (a) with the individual optimal fitness value stored in the previous iteration, reserving the individual position with smaller fitness value as the latest individual position, and updating the individual optimal fitness value;
s7, comparing the fitness value of the latest position of the current individual with the optimal fitness value of the population by using a greedy selection strategy, keeping the value with smaller fitness as the optimal fitness value of the population, and updating the optimal individual of the population;
s8, randomly selecting an individual in the population in the eyepiece debugging stageThe space calculation formula is shown as formula (1), if the space calculation formula is satisfiedThen->Designing a position updating formula of population individuals as formula (2), generating new magnification, and calculating the fitness value of the modified target object;
（1）；
（2）；
in the method, in the process of the invention,is an adaptive proportional amplification coefficient;
s9, obtainingThen greedy selection is carried out again to obtain next generation population individuals;
s10, judging whether the maximum iteration times are reached, if so, optimizing and stopping outputting optimal parameters, otherwise, returning to S5 to continue optimizing;
and S11, assigning the data of the optimal solution to Kp, ki and Kd, and transmitting the data into the controlled object for simulation to obtain a model output result under the condition of the optimal data.
2. The method according to claim 1, wherein in the first step, the magnetic field directional control system model of the aircraft brake cooling fan comprises a Clark conversion module, a Park conversion module, an IPark conversion module, a speed PID controller module, two current PID controller modules, a SVPWM module, a flux linkage and position estimation module, a signal acquisition module, an inverter module, and an ac asynchronous motor module.
3. The method for optimal robust control of an aircraft brake cooling fan according to claim 1, wherein in the second step, the PID controller model includes a deviation calculation module of a target rotational speed and a real-time rotational speed, a PID controller module, an improved optical microscope optimization algorithm module, an inverter module, an ac asynchronous motor module, e (t) is a difference between the target rotational speed and the real-time rotational speed, u (t) is a control amount adjusted by a speed PID controller, and y (t) is an output current adjusted by a PID.
4. The optimal robust control method based on an improved aircraft brake cooling fan of claim 1, wherein in step four, greedy selection is performed in the following manner in S6, S7 and S9:
；
in the method, in the process of the invention,the function is an objective function that returns an expected value representing the fitness value of the population of individuals.
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