A gas flow adaptive control method for solid fuel injection engine based on variable rate integral gain scheduling

Abstract

The present invention provides a gas flow adaptive control method for a solid-fuel ramjet engine based on variable rate integral gain scheduling, including: S1, constructing a gas flow control internal ballistic model of the solid-fuel ramjet engine, and designing a gain scheduling main controller; S2, considering the suppression of negative regulation characteristics, designing a tracking instruction pre-filter; S3, considering the uncertainty of the dynamic response rate of the solid-fuel ramjet engine, designing a variable rate integral control strategy; S4, combining steps S1 and S3, constructing a gas flow integrated controller for the solid-fuel ramjet engine, and performing digital simulation of the gas flow regulation control of the solid-fuel ramjet engine with strong time-varying and non-minimum phase characteristics. In order to improve the performance of the gas flow control of the solid-fuel ramjet engine, the present invention improves the adaptability of the gas flow controller of the solid-fuel ramjet engine to the time-varying characteristics of parameters and the suppression ability of the negative regulation characteristics caused by the non-minimum phase on the basis of the gas flow regulation internal ballistic model, thereby ensuring the control accuracy of the solid-fuel ramjet engine and improving the robustness of the engine control.

Description

Technical Field

[0001] The present invention relates to the technical field of solid ramjet engine control, and in particular to a solid ramjet engine fuel gas flow adaptive control method based on variable rate integral gain scheduling. Background Art

[0002] High-performance and high-reliability control of gas flow in solid ramjet engines is a key technology in the design of ramjet-powered missile control systems. To achieve high-reliability and stable operation of the aircraft, the gas flow control system must not only be able to accurately track gas flow instructions under full envelope and fast time-varying conditions, but also be robust enough to continuously monitor relevant system parameters to cope with disturbances and constraints caused by changes in flight status (altitude, speed, angle of attack), changes in propellant combustion status, and environmental uncertainties.

[0003] However, the solid-fuel engine system is a highly uncertain system with a strong time-varying non-minimum phase. During the gas flow regulation process, the system dynamic regulation characteristics will change significantly with the free volume and pressure. The negative regulation caused by the non-minimum phase characteristics will seriously affect the system safety, and the model uncertainty will cause the performance of the designed control system to be seriously degraded. The current solid-fuel engine gas flow control strategy does not take into account the strong time-varying characteristics during its operation, lacks a strategy to suppress the negative regulation characteristics brought by the non-minimum phase system of the engine system, and lacks countermeasures for the mismatch between the controller and the control object caused by the model uncertainty characteristics. Summary of the invention

[0004] According to the technical problems raised above, a method for adaptive control of gas flow of a solid ramjet engine based on variable rate integral gain scheduling is provided. In order to improve the performance of gas flow control of a solid ramjet engine, the present invention improves the adaptability of the gas flow controller of the solid ramjet engine to the time-varying characteristics of parameters and the ability to suppress the negative regulation characteristics caused by the non-minimum phase on the basis of the gas flow regulation internal ballistic model, thereby ensuring the control accuracy of the solid ramjet engine and improving the robustness of the engine control.

[0005] The technical means adopted by the present invention are as follows:

[0006] A gas flow adaptive control method for a solid fuel injection engine based on variable rate integral gain scheduling, comprising:

[0007] S1. Construct the gas flow control interior ballistic model of the solid rocket engine and design the gain scheduling main controller;

[0008] S2. Considering the suppression of negative regulation characteristics, design the tracking command pre-filter;

[0009] S3. Considering the uncertainty of the dynamic response rate of the solid ramjet engine, a variable rate integral control strategy is designed;

[0010] S4. Combining steps S1 to S3, a comprehensive controller for the gas flow of a solid-fuel ramjet engine is constructed to perform digital simulation of the gas flow regulation control of a solid-fuel ramjet engine with strong time-varying and non-minimum phase characteristics.

[0011] Furthermore, step S1 specifically includes:

[0012] S11. Considering the engine operating flight altitude and Mach number conditions, a gas flow regulation model of the solid fuel injection engine within its operating range is established, and the gas flow regulation range is determined. The gas flow regulation model is as follows:

[0013]

[0014] In the formula, is the internal pressure of the gas generator, is the gas constant, is the gas temperature, is the free volume in the gas generator, is the propellant density, is the propellant combustion area, and n are the burning rate coefficient and pressure exponent coefficient of solid propellant, respectively, 0< n <1, The term is the burning rate temperature correction term, is the external operating temperature of the engine, is the nominal state temperature of the propellant, is the throat area, is the characteristic speed;

[0015] S12, the gas flow rate regulation model established in step S11 is adjusted according to the pressure and free volume of the gas generator of the solid rocket engine. The characteristic points are divided and the model is linearized through Laplace transformation to obtain the transfer function between the throat area of ​​the gas generator and the gas generator pressure. Transfer function between gas generator throat area and gas flow rate ,as follows:

[0016]

[0017]

[0018] In the formula, , , , , , , , All are greater than 0; Indicates the steady-state regulation increment of gas flow;

[0019] S13. Embed the PI controller into the linearized model of the gas generator at a given characteristic point In the example above, we get the given feature points. The transfer function from the gas generator pressure command to the response output is as follows:

[0020]

[0021] In the formula, is the pressure command, and is the gain of the adaptive PI controller;

[0022] S14, the damping ratio and natural frequency Introducing the transfer function of step S13, the transfer function of step S13 is rewritten as follows:

[0023]

[0024] In the formula, , , , represents an intermediate variable;

[0025] S15, perform inverse Laplace transform on the transfer function rewritten in step S14 to obtain the system unit step time domain response, as follows:

[0026]

[0027] In the formula, , , , , , , , An intermediate variable introduced to make the formula in step S15 more concise;

[0028] S16. According to the formula in step S15, the overshoot and rise time of the key response indicators of the system under the unit step time domain response are obtained as follows:

[0029]

[0030]

[0031] S17. Define auxiliary variables as follows:

[0032]

[0033] In the formula, the definition is the ratio of the real part of the zero point to the real part of the pole of the transfer function rewritten in step S13, then the overshoot and rise time Rewritten as:

[0034]

[0035]

[0036] S18. Determine the expected control index at the characteristic point according to the expected control performance index. and , refer to the feature point , and , Relationship, select satisfy , Constrained , The boundary is obtained, and the problem of solving the system control gain at this characteristic point is transformed into an optimization problem, as follows:

[0037]

[0038] In the formula, and for The upper and lower bounds of and for upper and lower bounds; is the overshoot constraint, To optimize the objective function value;

[0039] S19, based on the solution of step S18 , , according to the formula , , Inverse control gain and , check whether all feature points have been calculated, if so, save the control gain parameters under all feature points and go to step S20, otherwise select a new feature point and return to step S12;

[0040] S20, according to the calculation result of step S19, based on the calculated gain at different feature points and , an adaptive gain scheduling controller with pressure and free volume as scheduling variables is designed by table lookup interpolation method.

[0041] Further, step S2 specifically includes:

[0042] S21, according to the pressure and free volume Divide the characteristic points, linearize the gas generator model of the solid ramjet engine according to the transfer function in step S12 and the transfer function in step S13, and obtain the closed-loop transfer function from system pressure to gas flow, as follows:

[0043]

[0044] S22, sequentially select the system characteristic points of the division, and calculate the positive zero value of the system transfer function under the given characteristic points ( ), and define the state variables describing the negative modulation characteristics caused by the non-minimum phase of the solid ramjet engine as follows:

[0045]

[0046]

[0047] In the formula, is the relative negative regulation of the system, is the minimum negative regulation duration of the system under the given negative regulation constraint; and They are the maximum negative regulation increment and steady-state regulation increment of gas flow respectively;

[0048] S23, based on step S22, a non-minimum phase system negative regulation with a positive zero point is given and stabilization time The relationship between them is as follows:

[0049]

[0050]

[0051] Draw the fastest response time-minimum negative regulation relationship curve according to the above formula;

[0052] S24, select the negative adjustment of the inflection point position in the fastest response time-minimum negative adjustment relationship curve under the specified feature point As a system regulation limit protection constraint;

[0053] S25. Based on the tracking differentiator, the gas flow negative regulation suppression command filter is designed as follows:

[0054]

[0055] The designed gas flow negative regulation suppression command filter is essentially a command softening link, which can soften the gas generator tracking command; where, is the reference input, To arrange the transition process output, represents the filtering factor, The smaller the value, the stronger the effect. is the speed factor, The smaller the value, the stronger the softening effect on the command;

[0056] S26, combining the gas flow response system containing the closed-loop controller under given characteristics with the TD adaptive tracking differentiator, and using the negative adjustment limit obtained in step S24 To constrain, the speed factor of the TD adaptive tracking differentiator is adjusted from large to small. Until the negative adjustment of the unit step response of the closed-loop control system at this characteristic point is less than ;

[0057] S27, check whether all feature points have been calculated, if so, calculate the speed factor of all feature points If the parameters are saved, go to step S28, otherwise return to step S22;

[0058] S28. Based on the calculation result of step S27, a speed factor gain scheduling regulator of an adaptive TD adaptive tracking differentiator is designed with pressure and free volume as scheduling variables.

[0059] Furthermore, step S3 specifically includes:

[0060] S31. Define the variable rate integral function of the controller integral control gain as follows:

[0061]

[0062] In the formula, is the output of the variable rate integral function, which is the gain scheduling controller The gain weight of the control gain is about the free volume And the closed-loop system tracking error The function of represents the clipping function, , , is the regulating variable of the variable rate integral function;

[0063] S32. Design a nominal gas flow closed-loop control system model containing an adaptive gain scheduling controller as a reference model for training variable rate integrals, as follows:

[0064]

[0065] In the formula, is the nominal closed-loop system pressure response, is the control input of the nominal control system, Provides system pressure tracking instructions;

[0066] S33, construct a training model with parameter uncertainty and time lag characteristics to simulate system uncertainty and slow response characteristics to train the variable speed integral neural network. The uncertainty model is as follows:

[0067] In the formula, is the pressure response of the training model, Control system input for training model; The pressure delay for training the model;

[0068] S34, define the initialization parameters of the variable rate integral function, and design the system uncertainty bias term according to the uncertainty of the control system and , set simulation tracking instructions, calculate the response deviation of the nominal closed-loop reference model and the variable rate integral training model , and use the response deviation to adjust the parameters of the variable rate integral function using the gradient descent method , , , until the error converges to the specified value;

[0069] S35. Bind the variable rate integral parameters obtained by the convergence of the calculation into the variable rate integral function to obtain the final variable rate integral function.

[0070] Further, step S4 specifically includes:

[0071] S41, combining the command gain scheduling controller, command pre-filter, and variable rate integral controller mentioned above to construct a comprehensive controller for the gas flow of a solid fuel injection engine;

[0072] S42, obtaining the working state of the solid ramjet engine at the current moment, and obtaining the gas generator pressure command by interpolation according to the flight altitude, Mach number, angle of attack, sideslip angle and thrust demand;

[0073] S43, inputting the instruction obtained by interpolation into the instruction pre-filter, and obtaining a new instruction after filtering through a transition process;

[0074] S44, calculate the tracking error based on the filter instruction and the pressure feedback obtained by simulation, obtain the control input through the gain scheduling controller and the variable rate integral control strategy, and send the control input to the solid fuel injection engine gas flow integrated controller to obtain the simulation response.

[0075] Compared with the prior art, the present invention has the following advantages:

[0076] 1. The present invention provides a method for adaptively controlling the gas flow of a solid rocket engine based on variable rate integral gain scheduling. The method adopts an adaptive gain scheduling controller, which can adaptively adjust the controller control gain according to the external flight state and the internal working state of the gas generator, thereby improving the adaptive ability of the controller.

[0077] 2. The present invention provides a method for adaptively controlling the gas flow of a solid fuel injection engine based on variable rate integral gain scheduling. The designed adaptive differential tracker can actively pre-filter the system instructions and improve the system's negative regulation suppression capability.

[0078] 3. The present invention provides a method for adaptive control of the fuel gas flow of a solid fuel engine based on variable rate integral gain scheduling. The designed variable rate integral strategy can effectively cope with the strong uncertainty state of the engine, improve the dynamic response characteristics of the engine, especially improve the long response time of the regulation end and suppress system overshoot during the sudden change of command.

[0079] Based on the above reasons, the present invention can be widely promoted in the fields of solid ramjet engine control and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0081] Figure 1 This is a diagram of the architecture of the integrated control system for the gas flow of a solid fuel injection engine according to the present invention.

[0082] Figure 2 This is a diagram showing the gas flow control effect of a solid ramjet engine under time-varying conditions provided by the present invention.

[0083] Figure 3 This is a gas flow control effect diagram considering negative regulation suppression provided by the present invention.

[0084] Figure 4 This is a simulation effect diagram of the variable rate integral control strategy provided by the present invention. DETAILED DESCRIPTION

[0085] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0086] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0087] like Figure 1 The figure shows the architecture diagram of the variable rate integral gain scheduling adaptive solid ram engine gas flow control method used in the method of the present invention. The specific implementation steps of the method of the present invention are as follows:

[0088] S1. Construct the gas flow control interior ballistic model of the solid rocket engine and design the gain scheduling main controller;

[0089] S2. Considering the suppression of negative regulation characteristics, design the tracking command pre-filter;

[0090] S3. Considering the uncertainty of the dynamic response rate of the solid ramjet engine, a variable rate integral control strategy is designed;

[0091] S4. Combining steps S1 to S3, a comprehensive controller for the gas flow of a solid-fuel ramjet engine is constructed to perform digital simulation of the gas flow regulation control of a solid-fuel ramjet engine with strong time-varying and non-minimum phase characteristics.

[0092] In the specific implementation, it is considered as a preferred embodiment of the present invention, in step S1, a certain type of solid ram engine is in a cruising working state, , taking the working state at an altitude of 20 km as an example, it specifically includes:

[0093] S11. Considering the engine operating flight altitude and Mach number conditions, a gas flow regulation model of the solid fuel injection engine within its operating range is established, and the gas flow regulation range is determined. The gas flow regulation model is as follows:

[0094]

[0095] In the formula, is the internal pressure of the gas generator, is the gas constant, is the gas temperature, is the free volume in the gas generator, is the propellant density, is the propellant combustion area, and n are the burning rate coefficient and pressure exponent coefficient of solid propellant, respectively, 0< n <1, The term is the burning rate temperature correction term, is the external operating temperature of the engine, is the nominal state temperature of the propellant, is the throat area, is the characteristic speed;

[0096] S12, the gas flow rate regulation model established in step S11 is adjusted according to the pressure and free volume of the gas generator of the solid rocket engine. Characteristic points are divided, where the pressure range is 0.3 MPa – 3.5 MPa and the free volume range is 0.001 m 3 -0.15 m 3 The transfer function between the gas generator throat area and the gas generator pressure is obtained by linearizing the model through Laplace transformation. Transfer function between gas generator throat area and gas flow rate ,as follows:

[0097]

[0098]

[0099] In the formula, , , , , , , , All are greater than 0; Indicates the steady-state regulation increment of gas flow;

[0100] S13. Embed the PI controller into the linearized model of the gas generator at a given characteristic point In the example above, we get the given feature points. The transfer function from the gas generator pressure command to the response output is as follows:

[0101]

[0102] In the formula, is the pressure command, and is the gain of the adaptive PI controller;

[0103] S14, the damping ratio and natural frequency Introducing the transfer function of step S13, the transfer function of step S13 is rewritten as follows:

[0104]

[0105] In the formula, , , , represents an intermediate variable;

[0106] S15, perform inverse Laplace transform on the transfer function rewritten in step S14 to obtain the system unit step time domain response, as follows:

[0107]

[0108] In the formula, , , , , , , , An intermediate variable introduced to make the formula in step S15 more concise;

[0109] S16. According to the formula in step S15, the overshoot and rise time of the key response indicators of the system under the unit step time domain response are obtained as follows:

[0110]

[0111]

[0112] S17. Define auxiliary variables as follows:

[0113]

[0114] In the formula, the definition is the ratio of the real part of the zero point to the real part of the pole of the transfer function rewritten in step S13, then the overshoot and rise time Rewritten as:

[0115]

[0116]

[0117] S18. Determine the expected control index at the characteristic point according to the expected control performance index. and , refer to the feature point , and , Relationship, select satisfy , Constrained , The boundary is obtained, and the problem of solving the system control gain at this characteristic point is transformed into an optimization problem, as follows:

[0118]

[0119] In the formula, and for The upper and lower bounds of and for upper and lower bounds; is the overshoot constraint, To optimize the objective function value, in this embodiment, the allowable overshoot is set to 2%.

[0120] S19, based on the solution of step S18 , , according to the formula , , Inverse control gain and , check whether all feature points have been calculated, if so, save the control gain parameters under all feature points and go to step S20, otherwise select a new feature point and return to step S12;

[0121] S20, according to the calculation result of step S19, based on the calculated gain at different feature points and , an adaptive gain scheduling controller with pressure and free volume as scheduling variables is designed by table lookup interpolation method. The controller is integrated into the nonlinear model of solid ramjet engine system and numerical simulation is carried out to verify it. Figure 2The adaptive control effect of the gas generator under different pressures and free volumes is demonstrated.

[0122] In the specific implementation, it is considered as a preferred embodiment of the present invention, step S2 specifically includes:

[0123] S21, according to the pressure and free volume Divide the characteristic points, linearize the gas generator model of the solid ramjet engine according to the transfer function in step S12 and the transfer function in step S13, and obtain the closed-loop transfer function from system pressure to gas flow, as follows:

[0124]

[0125] S22, sequentially select the system characteristic points of the division, and calculate the positive zero value of the system transfer function under the given characteristic points ( ), and define the state variables describing the negative modulation characteristics caused by the non-minimum phase of the solid ramjet engine as follows:

[0126]

[0127]

[0128] In the formula, is the relative negative regulation of the system, is the minimum negative regulation duration of the system under the given negative regulation constraint; and They are the maximum negative regulation increment and steady-state regulation increment of gas flow respectively;

[0129] S23, based on step S22, a non-minimum phase system negative regulation with a positive zero point is given and stabilization time The relationship between them is as follows:

[0130]

[0131]

[0132] Draw the fastest response time-minimum negative regulation relationship curve according to the above formula;

[0133] S24, select the negative adjustment of the inflection point position (Pareto front position) in the fastest response time-minimum negative adjustment relationship curve under the specified feature point As a system regulation limit protection constraint;

[0134] S25. Based on the tracking differentiator, the gas flow negative regulation suppression command filter is designed as follows:

[0135]

[0136] The designed gas flow negative regulation suppression command filter is essentially a command softening link, which can soften the gas generator tracking command; where, is the reference input, To arrange the transition process output, represents the filtering factor, The smaller the value, the stronger the effect. is the speed factor, The smaller the value, the stronger the softening effect on the command;

[0137] S26, combining the gas flow response system containing the closed-loop controller under given characteristics with the TD adaptive tracking differentiator, and using the negative adjustment limit obtained in step S24 To constrain, the speed factor of the TD adaptive tracking differentiator is adjusted from large to small. Until the negative adjustment of the unit step response of the closed-loop control system at this characteristic point is less than ;

[0138] S27, check whether all feature points have been calculated, if so, calculate the speed factor of all feature points If the parameters are saved, go to step S28, otherwise return to step S22;

[0139] S28. According to the calculation result of step S27, a speed factor gain scheduling regulator of an adaptive TD adaptive tracking differentiator with pressure and free volume as scheduling variables is designed. The designed adaptive tracking differentiator is integrated into the system reference instruction as an additional module, and a numerical simulation evaluation is performed; Figure 3 As shown, the negative modulation suppression effect of the proposed adaptive tracking differentiator (TD) is demonstrated.

[0140] In the specific implementation, it is considered as a preferred embodiment of the present invention, step S3 specifically includes:

[0141] S31. Define the variable rate integral function of the controller integral control gain as follows:

[0142]

[0143] In the formula, is the output of the variable rate integral function, which is the gain scheduling controller The gain weight of the control gain is about the free volume And the closed-loop system tracking error The function of represents the clipping function, , , is the regulating variable of the variable rate integral function;

[0144] S32. Design a nominal gas flow closed-loop control system model containing an adaptive gain scheduling controller as a reference model for training variable rate integrals, as follows:

[0145]

[0146] In the formula, is the nominal closed-loop system pressure response, is the control input of the nominal control system, Provides system pressure tracking instructions;

[0147] S33, construct a training model with parameter uncertainty and time lag characteristics to simulate system uncertainty and slow response characteristics to train the variable speed integral neural network. The uncertainty model is as follows:

[0148] In the formula, is the pressure response of the training model, Control system input for training model; is the pressure delay of the training model; in this embodiment, the main difference between the closed-loop training system in step S33 and the closed-loop reference system in step S32 is that two additional terms are added, namely, the parameter perturbation terms and response delay term , represents the system delay time, which is used to quantify the uncertainty of the model. Since the uncertainty of the model is mainly reflected in the response rate, the important parameter term that affects the response rate is Multiply by the parameter correction factor ;

[0149] S34, define the initialization parameters of the variable rate integral function, and design the system uncertainty bias term according to the uncertainty of the control system and , set simulation tracking instructions, calculate the response deviation of the nominal closed-loop reference model and the variable rate integral training model , and use the response deviation to adjust the parameters of the variable rate integral function using the gradient descent method , , , until the error converges to the specified value;

[0150] S35, binding the variable rate integral parameter obtained by the calculation convergence into the variable rate integral function to obtain the final variable rate integral function. Figure 4 As shown, this is the simulation effect diagram of the variable rate integral control strategy.

[0151] In the specific implementation, it is considered as a preferred embodiment of the present invention, step S4 specifically includes:

[0152] S41, combining the command gain scheduling controller, command pre-filter, and variable rate integral controller mentioned above to construct a comprehensive controller for the gas flow of a solid fuel injection engine;

[0153] S42, obtaining the working state of the solid ramjet engine at the current moment, and obtaining the gas generator pressure command by interpolation according to the flight altitude, Mach number, angle of attack, sideslip angle and thrust demand;

[0154] S43, inputting the instruction obtained by interpolation into the instruction pre-filter, and obtaining a new instruction after filtering through a transition process;

[0155] S44, calculate the tracking error based on the filter instruction and the pressure feedback obtained by simulation, obtain the control input through the gain scheduling controller and the variable rate integral control strategy, and send the control input to the solid fuel injection engine gas flow integrated controller to obtain the simulation response.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive control of gas flow of solid fuel injection engine based on variable rate integral gain scheduling, characterized in that: include: S1. Construct the gas flow control interior ballistic model of the solid rocket engine and design the gain scheduling main controller; Step S1 specifically includes: S11. Considering the engine operating flight altitude and Mach number conditions, a gas flow regulation model of the solid fuel injection engine within its operating range is established, and the gas flow regulation range is determined. The gas flow regulation model is as follows: In the formula, is the internal pressure of the gas generator, is the gas constant, is the gas temperature, is the free volume in the gas generator, is the propellant density, is the propellant combustion area, and n are the burning rate coefficient and pressure exponent coefficient of solid propellant, respectively, 0< n <1, The term is the burning rate temperature correction term, is the external operating temperature of the engine, is the nominal state temperature of the propellant, is the throat area, is the characteristic speed; S12, the gas flow rate regulation model established in step S11 is adjusted according to the pressure and free volume of the gas generator of the solid rocket engine. The characteristic points are divided and the model is linearized through Laplace transformation to obtain the transfer function between the throat area of ​​the gas generator and the gas generator pressure. Transfer function between gas generator throat area and gas flow rate ,as follows: In the formula, , , , , , , , All are greater than 0; Indicates the steady-state regulation increment of gas flow; S13. Embed the PI controller into the linearized model of the gas generator at a given characteristic point In the example above, we get the given feature points. The transfer function from the gas generator pressure command to the response output is as follows: In the formula, is the pressure command, and is the gain of the adaptive PI controller; S14, the damping ratio and natural frequency Introducing the transfer function of step S13, the transfer function of step S13 is rewritten as follows: In the formula, , , , represents an intermediate variable; S15, perform inverse Laplace transform on the transfer function rewritten in step S14 to obtain the system unit step time domain response, as follows: In the formula, , , , , , , , An intermediate variable introduced to make the formula in step S15 more concise; S16. According to the formula in step S15, the overshoot and rise time of the key response indicators of the system under the unit step time domain response are obtained as follows: S17. Define auxiliary variables as follows: In the formula, the definition is the ratio of the real part of the zero point to the real part of the pole of the transfer function rewritten in step S13, then the overshoot and rise time Rewritten as: S18. Determine the expected control index at the characteristic point according to the expected control performance index. and , refer to the feature point , and , Relationship, select satisfy , Constrained , The boundary is obtained, and the problem of solving the system control gain at this characteristic point is transformed into an optimization problem, as follows: In the formula, and for The upper and lower bounds of and for upper and lower bounds; is the overshoot constraint, To optimize the objective function value; S19, based on the solution of step S18 , , according to the formula , , Inverse control gain and , check whether all feature points have been calculated, if so, save the control gain parameters under all feature points and go to step S20, otherwise select a new feature point and return to step S12; S20, according to the calculation result of step S19, based on the calculated gain at different feature points and , an adaptive gain scheduling controller with pressure and free volume as scheduling variables is designed by table lookup interpolation method; S2. Considering the suppression of negative regulation characteristics, design the tracking command pre-filter; S3. Considering the uncertainty of the dynamic response rate of the solid ramjet engine, a variable rate integral control strategy is designed; S4. Combining steps S1 to S3, a comprehensive controller for the gas flow of a solid-fuel ramjet engine is constructed to perform digital simulation of the gas flow regulation control of a solid-fuel ramjet engine with strong time-varying and non-minimum phase characteristics.

2. According to claim 1, a method for adaptively controlling the gas flow of a solid ramjet engine based on variable rate integral gain scheduling is characterized in that: Step S2 specifically includes: S21, according to the pressure and free volume Divide the characteristic points, linearize the gas generator model of the solid ramjet engine according to the transfer function in step S12 and the transfer function in step S13, and obtain the closed-loop transfer function from system pressure to gas flow, as follows: S22, sequentially select the system characteristic points of the division, and calculate the positive zero value of the system transfer function under the given characteristic points, , and define the state variables describing the negative adjustment characteristics caused by the non-minimum phase of the solid ramjet engine as follows: In the formula, is the relative negative regulation of the system, is the minimum negative regulation duration of the system under the given negative regulation constraint; and They are the maximum negative regulation increment and steady-state regulation increment of gas flow respectively; S23, based on step S22, a non-minimum phase system negative regulation with a positive zero point is given and stabilization time The relationship between them is as follows: Draw the fastest response time-minimum negative regulation relationship curve according to the above formula; S24, select the negative adjustment of the inflection point position in the fastest response time-minimum negative adjustment relationship curve under the specified feature point As a system regulation limit protection constraint; S25. Based on the tracking differentiator, the gas flow negative regulation suppression command filter is designed as follows: The designed gas flow negative regulation suppression command filter is essentially a command softening link, which can soften the gas generator tracking command; where, is the reference input, To arrange the transition process output, represents the filtering factor, The smaller the value, the stronger the effect. is the speed factor, The smaller the value, the stronger the softening effect on the command; S26, combining the gas flow response system containing the closed-loop controller under given characteristics with the TD adaptive tracking differentiator, and using the negative adjustment limit obtained in step S24 To constrain, the speed factor of the TD adaptive tracking differentiator is adjusted from large to small. Until the negative adjustment of the unit step response of the closed-loop control system at this characteristic point is less than ; S27, check whether all feature points have been calculated, if so, calculate the speed factor of all feature points If the parameters are saved, go to step S28, otherwise return to step S22; S28. Based on the calculation result of step S27, a speed factor gain scheduling regulator of an adaptive TD adaptive tracking differentiator is designed with pressure and free volume as scheduling variables.

3. The method for adaptively controlling the gas flow of a solid fuel injection engine based on variable rate integral gain scheduling according to claim 1 is characterized in that: Step S3 specifically includes: S31. Define the variable rate integral function of the controller integral control gain as follows: In the formula, is the output of the variable rate integral function, which is the output of the gain scheduling controller. The gain weight of the control gain is about the free volume And the closed-loop system tracking error The function of represents the clipping function, , , is the regulating variable of the variable rate integral function; S32. Design a nominal gas flow closed-loop control system model containing an adaptive gain scheduling controller as a reference model for training variable rate integrals, as follows: In the formula, is the nominal closed-loop system pressure response, is the control input of the nominal control system, Provides system pressure tracking instructions; S33, construct a training model with parameter uncertainty and time lag characteristics to simulate system uncertainty and slow response characteristics to train the variable speed integral neural network. The uncertainty model is as follows: In the formula, is the pressure response of the training model, Control system input for training model; The pressure delay for training the model; S34, define the initialization parameters of the variable rate integral function, and design the system uncertainty bias term according to the uncertainty of the control system and , set simulation tracking instructions, calculate the response deviation of the nominal closed-loop reference model and the variable rate integral training model , and use the response deviation to adjust the parameters of the variable rate integral function using the gradient descent method , , , until the error converges to the specified value; S35. Bind the variable rate integral parameters obtained by the convergence of the calculation into the variable rate integral function to obtain the final variable rate integral function.

4. The method for adaptively controlling the gas flow of a solid ramjet engine based on variable rate integral gain scheduling according to claim 1 is characterized in that: Step S4 specifically includes: S41, combining the gain scheduling main controller, tracking instruction pre-filter, and variable rate integral control strategy mentioned above to construct a comprehensive controller for the gas flow of the solid fuel injection engine; S42, obtaining the working state of the solid ramjet engine at the current moment, and obtaining the gas generator pressure command by interpolation according to the flight altitude, Mach number, angle of attack, sideslip angle and thrust demand; S43, inputting the instruction obtained by interpolation into the instruction pre-filter, and obtaining a new instruction after filtering through a transition process; S44, calculate the tracking error based on the filter instruction and the pressure feedback obtained by simulation, obtain the control input through the gain scheduling controller and the variable rate integral control strategy, and send the control input to the solid fuel injection engine gas flow integrated controller to obtain the simulation response.

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