Nghiên cứu xây dựng thuật toán nâng cao chất lượng xử lý tín hiệu kênh cao cho một lớp tên lửa hành trình đối hải trên cơ sở lý thuyết điều khiển hiện đại tt tiếng anh

MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENSE ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY PHAM DUC THOA RESEARCH ON THE CONSTRUCTION OF AN ALGORITHM FOR IMPROVING THE QUALITY OF THE PROCESS OF HEIGHT MEASUREMENT SIGNALS FOR A CLASS OF MARINE CRUISE MISSILES BASED ON THE MODERN CONTROL THEORY Major: Control Engineering and Automation Code : 52 02 16 SUMMARY OF TECHNICAL DOCTORAL DISSERTATION HA NOI – 2019 LIST OF PUBLISHED SCIENTIFIC WORKS The thesis was completed at ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY Supervisors: Dr Nguyen Quang Vinh 2 Dr Nguyen Xuan Can Review 1: Assoc Prof Dr Pham Trung Dung Military Technical Academy Review 2: Prof Dr Nguyen Doan Phuoc Hanoi University of Science and Technology Review 3: Assoc Prof Dr Tran Duc Thuan Academy of Military Science and Technology The dissertation was defended in front of the Doctoral Evaluating Committee at Academy level held at Academy of Military Science and Technology at …/…, 2019 More information on the dissertation can found from: - Academy of Military Science and Technology - National Library Pham Duc Thoa, Nguyen Quang Vinh, Nguyen Xuan Can, (2016), ”Building an algorithm for information processing in the compound altimeter of a flying vehicle”, Journal of Military Science and Technology, Academy of military science and technology, (9/2016) p.116122 Pham Duc Thoa, Nguyen Quang Vinh, Nguyen Xuan Can, Tran Ngoc Huong, (2018), “Application of the self-organnized algorithm for improving the signal processing quality of the linked high measuaring system”, Confference: “Apply high technology into practice”, Journal of Military Science and Technology, Academy of military science and technology, (8/2018) p.382-390 Nguyen Quang Vinh, Pham Duc Thoa, (2018), “Improving high quality in combination processing the high measurement signals”, The 7th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA2018); 29-30 November Pham Duc Thoa, To Ba Thanh, Nguyen Quang Vinh, Bui Minh Tuan, (2019), “The construction of a self-organizing algorithm for choosing a model of extrapolation in the combined process of signal of the height measurement”, Journal of Military Science and Technology, Academy of military science and technology, (3/2019), p269-278 Pham Duc Thoa, Nguyen Quang Vinh, Tran Ngoc Huong, (2019), “Evaluating the influence of the observability level to the exactness of processing signals in the combination of the inertia height meter and the radio height meter”, Journal of Military Science and Technology, Academy of military science and technology, (4/2019), p.73-80 PREFACE The necessity of the dissertation In terms of current combat operations, Electronic combat systems have superior features such as high combat capability both in mobility, the ability to suppress the operation of the system to lead the enemy in the form of different types of noise On the lead of many modern cruise missiles equipped with inertial navigation system (INS), due to large cumulative errors, from there, this error will cause a large error in the control Due to the characteristics of cruise missiles with the condition of low orbit in many altitude ranges, long flight times, under the influence of different types of noise, changing some special parameters corresponding to each combination of high measurement Resulting in high quality signal processing when combining each high measuring set with inertial height measuring kit at each flight condition is different The dissertation “Research on the construction of an algorithm for improving the quality of the process of height measurement signals for a class of marine cruise missiles based on the modern control theory” studying the solution of combining high-measuring set and signal processing algorithm to optimize the structure of the high-measurement combination, ensuring that the height information of cruise missiles is continuous and accurate; Construction of an extrapolation model in the combined process of height measurement by using a self-organization algorithm Research targets for the dissertation Applying modern control theory on the basis of self-organizing algorithm (SOA) and standard of observing the observed level of variables in the state space to build intelligent high-quality measurement, improve quality in the combination of high measurement signals Some main contents of the dissertation - Construction of an algorithm for the combined process of height measurement signals and choice of the structure of the combined height meter based on the evaluation of the observably level - Construction of an extrapolation model in the combined process of height measurement by using a self-organization algorithm with the condition of an in consonant observably level of status variable Object and scope of the dissertation research Modern high-altitude measuring system in the high-channel control system of a class of maritime cruise missiles Approaches of study Combining method of theoretical research and using simulation techniques to test and evaluate algorithms Scientific and practical benefits of this project Use the standard of observable level and SOA to build an extrapolation model to optimize signal processing in high measurement combination Overcoming the limitations that the Kalman filter cannot solve during processing combined with high measurement signals The results of the thesis can be used for designing and improving the control system-stabilizing the height for a class of cruise missiles, adding methodologies and knowledge to serve the training and teaching activities Teaching and researching in research institutes, Academies, Schools in the Army The layout of the dissertation The whole thesis consists of 128 pages presented in chapters with the Introduction, Conclusion, List of published scientific works, References and Appendixes Chapter OVERVIEW OF HIGH MEASUREMENT METHODS AND PROCESSING OF SIGNALS OF HIGH CHANNEL OF CRUISE MISSILE 1.1 Overview of high measurement methods On general flight vehicles, to measure the altitude of a flying device typically uses two measurement systems: The high measurement system does not use magnetic (no-radio) electromagnetic waves and high measurement systems using electromagnetic (radio) waves 1.2 The situation of research on processing and combining high measuring signals 1.2.1 Research situation in the world The application of SOA for the construction of the model used to extrapolate the error according to the horizontal channel INS [48], [53], [54] yet clearly mentioned explicitlyin the form of algorithms, evaluation results also improved qualitative characteristics, especially not the proposal was time of application algorithms The application of SOA for combined processing problems in high measuring combination when changing flight conditions has not been resolved 1.2.2 Research situation in our country The research published in the country on the construction of algorithms to combine high measurement signals is performed by Kalman filtering algorithm and given certain results on improving the quality of processing and combining high measuring signals However, these studies have not fully addressed the limitations and remedies that result in high combined measurement errors when using Kalman filters From the analysis of domestic and foreign research situation, the thesis raises the problem to be solved - For high-measurement combinations with many high-altitude measurement units combined with different flight conditions, the algorithm-based optimization selects a high-order measurement structure combined in a high-measurement combination in one thing Specific flight packages are needed - In the case of the observation of improper state variables, the Kalman filter works ineffectively for a certain period of time, using a model-building algorithm to extrapolate an alternative state error, resulting in Adjusting the error of INS state 1.3 The problem of combining high measurement signals on cruise missiles When processing signals in a combined high-level measurement, compensation or correction methods are often used The synthetic results are treated with various filters such as Kalman filter, adaptive filter after the filter receives the error estimates of the high-altitude signal treated with markedly improved quality in the flight of the missiles 1.4 Application of Kalman filtering algorithm and the problem of selecting structure in high measurement combination 1.4.1 The Kalman filter algorithm treats the combination of high measurement signals 1.4.2 Select a high-gauge set structure in combination with the observation standard In the combination of high measurement the high combined meters are measuring a generic parameter is the elevation, to consider the remaining components of the state vector to assess whether to calculate (the observed) and calculate exactly how (the level observed) through the component is measured directly as a height Selection, search out the combined height structure consistent with the specific flight conditions in order to improve the quality of the processed signal height combinations when the combination of high measurement are more combined height, use standard reviews the level of observed status variables Interms of specific flights, the quality of the measuring signal match processors willbe increased markedly, when the combined height level of the larger states respectively, will choosing to handle the high measuring signal matching 1.5 The instability of Kalman filtering algorithm and application of self-organization algorithm to improve processing quality combined with high measurement signal 1.5.1 The instability of Kalman filtering algorithm and method of constructing extrapolation model In order to overcome the decomposition of the Kalman filter, many m ethods are given as the offset method, the Kalman filter structure method, the method of constructing the Kalman filter structure is more appropriate, however, when initial prognosis is incorrect (mathematical modeling, input interference, measuring noise etc.), the use of the above methods does not bring high efficiency Alternative methods can then be used instead: neural networks, self-organizing algorithms, genetic algorithms [54], [63], [64]… That at some time (tAtB) to use the model construction algorithms extrapolate (figure 1.8) then get estimates before time tA the set sample value zi = z1, z2, z3, …,zN updated results matching measuring signal processing of high, the algorithm will use this data to evaluate new construction extrapolation models from the base models Figure 1.8 Overview of constructing models of extrapolating: BH- The base height; MCA- Model construction algorithms; PA- Prediction algorithm 1.5.2 Self-organizing algorithm in processing high-signal matching When the high-level detectors combine signal processing using Kalman filter, it is not allowed to evaluate the system state accurately enough with high measuring intensity Then, the criteria for assessing the observed level of state variables for the evaluation value not exceed the observation threshold, the signal processing results with large errors for the measurement At this point, correcting the high measurement errors of the base measurement system needs to use a new algorithm to model the extrapolation of their status errors instead The thesis proposes to use SOA to solve this problem 1.6 Conclusion chapter On the basis of analyzing domestic and foreign research works related to the problem of combining high measurement signals, given the limitations of the Kalman filter algorithm associated with the combined high gauge structure, it shows that it is necessary to solve the problem using the observation level evaluation criteria to optimize the structure in the high measuring assembly (selection) Choose the appropriate combination plan - chapter 2) Application of self-organizing algorithm to model extrapolation of state errors when Kalman filtering algorithm is not stable (chapter 3) Chapter ALGORITHMS FOR INFORMATION PROCCESSING AND SELECTION OF COMBINED HIGH MEASUREMENT SET STRUCTURE 2.1 Diagram of structure adjustment parameter of high channel state When connecting the circuit of feedback to the corresponding point in the structure IHM, Kalman form status equation the following discrete [56]: (2.1) xk  k,k 1xk 1  uk 1   k 1w k 1 ; k,k-1 is the matrix system; k,k-1 is the input interference matrix; elements in the matrix k,k-1, k,k-1 received from the mathematical model of the associated high input signal error; wk-1 is the input noise vector; uk1 is a vector of calibration signals, constructed from optimal estimates of the filter [56] accelerometer - + + + - TP1 - - + + + TP2 + + 2g/R g IHM Figure 2.1 The scheme of IHM with the feedbacks of the cumulative error 2.2 Build kinematic model of high measurement error 2.2.1 Differential kinematic model on high channel of inertial navigation system The model error of the IHM will be described by differential equations simpler than [56]:   H  V   g  V    H  a  g  R   a  a  u a    g  g  u g (2.6) In formula (2.6) height error H(t) include: the wrong number of accelerometers under the vertical channel δa(t) = δay(t) and the error due to the attractive uncertainty g(t) 2.2.2 Kinetic model of error of the radio height meter The process error Hvt(t) be performed as standard Mackop process satisfy the differential equation a [56]:  H vt  t   vt  vt  t   u vt  t  (2.8) in that:  vt = 1/vt; vt - the constant correlation movement u Hvt -the white junk Gauss with mathematical expectation of zero and the correlation function:   BuH    m u Hvt  t  u Hvt  t     2vt 2vt   t    vt (2.9) 2.2.3 Kinetic model of error of the micro-barometer The cause of the error is primarily the impact of the noise sourcedue to the fluctuations of motion speed cruise missiles, satisfy the quadratic linear differential equation for [56]  '  H ka   H ka ;   H' ka  (0, 67V   ) H '  0, 67 V  H  3, 2.10  u B ka ka ka B ka ka ka  (2.12) in that: ka- Constant pressure differential correlation, VB- High-speed change; uka - The speed of the flying device; uka -the white junk input noise described in the form of random process stops with the correlation function   (2.13) BH     2H e ka ka ka 2.4 Develop standards for assessing the observed level for combined high measuring sets 2.4.1 Observed and controlled according to Kalman standards On the level observed by В.Н Афанасьев and К.А Неусыпин there is a specific concept, considered the precision of approximation of the status vectors and analyzed the measurement noise as well: the observability level defines the variance ratio of an arbitrary status element and the variance of the status vector is measured directly considering the variance of the measurement noise 2.4.2 Develop standards for assessing the level of observation in the high-altitude meter IHM-RHM When considering the exact characteristics of the high measuring IHM-RHM need to transfer the persistent Kalman filtering algorithm to discrete, we have the equation and state equations in discrete form for measuring the combined height IHM-RHM (2.19), (2.20) in that: xk  k,k 1xk 1   k,k 1w k 1 (2.19) z k  H k xk  v k (2.20)  H    V   2gT / R  ;    x k  a   E  F(t k )T     k,k 1   g   H vt       T 0   0  T  0  vt T  T T T 0 ; 0    ; 2 T T   T  0  k  k,k 1 (t k )T    T  T  1    T T        0 T  Tvt  1   Initial conditions: x(0 / 0)  0 ; P  / 0  diag 2vt 2v 2a 2g 2vt  ;   Equations in matrix form measurement z  x k   O*z*k (2.24) Suppose that when calculating the level of observed status vector components of the heating system only includes ameasurement, that is in the case Hk = [1 … -1] Measurement of equation z(xk) in scalar form, with the size of the matrix system of n = would be: z  H   z k  z k  (2.25) z  V    21,k z k   22,k z k 1    25,k z k  z  a   31,k z k  32,k z k 1   35,k z k  z  g    41,k z k   42,k z k 1    45,k z k  The coefficient of i j ,k ( j  1, 2, , 5) is the row of a matrix O* at time tk Calculate the variance of the error estimate of the state variables at time k according to the formula (2.26) n M  x i,k       xˆ k 1 n i,k (2.26) in that: i = 1,2,3,4 corresponds to the element status H,V,a,g For arbitrary elements of the statusvector, vector measurement of 10 n M j  x i,k       xˆ k 1 i,k ; (2.38) n in that: i = 1,2,3,4 corresponding to the state variables of the ĐCQT H,V,a,g; j = 1, 2, is the index correspond to the sets of measures combining IHM-RHM, IHM-AHM, IHM-RHM-AHM - Magazine variance measure corresponding to each element in the status vector measure combines high: 2 (2.39) R *ik   i1,k    i2,k     im,k   R 0k   in that: i = 2,3,4 corresponding status vector elements V,a,g; m = 5,6,7- the size of system matrix correspond to the sets of measures combning IHM-RHM, IHM-AHM, IHM-RHM-AHM; k- the time calculated at time tk - The level of the state variables in the set of matching height Choosing the appropriate height structure on the basis of reviews the level of observed status variables M j  x i,k     D j  x i,k   m M  H    j1  ij,k    (2.40) in that: i = 2,3,4 respectively with the status vector element V,a,g; m = 5,6,7- the size of system matrix correspond to the sets of measures combining IHM- RHM, IHM- AHM, IHM- RHM-AHM; j = 1,2,3- the index correspond to the sets of measures combining IHM-RHM, IHMAHM, IHM-RHM-AHM; k the time calculated at time tk 2.6 The conclusions chapter In this chapter, the error model of a number of high measuring sets has been developed, constructing the structure of the high-measurement assembly to select the combined high-gauge structure Research and standard analysis assess the level of observation of state variables in combined information processing Applying the research results, developing algorithms to select the appropriate combination of high gauges by assessing the degree of observation of state variables to improve the quality of processing high measuring signals for the selected combination of high gauges when considering characteristic parameters corresponding to specific flight 11 conditions Proposing measures to overcome the limitations of the algorithm in case the ability to observe state variables does not exceed the observed threshold, the initial a priori information is not sufficient for Kalman filtering algorithm Research results of chapter will be proved by simulation in chapter and shown on works [3], [5] of the author Chapter BUILDING EXTRAPOLATING MODEL IN HANDLING HIGH-QUALITY SIGNALS ALGORITHMS APPLICATION SELFORGANIZATION 3.1 Basic principles when implementing SOA 3.2 The structure of the SOA The construction of self-organizing algorithm consists of the following basic steps: 3.2.1 Enter input data base Fact based on the experimental process of research subjects, the data about the technical characteristics of measuring complex high on a particular cruise missile classand level, the capacity estimated by the design of variable trend status error will choose the basis functions and limits the number of basis functions properly and considering it is the wrong model of simplified base accepted General form of the basis functions arespecified in section 1.5.2.1; 3.2.2 Organize improving the quality of the model The method of finding the coefficient for the linear model [67] Data is divided nto parts: A- school section; B- the test section is described on figure 3.2 To time tm in the study A will for a value y(tm) Figure 3.2 Performing split model to construct and model reviews To find the coefficients for the linear format model, using the method 12 of minimum squared in A test set [67] With combinations of y(a,t) depends on the linear coefficient vector A: y  a, t   a1g1  t   a 2g  t   a 3g t    a ng n t  A   GTG  in that: 1 G Y  T (3.8) *  y  t1    g1  t1  g  t1     yt  g  t  g2  t  Y*    ;[G]         y t g t g     tm  m m    g n  t1    a1   a  g n  t   ;A          g n  t m   a n  ; To enhance the complexity of the model can use the methods of the following organizations: - Organized by combination method: Each level of complexity i of the model is the short-i of n complex models, the model is a combination of the form of the selected combination function; - Organized by selective combination method: Each level of organization will pick out a fixed amount the best models, the best model that will participate in the next organization level and aggregated with all of the remaining base model; - Organized by the method of multi-sequence iteration: Each level of organization will be fixed the number of the best model was selected (each model is considered as a variable) At times the next organization each pair of variables is taken at random from among the best models that will the combination out of a new model in the form of fixed combination function; - Organized by the repeat method software: Similar to that held by the method of multi-sequence iteration, this organization methods each organization level select fixed number of best model In the next level of organization, with other organizations under the organization method of multi-sequence iteration, the best model was selected at the organization level before will the combination with the original base model remains tonew models out combinations according to the functional form of fixed combinations 3.2.3 Evaluate the selection of models After each level the organization of certain organization methods, conducting reviews and select the optimal model to select best 13 extrapolating model or select the number of the best model to use for the next organization level To evaluate the new constructing extrapolating model after each level of organization, we use the standard assessment in item 1.5.2.2 on the bases of value patterns in previous Kalman estimation process However, the need to incorporate the criteria for evaluation and selection of models extrapolate according to form: (3.9)   w1n dc2  w 22  B  w 3Bi2 ; đó: n dc2 ; 2  B ; Bi2 is the standard minimum shift; uniform standards and criteria of balance is calculated by (1.27),(1.23), (1.34); w1, w2, w3 is the weights (w1 + w2 + w3 = 1), the value of wi depend on reviews of the designer about the importance of each criteria, if the standard would be considered more important than the value of the corresponding wi will have greater value than the remaining weight When execute the algorithm self -organization, it is often combine standard shift state  n dc2  and uniform standards   B to evaluate and select extrapolating model, the evaluation and selection of extrapolating model not only between new constructing models at each organization level, but also to proceed to select the best model between pattern of organization level 3.2.4 Conditions for the end of the algorithm Based on the value evaluation model extrapolate according to (3.9); self-organization algorithm will stop when satisfied one of the following two conditions: - If the value of reviews () of the best models of organization level are considering the larger the value of reviews () of the best models of organization level, the algorithm stops; Means: level (k+1)>level k; k = 2, 3, 4, ….,n; n- the number of the base model in the input data of the algorithm; - With the various problem conditions will advance to the algorithm stops after some level of organization or after a working time of the algorithm This weighing on demand computer fast impact of math, as well as the complexity of the model and the possibility of dedicated 14 computer calculations, the designer will put time self-organization algorithm for matching The result of the best model would be valuable model sreviews the smallest of levels of organization have made of the algorithm From the analys is of the structure of the self-organizing algorithm with four basic tasks are done; I have the save map of self-organizing algorithm is shown on figure 3.8 Figure 3.8 The scheme of self-organizating algorithm of signal processing 3.3 High-altitude combination of SOA applications In figure 3.9 introduces high measuring complex diagrams using self organizing algorithm of constructing and extrapolating model selection Figure 3.9 Automatic height combination chart using of SOA: H -is the practical height information which we need to measure; xk - the error vector IHM; 𝑥𝑘 - vector model extrapolation of errors; EOL - evaluating the observability level 3.4 Develop an extrapolation model of error of SOA application state variables 15 3.4.1 Collect and build input base data for SOA Choose data base algorithm input the wrong number of selforganization of the altitude is simplified by the variability of the base model 24 (3.10) 3.4.2 Improve the quality of the model by selective organization method In the method of selected complex organizations, each organization level enhance the complexity of the model will pick out a fixed amount the best models the best models, will participate in the next sequence and combination with allremaining models Select the funtion associated to the organization level are linear: (3.11) y  a,t   y  g1,g , ,g n   a1g1  t   a 2g  t    a ng n  t  ; The standard of review for the models built for the level held: (3.14) 3.4.3 Check the stop condition of the algorithm After each organization level reviews to select the best model Using standard algorithm reviews by (3.14), while the value increased rating, meaning the good level of the model decreased then the algorithm would stop The results of calculations for the advanced degree organization level of complex models when constructing the model error of extrapolating a height ofitem 3.4.2: y(10) (a, t)  a1(10) t1,5  a (10) t 0,5  a 3(10) t  a (10) sin(0,1.t)  a 5(10)sin(0,2.t)  ; (3.19) a (10) sin(0,5.t)  a (10) sin(t)  a 8(10)exp(5.t)  a 9(10) exp(3t)  a10(10)exp( t) a1(10)  0.574992279351559;a (10)  0.171280269751940;a 3(10)  0.00784684354414533; a (10)  0.0212227452780124;a 5(10)  0.0446385963644056;a 6(10)  0.0181295025740750; a (10)  0.00736648717194733;a 8(10)  0.245347972257312;a 9(10)  0.215674527823339; a10(10)  0.288322982796649 The algorithm can be stopped in the following terms the number of predetermined organization level, by the designer based on the data and computing power of dedicated computer 16 3.5 Construct the model error of extrapolating the velocity and acceleration of self-organizing algorithm application The input data to construct models that extrapolate the velocity error and the error under acceleration (3.20), (3.21) Construction and evaluation of model error in the process of improving the quality of the model 3.6 The conclusions chapter In this chapter, we present the structure of self-organizing algorithm to model the extrapolation of errors of state vector elements and apply them in case of the observable level of the state vectors below Threshold, the Kalman filter is diverged during processing associated with a high measuring signal Implement self-organizing algorithm to model extrapolation of height error of state vectors by selective combination method when organizing to improve the quality of the model Assess the advantages and disadvantages when selecting organizational methods to improve the quality of the model in the process of building extrapolation model of self-organizing algorithm It has been demonstrated by the algorithm itself to build an extrapolation model to correct the status error of the high combined measurement when the Kalman filter algorithm is not stable (divergent Kalman filter) The research results will be proved by simulation in the following chapter and shown on the author's works [1], [2], [5] Chapter DESCRIPTION OF SURVEY, ASSESSMENT OF HIGH-QUALITY SIGNAL HANDLING 4.1 Set simulation with assumptions, input data of the problem 4.1.1 Building simulation program Construction of simulation algorithm of program reviews the high measurement quality improvement associate use standard reviews the level of observed and self-organization algorithm construct extrapolate errors The steps are done according to the save map image algorithm figure 4.1 17 Figure 4.1 The scheme of the algorithm to enhance the quality of processing measurement signals higher combined standard the criterion for evaluating the observability level applications and self-organization algorithm 4.1.2 Assumptions, data for simulation - In the dissertation will not go to analyze the dependence of characteristic parameters of the object of research on the fluctuations of atmospheric gas and earth to choose the correct characteristic parameters with the fact That the value of this characteristic paramet; terms of assumptions and retrieved the document [56]; - The steps preliminary to the signal processing board for the parameters from the measured high and the conversion between the coordinate system as was done The causes of the error from the high measurement related to hard ware calibration before was put to work; - The orbit plane of a dissertation review class cruise missiles with altitude strip mixture; 18 - The choice of input data for self-organizing algorithm base on the rules change the error of the status element on the basis of the data set to assume (3.10), (3.20), (3.21) 4.2 Survey results of algorithm for selecting combined high gauge structure 0,435 0,293 0,162 Figure 4.2 Evaluating the observation level of the velocity error of combination height measurement at vt =10s, ka =25s, g =9,7803 m/s2 Figure 4.3 The height error by using the Kalman filter of combination height measurements when vt =10s, ka =25s, g =9,7803 m/s2 In figures 4.2, 4.4: 1- The observability level δV of the complex IHMRHM; 2-The observability level δV of the complex IHM-AHM; 3- The observability level δV of the complex IHM-RHM-AHM 0,325 0,248 0,094 Figure 4.4 Evaluating the observation level of the velocity error of combination height measurement when vt = 15s, ka = 30s, g = 9,7786 m/s2 Figure 4.5 The height error by using the Kalman filter of combination height measurements when vt = 15s, ka = 30s, g = 9,7786 m/s2 In figures 4.3, 4.5: 1- Evaluating the height error of the complex IHM-RHM; 2- Evaluating the height error of the complex IHM-AHM; 3Evaluating the height error of the complex IHM-RHM-AHM, 4- The actual height error; 19 Bảng 4.1 Evaluating the height error of combination height measurements The mean of the error (m) IHM-RHM IHM-AHM IHM-RHM-AHM H =15m 0,0205 0,0254 0,0293 The variance of the Standard deviation (m) error (m2) H =14km 0,0427 0,0221 0,1986 H =15m 0,000325 0,000532 0,000686 H =14km 0,00126 0,000373 0,00242 H =15m 0,018 0,0231 0,0262 H =14km 0,0355 0,0193 0,162 4.3 Application of the standard for evaluating the degree of observation with improved processing quality for the high IHM-HM 0.268 0.175 Figure 4.6 The observability level of the velocity error in different discrete intervals T =0,1s (graph 1) T =0,2s (graph 2) Figure 4.7 The velocity error by using the Kalman filter at different T: 1- The actual error value; 2- The error value with T = 0,1s; 3- The error value with T = 0,2s Bảng 4.2 Evaluating the velocity error at different T in the set status Discrete range (s) The mean of the error (m/s) The variance of the error (m2/s2) Standard deviation (m/s) 0,1 1,241 1,994 1,412 0,2 2,514 7,518 2,742 Figure 4.8 The observability level of the velocity error when vt change: 1- The observability level of the velocity error when vt=5s; 2- The observability level of the velocity error when vt=20s Figure 4.9 The velocity error in combination processing the high measurement signals by using the Kalman filter when vt change 20 In figures 4.9: 1- The actual error value; 2- The observability level of the velocity error when vt=5s; 3- The observability level of the velocity error when vt=20s Figure 4.11 The acceleration error in combination processing the high measurement signals by using the Kalman filter when vt change: 1- The actual error value; 2-The observability level of the acceleration error when vt=5s; 3-The observability level of the acceleration error when vt=20s Figure 4.10 The observability level of the acceleration error when vt change: 1- The observability level of the acceleration error when vt=5s; 2- The observability level of the acceleration error when vt=20s Bảng 4.3 Evaluating the velocity error, the acceleration error at vt different The mean of the error (m/s), (m/s2) velocity error acceleration error The variance of the error Standard deviation (m2/s2), (m2/s4) (m/s), (m/s2) vt =5s vt =20s vt =5s vt =20s vt =5s 0,8352 1,3565 0,5964 1,8692 0,7723 0,568.10 -2 0,973.10 -2 0,1332 10 -4 0,6839.10 -4 vt =20s 1,3672 -2 0,365.10 0,827.10-2 4.4 Assessing the quality of processing and combining high measuring signals using self-organizing algorithm Figure 4.12 The velocity error in combination processing the high measurement signals by using the Kalman filter and using self organizing algorithm In figure 4.12, 4.13: 1- The actual error value 2- Evaluating the error by using the Kalman filter; 3- Evaluating the error by using self organizing algorithm 21 Figure 4.13 The height error in combination processing the high measurement signals by using the Kalman filter and using self organizing algorithm Table 4.4 The table of values of evaluating the velocity error Time (s) The mean of the error (m/s) The variance of the error (m2/s2) Standard deviation (m/s) 40  200 200  280 Kalman self organizion Kalman 1,2749 2,1536 0,9332 17,962 2,985 3,0848.10 self organizion Kalman self organizion 5,626 0,966 2,372 8,1396 17,5638 2,853 Table 4.6 The table of values of evaluating the height error Time (s) The mean of the error (m) The variance of the error(m2) Standard deviation (m) Kalman self organizion Kalman 40  200 0,0244 0,0896 3,2446.10 200  280 0,3997 0,0892 0,6391 self organizion Kalman self organizion -4 0,0045 0,018 0,0670 0,0297 0,7994 0,1723 4.5 The results of the algorithm survey organize the construction and select extrapolation models Figure 4.14 The height error of combination Figure 4.15 The height error of combination height measurements at H =15m when height measurements at H =14km when In figures 4.14, 4.15: 1,2,3- extrapolate using self organizing algorithm; 4,5,6- using the Kalman filter; 7- Evaluating of the actual error value; (in that: 1,4- the height error value of IHM-RHM; 2,5- the 22 height error value of IHM-AHM; 3,6- the height error value of IHMRHM-AHM) Table 4.7 Evaluating the height error of combination height measurements Time (s) IHM-RHM Kalman self organizion IHM-AHM IHM-RHM-AHM Kalman self organizion Kalman self organizion The variance of the height error (m2) at H =15m t =(40 ÷200) 0,000325 t = (200 ÷280) 0,6391 0,0045 0,000532 0,0081 0,000686 0,0116 0,0297 0,8577 0,0358 0,9789 0,0527 The variance of the height error (m ) at H =14km t = (40200) 0,00126 0,0099 0,000373 0,0060 0,00242 0,0452 t = (200280) 0,9671 0,088 0,6629 0,0698 0,9944 0,1357 4.6 Conclusion chapter Chapter has conducted simulation evaluation of processing quality combined with high measuring signals, when using the observation level assessment criteria to optimize the structure in the high measuring assembly, select the set structure Combined high gauge suits specific flight conditions In case the observation level of state variables is smaller than the observed threshold, the Kalman filter or more diverges, using the SOA to model the state error extrapolation model instead The simulation results confirm that the processing accuracy associated with high measuring signals is increased when: - Using the criteria for evaluating the degree of observation of the state variables, selecting the high measuring structure and optimal combination in the high measuring combination - Using self-organizing algorithm to model extrapolation of state errors in processing in combination with high measuring signals CONCLUSIONS The thesis has studied and applied modern algorithms and evaluation criteria to combine high measurement signals to optimize high measurement, improve the quality of control process - stabilize the height 23 of cruise missiles when conditions change Research results of the thesis have built intelligent high-altitude combination using standards to assess the level of observation and self-organizing algorithms in combining high measurement signals The thesis got main results as follows We analyzed clearly the eveluation criterion (of the quantity) of the observability degree and the self-organizing algorithm for constructing an extrapolation model Applied them in the combination process of height measurement signals We improved the quality of the combination process of height measurement by choosing a suitable structure of the combined height meter in a height measurement combination with many working height meters based on use of the valuation criterion of the observability degree of the status vector's components among combined height meterd within the same flight conditions We improved the quality of the process of height measurement signals for the combined height meter when considering the characteristic parameter of the combined height meter corresponding a concrete flight condition The obtained results showed that when the observability degree of status variables increases, the exactness of the combination process of height measurement signals increases When the status variable has a low observability degree due to the action of external conditions, ues of the self-organizing algorithm for constructing an extrapolation model of the status error auguments the exactness of the combined process of height measurement signals The research results were tested by solfware Malab/Simulink on computers and verified the correctness of proposed algorithms Summary of new conclusions of the dissertation Construction of an algorithm for the combined process of height measurement signals and choice of the structure of the combined height meter based on the evaluation of the observably level Construction of an extrapolation model in the combined process of 24 height measurement by using a self-organization algorithm with the condition of an in consonant observably level of status variable Suggestions for future research Study and evaluate the quality of the control ring - stabilize the height when putting the high measurement combination to apply the standard on the evaluation the observed level and the algorithm will organize itself in a closed loop Continue to research and apply modern control algorithms for control loops - height stability, improve control quality - stabilize altitude for a specific type of cruise missiles included in military equipment ... (FICTA2018); 29-30 November Pham Duc Thoa, To Ba Thanh, Nguyen Quang Vinh, Bui Minh Tuan, (2019), “The construction of a self-organizing algorithm for choosing a model of extrapolation in the combined... Military Science and Technology The dissertation was defended in front of the Doctoral Evaluating Committee at Academy level held at Academy of Military Science and Technology at …/…, 2019 More information... dissertation - Construction of an algorithm for the combined process of height measurement signals and choice of the structure of the combined height meter based on the evaluation of the observably level