PID control method for intake environment simulation system based on adaptive homogeneous differentiator

Abstract

The PID control method of the intake environment simulation system based on the adaptive homogeneous differentiator disclosed in the present invention includes step 1: constructing a homogeneous differentiator algorithm with variable structure characteristics based on a second-order series system; step 2: designing an adaptive law based on error feedback for the core parameter α of the homogeneous differentiator algorithm; step 3: performing stability analysis on the designed adaptive law design Lyapunov function to prove the existence of the adjustable parameter θ that characterizes the constraint relationship between the system parameter and the signal; step 4: inputting the pressure signal in the intake environment simulation control system into the designed adaptive homogeneous differentiator to obtain real-time filtering and differential signals; step 5: comparing the filtering signal and differential signal obtained in step 4 with the filtering and differential signals of a given input signal, and designing a PID controller algorithm based on the error. Step 6: repeating the above steps 4 to 5.

Description

Technical Field

[0001] The invention belongs to the field of aero-engine testing, and in particular relates to a PID control method for an intake environment simulation system based on an adaptive homogeneous differentiator. Background Art

[0002] The aircraft engine high-altitude simulation test bench (referred to as the high-altitude test bench) is a test bench on the ground that can simulate the altitude, speed and intake distortion conditions of various types of aircraft engines in the air. It is one of the indispensable and most effective experimental equipment for the development of advanced aircraft engines and propulsion systems. The flight environment simulation control system is an important part of the high-altitude test bench, especially the intake environment simulation control system, which plays an extremely important role in the entire high-altitude simulation test. Its control quality and regulation performance are directly related to the test safety of the tested engine and the accuracy of the simulation of aerodynamic boundary conditions.

[0003] The current flight environment simulation control technology has shifted from conventional environment simulation control problems such as "steady state", "slow transition state", and "fixed-point simulation" to complex environment simulation control problems such as "fast target tracking", "strong anti-disturbance" and "continuous trajectory simulation". In the transition state test of aircraft engines, a series of fast and high-load disturbance impact tests are required, such as rapid pushing and pulling of the throttle lever, short-term acceleration and deceleration to the target power, etc. Due to the stringent test requirements during the transition state test, the sensor's own performance and the disturbance of the measurement channel, the frequency of the environmental pressure signal has the characteristics of mutation and random noise of different intensities. Specifically, the pressure signal changes with the change of the simulation test conditions, causing the frequency of the signal to change within a certain bandwidth. In addition, the structure and composition of the flight environment simulation intake control system are complex, and the pipe network cavity, valve system and other characteristics are large in size, strong nonlinear characteristics, and there are multiple sources of unknown disturbances, which further bring great challenges to the flight environment simulation intake control system.

[0004] In response to the above technical difficulties, domestic and foreign units with high-altitude simulation tests for aircraft engines have carried out a large number of studies on transition state control methods. The Arnold Engineering Development Center (AEDC) in the United States has carried out a large number of basic equipment characteristic modeling and simulation work for the controlled system, established a relatively complete system model, and on this basis, carried out classical control algorithms such as gain scheduling control, adaptive control, and model predictive control. These research results have greatly improved the level of simulation control of the flight environment of aircraft engines and provided a solid foundation for the development of new aircraft engines. The University of Stuttgart in Germany has established a digital flight environment simulation control simulation system and a hardware-in-the-loop simulation system for its high-altitude simulation test bench. Based on this simulation system, composite control and feedforward control technology have been developed, which greatly improves the adjustment ability of the intake control system in the transition state test. The flight environment simulation control system of my country's high-altitude platform basically adopts the classical error feedback control mode based on sensor signals, and combines fuzzy control to adjust the relevant control parameters. In addition, on the basis of improving the mathematical model of the controlled object, my country has also carried out the development of an anti-disturbance control algorithm based on signal processing and estimation for the transition state test.

[0005] In general, the technology actually used in the current high-altitude platform flight environment simulation system still revolves around the classic error feedback PID control. In PID control, the differential link reflects the rate of change of the system deviation signal, which is predictive and can predict the trend of deviation change, thereby producing an advanced control effect, that is, before the deviation is formed, it has been eliminated by the differential adjustment effect. For this reason, the differential can improve the dynamic performance of the system. In addition, if the differential time is selected appropriately, overshoot can be reduced and the adjustment time can be reduced. However, due to the fact that the pressure signal has a changing bandwidth and is mixed with random noise of different intensities due to changes in test conditions, these have caused the differential link to amplify high-frequency noise and make it difficult to obtain an effective differential signal, so that the differential link with the ability to respond in advance and enhance system stability is often abandoned in PID control, and the effect of PID control is also greatly reduced. Therefore, for the intake control system simulating the flight environment of the high-altitude platform, how to obtain its effective pressure signal and pressure differential signal from the real-time pressure signal, especially the tracking filtering and differential signal of the signal transient process and the mutation process under the action of short-term impact disturbance, and apply it to the PID feedback control of the intake regulating valve, is of great significance for its rapid, accurate and real-time completion of the control goals.

[0006] Since the ideal differentiator is not achievable, the commonly used method for signal filtering and differential signal acquisition in practical engineering is the classical differentiator. The classical differentiator uses a small time constant inertia link to obtain the differential approximate formula. The principle of this method is simple and easy to implement. However, when the input signal is contaminated by random noise, a noise amplification effect will occur, so this algorithm is difficult to be applied in the actual engineering. In order to reduce the noise amplification effect, a differential extraction algorithm with two small time constant inertia links subtracted is used to suppress the noise amplification phenomenon to a certain extent. However, it is found from a large number of simulations that for low-sampled signals, this method is difficult to achieve the effect of suppressing noise, and it will bring high-frequency chatter while suppressing noise. Chinese scholar Han Jingqing proposed an idea based on bang-bang control and introduced a nonlinear tracking differentiator algorithm. This method has the form of a discrete algorithm, and its advantage is that it can track the input signal without overshoot and extract effective differential signals at the same time. However, when there is a sudden change in the processed signal, the transient performance of signal tracking filtering and differential extraction is greatly reduced. The sliding mode differentiator can effectively extract the differential of the signal and apply it to the sliding mode control, so that the system can achieve robust and stable tracking control under disturbance and uncertainty conditions. The differentiator is both a dual sliding mode algorithm and a continuous form of differential algorithm. When the input signal frequency band changes, the dynamic process of the differentiator in differential signal extraction is slow. In the high-altitude platform flight environment simulation intake control system, high requirements are placed on transient filtering and differential extraction performance when the pressure signal changes suddenly. Once the transient process is slow, the performance of PID control will be greatly reduced. Summary of the invention

[0007] In view of this, a PID control method for intake environment simulation system based on adaptive homogeneous differentiator is proposed to solve the problem of designing intake controller for flight environment simulation in aircraft engine transient state test.

[0008] A PID control method for an intake environment simulation system based on an adaptive homogeneous differentiator is provided, which is suitable for experiments of high-altitude simulation devices, including the use of an intake environment simulation control system, and includes the following steps:

[0009] S101: Based on a second-order series system, a homogeneous differentiator algorithm with variable structure characteristics is constructed, and a core parameter α of the homogeneous differentiator algorithm is determined;

[0010] S102: designing an adaptive law according to the core parameter α of the homogeneous differentiator algorithm and based on error feedback;

[0011] S103: The design of the adaptive law is subjected to stability analysis based on the Lyapunov function to determine the existence of an adjustable parameter θ in the constraint relationship between the system parameters and the signal;

[0012] S104: inputting the pressure signal in the intake environment simulation control system into the designed adaptive homogeneous differentiator to obtain filtering and differential signals in real time;

[0013] S105: comparing the filtered signal and the differential signal with the filtered and differential signals of a given input signal to determine an error, and determining a PID controller algorithm according to the error;

[0014] S106: Repeat S104 and S105 to track, filter and differentiate the pressure signal in the flight environment simulation control system test in real time, and feed it back to the main controller to realize real-time control and adjustment of the flight environment simulation control system.

[0015] The technical beneficial effects of the present invention are:

[0016] The present invention aims at the technical problem of flight environment simulation intake control in the transient state test of aircraft engines, and designs a PID control method based on an adaptive homogeneous differentiator. The adaptive homogeneous differentiator can effectively improve the filtering and differential extraction accuracy of transient signals in the process of pressure signal mutation, thereby greatly improving the performance of PID control. The existence of the adjustable parameter θ in the constraint relationship between the system parameters and the signal is determined. On this basis, for the transient process of tracking and differential extraction of the input signal, the proposed adaptive homogeneous differentiator algorithm has the advantages of fast speed and high precision compared with the sliding mode differentiator algorithm of subtracting two inertial links, which meets the engineering characteristics of high transient performance requirements in the transient state test of the flight environment simulation control system. When the signal suddenly changes, that is, corresponding to different operating conditions in the actual system, the proposed algorithm can achieve the characteristics of fast tracking and high differential accuracy. Compared with the commonly used differentiator algorithms in engineering, the adaptive homogeneous differentiator algorithm proposed in the present invention significantly improves the transient performance of tracking, filtering and differential extraction of a given signal under the condition of signal mutation, improves the robustness of the control system, that is, improves the ability to resist disturbance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 Schematic diagram of the adaptive homogeneous differentiator algorithm;

[0019] Figure 2 Principle diagram of PID control algorithm for intake environment simulation system based on adaptive homogeneous differentiator;

[0020] Figure 3Comparison of tracking filtering effects between adaptive homogeneous differentiator and sliding mode differentiator in transient process

[0021] Figure 4 Comparison of differential extraction effects between adaptive homogeneous differentiator and sliding mode differentiator in transient process;

[0022] Figure 5 Comparison of tracking filtering effects between adaptive homogeneous differentiator and sliding mode differentiator under signal mutation;

[0023] Figure 6 Comparison of differential extraction effects between adaptive homogeneous differentiator and sliding mode differentiator under signal mutation;

[0024] Figure 7 Comparison chart of the effects of PID control based on adaptive homogeneous differentiator and classic PID control. DETAILED DESCRIPTION

[0025] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0026] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0027] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0028] In order to solve the problem of designing the intake controller for flight environment simulation in the transient state test of aircraft engines, a PID control method based on an adaptive homogeneous differentiator is designed. The adaptive homogeneous differentiator can effectively improve the filtering and differential extraction accuracy of transient signals during the sudden change of pressure signals. The schematic diagram of the specific adaptive homogeneous differentiator algorithm can be found in Figure 1 As shown in the figure, it mainly includes a homogeneous differentiator algorithm with variable structure characteristics and the design of an adaptive law for parameter adjustment. The structure diagram of the PID control algorithm for the intake environment simulation system based on an adaptive homogeneous differentiator is shown in the figure. Figure 2 As shown, the controlled object is the high-altitude platform air intake environment simulation system. The system is affected by internal and external disturbances during operation, which affects the control performance. The real-time pressure signal is obtained by measuring the pressure sensor. The frequency of the pressure signal changes with the change of operating conditions, and it also contains a certain intensity of random noise. The output and given input signals of the system are tracked and filtered by the set adaptive homogeneous differentiator, and the differential signal is extracted. The main controller calculates the feedback control quantity according to the processed signal, which is finally used to control the controlled pressure. A method for simulating air intake environment PID control based on an adaptive homogeneous differentiator. The specific implementation steps are as follows.

[0029] In short:

[0030] Step 1: Based on the second-order series system, a homogeneous differentiator algorithm with variable structure characteristics is constructed; Step 2: Based on the core parameter α of the homogeneous differentiator algorithm, an adaptive law is designed based on error feedback; Step 3: The designed adaptive law is designed to perform stability analysis on the Lyapunov function, proving the existence of the adjustable parameter θ that characterizes the constraint relationship between the system parameters and the signal; Step 4: Input the pressure signal in the intake environment simulation control system into the designed adaptive homogeneous differentiator to obtain real-time filtering and differential signals; Step 5: Compare the filtered signal and differential signal obtained in step 4 with the filtered and differential signals of the given input signal, and design the PID controller algorithm based on the error. Step 6: Repeat the above steps 4 to 5, specifically:

[0031] Step 1: Based on the following second-order series system, the algorithm for constructing a homogeneous differentiator with variable structure characteristics is as follows:

[0032]

[0033] Among them, x1 and x2 are two system states of the second-order series system, X1 is the filter output signal, and X2 is the differential output signal; s=sign(x1-v) is the sign function; v is the noisy sensor detection signal; p and q are two adjustable parameters, the purpose is to make the above two differential equations have homogeneous characteristics, so that the corresponding differential equations satisfy the homogeneous equation, where p=1-m, q=1-2m, m>0 is an adjustable parameter; α and β are two core parameters for adjusting the filtering and differential characteristics of the differentiator. For the fixed parameter differentiator algorithm, α and β can be adjusted according to the empirical formula, where α=2ζA m W n , In the formula, A is the effective amplitude of the detection signal V, Wn is the frequency band of the given input signal, and ζ is the damping coefficient of the system. In a general system, (W0 is the bandwidth of the differentiator); the above homogeneous differentiator algorithm can resist system disturbances during the state transfer process and has strong robustness due to the introduction of variable structure characteristics and homogeneous characteristics.

[0034] Step 2:

[0035] The core parameter a of the homogeneous differentiator is closely related to the transient characteristics and accuracy of the differentiator filtering and differential extraction. For this reason, the error feedback e=x1-v is constructed, thereby constructing the following piecewise function with homogeneous parameter p based on the error feedback threshold, so that the core parameter a of the homogeneous differentiator can be adaptively adjusted according to the actual system error situation. The specific adaptive piecewise function is as follows:

[0036] Where α0 is the adaptive homogeneous differential

[0037] The initial value of the parameter, the adjustable parameter θ is to characterize the constraint relationship between the system parameter and the signal, and ε is the error threshold, which can be determined according to the actual system situation.

[0038] Step 3:

[0039] In order to obtain the constraint relationship between parameters and signals, the following Lyapunov function is designed for the designed adaptive homogeneous differentiator to conduct stability analysis: Construct Lyapunov function

[0040]

[0041] Obviously, the function itself is radially infinite. Next, we examine the derivative of the function, namely

[0042]

[0043] To make must

[0044]

[0045] Where γ is an arbitrary given parameter related to the input signal), so as long as There is always Therefore, when the error meets certain conditions, the filtering and differentiation accuracy of the proposed adaptive homogeneous differential algorithm are guaranteed. Through the above derivation, the existence of the adjustable parameter θ is proved, which reflects the effectiveness of the adaptive piecewise function.

[0046] Step 4:

[0047] For the transient state test of the high-altitude platform air intake environment simulation system, the signal of the pressure sensor is obtained in real time, denoted as v p Based on the above adaptive homogeneous differentiator algorithm, the following algorithm is designed for the pressure sensor input:

[0048]

[0049] Among them, X1p is the tracking filter signal of the pressure sensor, X2P is the differential signal of the pressure sensor, and the other parameters remain unchanged. The selection of core parameters and the constraints between the parameters and the input signal meet the requirements of steps 2 and 3 above.

[0050] Step 5:

[0051] For the given pressure signal r in the transient state test of the flight environment simulation control system, the corresponding adaptive homogeneous differentiator is designed to obtain the filtering and differential signals of the given signal. The purpose of this step is to buffer the given input pressure signal and largely eliminate the random noise signal contained in the given signal. The tracking filtered signal of the given signal is denoted as r1, and the corresponding differential signal is denoted as r2. Based on the filtering and differential signals of the real-time signal of the pressure sensor given in step 4, the following PID control algorithm is designed:

[0052] Where Kp is the adjustable proportional gain; Ki is the adjustable integral gain; Kd is the adjustable differential gain; t is the current time; τ is the integral variable, and the definitions of the remaining variables are the same as described in the above steps.

[0053] Furthermore, the specific contents of step 6 are:

[0054] Repeat steps 4 and 5 above to track, filter and differentiate the pressure signal in the flight environment simulation control system test in real time, and then feed it back to the main controller to achieve real-time control and adjustment of the flight environment simulation control system.

[0055] Implementation Effect

[0056] In order to verify the tracking filtering and differential extraction effects of the adaptive homogeneous differentiator algorithm proposed in the present invention under a given input signal and the control effect of the PID controller based on the differentiator algorithm in the high-altitude platform flight environment simulation control system, the following numerical simulation and system simulation are used for explanation. In the numerical simulation process, the given input signal is v(t)=sin(t)+cos(0.01t)+0.001*rand(1), where 0.001*rand(1) is a random noise sequence with an energy of 1. For the sliding mode differential differentiator algorithm with two inertial links subtracted, the parameters are adjusted by trial and error, and finally the two time constants are selected as λ1=0.01 and λ2=0.02 respectively; in the proposed adaptive homogeneous differentiator algorithm, the adjustable parameter m=1 / 3, and the α core parameter is adjusted according to the corresponding adaptive adjustment law. In the high-altitude platform flight environment simulation control system, the throttle lever is simulated to move suddenly during the 80s-160s process, resulting in a rapid change in the engine intake flow. The adjustable parameter m of the adaptive homogeneous differentiator algorithm is 1 / 2, and the three parameters of the PID controller are selected as: K p =15; K i =5.5; K d =0.6.

[0057] from Figure 3 , Figure 4 , Figure 5 , Figure 6 From the numerical simulation, it can be seen that for the transient process of input signal tracking and differential extraction, the proposed adaptive homogeneous differentiator algorithm has the advantages of fastness and high precision compared with the sliding mode differentiator algorithm of subtracting two inertial links. This is in line with the engineering characteristics of high requirements for transient performance in the transition state test of the flight environment simulation control system. When the signal changes suddenly, that is, corresponding to different operating conditions in the actual system, the proposed algorithm can achieve the characteristics of fast tracking and high differential accuracy. Compared with the differentiator algorithms commonly used in engineering, the adaptive homogeneous differentiator algorithm proposed in the present invention significantly improves the transient performance of tracking, filtering and differential extraction of a given signal under the condition of signal mutation. Based on Figure 7 It can be seen that for the flight environment simulation control system transition state test, the proposed PID control architecture and method based on adaptive homogeneous differentiator can greatly reduce the deviation value of the intake pressure, enable the system to quickly enter stable convergence, and improve the transient and steady-state control quality of the system.

[0058] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A PID control method for an intake environment simulation system based on an adaptive homogeneous differentiator, suitable for experiments of high-altitude simulation devices, including the use of an intake environment simulation control system, characterized in that: The steps include: S101: Based on the second-order series system, construct a homogeneous differentiator algorithm with variable structure characteristics and determine the core parameters of the homogeneous differentiator algorithm , where a homogeneous differentiator algorithm with variable structure characteristics is constructed based on the second-order series system and satisfies: , where x1 and x2 represent the two system states of the second-order series system, x1 is the filter output signal, and x2 is the differential output signal; is a symbolic function; v is the noisy sensor detection signal; p and q are two adjustable parameters, so that the corresponding differential equation satisfies the homogeneous equation, and P=1-m, q=1-2m, m>0 is an adjustable parameter; These are the two core parameters for adjusting the differentiator filtering and differential characteristics. For the fixed parameter differentiator algorithm, the homogeneous differentiator algorithm introduces variable structure characteristics and homogeneous characteristics, which can resist system disturbances during the state transfer process and has strong robustness. S102: According to the core parameters of the homogeneous differentiator algorithm And design an adaptive law based on error feedback, including the core parameters It is closely related to the transient characteristics and accuracy of the differentiator filtering and differential extraction, and constructs error feedback , so that the core parameters It can be adjusted adaptively according to the actual system error and meet the following requirements: in, is the initial value of the adaptive homogeneous differential parameter, the adjustable parameter It is the constraint relationship between the system parameters and the signal. is the error threshold; S103: The design of the adaptive law performs stability analysis based on the Lyapunov function to determine the adjustable parameters in the constraint relationship between the system parameters and the signal. The existence of includes, in order to obtain the constraint relationship between parameters and signals, the following Lyapunov function is designed for the designed adaptive homogeneous differentiator to conduct stability analysis, and the Lyapunov function is constructed and satisfies: ； The Lyapunov function satisfies radial infinity, and its derivative satisfies: ； To satisfy hour, ,in: is an arbitrary given parameter and is related to the input signal, so as long as , which satisfies: , ensuring the filtering and differentiation accuracy of the adaptive homogeneous differential algorithm; S104: inputting the pressure signal in the intake environment simulation control system into the designed adaptive homogeneous differentiator to obtain filtering and differential signals in real time; S105: comparing the filtered signal and the differential signal with the filtered and differential signals of a given input signal to determine an error, and determining a PID controller algorithm according to the error; S106: Repeat S104 and S105 to track, filter and differentiate the pressure signal in the flight environment simulation control system test in real time, and feed it back to the main controller to realize real-time control and adjustment of the flight environment simulation control system.

2. The PID control method for the intake environment simulation system based on the adaptive homogeneous differentiator according to claim 1 is characterized in that: According to the intake environment simulation system, a signal processing unit including an adaptive homogeneous differentiator and a state feedback PID control based on the signal processing result is provided.

3. The PID control method for the intake environment simulation system based on the adaptive homogeneous differentiator according to claim 1 is characterized in that: S106 is a given pressure signal in the transient state test of the flight environment simulation control system , to match the adaptive homogeneous differentiator for obtaining the filtering and differentiation signals of the given signal, buffering the given input pressure signal, and eliminating the random noise signal contained in the given signal; The tracking filtered signal of a given signal is denoted as r1, and the corresponding differential signal is denoted as r2; According to the filtering and differential signal of the real-time signal of the pressure sensor given in S104, the PID control algorithm satisfies: Where, Kp is the adjustable proportional gain; Ki is the adjustable integral gain; Kd is the adjustable differential gain; t is the current time; is the integrating variable.

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