Intelligent design system and method for parameters of carrier rocket controller

Abstract

The invention discloses a carrier rocket controller parameter intelligent design system and method, and belongs to the technical field of carrier rocket attitude control. Aiming at the defects of long manual design time, low efficiency, dependence on empirical values, high error rate and the like of a traditional attitude control system, an expert system is adopted for carrying out carrier rocket controller parameter intelligent design, and the purpose of intelligently designing control parameters can be quickly and effectively achieved. By using the method, the efficiency of manually adjusting attitude control design parameters can be greatly improved, and the attitude control design efficiency under the dense emission background is improved by more than 80%. According to the method, successful model design parameters can be rapidly used, or combined parameters can be adapted to the successful model design parameters, and even new design parameters adapted to design requirements can be rapidly and intelligently generated according to an autonomous parameter selection method, and the parameters have an effect of good stability, so that the method has a relatively high practical value for the design of an attitude control system of a carrier rocket in active service.

Description

Technical Field

[0001] The invention relates to a launch vehicle controller parameter intelligent design system and method, belonging to the technical field of launch vehicle attitude control. Background Art

[0002] The traditional attitude control design of the current launch vehicle models mainly adopts the manual parameter adjustment mode, and the process is mainly divided into two steps. First, the designer queries the previous launch missions and corresponding control parameters based on the payload and other information of the current launch and directly uses them. The simulation verifies whether they meet the design mission requirements. If feasible, they are directly used. This link takes a short time, but it relies too much on design experience, and the historical control parameter query is incomplete. Secondly, if the control parameters of previous successful missions cannot be directly applied, the designer needs to manually adjust the parameters based on similar mission control, iteratively adjust the correction network and dynamic and static amplification factors compatible with the rocket body characteristics of each characteristic second point, until all the margins of the system frequency domain design meet the index requirements. This design process is iterative, time-consuming, and inefficient. The traditional manual parameter adjustment mode has become increasingly unable to meet the mission design requirements of aerospace under the background of high-density launches. It is necessary to develop an intelligent parameter adjustment mode to reduce the attitude control design cycle, improve design efficiency, and fully use historical design parameters to improve system reliability. Summary of the invention

[0003] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art and provide a system and method for intelligent design of launch vehicle controller parameters, which can quickly and intelligently determine whether the control parameters of a successful model can be used and output without relying on human intervention, or intelligently adjust and design the control parameters of the launch vehicle to meet the stability requirements based on the successful control parameters.

[0004] The technical solution of the present invention is: in the first aspect, a launch vehicle controller parameter intelligent design system, comprising:

[0005] Parameter database module, including control parameter data of historical successful launch models, margin indicators and their envelopes;

[0006] Automatically use the historical module, according to the task requirements, use the whole set of control parameters of the successfully issued model in the parameter database module, and determine whether to use the control parameters of the historical model according to the margin comprehensive result; if the use is successful, the design is completed; otherwise, the free combination parameter selection module is called;

[0007] The free combination parameter selection module packs and cross-references the previous correction networks and static and dynamic amplification coefficients in the parameter database module, uses a recursive backtracking method to traverse, adds constraints and thresholds during the traversal, and adapts the control parameters of the attitude control design suitable for the mission requirements by formulating a comprehensive sorting rule for margin results; if the adaptation is successful, the design is completed; otherwise, the autonomous optimization parameter search module is called;

[0008] The autonomous optimization parameter-seeking module sets the initial correction network based on the simplex method, configures the initial simplex for the first and second stage rocket bodies and network characteristics respectively, selects the optimization target value according to the comprehensive margin in the frequency domain and performs algorithm evaluation, and obtains the design network that meets the requirements through iterative solution and selection of margins, thus completing the intelligent design of control parameters.

[0009] Furthermore, the parameter database module includes the model code, design stage, design channel, correction network, network switching time, static amplification factor, amplification factor switching time, corresponding level rigid-shaking comprehensive margin index data, and corresponding level rigid-shaking comprehensive margin envelope data of the control parameters of the successful launch history of the carrier attitude control design; the parameter database module data is packaged into an extractable form for free configuration of the free combination parameter selection and autonomous optimization parameter search module; the control parameters are serially numbered to facilitate the statistical call output of the free combination parameter selection module.

[0010] Furthermore, the automatic use of historical modules uses the entire set of control parameters of the successfully issued models in the parameter database module, takes the correction network, amplification factor value and switching time corresponding to the model as the overall design parameters, and completely traverses the design parameters of each historical issue and uses them; margin synthesis and statistics are performed according to each set of control parameters in the parameter database module, and a final margin synthesis report is output to determine whether the control parameters of the historical models can be used.

[0011] Furthermore, the free combination parameter selection module packages and cross-references all the previous correction networks and static and dynamic amplification factors in the parameter database module, traverses them using a recursive backtracking method, adds constraints and thresholds during the traversal, and formulates a comprehensive sorting rule for margin results. According to the comprehensive sorting of margins, it is screened whether the historical design parameters can be combined and referenced, and the historical design parameters that are adapted are sorted, and finally the control parameters of the attitude control design that meet the mission requirements are adapted.

[0012] Furthermore, the comprehensive sorting rules of the margin results include:

[0013] Count the sum of all comprehensive margins of the corresponding level, and give priority to the control parameters that maximize the sum of margins;

[0014] Secondly, select the control parameters with fewer network sets;

[0015] Again, select control parameters with less static gain variation.

[0016] Furthermore, all the comprehensive margins include: low-frequency amplitude margin, rigid body phase margin, high-frequency amplitude margin, sway phase margin, first-order elastic phase margin, and second-order and above elastic amplitude margin.

[0017] Furthermore, the autonomous optimization parameter search module finds a matching design correction network based on the simplex method, including:

[0018] Determine digital network configuration based on experience;

[0019] According to the comprehensive margin characteristics of the launch vehicle body, the initial network is determined and the initial simplex is constructed using the long coefficient method.

[0020] The optimization margin target value is determined and evaluated based on the frequency domain comprehensive margin. The margin is screened through iterative solution to find the optimal design correction network that meets the requirements.

[0021] Furthermore, the initial simplex includes 16 parameters corresponding to the 8th order selected initial network.

[0022] In a second aspect, a method for intelligent design of launch vehicle controller parameters includes:

[0023] Establish a database including control parameter data, margin indicators and their envelopes of historical successful launch models;

[0024] According to the task requirements, the whole set of control parameters of the previously successfully issued models in the database is used, and whether to use the control parameters of the previously issued models is determined according to the margin comprehensive results; if the use is successful, the design is completed; otherwise, continue;

[0025] The previous correction networks and static and dynamic magnification coefficients in the database are packaged and cross-referenced, and a recursive backtracking method is used to traverse, and constraints and thresholds are added during the traversal process. By formulating a comprehensive sorting rule for margin results, the control parameters of the attitude control design suitable for the mission requirements are adapted; if the adaptation is successful, the design is completed; otherwise, continue;

[0026] Based on the simplex method, the initial correction network is set, and the initial simplex is configured for the first and second stage rocket bodies and network characteristics respectively. The optimization target value is selected according to the comprehensive margin in the frequency domain and the algorithm is evaluated. The margin is screened through iterative solution, and the design network that meets the requirements is obtained, and the intelligent design of control parameters is completed.

[0027] Furthermore, the database includes the model code, design stage, design channel, correction network, network switching time, static amplification factor, amplification factor switching time, corresponding level rigid-shaking comprehensive margin index data, and corresponding level rigid-shaking comprehensive margin envelope data of the control parameters of the successful launch of the carrier attitude control design history; the database data is packaged into an extractable form for free configuration of the autonomous optimization parameter-seeking module; the control parameters are serially numbered to facilitate the output of the statistical call status of the autonomous optimization parameter-seeking module;

[0028] The entire set of control parameters of the successfully launched models in the database is used, and the correction network, amplification coefficient value and switching time corresponding to the model are used as the overall design parameters, and the design parameters of each historical launch are completely traversed and adopted; the margin is integrated and counted according to each set of control parameters in the database, and the final margin comprehensive report is output to determine whether the control parameters of the previously launched models can be used;

[0029] The previous correction networks and static and dynamic amplification factors in the database are packaged and cross-referenced, and a recursive backtracking method is used to traverse, and constraints and thresholds are added during the traversal process. By formulating a comprehensive sorting rule for margin results, it is screened whether the historical design parameters can be combined and referenced according to the comprehensive sorting of margins, and the historical design parameters that are adapted are sorted, and finally the control parameters of the attitude control design that meet the mission requirements are adapted;

[0030] The comprehensive sorting rules of the margin results include:

[0031] Count the sum of all comprehensive margins of the corresponding level, and give priority to the control parameters that maximize the sum of margins;

[0032] Secondly, select the control parameters with fewer network sets;

[0033] Again, select the control parameters with less static gain changes;

[0034] All the comprehensive margins include: low-frequency amplitude margin, rigid body phase margin, high-frequency amplitude margin, sway phase margin, first-order elastic phase margin, second-order and above elastic amplitude margin;

[0035] Based on the simplex method, a matching design correction network is found, including:

[0036] Determine digital network configuration based on experience;

[0037] According to the comprehensive margin characteristics of the launch vehicle body, the 8th-order initial network is determined, and the 16-dimensional space initial simplex corresponding to the parameters of the initial network is constructed using the long coefficient method.

[0038] The optimization margin target value is determined and evaluated based on the frequency domain comprehensive margin. The margin is screened through iterative solution to find the optimal design correction network that meets the requirements.

[0039] The advantages of the present invention compared with the prior art are:

[0040] 1) It is easy to miss the control parameters of the past when selecting the manual mode, and the local control parameters are incomplete, and even the historical parameters are inaccurate. The intelligent design method proposed by the present invention establishes a control parameter database of successful past models. The control parameters in the database are complete and can automatically match the historical control parameters according to the model status. It can quickly, intelligently, comprehensively and accurately call the use and verification of previous control parameters;

[0041] 2) The whole process of manually selecting the parameters for comprehensive design takes too long, from one day to one week. The intelligent design method proposed in the present invention is different from the conventional design. It adopts the automatic matching of the control parameters of the historical successful models based on the database, and quickly adapts the control parameters of the historical models in about one to ten minutes. The output is fully automatic, efficient, and the frequency domain comprehensive result display effect is good;

[0042] 3) Manual adjustment of the correction network is very inefficient, ranging from one week to several weeks, and the adjustment process iteration cycle is very long. It is necessary to manually check whether the network matches all the second points of the launch, and finally manually confirm the characteristics of the newly designed network. The intelligent design method proposed in the present invention adjusts the control parameters by introducing an improved simplex method without increasing the complexity of the launch vehicle attitude control algorithm, and intelligently designs the correction network. It takes a short time and can shorten the time required for control parameter adjustment by about 80%. Its efficiency is better than the method in which designers adjust parameters based on experience, and the newly generated correction network characteristics are output together for easy screening, which fully guarantees the stability of the attitude control system and enhances the adaptability characteristics of the correction network. It greatly improves the design efficiency, accuracy, intelligence and effectiveness of the attitude control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 It is a flow chart of control parameters of frequency domain integrated intelligent attitude control design of the present invention;

[0045] Figure 2 It is a flowchart of the free combination parameter selection method of the present invention;

[0046] Figure 3 It is a flowchart of the autonomous optimization parameter-seeking method of the present invention. DETAILED DESCRIPTION

[0047] In order to better understand the above technical scheme, the technical scheme of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical scheme of the present invention, rather than limitations on the technical scheme of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0048] The following is a further detailed description of a launch vehicle controller parameter intelligent design system and method provided by an embodiment of the present invention in conjunction with the accompanying drawings of the specification. The specific implementation method may include:

[0049] In a first aspect, a launch vehicle controller parameter intelligent design system includes:

[0050] Parameter database module, including control parameter data of historical successful launch models, margin indicators and their envelopes;

[0051] Automatically use the historical module, according to the task requirements, use the whole set of control parameters of the successfully used models in the database, and determine whether to use the control parameters of the historical models according to the margin comprehensive results; if the use is successful, the design is completed; otherwise, the free combination parameter selection module is called;

[0052] The free combination parameter selection module packs and cross-references the previous correction networks and static and dynamic amplification coefficients in the parameter database module, uses a recursive backtracking method to traverse, adds constraints and thresholds during the traversal process, reduces the amount of calculation during the traversal process, and quickly adapts the historical design parameters. By formulating a comprehensive sorting rule for margin results, the control parameters of the attitude control design suitable for the mission requirements are adapted; if the adaptation is successful, the design is completed; otherwise, the autonomous optimization parameter search module is called;

[0053] The autonomous optimization parameter-seeking module sets the initial correction network based on the simplex method, configures the initial simplex for the first and second stage rocket bodies and network characteristics respectively, selects the optimization target value according to the comprehensive margin in the frequency domain and performs algorithm evaluation, and obtains the design network that meets the requirements through iterative solution and selection of margins, thus completing the intelligent design of control parameters.

[0054] Second, as Figure 1 Based on the same inventive concept, the present invention also provides a method for intelligent design of launch vehicle controller parameters, including:

[0055] Establish a control parameter database of historical successful launch models and a database of margin indicators and envelopes;

[0056] According to the task requirements, the whole set of control parameters of the previously successfully issued models in the database is used, and whether to use the control parameters of the previously issued models is determined according to the margin comprehensive results; if the use is successful, the design is completed; otherwise, continue;

[0057] like Figure 2 , cross-reference the previous correction networks and static and dynamic amplification coefficients in the database, traverse them using a recursive backtracking method, add constraints and thresholds during the traversal process, reduce the amount of calculation during the traversal process, and quickly adapt the historical design parameters, and adapt the control parameters of the attitude control design that are suitable for the mission requirements by formulating a comprehensive sorting rule for margin results; if the adaptation is successful, the design is completed; otherwise, continue;

[0058] like Figure 3 Based on the simplex method, the initial correction network is set, and the initial simplex is configured for the first and second stage rocket bodies and network characteristics respectively. The optimization target value is selected according to the comprehensive margin in the frequency domain and the algorithm is evaluated. The margin is screened through iterative solution, and the design network that meets the requirements is obtained through autonomous optimization, and the intelligent design of control parameters is completed.

[0059] In the solution provided in the embodiment of the present invention, the design method specifically includes the following steps:

[0060] Establish a control parameter database of historical successful launch models.

[0061] According to the attitude control design reports of successful models launched in the past, the attitude control design parameters of the successfully launched models are organized into a set of parameter databases in the form of data. The design parameter database includes the model code, design stage, design channel, correction network, network switching time, static amplification factor, and amplification factor switching time in the control parameters of the successful launch of the carrier attitude control design history. Synchronously, according to the attitude control design specifications and design reports, a full set of corresponding level rigid-shaking comprehensive margin index databases and rigid-shaking comprehensive margin envelope databases are configured to determine whether the envelope and index criteria are met.

[0062] Furthermore, the database data is packaged into an extractable form for free configuration of the subsequent intelligent design system. The control parameters are numbered to facilitate the statistical call output in the subsequent intelligent design system.

[0063] In the solution provided in the embodiment of the present invention, according to the task requirements, the whole set of control parameters of the successful models in the database are used, and whether to use the control parameters of the successful models in the past is determined according to the comprehensive results of the margin.

[0064] The default call to the database of the corresponding model is determined according to the model of attitude control design. For example, for the attitude control design of CZ-2X model, the default query automatically uses the control parameters and design envelope of the CZ-2X historical model.

[0065] The model-corresponding correction network, amplification factor value and switching time are taken as the overall design parameters. The software automatically and completely traverses the historical design parameters and uses them.

[0066] Taking 50 sets of design parameters as an example, during the automatic traversal process, the operation information is added to the statistics of the parameter margin used for each launch, such as "The selection of the first-stage pitch CZ-2X YXX parameters has been completed, and the cumulative margin calculation has been 312 times, which has taken 1.1s." After traversing the design parameters, it is displayed "The traversal of the attitude control parameters of the launches has been completed, which took 37.0s, and the cumulative margin calculation has been 10167 times."

[0067] Margin synthesis and statistics are performed according to each set of control parameters in the database, and the final margin synthesis report is output to determine whether the control parameters of the historical model can be used.

[0068] The final margin synthesis report consists of two parts: one is the worst margin table under the design parameters of each historical successful model, and the other is the frequency domain synthesis result of each characteristic second point of this flight under each design parameter. If the margin synthesis result uses the design parameters of a certain flight and the margin meets the envelope requirements, that is, the parameters of this flight can be used, then "yes" will be displayed in the worst margin table corresponding to this set of parameters, otherwise "no" will be displayed.

[0069] The report content allows designers to intuitively see the historical design parameters that can be used, and also see the reasons why certain design parameters cannot be used.

[0070] If the adoption is successful, the design is complete; otherwise, continue;

[0071] According to the above intelligent design method, the intelligent design system runs the "automatically follow the history" module, which can output the frequency domain comprehensive margin statistics of the current arrow under various sets of control parameters within one minute, allowing designers to quickly screen and select the control parameters of the model that has been successfully launched in history. The whole process takes a short time and the effect is clear at a glance.

[0072] In the solution provided in the embodiment of the present invention, the previous correction networks and static and dynamic amplification factors in the database are packaged and cross-referenced, and are traversed using a recursive backtracking method. Constraints and thresholds are added during the traversal process to reduce the amount of calculation in the traversal process so as to quickly adapt the historical design parameters. By formulating comprehensive sorting rules for margin results, the control parameters of the attitude control design suitable for the mission requirements are adapted.

[0073] The overall idea of ​​this design method is "free combination of design parameters and free selection of switching time". The previous correction networks and static and dynamic amplification factors in the database are cross-referenced, and all comprehensive margins of the corresponding level are statistically analyzed, mainly including low-frequency amplitude margin, rigid body phase margin, high-frequency amplitude margin, sway phase margin, first-order elastic phase margin, and second-order and above elastic amplitude margin. After cross-referencing the parameters, if the margin meets the envelope data requirements, the cross-reference is considered successful, otherwise the cross-reference fails.

[0074] Considering the diversity of successful design parameters in history, there will be too many design parameters after free combination, and this design method has the disadvantage of facing a long debugging phase. For example, there are 30 different networks and 26 different static amplification factors in the 27 parameters of the first level of CZ-2X. There are about 23 characteristic points in the design, of which about 10 point parameters need to be switched. The number of situations in which a new set of control parameters can be formed by completely free combination is close to infinite. Therefore, we must find ways to reduce the number of combinations.

[0075] Combined with the design of the rocket body characteristics and attitude control design requirements, traversal constraints can be added, such as limiting the number of network sets to no more than 3, the network switching time to no more than 2, the number of static amplification coefficients to no more than 7, and the coefficient switching time to no more than 6. Even so, the number of parameter sets combined will be very large. Combined with the average time of 1.5s per set of parameters in the previous method, the amount of calculation is still unacceptably large.

[0076] When cross-referencing, a recursive backtracking method is used for traversal. The traversal process is as follows: traverse all combination parameters at each flight feature point of each stage of the launch vehicle. Adjacent flight second points need to find common parameters to be switchable. When recursively traversing each feature point parameter node, find the next switchable feature point node 1 that meets the switching conditions. During the traversal process, every time a node is visited, it is added to determine whether the constraint conditions are met (such as limiting the number of network and amplification factor switching times, and the amplification factor gradually decreasing, etc.). For nodes that do not meet the conditions, no recursion will be continued, but backtracking to the previous feature point node, and re-traversing the next switching feature point node 2. Repeat the iterative traversal in sequence until all nodes of the last second are traversed, and the traversal ends.

[0077] If the last second is accessed and the judgment condition is met, the parameter set consisting of all nodes on this path is recorded. The maximum feasible cross-reference parameter set threshold (such as 10,000) can be set to terminate the traversal. If the set threshold is exceeded, the constraint conditions are tightened and the traversal is re-performed to completely traverse all design parameters, which is conducive to reducing the amount of calculation and finding the best combination of historical design parameters.

[0078] Under each parameter of the successful cross-referenced history, the cumulative sum of all comprehensive margins of the corresponding level is calculated. Formulate the comprehensive sorting rules of margin results as follows:

[0079] First, the control parameters that maximize the total margin are selected; second, the control parameters with fewer network sets are selected; and third, the control parameters with less static gain changes are selected.

[0080] According to the comprehensive margin sorting, it is screened whether the historical design parameters can be combined and referenced, and the adaptive historical design parameters are intelligently sorted, and finally the control parameters of the attitude control design that meet the mission requirements are adapted; the adaptive control parameters and comprehensive margins are counted, and the control parameters that meet the stability requirements of different characteristic points of the rocket flight are displayed with serial numbers, which is convenient for designers to judge the bottleneck periods where historical parameters cannot be referenced.

[0081] When the design parameters can be quickly adapted, the operation interface response time is fast, and the adaptation can be successful within 200 seconds; when the design parameters are difficult to adapt, the operation interface response time is extended, the calculation efficiency and success rate are reduced, and the time for the second point is increased, about 500s to 800s. The main reason for the slow matching is that there are fewer successful design parameters that can be matched at some second points. The network and amplification factor constraints can be adjusted to increase the adaptation rate.

[0082] If the adaptation is successful, the design is completed, and the control parameters and the worst comprehensive margin of each frequency band that meet the design requirements are recorded, displayed and output; otherwise, the system continues and displays the reason why the parameter matching rate is 0, such as "there are no identical control parameters for the pitch feature points 80s and 90s (the parameters cannot be switched in this one second)".

[0083] According to the above intelligent design method, the intelligent design system runs the "free combination parameter selection" module, which can output the frequency domain synthesis results of the cross-referenced control parameters and the adapted control parameters in about ten minutes, and can increase the control parameter adaptation success rate to 80%.

[0084] In the solution provided by the embodiment of the present invention, based on the simplex method, an initial correction network is set, and initial simplexes are configured for the primary and secondary rocket bodies and network characteristics respectively. The optimization target value is selected according to the frequency domain comprehensive margin and the algorithm is evaluated. The margin is screened by iterative solution, and a matching design network is found autonomously.

[0085] For the above-mentioned attitude control design in the flight second segment that cannot adapt to the historical control parameters, this method can be used to intelligently design the parameters of the launch vehicle controller.

[0086] First, the digital network configuration is determined based on experience and is set to an 8th-order network, as follows:

[0087]

[0088] Among them, W 0 is the correction network function, ω i is the circular frequency of each order network link, ξ i Damping for each order network link.

[0089] In order to ensure that the margin of each frequency band achieves the optimal effect, combined with the comprehensive margin characteristics of the liquid carrier rocket body, the initial network is set according to the following principles: low frequency group 2nd order, high frequency group 4th order, sway frequency 2nd order. Among them, the frequency of the low frequency group is less than the sway frequency of the rocket body, the frequency of the high frequency group is greater than the first-order elastic frequency, the second-order network frequency at the sway frequency takes the average of the sway frequency, and the damping is between 0.2 and 0.8. The above selections are all used to meet the comprehensive requirements of rocket rigid body, sway, and elastic margin.

[0090] According to the initial network, the improved simplex method is used, and the long coefficient method is used to construct the 16-dimensional space initial simplex. The comprehensive margin under the system frequency domain analysis is evaluated, and the deviation between the comprehensive margin and the margin envelope data in the above database is established as the margin objective function. According to the margin requirements of the launch vehicle body, the penalty functions of each level of the launch vehicle body are determined, and the constraint function is determined for the damping coefficient of the correction network; and the corresponding weight coefficients are analyzed and determined. The weight coefficients of the objective function and the penalty function can be selected between 0.5 and 2 according to the importance of the margin, and the weight coefficient of the constraint function is set to be greater than 50; according to the weighted margin objective function, penalty function and constraint function, the optimization functions of each level of the launch vehicle body that meet the optimization requirements are established. Through the iterative solution of the improved simplex method, the uniform step size is not used for parameter search, the step size of the low frequency band is smaller than the step size of the high frequency band, and the screening margin continuously replaces the vertex of the initial simplex. Finally, the intelligent optimization is performed to obtain the design correction network that meets the requirements of the stability of the launch vehicle body characteristics. At this point, the intelligent design optimization of the controller parameters is successful.

[0091] According to the above intelligent design method, the intelligent design system runs the "autonomous optimization parameter search" module, which can intelligently find the control parameters that meet the design requirements in about a few minutes, and can improve the efficiency of attitude control design by more than 80%.

[0092] In summary, the intelligent design system and method of launch vehicle controller parameters of the present invention solves the long-standing problem of low efficiency and reliance on experience values ​​in manual parameter adjustment of rocket attitude control design by adopting a complete set of design methods of automatically following the success of previous launches, freely combining and selecting parameters, and autonomously optimizing and searching for parameters. Based on the existing successful control parameters of previous launches, the invention gives priority to following the control parameters of the entire set of successful models. When the entire set of control parameters of the successful models of previous launches cannot be followed, the control parameters of the previous models are packaged and freely combined and cross-referenced, and the recursive backtracking method is used to traverse, so as to adapt the control parameters of the successful models that meet the mission requirements, and quickly and comprehensively improve the adaptation and output of the design parameters of the historical successful launch models. When the design parameters of the successful models cannot be adapted, the control parameters that meet the requirements of the rocket body stability can be intelligently generated by the autonomous optimization and searching method. The system design efficiency is greatly improved, and the drawbacks of relying on experience and being prone to errors in manual attitude control design are avoided, thereby realizing process optimization under the background of high-density launches, and ensuring the reliability of the design parameters. The invention has great application value in the design of attitude control systems of active launch models.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0094] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A launch vehicle controller parameter intelligent design system, characterized in that: include: Parameter database module, including control parameter data of historical successful launch models, margin indicators and their envelopes; Automatically use the historical module, according to the task requirements, use the whole set of control parameters of the successfully issued model in the parameter database module, and determine whether to use the control parameters of the historical model according to the margin comprehensive result; if the use is successful, the design is completed; otherwise, the free combination parameter selection module is called; The free combination parameter selection module packs and cross-references the previous correction networks and static and dynamic amplification coefficients in the parameter database module, uses a recursive backtracking method to traverse, adds constraints and thresholds during the traversal, and adapts the control parameters of the attitude control design suitable for the mission requirements by formulating a comprehensive sorting rule for margin results; if the adaptation is successful, the design is completed; otherwise, the autonomous optimization parameter search module is called; The autonomous optimization parameter-seeking module sets the initial correction network based on the simplex method, configures the initial simplex for the first and second stage rocket bodies and network characteristics respectively, selects the optimization target value according to the comprehensive margin in the frequency domain and performs algorithm evaluation, and obtains the design network that meets the requirements through iterative solution and selection of margins, thus completing the intelligent design of control parameters.

2. The intelligent design system for launch vehicle controller parameters according to claim 1, characterized in that: The parameter database module includes the model code, design stage, design channel, correction network, network switching time, static amplification factor, amplification factor switching time, corresponding level rigid sloshing comprehensive margin index data, and corresponding level rigid sloshing comprehensive margin envelope data of the control parameters of the carrier attitude control design history successfully launched; The parameter database module data is packaged into an extractable form for free configuration of the free combination parameter selection and autonomous optimization parameter search module; the control parameters are numbered to facilitate the output of statistical call status of the free combination parameter selection module.

3. The intelligent design system for launch vehicle controller parameters according to claim 1, characterized in that: The automatic use of historical modules uses the entire set of control parameters of the successfully issued models in the parameter database module, takes the correction network, amplification factor value and switching time corresponding to the model as the overall design parameters, and completely traverses the design parameters of each historical issue and uses them; margin synthesis and statistics are performed based on each set of control parameters in the parameter database module, and a final margin synthesis report is output to determine whether the control parameters of the historical models can be used.

4. The intelligent design system for launch vehicle controller parameters according to claim 1, characterized in that: The free combination parameter selection module packages and cross-references all the previous correction networks and static and dynamic amplification factors in the parameter database module, traverses them using a recursive backtracking method, adds constraints and thresholds during the traversal, and formulates a comprehensive sorting rule for margin results. According to the comprehensive sorting of margins, it is screened whether the historical design parameters can be combined and referenced, and the historical design parameters that are adapted are sorted, and finally the control parameters of the attitude control design that meet the mission requirements are adapted.

5. The intelligent design system for launch vehicle controller parameters according to claim 4, characterized in that: The comprehensive sorting rules of the margin results include: Count the sum of all comprehensive margins of the corresponding level, and give priority to the control parameters that maximize the sum of margins; Secondly, select the control parameters with fewer network sets; Again, select control parameters with less static gain variation.

6. The intelligent design system for launch vehicle controller parameters according to claim 5, characterized in that: The comprehensive margins include low-frequency amplitude margin, rigid body phase margin, high-frequency amplitude margin, sway phase margin, first-order elastic phase margin, and second-order and above elastic amplitude margin.

7. The intelligent design system for launch vehicle controller parameters according to claim 1, characterized in that: The autonomous optimization parameter search module finds a matching design correction network based on the simplex method, including: Determine digital network configuration based on experience; According to the comprehensive margin characteristics of the launch vehicle body, the initial network is determined and the initial simplex is constructed using the long coefficient method. The optimization margin target value is determined and evaluated according to the comprehensive margin in the frequency domain. The margin is screened through iterative solution to find the optimal design correction network that meets the requirements.

8. The intelligent design system for launch vehicle controller parameters according to claim 7, characterized in that: The initial simplex includes 16 parameters corresponding to an 8th order selected initial network.

9. A method for intelligent design of launch vehicle controller parameters, characterized in that: include: Establish a database including control parameter data, margin indicators and their envelopes of historical successful launch models; According to the task requirements, the whole set of control parameters of the previously successfully issued models in the database is used, and whether to use the control parameters of the previously issued models is determined according to the margin comprehensive results; if the use is successful, the design is completed; otherwise, continue; The previous correction networks and static and dynamic magnification coefficients in the database are packaged and cross-referenced, and a recursive backtracking method is used to traverse, and constraints and thresholds are added during the traversal process. By formulating a comprehensive sorting rule for margin results, the control parameters of the attitude control design suitable for the mission requirements are adapted; if the adaptation is successful, the design is completed; otherwise, continue; Based on the simplex method, the initial correction network is set, and the initial simplex is configured for the first and second stage rocket bodies and network characteristics respectively. The optimization target value is selected according to the comprehensive margin in the frequency domain and the algorithm is evaluated. The margin is screened through iterative solution, and the design network that meets the requirements is obtained, and the intelligent design of control parameters is completed.

10. The intelligent design method for launch vehicle controller parameters according to claim 9, characterized in that: The database includes the model code, design stage, design channel, correction network, network switching time, static amplification factor, amplification factor switching time, corresponding level rigid sloshing bomb comprehensive margin index data, and corresponding level rigid sloshing bomb comprehensive margin envelope data in the control parameters of the carrier attitude control design history successfully launched; The database data is packaged into an extractable form for free configuration of the autonomous optimization parameter-seeking module; the control parameters are numbered to facilitate the output of the statistical call status of the autonomous optimization parameter-seeking module; The entire set of control parameters of the successfully launched models in the database is used, and the correction network, amplification coefficient value and switching time corresponding to the model are used as the overall design parameters, and the design parameters of each historical launch are completely traversed and adopted; the margin is integrated and counted according to each set of control parameters in the database, and the final margin comprehensive report is output to determine whether the control parameters of the previously launched models can be used; The previous correction networks and static and dynamic amplification factors in the database are packaged and cross-referenced, and a recursive backtracking method is used to traverse, and constraints and thresholds are added during the traversal process. By formulating a comprehensive sorting rule for margin results, it is screened whether the historical design parameters can be combined and referenced according to the comprehensive sorting of margins, and the historical design parameters that are adapted are sorted, and finally the control parameters of the attitude control design that meet the mission requirements are adapted; The comprehensive sorting rules of the margin results include: Count the sum of all comprehensive margins of the corresponding level, and give priority to the control parameters that maximize the sum of margins; Secondly, select the control parameters with fewer network sets; Again, select the control parameters with less static gain changes; All the comprehensive margins include: low-frequency amplitude margin, rigid body phase margin, high-frequency amplitude margin, sway phase margin, first-order elastic phase margin, second-order and above elastic amplitude margin; Based on the simplex method, a matching design correction network is found, including: Determine digital network configuration based on experience; According to the comprehensive margin characteristics of the launch vehicle body, the 8th-order initial network is determined, and the 16-dimensional space initial simplex corresponding to the parameters of the initial network is constructed using the long coefficient method. The optimization margin target value is determined and evaluated according to the comprehensive margin in the frequency domain. The margin is screened through iterative solution to find the optimal design correction network that meets the requirements.

Images