CN118226763B - Variable cycle engine multivariable control design method based on intelligent anti-interference strategy - Google Patents
Variable cycle engine multivariable control design method based on intelligent anti-interference strategy Download PDFInfo
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Abstract
The invention belongs to the field of aeroengine control, and discloses a variable cycle engine multivariable control design method based on an intelligent disturbance rejection strategy, which utilizes an improved particle swarm algorithm to optimize fuel consumption under a typical working condition point and takes the fuel consumption as a control reference instruction value, and then adopts a four-variable closed-loop multivariable disturbance rejection control strategy; the nonlinear function of an extended state observer in the active disturbance rejection controller is improved, and the robust control effect is improved; meanwhile, the intelligent setting of the parameters of the controller is realized by utilizing an improved particle swarm optimization algorithm, the control precision and the control speed are improved, and the parameter setting difficulty is reduced, so that the method has important engineering significance in the application of the variable cycle engine.
Description
Technical Field
The invention belongs to the field of aeroengine control, and relates to a variable cycle engine multivariable control design method based on an intelligent anti-interference strategy.
Background
Variable cycle engines possess complex structures, broad flight envelopes, a variety of adjustable geometric variables, and a variety of cycle modes. The control system of a variable cycle engine comprises two parts: control plans and control algorithms. The variable cycle engine may be operated according to a desired control schedule using a controller designed with a control algorithm. The traditional variable cycle engine control systems are all single variable control of fuel oil-rotating speed, and relatively simplified control algorithms are applied. Because the variable cycle engine belongs to a highly complex nonlinear system, the required control precision is difficult to achieve by means of a single variable control means, and therefore, a control algorithm capable of simultaneously and cooperatively processing a plurality of control variables and feedback signals is required to be adopted to design an engine control system meeting control specifications.
Aiming at the problem that the stability and the anti-interference performance of an engine under various working conditions are difficult to ensure by the traditional univariate control method, domestic and foreign scholars start to turn to the multivariate control technology, which gradually becomes an important research direction in the field of variable cycle engine control. Han Jing teaches that an active disturbance rejection algorithm is proposed in the 'PID technology to the active disturbance rejection technology', so that a control strategy of multiple variables and multiple loops can be established, and the coupling between different variables can be regarded as disturbance in an engine and can be automatically compensated in an observation and compensation mode, so that the system has stronger anti-disturbance performance and robustness. However, the active disturbance rejection algorithm needs a plurality of setting parameters, the setting is complicated, and the intelligent setting of the control parameters is necessary to be realized by using a novel optimization algorithm.
In order to overcome the defects of the current variable cycle engine control system design, the invention provides a variable cycle engine multivariable control design based on an intelligent disturbance rejection strategy, an improved particle swarm algorithm is utilized to optimize the fuel consumption under a typical working condition point and takes the fuel consumption as a control reference command value, and then a four-variable closed-loop multivariable disturbance rejection control strategy is adopted; the nonlinear function of an extended state observer in the active disturbance rejection controller is improved, and the robust control effect is improved; meanwhile, the intelligent setting of the parameters of the controller is realized by utilizing an improved particle swarm optimization algorithm, the control precision and the control speed are improved, and the parameter setting difficulty is reduced, so that the method has important engineering significance in the application of the variable cycle engine.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a variable cycle engine multivariable control design method based on an intelligent anti-interference strategy.
The technical scheme of the invention is as follows:
A variable cycle engine multivariable control design method based on intelligent disturbance rejection strategy comprises the following steps:
s1: an improved particle swarm algorithm is designed, and an optimization strategy for specifying a search direction is specifically provided for optimizing fuel consumption rate under subsonic speed typical working conditions
S1.1: setting the height, mach number and reference thrust required by optimizing fuel consumption of a variable cycle engine model;
s1.2: initializing a particle population N, a maximum iteration number item, the position and speed of the population and the dimension dim of the population;
s1.3: substituting the population position into a variable cycle engine model to be optimized to obtain a fitness value, namely the initial optimal position of the population; then, when the first M iterations are performed, replacing the initial optimal position of the population with a designated optimal position, namely a designated searching direction; m represents the number of times that the designated search direction needs to be maintained, and is not more than 5;
s1.4: updating the position and speed of the population;
in the method, in the process of the invention, The position of the ith particle is the dimension；Is the speed of the ith particle; wherein i=1, 2, …, N is the total number of particle groups; k is the current iteration number; the update formula for the j-th dimension velocity of the i-th particle is as follows:
in the method, in the process of the invention, A historical optimum value for the whole population of particles; is the historical optimum of the ith particle; And Is a learning factor, is a non-negative constant; And Is a random number uniformly distributed in the interval of [0,1 ]; is an inertial weight; representing a specified historical best location, i.e., a specified search direction;
s1.5: substituting the updated population position into a variable cycle engine model to be optimized to obtain an updated fitness value;
S1.6: repeating the steps S1.4 and S1.5, finding out an optimal solution, namely a group of engine control amounts corresponding to the lowest fuel consumption rate, through multiple iterations, and outputting the optimal solution;
S2: based on the results of improving the particle swarm optimization fuel consumption rate, four groups of control loops are selected: the method comprises the steps of establishing a variable cycle multivariable control system by using an improved active disturbance rejection algorithm, wherein the variable cycle multivariable control system comprises the relative rotation speed of a fuel-high pressure compressor, the critical area-total pressure ratio of a tail nozzle, the guide vane angle of a core fan, the post-fan inclusion pressure, the guide vane angle of the high pressure compressor and the post-high pressure compressor pressure:
s2.1: designing tracking differentiators TD according to the principle of immunity algorithm
The tracking differentiator functions to schedule transients and extract the differentiated signal to reduce overshoot. In a general control system, the error is obtained by directly making a difference between a set value and an output value of a controlled object, and the error taking method makes the initial error of the control system large and easy to cause overshoot. Therefore, the TD is adopted to firstly arrange a reasonable transition process, and the transition process and the output of the controlled object are subjected to poor operation, which is a limited method for solving the contradiction between the rapidity and overshoot of the PID control algorithm, and is a method for improving the robustness of the system. The purpose of the transient is to make the input smoother by low pass filtering the desired values, which reduces abrupt and unstable changes and helps the system respond more stably to the reference command. Han Jing teaches in "from PID technology to active disturbance rejection technology" a fastest discrete tracking differentiator that enables instruction tracking by fastest control:
Wherein, The function is a composite function of the fastest control, and is expressed as follows:
Wherein, 、、、、Is an intermediate variable; Is that And (3) withThe difference between the two,Equal to；Is the simulation step length, when the system has no noise interference, the simulation step length is takenIs equal to the value of；As a result of the filtering factor,The larger the filtering effect is, the better; For the speed factor, the speed at which the scheduled transient tracking reference command value is determined, in actual control, The larger the expected response time the shorter; is a sign function; as a reference to the instruction(s), The system variables are representative of the scheduled transition,The system variables represent the differential of the scheduled transition,In the function of，；
S2.2: design improvement nonlinear expansion observer
S2.2.1: designing a traditional nonlinear extended observer NLESO according to the principle of an anti-interference algorithm
The expansion observer is essentially a high-gain observer, is the most core part in the anti-interference technology, can observe the state and disturbance of a nonlinear system, and compensates through feedback so as to play a role in anti-interference. When the engine is a controlled object, the sensor noise interference, the input loop disturbance, the model parameter perturbation (pulling deviation) and the environmental disturbance can be observed through the expansion observer, and meanwhile, the nonlinear error feedback is utilized to timely compensate the observed disturbance error. Firstly, constructing a traditional nonlinear dilation observer:
Wherein y is the output of the system, Is to the state quantity of the systemAn estimated value of (2),Is to the state quantity of the systemAn estimated value of the rate of changeIs in an expanded stateIs a function of the estimated value of (2); Is that Derivative of (2),Is thatDerivative of (2),Is thatIs a derivative of (2); e isAnd (3) withA difference between; For observer gain, the observation effect is controlled to be an adjustable parameter, concerning Tuning of parameters, gao Zhijiang teaches bandwidth tuning methods, i.e. configuring parameters separately for second order controlled objectsConverting three parameters to be set into bandwidthIs adjusted; the compensation term is constant; Is a control amount; as a nonlinear function, when actual simulation is performed, taking:
Wherein, Is a constant of 0 to 1 and,For the filter constant, determineThe size of the function linear interval enables the expansion observer to have a filtering function;
S2.2.2: design improvement nonlinear expansion observer
The fal function is an important nonlinear term whose convergence speed determines the performance of the extensional observer in observing system uncertainty and interference. The response speed and the anti-interference capability of the whole control system can be improved by improving the fal function. The present invention therefore employs an improved fal function as follows:
Wherein m and n are constants, and the selection is carried out according to actual conditions;
substituting the improved fal function into the nonlinear extended observer results in an improved nonlinear extended observer:
s2.3: design of nonlinear state error feedback NLSEF according to disturbance rejection algorithm principle
Classical PID control is a simple linear combination comprising three feedback control terms, proportional, integral and derivative, but this approach has certain limitations. In conventional variable cycle engine control systems, PI control algorithms are typically employed; the algorithm utilizes the integral feedback of the error to eliminate steady-state error, thereby improving the precision; however, the integral feedback also causes problems such as side effects of integral saturation. While NLSEF is based onAnd the nonlinear PD controller of the function can realize zero steady-state error through disturbance compensation within a certain observation error range. Therefore, the nonlinear PD controller can not only avoid the problem of integral saturation caused by integral feedback, but also ensure good steady-state accuracy, and is constructed as follows:
Wherein, The output of the non-linear error feedback,Is a transitional process of arrangementAnd to system state quantityEstimate of (2)Is used for the error of (a),Is the derivative of the scheduled transitionAnd to system state quantityEstimation of the rate of changeIs used for the error of (a),、Gain for the controller;、 A constant of 0 to 1;
due to expansion of internal and external disturbances and uncertainty items of the system into new states And utilize the opposite expansion stateEstimate of (2)Estimating it, designing proper compensation factorControl amount of variable cycle engineThe preparation method comprises the following steps:
s3: adopting an improved particle swarm algorithm to intelligently set parameters of an improved active disturbance rejection controller;
S3.1: dividing parameters to be regulated into four groups according to four-loop multivariable closed-loop control, and respectively optimizing and setting parameters generated in each group; first, the parameters to be adjusted in each group are analyzed, and the parameters generated in the tracking differentiator are as follows: 、 ; the parameters generated in the non-linear dilation observer are: m, n; the parameters generated in the nonlinear error feedback are: 、、、、 ; wherein the method comprises the steps of 、Respectively related to the filtering and tracking speed, and thus is directly set manually; other parameters are optimized and set through an algorithm; then one of the three groups of loops is closed-loop controlled, and the other three groups of loops are input into the variable cycle engine together with an open-loop control plan in an open-loop mode;
S3.2: corresponding limiting constraint conditions are provided for dynamic indexes (thrust fluctuation amount and thrust adjustment time) and steady-state indexes (steady-state errors) in the control process, and the corresponding limiting constraint conditions are converted into an optimization problem:
Wherein the method comprises the steps of Is the sum of the errors of the two different types of the data,As a steady-state error of the thrust,Is the steady-state error of the relative rotation speed of the high-pressure compressor,As a steady-state error of the total pressure ratio,Is the steady-state error of the pressure after the high-pressure compressor,Is the steady state error of the internal pressure behind the fan,For maximum dynamic thrust during the switching process,Is a steady-state thrust force,The thrust adjustment time.、The constant is obtained according to the error requirement, and the steady-state error of the relative rotation speed of the high-pressure compressor and the steady-state error of the total pressure ratio are amplified in a coefficient multiplication mode because the magnitude order of the steady-state error is smaller.
S3.3: and optimizing the parameters to be regulated of each group by utilizing an improved particle swarm algorithm, and adding another group of closed-loop control loops after the parameters of one group of control loops are successfully regulated until the parameter regulation of four groups of closed-loop control loops is completed.
S4: and verifying the superiority of the multivariable control based on the intelligent anti-interference strategy, and constructing a Simulink model in Matlab to carry out simulation experiments.
The invention has the beneficial effects that: in the invention, when the variable cycle engine is switched from the normal mode to the lowest oil consumption mode, the traditional PID control mode is difficult to ensure the immunity. According to the invention, an improved particle swarm algorithm is adopted to optimize the fuel consumption rate of the engine on the premise of ensuring the safety of the variable cycle engine, so that the cruising capacity of the engine is improved; then, taking the optimized result as a control reference instruction value, adopting a four-variable closed-loop multivariable disturbance rejection control strategy and improving the extended observer, thereby improving the robustness and disturbance rejection of the engine control system; finally, the improved particle swarm algorithm is adopted to intelligently set the parameters of the disturbance rejection controller, so that the control precision and the control speed are improved, and the parameter setting difficulty is reduced.
Drawings
FIG. 1 is a flow chart of a variable cycle engine multivariable control design based on intelligent immunity strategy;
FIG. 2 is a flow chart for optimizing fuel consumption rate by an improved particle swarm algorithm;
FIG. 3 is a schematic diagram of an active disturbance rejection control architecture;
FIG. 4 is a diagram of a parameter framework of an intelligent tuning controller;
FIG. 5 is a graph showing the fuel consumption rate control effect of a variable cycle engine in a minimum fuel consumption mode;
FIG. 6 is a graph showing the thrust control effect of a variable cycle engine in a minimum fuel consumption mode;
FIG. 7 is a graph showing the effect of controlling the rotational speed of a variable cycle engine in a minimum fuel consumption mode;
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings and technical schemes.
As shown in FIG. 1, a variable cycle engine multivariable control design based on intelligent immunity strategy mainly comprises the following steps:
s1: improving the traditional particle swarm algorithm, and designing a particle swarm algorithm with a specified searching direction;
S2: optimizing the fuel consumption rate of the subsonic cruise typical working condition point by utilizing an improved particle swarm algorithm;
S3: according to the basic principle of active disturbance rejection, three parts of a tracking differentiator TD, a nonlinear extended state observer NLESO and a nonlinear state error feedback NLSEF are built, and a nonlinear function fal is improved to enhance the disturbance rejection of the controller;
s4: the improved particle swarm algorithm is utilized to intelligently set the parameters of the controller generated by the four closed-loop control circuits respectively;
S5: and verifying the superiority of the variable cycle engine multi-variable control, and constructing a Simulink model in Matlab to carry out simulation experiments.
As shown in fig. 2, the step of optimizing the fuel consumption rate at the typical operating point by using the improved particle swarm algorithm is as follows:
S2.1: initializing the position and the speed of a particle swarm, wherein the number of variables to be optimized is 7, so that the population dimension is 7, the iteration number is set to 50, the initial position is input into a variable cycle engine model in an open-loop mode, and the engine output value is used as an initial fitness value.
S2.2: when the first M iterations are performed, replacing the initial optimal position of the population with a designated optimal position (searching direction), and updating the position and the speed by using an updating formula;
s2.3: substituting the updated particles into a model to be optimized to obtain the latest fitness value;
S2.4: judging whether the iteration times are reached, if so, finishing optimization, and if not, jumping to S2.2 to continue iteration;
As shown in fig. 3, three parts of the tracking differentiator TD, the nonlinear extended state observer NLESO and the nonlinear state error feedback NLSEF are built, and the nonlinear function fal is improved:
s3.1: design Tracking Differentiator (TD)
The tracking differentiator functions to schedule transients and extract the differentiated signal to reduce overshoot. Han Jingqing teaches in the paper a fastest discrete tracking differentiator that achieves instruction tracking by fastest control:
Wherein, The function is a composite function of the fastest control, and is expressed as follows:
Wherein the method comprises the steps of Is the step size of the simulation,As a result of the filtering factor,For the speed factor, the speed at which the scheduled transient tracking reference command value is determined, in actual control,The larger the expected response time the shorter; as a function of the sign of the symbol, In the function of，。
S3.2: design nonlinear expansion observer (NLESO)
S3.2.1: traditional nonlinear distention observer (NLESO)
When the engine is a controlled object, the sensor noise interference, the input loop disturbance, the model parameter perturbation (pulling deviation) and the environmental disturbance can be observed through the expansion observer, and meanwhile, the nonlinear error feedback is utilized to timely compensate the observed disturbance error. The following first constructs a conventional nonlinear dilation observer:
Wherein, Is to the state quantity of the systemAn estimated value of (2),Is to the state quantity of the systemAn estimated value of the rate of changeIs in an expanded stateIs a function of the estimated value of (2); For observer gain, the observation effect is controlled to be an adjustable parameter, concerning Tuning of parameters, gao Zhijiang teaches bandwidth tuning methods, i.e. configuring parameters separately for second order controlled objectsConverting three parameters to be set into bandwidthIs adjusted; As a nonlinear function, in performing actual simulation, usually take:
Wherein the method comprises the steps of A constant typically 0 to 1; For the filter constant, determine The size of the linear interval of the function enables the extended observer to have a filtering function.
S3.2.2: improved nonlinear dilation observer
The invention adopts an improved fal function as shown in the following formula:
substituting the improved fal function into the nonlinear dilation observer results in an improved dilation observer:
When (when) When the novel extended observer has higher anti-interference capability than the traditional extended observer, the improved anti-interference control algorithm is applied to the engine control system, and the method is thatThe setting of the parameters needs to be guaranteed to be within limits. The replacement of the fal function does not change the stability of the extended observer, but only accelerates the convergence speed of the observer and enhances the anti-interference capability of the system.
S3.3: design nonlinear state error feedback (NLSEF)
NLSEF is based onNonlinear PD controller of function, its outputAiming at the converted integral serial system, the steady-state error can be zero through disturbance compensation within a certain range of observation errors. Therefore, the nonlinear PD controller can not only avoid the problem of integral saturation caused by integral feedback, but also ensure good steady-state accuracy, and is constructed as follows:
Wherein, The output of the non-linear error feedback,Is a transitional process of arrangementAnd to system state quantityEstimate of (2)Is used for the error of (a),Is the derivative of the scheduled transitionAnd to system state quantityEstimation of the rate of changeIs used for the error of (a),、Gain for the controller.
By expanding the internal and external disturbance and uncertainty items of the system to a new state and utilizing the expanded stateEstimate of (2)Estimating it and designing proper compensation factorControl amount of systemThe method can be taken as follows:
as shown in fig. 4, the controller parameters were intelligently set using the modified particle swarm algorithm:
s4.1: first, the parameters to be adjusted in each group are analyzed, and the parameters generated in the tracking differentiator are as follows: 、 ; the parameters generated in the non-linear dilation observer are: m, n; the parameters generated in the nonlinear error feedback are: 、、、、 ; wherein the method comprises the steps of 、Respectively to the filtering and tracking speed, and can thus be set directly manually. Then one of the three groups of circuits is closed-loop controlled, and the other three groups of circuits are input into the engine together with an open-loop control plan in an open-loop mode.
S4.2: corresponding limiting constraint conditions are provided for dynamic indexes (thrust fluctuation amount and thrust adjustment time) and steady-state indexes (steady-state errors) in the control process, and the corresponding limiting constraint conditions are converted into an optimization problem:
Wherein the method comprises the steps of As a steady-state error of the thrust,Is the steady-state error of the relative rotation speed of the high-pressure compressor,As a steady-state error of the total pressure ratio,Is the steady-state error of the pressure after the high-pressure compressor,Is the steady state error of the internal pressure behind the fan,For maximum dynamic thrust during the switching process,Is a steady-state thrust force,The thrust adjustment time. Since the steady-state error of the relative rotation speed of the high-pressure compressor is smaller than the steady-state error of the total pressure by an order of magnitude, the high-pressure compressor is amplified in a form of coefficient multiplication.
S4.3: initializing the position and speed of a particle swarm, wherein the number of variables to be optimized is 10, the population dimension is 10, the iteration number is set to 50, the initial position is used as a controller parameter to be input into a variable cycle engine model, and the engine output value is used as an initial fitness value.
S4.4: when the first M iterations are performed, replacing the initial optimal position of the population with a designated optimal position (searching direction), and updating the position and the speed by using an updating formula;
s4.5: substituting the updated particles into a variable cycle engine model to obtain the latest fitness value;
S4.6: judging whether the iteration times are reached, if so, finishing optimization, and if not, jumping to S4.4 to continue iteration;
As shown in fig. 5, 6 and 7, on the premise of providing a reference command value with an optimized result, the variable-cycle engine is subjected to multivariable immunity control, so that the engine stably operates in a multi-closed loop and the fuel consumption rate is greatly reduced in subsonic cruising.
In conclusion, the variable cycle engine multivariable control design method based on the intelligent anti-interference strategy can effectively compensate disturbance generated by internal and external factors of the system to realize stable operation of the control system, and meanwhile, the improved particle swarm algorithm can be utilized to optimize fuel consumption under typical working condition points and is used as a control reference instruction value to realize multivariable control of the engine.
Claims (1)
1. A variable cycle engine multivariable control design method based on an intelligent disturbance rejection strategy is characterized by comprising the following steps:
S1: an improved particle swarm algorithm is designed, an optimization strategy for specifying the search direction is provided, and the fuel consumption rate under subsonic typical working conditions is optimized
S1.1: setting the height, mach number and fuel consumption rate of a variable cycle engine model, and optimizing the required reference thrust;
S1.2: initializing a particle population N, a maximum iteration number item, a population position and speed and a population dimension dim;
s1.3: substituting the population position into a variable cycle engine model to be optimized to obtain a fitness value, namely the initial optimal position of the population; then, when the first M iterations are performed, replacing the initial optimal position of the population with a designated optimal position, namely a designated searching direction; m represents the number of times that the designated search direction needs to be maintained, and is not more than 5;
s1.4: updating the position and speed of the population;
in the method, in the process of the invention, The position of the ith particle is the dimension；Is the speed of the ith particle; wherein i=1, 2, …, N is the total number of particle groups; k is the current iteration number; the update formula for the j-th dimension velocity of the i-th particle is as follows:
in the method, in the process of the invention, A historical optimum value for the whole population of particles; is the historical optimum of the ith particle; And Is a learning factor, is a non-negative constant; And Is a random number uniformly distributed in the interval of [0,1 ]; is an inertial weight; representing a specified historical best location, i.e., a specified search direction;
s1.5: substituting the updated population position into a variable cycle engine model to be optimized to obtain an updated fitness value;
S1.6: repeating the steps S1.4 and S1.5, finding out an optimal solution, namely a group of engine control amounts corresponding to the lowest fuel consumption rate, through multiple iterations, and outputting the optimal solution;
s2: based on the results of optimizing fuel consumption rate by the improved particle swarm algorithm, four groups of control loops are selected: the method comprises the steps of establishing a variable cycle multivariable control system by using an improved active disturbance rejection algorithm, wherein the variable cycle multivariable control system comprises the relative rotation speed of a fuel-high pressure compressor, the critical area-total pressure ratio of a tail nozzle, the guide vane angle of a core fan, the post-fan inclusion pressure, the guide vane angle of the high pressure compressor and the post-high pressure compressor pressure:
s2.1: designing tracking differentiators TD according to the principle of immunity algorithm
Establishing a fastest discrete tracking differentiator:
Wherein, The function is a composite function of the fastest control, and is expressed as follows:
Wherein, 、、、、Is an intermediate variable; Is that And (3) withThe difference between the two,Equal to；Is the simulation step length, when the system has no noise interference, the simulation step length is takenIs equal to the value of；As a result of the filtering factor,The larger the filtering effect is, the better; For the speed factor, the speed at which the scheduled transient tracking reference command value is determined, in actual control, The larger the expected response time the shorter; is a sign function; as a reference to the instruction(s), The system variables are representative of the scheduled transition,The system variables represent the differential of the scheduled transition,In the function of，；
S2.2: design improvement nonlinear expansion observer
S2.2.1: design of nonlinear extended observer NLESO according to disturbance rejection algorithm principle
Constructing a nonlinear dilation observer:
Wherein y is the output of the system, Is to the state quantity of the systemAn estimated value of (2),Is to the state quantity of the systemAn estimated value of the rate of changeIs in an expanded stateIs a function of the estimated value of (2); Is that Derivative of (2),Is thatDerivative of (2),Is thatIs a derivative of (2); e isAnd (3) withA difference between; For observer gain, the observation effect is controlled to be an adjustable parameter, concerning Setting parameters, namely respectively configuring the parameters for second-order controlled objects asConverting three parameters to be set into bandwidthIs adjusted; the compensation term is constant; Is a control amount; as a nonlinear function, when actual simulation is performed, taking:
Wherein, Is a constant of 0 to 1 and,For the filter constant, determineThe size of the function linear interval enables the nonlinear extended observer to have a filtering function;
S2.2.2: design improvement nonlinear expansion observer
The following formula is used for the modified fal function:
Wherein m and n are constants, and the selection is carried out according to actual conditions;
substituting the improved fal function into the nonlinear extended observer results in an improved nonlinear extended observer:
s2.3: design of nonlinear state error feedback NLSEF according to disturbance rejection algorithm principle
NLSEF is based onA nonlinear PD controller of a function, the nonlinear PD controller comprising the following formula:
Wherein, The output of the non-linear error feedback,Is a transitional process of arrangementAnd to system state quantityEstimate of (2)Is used for the error of (a),Is the derivative of the scheduled transitionAnd to system state quantityEstimation of the rate of changeIs used for the error of (a),、Gain for the controller;、 A constant of 0 to 1;
by expanding internal and external disturbances and uncertainty of variable cycle engines to new conditions and using the same Estimate of (2)Estimating it, designing proper compensation factorControl amount of variable cycle engineThe preparation method comprises the following steps:
s3: adopting an improved particle swarm algorithm to intelligently set parameters of an improved active disturbance rejection controller;
s3.1: dividing parameters to be regulated into four groups according to four groups of loop multivariable closed-loop control, and respectively optimizing and setting parameters generated in each group; first, the parameters to be adjusted in each group are analyzed, and the parameters generated in the tracking differentiator are as follows: 、 ; the parameters generated in the non-linear dilation observer are: m, n; the parameters generated in the nonlinear error feedback are: 、、、、 ; wherein the method comprises the steps of 、Respectively related to filtering and tracking speed, and is directly arranged; the other parameters are optimized and set through an improved particle swarm algorithm; then one group of loop multivariable closed-loop control is carried out, and the other three groups of multivariable loops are input into the variable cycle engine together with an open-loop control plan in an open-loop mode;
S3.2: corresponding constraint conditions are provided for dynamic indexes and steady-state indexes in the control process, and the constraint conditions are converted into optimization problems:
the dynamic index comprises thrust fluctuation amount and thrust adjustment time, and the steady-state index is steady-state error; Is the sum of the errors of the two different types of the data, As a steady-state error of the thrust,Is the steady-state error of the relative rotation speed of the high-pressure compressor,As a steady-state error of the total pressure ratio,Is the steady-state error of the pressure after the high-pressure compressor,Is the steady state error of the internal pressure behind the fan,、In order to amplify the coefficient of the power,For maximum dynamic thrust during the switching process,Is a steady-state thrust force,The thrust force is adjusted for time;
S3.3: and optimizing the parameters to be regulated of each group by utilizing an improved particle swarm algorithm, and adding another group of closed-loop control loops after the parameters of one group of loops are successfully regulated until the parameter regulation of four groups of closed-loop control loops is completed.
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