A solid engine shell dynamic loading adaptive control method and system

Abstract

The application discloses a kind of solid engine shell dynamic loading adaptive control method and system, comprising: for the combination of multi-cylinder combined hydraulic loading device and shell test piece, the element level model of dynamic loading device is established with mathematical analysis model;Uncertain factors including friction, leakage, pressure fluctuation are introduced in element level model to simulate real loading device, and then it is used as test platform, and the joint simulation interface of signal interaction between test platform and controller is established;Based on mathematical analysis model, the reference input signal is generated using the nonlinear sliding surface of error integral power function and exponential approach rate introduced, to limit error convergence time, establish reference model;The compensation signal is generated to eliminate the force output error between reference model and test platform using neural network sliding mode controller for the uncertain factors existing in test platform.The application can quickly track the predetermined output force trajectory, effectively reduce test cost and improve overall control performance.

Description

Technical Field

[0001] The invention belongs to the field of hydraulic system control simulation, and in particular relates to a method and system for adaptively controlling dynamic loading of a solid motor casing. Background Art

[0002] Solid rocket motors are the power source of missiles. Their dynamic characteristics are directly related to the weapon's flight performance, maneuverability, and high-speed penetration capability. They are the prerequisite and foundation for the weapon's combat capability and the core technology driving the upgrade of missile weapons. As the main load-bearing component of the engine, the composite casing must not only withstand the combustion chamber pressure exceeding 10MPa, but also the loads from the nozzle vector control force and various engine loads during launch and flight. With the continuous improvement of the combat capability requirements of solid rocket motors, the mechanical loads faced by the engine casing are becoming more complex. Therefore, dynamic loading test research simulating real-world conditions is necessary.

[0003] Although we now have a solid rocket motor structural strength combined loading test system that can conduct solid rocket motor structural strength tests and possesses conventional engine structural performance assessment conditions and conventional engine structural performance assessment and verification capabilities, the design of control algorithms has become a key breakthrough point in order to improve dynamic loading simulation capabilities. A carefully designed control algorithm can not only effectively handle the system's complex nonlinearities and uncertainties during dynamic loading, but also accurately predict and feedback control the system's response under various loading conditions. This can significantly enhance the functionality of the simulation verification and analysis software platform, optimize the dynamic loading simulation system, and improve the accuracy and effectiveness of loading device performance testing, thereby ensuring the comprehensiveness and reliability of test verification. Therefore, the innovative design of control algorithms is of considerable theoretical and practical importance in improving the overall technical level and perfecting solid rocket motor dynamic loading simulation technology. Summary of the Invention

[0004] This invention addresses these challenges by developing an adaptive control method for dynamic loading of a solid motor casing based on precise modeling of a hydraulic loading device. This method rapidly tracks a given output force trajectory, enabling highly accurate force control feedback and output verification, thus meeting the dynamic loading requirements of the solid motor casing under various operating conditions.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for adaptively controlling dynamic loading of a solid rocket motor casing, comprising the following steps:

[0007] For the combination of multi-cylinder hydraulic loading device and shell specimen, the component-level model and mathematical analytical model of the dynamic loading device are established;

[0008] Uncertain factors such as friction, leakage, and pressure fluctuations are introduced into the component-level model to simulate a real loading device, which is then used as a test platform. A co-simulation interface for signal interaction between the test platform and the control algorithm is established.

[0009] Based on a mathematical analytical model, a nonlinear sliding surface and an exponential convergence rate that introduce an error integral power function are used to generate a reference input signal to limit the error convergence time and establish a reference model.

[0010] A neural network sliding mode controller is used to generate compensation signals for the uncertainties existing in the test platform to eliminate the force output error between the reference model and the test platform.

[0011] As a further improvement of the present invention, the establishment of the component-level model and the mathematical analysis model of the dynamic loading device includes:

[0012] A component-level model was established based on the hydraulic loading device. A two-channel force system was used to load the solid rocket motor casing to apply a specified output force to the casing. The simulation structure of the loading device under study was then clarified, and the component-level model and mathematical analytical model of the dynamic loading device were obtained.

[0013] The force output components of the hydraulic loading device include a hydraulic pump, a hydraulic cylinder, a control valve, a load and pipelines.

[0014] As a further improvement of the present invention, a valve core with an irregularly shaped throttle hole is used to simulate a pressure reducing valve, and the key parameters of the valve core are determined including: flow area, delay characteristics;

[0015] The flow area of ​​the valve core at different openings Calculate according to the relevant data of the actual pressure reducing valve using the following formula:

[0016]

[0017] is the mass flow rate, is the hydraulic oil density corresponding to the pressure after the pressure reducing valve, is the pressure drop of the pressure reducing valve, is the bulk modulus of hydraulic oil, is the orifice flow coefficient, which is determined by the orifice shape of the pressure reducing valve;

[0018] The delay characteristic of the valve core is to approximate the valve core movement process of the pressure reducing valve as a second-order system response. After a given input, the valve core reaches the specified position after a short oscillation process. The transfer function is:

[0019]

[0020] in, are the damping ratio and response frequency of the pressure reducing valve, is the gain coefficient; in the experiment, a step signal is applied to the pressure reducing valve and the step response of the pressure reducing valve is observed. and Calculated by the following formula:

[0021]

[0022]

[0023] and are the adjustment time and overshoot of the valve to the step signal respectively.

[0024] As a further improvement of the present invention, the nonlinear sliding surface is determined by the following formula:

[0025]

[0026] in is the error between the output force of the reference model and the expected value, All are positive hyperparameters;

[0027] The convergence rate is determined by the following formula:

[0028]

[0029] Where, is the exponential approach rate, is the linear approach rate, is a symbolic function.

[0030] As a further improvement of the present invention, before establishing the reference model, the method includes:

[0031] The displacement, speed and output force of the hydraulic cylinder piston rod of the loading device are selected as the three state variables: , the loading device is analytically modeled by simulating the signal flow process, and the state space expression is:

[0032]

[0033] in are the damping coefficient, stiffness and mass after load equivalence; The calculation formula is:

[0034] When u>0

[0035]

[0036] When u<0

[0037]

[0038] The calculation formula is:

[0039]

[0040] in, is the flow area of ​​the pressure reducing valve, is the valve core displacement, is the gain coefficient between the input signal and the valve core displacement, is the flow area of ​​the reversing valve oil outlet, is the average density of the hydraulic oil before and after the valve, is the cross-sectional area of ​​the high-pressure chamber and the low-pressure chamber, is the elastic modulus of hydraulic oil, and is the orifice flow coefficient corresponding to the pressure reducing valve and the reversing valve, is the pump pressure, It is divided into the pressure of the chamber directly connected to the pump source, is the pressure of the other chamber, is the corresponding volume.

[0041] As a further improvement of the present invention, after the state space, the following control algorithm is used to complete the design of the reference model:

[0042] The input signal u needs to make the output converge within a finite time. Based on the nonlinear sliding surface of the PI controller, the power function of the error integral is introduced on the basis of the sliding surface design, and its expression is:

[0043]

[0044] in, is the error between the output force of the reference model and the expected value, ;

[0045] After entering the sliding mode, if the system state is far away from zero, the convergence time is mainly determined by the attractor If the system state is close to zero, the convergence time is determined by the sliding mode control law:

[0046] The exponential reaching law is used to ensure that s converges to 0 in a finite time, and the system enters the sliding mode:

[0047]

[0048] Where, is the exponential approach rate, is the linear approach rate, is a symbolic function.

[0049] System status point reached After that, from this state Converges to The time is:

[0050]

[0051] By reasonably selecting the values ​​of each parameter and converging within the required time, the expression of the reference control signal u for the pressure reducing valve is obtained as follows:

[0052]

[0053] in is the expected output force.

[0054] As a further improvement of the present invention, a neural network sliding mode controller is used to generate a compensation signal for the uncertain factors existing in the test platform to eliminate the force output error between the reference model and the test platform, including:

[0055] The errors between the high-pressure cavity and low-pressure cavity of the reference model and the test platform are used as the input of the neural network, and the radial basis function is used as the activation function of the hidden layer neurons. The adaptive rate of the neural network parameters is determined by Lyapunov's second method, and the uncertainty of the output force error derivative is obtained. The uncertainty is used as the output of the neural network. The output of the neural network is substituted into the exponential reaching law of the sliding mode control to obtain the compensation signal calculation formula, so as to achieve the purpose of eliminating the tracking error between the test platform and the reference model.

[0056] As a further improvement of the present invention, the neural network sliding mode controller is used to generate a compensation signal for the uncertain factors existing in the test platform to eliminate the force output error between the reference model and the test platform, specifically including:

[0057] Based on sliding mode control and combined with neural network adaptive control theory, a compensation signal is calculated to eliminate the error between the test platform and the reference model, so that the test platform is loaded according to the set trajectory of the reference model; including:

[0058] S1, clarify the system uncertainty; by analytically modeling the hydraulic loading device, the loading device state space with the hydraulic cylinder output force as the observation indicator is obtained; in the reference model, the output force change rate is calculated by the following formula:

[0059]

[0060] Incorporating the influence of uncertainty, the output force change rate of the test platform is rewritten as

[0061]

[0062] To compensate the signal, define the tracking error:

[0063]

[0064] Derivative of the tracking error:

[0065]

[0066] The uncertainty of the error F is written as:

[0067]

[0068] The input of the neural network is the pressure error between the high-pressure chamber and the low-pressure chamber between the test platform and the reference model, and the input is recorded as ; Theoretical optimal weight Adaptively update weights Instead, we get the neural network error F estimate:

[0069]

[0070] The error between the estimated value and the true value:

[0071]

[0072] is the activation function, The error between the estimated weights and the ideal weights for a neural network ， , is a high-order uncertainty with an upper bound, that is ;

[0073] S2, calculate the relevant parameters of the neural network; use the RBF neural network to approximate the uncertainty in S1, and the activation function of each hidden layer neuron is:

[0074]

[0075] In the formula c j and b j For the j The central node and radiation width of the hidden layer neurons are selected as fixed values;

[0076] To obtain the adaptive rate of neural network related parameters, first construct the integral sliding surface

[0077]

[0078] in is the error integral gain.

[0079] Design the Lyapunov function as:

[0080]

[0081] For the estimation of , by Lyapunov second method, the sufficient condition of system stability is that the derivative of V is negative, and the calculation formula is:

[0082]

[0083] Since the above formula is uncertain, in order to eliminate its influence, let its coefficient be equal to 0, and get At this time, the neural network related parameter adaptive rate is:

[0084]

[0085]

[0086]

[0087] S3, generate the final sliding mode control signal; obtain the neural network approximation value of the uncertain term F , take the exponential approach law to increase the adjustment rate of the system when the error is large:

[0088]

[0089] Wherein is the exponential approach rate, is a sign function, and is designed in the form of hyperbolic tangent function to reduce the chattering of the input signal:

[0090]

[0091] Is a normal number, representing the smoothing width.

[0092] The expression of the compensation control signal is:

[0093]

[0094] The compensation signal is superimposed with the reference signal , and is used as the input signal of the test platform pressure reducing valve.

[0095] As a further improvement of the present application, the center node and the radiation width adopt K-means clustering algorithm to determine the value, specifically including:

[0096] First, initialize each hidden layer neuron;

[0097] Each input vector is then classified into the class to which it belongs, which is the center point closest to it:

[0098]

[0099] Then update the center point:

[0100]

[0101] is the learning rate, and n is the number of iterations.

[0102] The radiation width calculation formula is:

[0103]

[0104] is the maximum distance between the center points, is the number of neurons.

[0105] In a second aspect, the present invention provides a solid rocket motor casing dynamic loading adaptive control system, comprising:

[0106] Model building module, used to build component-level model and mathematical analytical model of dynamic loading device for multi-cylinder combined hydraulic loading device and shell specimen combination;

[0107] A simulation module is used to introduce uncertainties such as friction, leakage, and pressure fluctuations into the component-level model to simulate a real loading device, thereby using it as a test platform and establishing a co-simulation interface for signal interaction between the test platform and the control algorithm;

[0108] A reference model establishment module is used to generate a reference input signal based on a mathematical analytical model by using a nonlinear sliding surface and an exponential approach rate that introduce an error integral power function to limit the error convergence time and establish a reference model;

[0109] The error elimination module is used to generate compensation signals for the uncertain factors existing in the test platform using a neural network sliding mode controller to eliminate the force output error between the reference model and the test platform.

[0110] The beneficial effects of the present invention are embodied in:

[0111] The present invention designs a method for adaptive control of dynamic loading of a solid engine casing, which mainly includes comprehensive modeling of a hydraulic dynamic loading device and the design of an adaptive control algorithm. The comprehensive modeling method combines component-level modeling and analytical modeling, and introduces error factors in the actual loading process into the component-level modeling, which serves as a test platform for the performance of the adaptive control algorithm, thereby improving the accuracy and predictability of the system model; the adaptive control algorithm adopts a model reference adaptive structure, introduces a nonlinear sliding surface on the basis of analytical modeling, establishes a reference model, generates a reference input signal, and then uses a neural network sliding mode controller to compensate for the error between the reference model and the test platform caused by uncertain factors, thereby improving the loading device's precise control of the output force. The invention can quickly track the predetermined output force trajectory, meet the dynamic loading requirements of the engine casing under complex working conditions, effectively reduce test costs and improve overall control performance. Obtain more detailed parameters: The reference model can obtain the performance characteristics and response behavior of each component in the system, provide a scientific basis for the design of the control algorithm, and improve the intuitiveness and explainability of the control strategy. It has the following specific advantages:

[0112] Strong flexibility: The reference model independently generates control signals and output forces, is not affected by the test platform, and can be combined with other control algorithms to enhance adaptability and scalability.

[0113] Reduce experimental costs: Parameter tuning can be performed in the reference model to generate simulation results and select the optimal parameter combination, significantly reducing the consumption of specimens and equipment and improving optimization efficiency and accuracy.

[0114] Loading synchronization requirements: In a dual-cylinder system, the expected output signal generated by the reference model can simultaneously control the two hydraulic cylinders to achieve master-slave control, effectively meeting the synchronization requirements during the loading process and ensuring coordinated operation under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0116] Figure 1 It is a schematic diagram of the overall design structure of model reference adaptive control;

[0117] Figure 2 This is a schematic structural diagram of a real loading device as referenced by the embodiments of the present invention;

[0118] Figure 3This is a schematic diagram of the pressure reducing valve modeling structure in AMEsim;

[0119] Figure 4 This is a schematic diagram of the AMEsim / Simulink joint simulation structure;

[0120] Figure 5 It is a schematic diagram of the simulation signal flow process;

[0121] Figure 6 is a reference model schematic;

[0122] Figure 7 It is a schematic diagram of the RBF neural network structure and related parameters used;

[0123] Figure 8 It is a structural diagram of the control algorithm implementation method. DETAILED DESCRIPTION

[0124] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0125] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0126] The first part of the present invention provides a comprehensive modeling method for a hydraulic dynamic loading device, which combines component-level modeling and analytical modeling techniques to improve the accuracy and feasibility of system modeling, including the following steps:

[0127] For the combination of multi-cylinder hydraulic loading device and shell specimen, the component-level model and mathematical analytical model of the dynamic loading device are established;

[0128] Uncertain factors such as friction, leakage, and pressure fluctuations are introduced into the component-level model to simulate a real loading device, which is then used as a test platform for the performance of the adaptive control algorithm. A co-simulation interface for signal interaction between the test platform and the control algorithm is established.

[0129] Based on a mathematical analytical model, a nonlinear sliding surface and an exponential convergence rate that introduce an error integral power function are used to generate a reference input signal to limit the error convergence time and establish a reference model.

[0130] A neural network sliding mode controller is used to generate compensation signals for the uncertainties existing in the test platform to eliminate the force output error between the reference model and the test platform.

[0131] The first part of the present invention proposes a comprehensive modeling method for a hydraulic dynamic loading device, the principle of which is as follows:

[0132] Combining component-level modeling with mathematical analytical models: Component-level models utilize professional hydraulic system simulation software (such as AMEsim) and a predefined hydraulic component design library (HCD library) to build a model of the actual loading device. This model is connected according to the actual system topology, including core components such as hydraulic pumps, hydraulic cylinders, control valves, loads, and piping, to truly reflect the physical layout and workflow of the actual equipment.

[0133] Mathematical analytical models: Based on the system's physical characteristics and operating principles, mathematical equations are established to describe the system's dynamic behavior. This model can be used to generate reference input signals to limit error convergence time and serve as the basis for control algorithm design and verification.

[0134] Introducing uncertainties: Uncertainties such as friction, leakage, and pressure fluctuations are introduced into the component-level model to simulate the complexity and nonlinear characteristics of real loading devices. This helps to more accurately evaluate system performance and the effectiveness of control algorithms.

[0135] Signal interaction and co-simulation: Establishing a signal interaction interface between the test platform and the control algorithm enables real-time data exchange between the test platform and the control system. This helps verify the performance of the control algorithm in a simulation environment and make necessary adjustments and optimizations.

[0136] Nonlinear sliding mode control and neural network compensation: A nonlinear sliding mode surface and exponential convergence rate are used to generate a reference input signal to limit the error convergence time. Simultaneously, a neural network sliding mode controller is used to generate compensation signals for uncertainties in the test platform to eliminate the force output error between the reference model and the test platform. This approach improves system robustness and control accuracy.

[0137] As an example, using an error-introduced component-level model as a test platform to simulate a real hydraulic device allows for the development and verification of control algorithms in a simulation environment, saving testing costs. Based on a mathematical analytical model, parameters that cannot be directly measured experimentally are fully utilized. A nonlinear sliding surface and exponential convergence rate that introduce an error integral power function are used to establish a reference model, generate a reference input signal, and limit the error convergence time. A neural network sliding mode controller is used to generate compensation signals for the uncertainties of the test platform, eliminating the force output error between the reference model and the test platform, ensuring that the test platform accurately outputs the loading force along the predetermined trajectory.

[0138] For component-level modeling, the professional hydraulic system simulation software AMEsim was used to build a realistic hydraulic loading device model using a predefined Hydraulic Component Design (HCD) library. During the modeling process, connections were made according to the topology of the actual loading device, ensuring that the simulation model reflects the physical layout and workflow of the actual equipment. Specifically, core components such as hydraulic pumps, hydraulic cylinders, control valves, loads, and piping were included. This intuitive and highly interactive component-level modeling approach enables real-time adjustment and optimization of system parameters, providing a reliable simulation testing environment for control algorithms. Component-level modeling enables multiple experiments and optimizations to be performed in a virtual environment, significantly reducing the cost and time of actual testing and improving development efficiency.

[0139] In analytical modeling, detailed mathematical models are established for key components in the hydraulic dynamic loading device, such as hydraulic cylinders and servo valves. This involves utilizing nonlinear dynamic equations, basic laws of fluid mechanics, and dynamic equations that consider the properties of compressible fluids. After obtaining the nonlinear dynamic equations for these key components, a state-space model of the force closed-loop control system is constructed. The state-space model describes the dynamic behavior of the system through state variables and can accurately reflect the system's topological structure and the coupling and interaction between its core components, making the system more controllable and predictable at the mathematical level. It serves as the basis for subsequent precise control strategy design and stability analysis.

[0140] The second part of the present invention provides a model reference adaptive control algorithm, which specifically includes: a reference model control algorithm and an adaptive feedback control algorithm. Its overall structural diagram is shown as follows Figure 1 Its implementation includes the following steps:

[0141] S1, based on the analytical modeling in the first part, according to the given desired output trajectory, uses the nonlinear sliding surface and exponential approach rate introduced by the error integral power function to generate a reference input signal to limit the error convergence time and complete the design of the reference model.

[0142] S2. In this application, all steps are completed in a simulation environment. In order to distinguish it from the reference model, the component-level modeling model in the first part is called a "test platform". Its function is to simulate the real loading environment and provide support for the design and verification of the control algorithm.

[0143] Furthermore, the role of the test platform of the present application is to simulate real hydraulic actuators, and the role of mathematical analytical modeling is to design control algorithms. Whether the control algorithm is effective is based on the output of the component-level modeling model.

[0144] Due to numerous uncertainties in the real-world loading process, such as the influence of hydraulic cylinder volume effects, valve deadband characteristics, and the time-varying nature of various soft parameters, a certain error in force output will occur between the test platform and the reference model when the same control signal is applied to the valve. Therefore, a neural network sliding mode controller was designed in MATLAB / Simulink to generate a compensation signal that is superimposed on the reference input signal in S1 and applied to the test platform. The errors between the high-pressure and low-pressure chambers of the reference model and the test platform are used as the neural network inputs, and radial basis functions are used as the activation functions of the hidden layer neurons. The adaptive rate of the neural network parameters is determined using Lyapunov's second method, and the uncertainty of the output force error derivative is obtained, which is used as the neural network output. Substituting the neural network output into the exponential reaching law of the sliding mode control yields a formula for calculating the compensation signal, thereby eliminating the tracking error between the test platform and the reference model.

[0145] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0146] Example 1

[0147] This embodiment refers to Figure 2 The component-level model of the real hydraulic loading device shown is established. Its application background is to use a two-channel force system to load the solid motor casing, with the aim of quickly and accurately applying a specified output force to the casing.

[0148] The purpose of this example is to clarify the simulation structure of the loading device under study and provide a foundation for the subsequent description of the control algorithm. Therefore, the generalized modeling process of other components is not described in detail. The pressure reducing valve, as the core component for controlling the force output of the loading device, has a critical impact on the simulation accuracy. However, the pressure reducing valve design in the AMEsim hydraulic component design library differs from the actual pressure reducing valve. To ensure that the characteristics of this key component are as consistent as possible with the actual valve, this example adopts the following method:

[0149] 1) Determination of basic valve parameters

[0150] The valve core with irregular shaped throttle hole in AMEsim hydraulic component design library is used to simulate the pressure reducing valve. Its structure is as follows Figure 3 As shown, the different opening According to the relevant data of the actual pressure reducing valve, it is determined by the following formula:

[0151]

[0152] is the mass flow rate, is the hydraulic oil density corresponding to the pressure after the pressure reducing valve, is the pressure drop of the pressure reducing valve, is the bulk modulus of hydraulic oil, is the orifice flow coefficient, which is determined by the orifice shape of the pressure reducing valve.

[0153] Its piston diameter With piston rod diameter The following formula should be satisfied:

[0154]

[0155] in is the maximum stroke of the valve core, It is the maximum volume of the pressure reducing valve chamber.

[0156] 2) Valve delay characteristics

[0157] Mechanical inertia causes delayed response of moving parts, dynamic sluggishness due to hydraulic oil flow and compression, buffering introduced by internal valve damping and spring effects, and electrical signal delays due to the inductance and resistance of the solenoid coil. These factors combine to create a time delay between the pressure reducing valve receiving the control signal and its actual response. The valve spool's motion can be roughly viewed as a second-order system. Given an input, the valve spool undergoes a brief oscillation before reaching the desired position. Its transfer function can be written as:

[0158]

[0159] in are the damping ratio and response frequency of the pressure reducing valve, is the gain coefficient.

[0160] The valve core with the irregularly shaped orifice mentioned in step 1) moves immediately with the input signal without delay, which is different from the action process of the actual valve and needs to be corrected.

[0161] right The impulse response function of the pressure reducing valve obtained by performing inverse Laplace transform is:

[0162]

[0163] In order to determine the damping ratio and response frequency of the pressure reducing valve, a step signal can be applied to the pressure reducing valve in the experiment to observe the step response of the pressure reducing valve. and Calculated by the following formula:

[0164]

[0165]

[0166] and are the adjustment time and overshoot of the valve to the step signal respectively.

[0167] After obtaining the damping ratio and response frequency of the valve, the second-order transfer function of formula (3) is substituted into the input signal to process it, thereby simulating the delay characteristics of the valve.

[0168] Since AMEsim has advantages in modeling hydraulic loading devices but is slightly insufficient in generating control signals, and MATLAB has powerful data processing capabilities, an AMEsim / Simulink joint simulation platform was built to couple the test platform with the controller designed in MATLAB. The overall structure is as follows: Figure 4 As shown in the figure, pressure sensors are installed at the oil inlet and outlet of the hydraulic cylinder, and a force sensor is installed at the connection between the hydraulic cylinder piston rod and the load. The sensor output is imported into the Simucosim interface. The controller in MATLAB generates control signals to control the operation of the hydraulic components in the AMEsim test platform, completing the design and verification of the control algorithm. This method can significantly improve the analysis and design efficiency of hydraulic loading devices.

[0169] Example 2

[0170] The purpose of this embodiment is to perform mathematical analytical modeling on the loading device and establish a reference model based on this model, which serves to provide a reference input signal for the test platform of Example 1 through the interface of the joint simulation platform.

[0171] Since the control goal of the loading device is to achieve closed-loop force control and focus on the change of system force, the displacement, speed and output force of the hydraulic cylinder piston rod of the loading device are selected as the three state variables, namely ,according to Figure 5 The simulation signal flow process shown is used to mathematically analyze the loading device in MATLAB / Simulink, and the expression is:

[0172]

[0173] in are the damping coefficient, stiffness and mass after load equivalence. The calculation formula is:

[0174] When u>0

[0175]

[0176] When u<0

[0177]

[0178] The calculation formula is:

[0179]

[0180] in, is the flow area of ​​the pressure reducing valve, is the valve core displacement, is the gain coefficient between the input signal and the valve core displacement, is the flow area of ​​the reversing valve oil outlet, is the average density of the hydraulic oil before and after the valve, is the cross-sectional area of ​​the high-pressure chamber and the low-pressure chamber, is the elastic modulus of hydraulic oil, and is the orifice flow coefficient corresponding to the pressure reducing valve and the reversing valve, is the pump pressure, It is divided into the pressure of the chamber directly connected to the pump source, is the pressure of the other chamber, is the corresponding volume.

[0181] After establishing the mathematical analytical model, the following control algorithm was designed on this basis to complete the design of the reference model:

[0182] Due to the inherent nonlinear characteristics, parameter uncertainty, and high requirements for response speed and accuracy of electro-hydraulic servo systems, conventional PID control often cannot meet dynamic performance requirements. However, sliding mode control has the advantages of robustness and fast response, and can effectively cope with parameter changes and external interference, providing strong anti-interference capabilities. Specifically, it can compensate for friction and dead zone in electro-hydraulic servo systems, as well as the time-varying nature of soft parameters. Therefore, sliding mode control is highly suitable for such systems.

[0183] For the dynamic loading test of the solid rocket motor case, it is required to generate a large output force within a specified time. Therefore, the input signal u needs to make the output converge within a finite time. The traditional sliding surface does not limit the error convergence time and cannot meet the requirements. In addition, since the expected output force is a constant value rather than a rapid change, the demand for differential control is not high. Therefore, this application designs a nonlinear sliding surface based on the PI controller concept and introduces the power function of the error integral on the basis of the traditional sliding surface design. Its expression is:

[0184]

[0185] in is the error between the reference model output force and the expected value, calculated by formula (7), .

[0186] This sliding mode control inherits the advantages of traditional sliding mode control. After the system enters the sliding mode, if the system state is far away from zero, the convergence time is mainly determined by the attractor. If the system state is close to zero, the convergence time is determined by the traditional sliding mode control law.

[0187] First, the exponential reaching law is used to ensure that s converges to 0 in a finite time, and the system enters the sliding mode:

[0188]

[0189] In the formula is the exponential approach rate, is the linear approach rate, is a symbolic function.

[0190] System status point reached After that, from this state Converges to The time is:

[0191]

[0192] By reasonably selecting the values ​​of each parameter, the system can converge within the required time, and the expression of the reference control signal u for the pressure reducing valve is obtained as follows:

[0193]

[0194] in is the expected output force.

[0195] The design of the reference model is now completed, and its overall schematic diagram is as follows: Figure 6 shown.

[0196] Example 3

[0197] There are many uncertain factors in the actual loading process, such as the influence of the hydraulic cylinder volume effect, the influence of the dead zone characteristics of the proportional valve, and the influence of the time-varying nature of various soft parameters. Therefore, if the same control signal shown in Equation (14) is applied to the valve, there will be a certain error in the force output between the test platform and the reference model that introduces uncertain factors.

[0198] To eliminate the force output error between the reference model and the test platform, this embodiment further describes the construction of a neural network adaptive control algorithm. This algorithm is based on an improvement in sliding mode control and incorporates neural network adaptive control theory. Its purpose is to calculate a compensation signal that superimposes the reference input signal to eliminate the error between the test platform and the reference model, ensuring that the test platform is loaded according to the trajectory set by the reference model. Specific implementation steps include:

[0199] S1, clarify the system uncertainty. Based on Example 2, by analytically modeling the hydraulic loading device, the loading device state space is obtained with the hydraulic cylinder output force as the observation indicator. In the reference model, the loading process is idealized, and its output force change rate is calculated by the following formula:

[0200]

[0201] Although there is uncertainty in the output force change rate during the actual loading process and it is difficult to obtain an accurate mathematical expression, the uncertainty does not affect the overall structure of the control equation. Therefore, referring to formula (15), the output force change rate of the test platform is rewritten by adding the influence of uncertainty as follows:

[0202]

[0203] In the formula To compensate the signal, define the tracking error:

[0204]

[0205] Derivative of the tracking error:

[0206]

[0207] The uncertainty F of the error can be written as:

[0208]

[0209] Although Simulink includes a built-in differentiation module that theoretically can directly calculate the derivative of the tracking error, this design still treats the derivative of the tracking error as an uncertain quantity and uses a neural network approximation. This is because the built-in differentiation module is susceptible to noise in co-simulation. Numerical differentiation amplifies high-frequency noise during the discretization process. Especially when the signal contains small high-frequency fluctuations, the differentiation module may introduce unnecessary oscillations, resulting in unstable simulation results. This is very disadvantageous in feedback control.

[0210] Since the pressure of the high-pressure chamber and the low-pressure chamber directly affects the change of the output force, the input of the neural network is the pressure error of the high-pressure chamber and the low-pressure chamber between the test platform and the reference model, and the input is recorded as The theoretical optimal weight Unknown, so the weights are updated adaptively Instead, we get the neural network error F estimate:

[0211]

[0212] The error between the estimated value and the true value:

[0213]

[0214] is the activation function, The error between the estimated weights and the ideal weights for a neural network ， , is a high-order uncertainty with an upper bound, that is .

[0215] S2, calculate the neural network related parameters. This control algorithm uses RBF neural network to approximate the uncertainty in S1. Its structure is as follows Figure 7 As shown, the activation function of each hidden layer neuron is:

[0216]

[0217] In the formula c j and b j For the j Generally speaking, the central node and radiation width of each hidden layer neuron can be selected as a certain value based on engineering experience.

[0218] As an optional solution, in order to enhance the adaptability of the neural network, this embodiment uses the K-means clustering algorithm to determine its value, and the steps are as follows:

[0219] First, initialize each hidden layer neuron, then classify each input vector, which belongs to the class of the nearest center point:

[0220]

[0221] Then update the center point:

[0222]

[0223] For learning rate, n is the number of iterations.

[0224] The radiation width calculation formula is:

[0225]

[0226] The maximum distance between the center points is The number of neurons.

[0227] In order to obtain the neural network related parameter adaptive rate, first construct the integral sliding surface

[0228]

[0229] The Lyapunov function is designed as:

[0230]

[0231] According to the second Lyapunov method, the sufficient condition for system stability is that the derivative of V is negative, and the calculation formula is:

[0232]

[0233] Since the above formula is uncertain, in order to eliminate its influence, let its coefficient equal to 0, and obtain , the neural network related parameter adaptive rate at this time is:

[0234]

[0235]

[0236]

[0237] S3, generate the final sliding mode control signal. The above obtains the neural network approximation value of the uncertain term F of formula (19) The last step of controller design is to obtain the compensation signal. Since the loading device has high requirements on dynamic performance, the exponential approach law is still taken to increase the adjustment rate of the system when the error is large.

[0238]

[0239] in is the exponential approach rate, is a sign function, designed as a hyperbolic tangent function to reduce the jitter of the input signal:

[0240]

[0241] In the formula Is a positive constant representing the smoothing width.

[0242] Substituting equation (26) into equation (32) yields the compensation control signal The expression is:

[0243]

[0244] The compensation signal With the reference input signal in Example 2 The superposition is used as the final input signal of the pressure reducing valve of the test platform to achieve the purpose of loading according to the expected trajectory. So far, the design of the model reference adaptive control algorithm has been completed. The overall implementation diagram is shown in the figure. Figure 8 shown.

[0245] To this end, the present invention also provides a solid motor casing dynamic loading adaptive control system, comprising:

[0246] Model building module, used to build component-level model and mathematical analytical model of dynamic loading device for multi-cylinder combined hydraulic loading device and shell specimen combination;

[0247] A simulation module is used to introduce uncertainties such as friction, leakage, and pressure fluctuations into the component-level model to simulate a real loading device, thereby using it as a test platform and establishing a co-simulation interface for signal interaction between the test platform and the control algorithm;

[0248] A reference model establishment module is used to generate a reference input signal based on a mathematical analytical model by using a nonlinear sliding surface and an exponential approach rate that introduce an error integral power function to limit the error convergence time and establish a reference model;

[0249] The error elimination module is used to generate compensation signals for the uncertain factors existing in the test platform using a neural network sliding mode controller to eliminate the force output error between the reference model and the test platform.

[0250] The third object of the embodiments of the present application is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the solid rocket engine case dynamic loading adaptive control method when executing the computer program.

[0251] The fourth object of the embodiments of the present application is to provide a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program implements the solid rocket engine case dynamic loading adaptive control method when executed by a processor.

[0252] The fifth object of the embodiments of the present application is to provide a computer program product, wherein the computer program product comprises computer instructions, and the computer instructions instruct a computer to execute the solid rocket engine case dynamic loading adaptive control method.

[0253] In summary, the present application has the following advantages:

[0254] By combining element-level modeling and analytical modeling, the dynamic behavior of the system can be more comprehensively described, improving the accuracy and feasibility of modeling. By performing multiple tests and optimizations in a virtual environment, the cost and time of actual tests are significantly reduced, and the development efficiency is improved. By introducing nonlinear sliding mode control and neural network compensation technology, the system's robustness and control accuracy are improved. The element-level modeling method is intuitive and interactive, allowing real-time adjustment and optimization of system parameters, providing a reliable simulation test environment for control algorithms.

[0255] In summary, the comprehensive modeling method of the present application significantly improves the modeling accuracy, feasibility, robustness, and control accuracy of the hydraulic dynamic loading device by combining element-level modeling and analytical modeling, and introducing nonlinear sliding mode control and neural network compensation technology, providing strong support for the design and verification of complex systems.

[0256] These computer program instructions can also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable memory produce a product including instruction means, which implements the functions specified in the flow Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0257] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowsheet or flowsheet blocks and / or functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowsheet or flowsheet blocks and / or functions specified in the flowchart or flowsheet block or blocks.

[0258] The present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, Read-Only Memory (ROM), programmable memory, and the like) embodying computer program instructions.

[0259] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and combinations of flows and / or blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processing machine, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowsheet or flowsheet blocks and / or functions specified in the flowchart or flowsheet block or blocks. Figure 1 one or more flowsheet or flowsheet blocks and / or functions specified in the flowchart or flowsheet block or blocks.

[0260] Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should belong to the protection scope of the present application.

[0261] Finally, it should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those of ordinary skill in the art should understand that the specific implementation manners of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the present application.

Claims

1. A method for adaptive control of dynamic loading of a solid rocket motor casing, characterized in that: The following steps are involved: For the combination of multi-cylinder hydraulic loading device and shell specimen, the component-level model and mathematical analytical model of the dynamic loading device are established; Uncertain factors such as friction, leakage, and pressure fluctuations are introduced into the component-level model to simulate a real loading device, which is then used as a test platform. A co-simulation interface for signal interaction between the test platform and the control algorithm is established. Based on a mathematical analytical model, a nonlinear sliding surface and an exponential convergence rate that introduce an error integral power function are used to generate a reference input signal to limit the error convergence time and establish a reference model. A neural network sliding mode controller is used to generate compensation signals for the uncertainties of the test platform to eliminate the force output error between the reference model and the test platform. The establishment of the component-level model and the mathematical analysis model of the dynamic loading device includes: A component-level model was established based on the hydraulic loading device. A two-channel force system was used to load the solid rocket motor casing to apply a specified output force to the casing. The simulation structure of the loading device under study was then clarified, and the component-level model and mathematical analytical model of the dynamic loading device were obtained. The force output elements of the hydraulic loading device include a hydraulic pump, a hydraulic cylinder, a control valve, a load, and a pipeline; the nonlinear sliding surface is determined by the following formula: in is the error between the output force of the reference model and the expected value, All are positive hyperparameters; The convergence rate is determined by the following formula: Where, is the exponential approach rate, is the linear approach rate, is a symbolic function; Before establishing the reference model, the following steps are included: The displacement, speed and output force of the hydraulic cylinder piston rod of the loading device are selected as the three state variables: , the loading device is analytically modeled by simulating the signal flow process, and the state space expression is: in are the damping coefficient, stiffness and mass after load equivalence; The calculation formula is: When u>0 When u<0 The calculation formula is: in, is the flow area of ​​the pressure reducing valve, is the valve core displacement, is the gain coefficient between the input signal and the valve core displacement, is the flow area of ​​the reversing valve oil outlet, is the average density of the hydraulic oil before and after the valve, is the cross-sectional area of ​​the high-pressure chamber and the low-pressure chamber, is the elastic modulus of hydraulic oil, and is the orifice flow coefficient corresponding to the pressure reducing valve and the reversing valve, is the pump pressure, It is divided into the pressure of the chamber directly connected to the pump source, is the pressure of the other chamber, is the corresponding volume.

2. The method for adaptively controlling dynamic loading of a solid rocket motor casing according to claim 1, characterized in that: A valve core with an irregularly shaped orifice is used to simulate a pressure reducing valve. The key parameters of the valve core include: flow area, delay characteristics; The flow area of ​​the valve core at different openings Calculate according to the relevant data of the actual pressure reducing valve using the following formula: is the mass flow rate, is the hydraulic oil density corresponding to the pressure after the pressure reducing valve, is the pressure drop of the pressure reducing valve, is the bulk modulus of hydraulic oil, is the orifice flow coefficient, which is determined by the orifice shape of the pressure reducing valve; The delay characteristic of the valve core is to approximate the valve core movement process of the pressure reducing valve as a second-order system response. After a given input, the valve core reaches the specified position after a short oscillation process. The transfer function is: in, are the damping ratio and response frequency of the pressure reducing valve, is the gain coefficient; in the experiment, a step signal is applied to the pressure reducing valve and the step response of the pressure reducing valve is observed. and Calculated by the following formula: and are the adjustment time and overshoot of the valve to the step signal respectively.

3. The method for adaptively controlling dynamic loading of a solid rocket motor casing according to claim 1, characterized in that: After the state space is described, the following control algorithm is used to complete the design of the reference model: The input signal u needs to make the output converge within a finite time. Based on the nonlinear sliding surface of the PI controller, the power function of the error integral is introduced on the basis of the sliding surface design, and its expression is: in, is the error between the output force of the reference model and the expected value, ; After entering the sliding mode, if the system state is far away from zero, the convergence time is mainly determined by the attractor If the system state is close to zero, the convergence time is determined by the sliding mode control law: The exponential reaching law is used to ensure that s converges to 0 in a finite time, and the system enters the sliding mode: Where, is the exponential approach rate, is the linear approach rate, is a symbolic function; System status point reached Then, from this current state Converges to The time is: By reasonably selecting the values ​​of each parameter and converging within the required time, the expression of the reference control signal u for the pressure reducing valve is obtained as follows: in is the expected output force.

4. The method for adaptively controlling dynamic loading of a solid rocket motor casing according to claim 1, characterized in that: A neural network sliding mode controller is used to generate compensation signals for the uncertainties of the test platform to eliminate the force output error between the reference model and the test platform, including: The errors between the high-pressure cavity and the low-pressure cavity of the reference model and the test platform are used as the input of the neural network, and the radial basis function is used as the activation function of the hidden layer neurons. The adaptive rate of the neural network parameters is determined by Lyapunov's second method, and the uncertainty of the output force error derivative is obtained. The uncertainty is used as the output of the neural network. The output of the neural network is substituted into the exponential reaching law of the sliding mode control to obtain the calculation formula of the compensation signal, so as to achieve the purpose of eliminating the tracking error between the test platform and the reference model.

5. The method for adaptively controlling dynamic loading of a solid rocket motor casing according to claim 4, characterized in that: The neural network sliding mode controller is used to generate a compensation signal for the uncertain factors existing in the test platform to eliminate the force output error between the reference model and the test platform, specifically including: Based on sliding mode control and combined with neural network adaptive control theory, a compensation signal is calculated to eliminate the error between the test platform and the reference model, so that the test platform is loaded according to the set trajectory of the reference model; including: S1, clarify the system uncertainty; by analytically modeling the hydraulic loading device, the loading device state space with the hydraulic cylinder output force as the observation indicator is obtained; in the reference model, the output force change rate is calculated by the following formula: Incorporating the influence of uncertainty, the output force change rate of the test platform is rewritten as To compensate the signal, define the tracking error: Derivative of the tracking error: The uncertainty of the error F is written as: The input of the neural network is the pressure error between the high-pressure chamber and the low-pressure chamber between the test platform and the reference model, and the input is recorded as ; Theoretical optimal weight Adaptively update weights Instead, we get the neural network error F estimate: The error between the estimated value and the true value: is the activation function, The error between the estimated weights and the ideal weights for a neural network ， , is a high-order uncertainty with an upper bound, that is ; S2, calculate the relevant parameters of the neural network; use the RBF neural network to approximate the uncertainty in S1, and the activation function of each hidden layer neuron is: In the formula c j and b j For the j The central node and radiation width of the hidden layer neurons are selected as fixed values; To obtain the adaptive rate of neural network related parameters, first construct the integral sliding surface in is the error integral gain; Design the Lyapunov function as: For The estimated value of , according to Lyapunov's second method, the sufficient condition for the stability of the system is that the derivative of V is negative definite, and the calculation formula is: Since in the above formula To eliminate its influence, let its coefficient be equal to 0, and we get , the adaptive rate of the neural network related parameters at this time is: S3, generate the final sliding mode control signal; obtain the neural network approximation value of the uncertainty term F , take the exponential reaching law to increase the adjustment rate of the system when the error is large: in is the exponential approach rate, is a sign function, designed as a hyperbolic tangent function to reduce the jitter of the input signal: Is a positive constant, representing the smoothing width; Obtain compensation control signal The expression is: The compensation signal With reference signal Superimposed and used as the input signal of the pressure reducing valve of the test platform.

6. The method for adaptively controlling dynamic loading of a solid rocket motor casing according to claim 5, characterized in that: The central node and the radiation width are determined using the K-means clustering algorithm, specifically including: First, initialize each hidden layer neuron; Each input vector is then classified into the class to which it belongs, which is the center point closest to it: Then update the center point: is the learning rate, n is the number of iterations; The radiation width calculation formula is: is the maximum distance between the center points, is the number of neurons.

7. A solid motor case dynamic loading adaptive control system, based on a solid motor case dynamic loading adaptive control method according to any one of claims 1 to 6, characterized in that: include: Model building module, used to build component-level model and mathematical analytical model of dynamic loading device for multi-cylinder combined hydraulic loading device and shell specimen combination; A simulation module is used to introduce uncertainties such as friction, leakage, and pressure fluctuations into the component-level model to simulate a real loading device, thereby using it as a test platform and establishing a co-simulation interface for signal interaction between the test platform and the control algorithm; A reference model establishment module is used to generate a reference input signal based on a mathematical analytical model by using a nonlinear sliding surface and an exponential approach rate that introduce an error integral power function to limit the error convergence time and establish a reference model; The error elimination module is used to generate compensation signals for the uncertain factors existing in the test platform using a neural network sliding mode controller to eliminate the force output error between the reference model and the test platform.

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