Online optimal guidance method and device based on secondary rectification and electronic equipment

Abstract

The invention provides an online optimal guidance method and device based on secondary rectification and electronic equipment, and the method comprises the steps: converting a first optimal control problem containing a process constraint into a second optimal control problem not containing the process constraint, solving the second optimal control problem through a model prediction static planning method, and solving the second optimal control problem through a model prediction static planning method. According to the method, a control quantity updating equation of an analysis iteration form is obtained, iteration correction is performed on a control quantity guess value based on the control quantity updating equation, an optimal guidance instruction is obtained, namely, a general solving algorithm with the analysis iteration form is realized, the form is simple, the time consumption of single iteration is extremely low, the total time consumption is relatively low, and the solving efficiency is greatly improved; and the first optimal control problem containing the process constraint is converted into the second optimal control problem without the process constraint, so that the solving accuracy is improved in a mode of solving the second optimal control problem through the model prediction static planning method, and the universality is good.

Description

Technical Field

[0001] The invention relates to the technical field of online guidance of aircraft, and in particular to an online optimal guidance method, device and electronic equipment based on secondary rectification. Background Art

[0002] In many cases, the online guidance problem of an aircraft can be reduced to an optimal control problem with process constraints that needs to be solved online, such as online trajectory planning of the recovery stage of a reusable rocket, online optimal guidance of an air-to-air missile under field of view constraints, etc. Studying methods for solving optimal control problems with process constraints has important engineering significance.

[0003] A similar problem to the optimal control problem with process constraints is the optimal control problem with terminal constraints. The online solution method for the optimal control problem with terminal constraints has developed relatively maturely, forming the famous model predictive control method. The model predictive control method is generally an algorithm with an analytical iterative form and has high computational efficiency.

[0004] There is no algorithm with analytical iteration form for optimal control problems with process constraints. The existing solution methods can be roughly divided into three categories: the first category is suboptimal methods, which are based on the optimal solution without process constraints and are iteratively corrected with a certain strategy until the constraints are satisfied. The disadvantage of this type of method is that the solution does not meet the optimality and may even be very different from the optimal solution. The second category is discrete optimization, which discretizes the dynamic equations, transforms the optimal control problem into a nonlinear programming problem, and solves it through some optimization methods. The disadvantage of this type of method is that the convergence depends on the optimization algorithm after discretization (if the optimization algorithm is not well selected, the convergence of the solution will be poor), and it is time-consuming to solve nonlinear programming problems online. The third category is convex optimization methods, which transform the original problem into a convex optimization problem through some special convexification methods, and then solve it through mature toolkits. The disadvantage of this type of method is that the convexification process requires a separate analysis of each problem, and only some problems can meet the convexification conditions, and problems that do not meet the convexification conditions cannot be solved.

[0005] In summary, the current solution methods for optimal control problems with process constraints have technical problems such as poor accuracy, low efficiency and poor versatility. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide an online optimal guidance method, device and electronic device based on secondary rectification to alleviate the technical problems of poor accuracy, low efficiency and poor versatility of existing solutions to optimal control problems with process constraints.

[0007] In a first aspect, an embodiment of the present invention provides an online optimal guidance method based on secondary rectification, comprising:

[0008] Build a dynamic model of the aircraft to obtain the first optimal control problem that needs to be solved for online guidance, including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations;

[0009] The first optimal control problem is converted into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations by state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectification function;

[0010] The second optimal control problem is discretized by using the Euler method to obtain a second optimal control problem in a discrete form;

[0011] Obtaining a control quantity guess value corresponding to the control quantity in the second optimal control problem in the discrete form, and calculating a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form;

[0012] Calculating the deviation between the final terminal output value and the expected terminal output value, and determining whether the deviation is less than a preset threshold;

[0013] If it is not less than the preset threshold, the control quantity guess value is updated based on the discrete state quantity, the deviation and the control quantity update formula in analytical iterative form to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem through a model prediction static programming method;

[0014] The updated control quantity guess value is used as the control quantity guess value, and the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in discrete form is returned to execute until the deviation is less than the preset threshold value, and the control quantity guess value corresponding to the deviation being less than the preset threshold value is used as the optimal guidance instruction.

[0015] Furthermore, the method further comprises:

[0016] If the deviation is smaller than the preset threshold, the control amount guess value is used as the optimal guidance instruction.

[0017] Furthermore, the first optimal control problem is transformed into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, including:

[0018] If the process constraint of the first optimal control problem is C(x(t), u(t))≤0, the state quantity x(t) has n dimensions, the process constraint C(x(t), u(t)) has m dimensions, and the terminal constraint ψ(x(t f ), t f ) has l dimensions, then delete the process constraints and add a new m-dimensional state quantity x n+i (t), i = 1, 2, ..., m, and x n+i (t0) = 0, and at the same time, a new m-dimensional terminal constraint ψ is added i (x(t f ), t f ), i=1, 2,...,m, and ψ i (x(t f ), t f )=x n+1 (t f )=0, where ReQU(x) represents the secondary rectification function, Indicates the control quantity;

[0019] The m-dimensional process constraint in the first optimal control problem is replaced by the newly added m-dimensional state quantity and the newly added m-dimensional terminal constraint to obtain the second optimal control problem, wherein the second optimal control problem is:

[0020] ψ * (x * (t f ), t f )=0, where the augmented state quantity x * (t) has n+m dimensions, the augmented terminal constraint ψ * (x * (t f ), t f ) has l+m dimensions, is the performance index, is the augmented nonlinear dynamic equation.

[0021] Further, the control amount guess value is updated based on the discrete state quantity, the deviation and the control amount update formula in analytical iterative form, including:

[0022] Update the formula according to the control quantity

[0023] The control amount guess value is updated to obtain the updated control amount guess value, where U k represents the updated control amount guess value, Q k Represents the weight matrix of the control amount, B krepresents the sensitivity matrix calculated based on the discrete state quantity, dY N represents the deviation, represents the estimated value of the control amount.

[0024] Furthermore, the process of solving the control amount update formula includes:

[0025] The second optimal control problem is discretized by using the Euler method to obtain a second optimal control problem in a discrete form;

[0026] Linearizing the final terminal output in the second optimal control problem in the discrete form to obtain a linearized terminal output error;

[0027] Linearizing the state equation in the second optimal control problem in the discrete form to obtain a linearized state quantity error;

[0028] Obtaining a new linearized terminal output error according to the linearized terminal output error and the linearized state quantity error;

[0029] Determining an augmented performance indicator according to the new linearized terminal output error and the performance indicator;

[0030] The augmented performance index is derived according to the first-order necessary condition to obtain a control quantity deviation formula;

[0031] The control amount update formula is derived according to the control amount deviation formula and the new linear terminal output error.

[0032] Further, calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in discrete form includes:

[0033] Substitute the control quantity guess value into the second optimal control problem in discrete form for iterative calculation to obtain the discrete state quantity and the final terminal output value.

[0034] In a second aspect, an embodiment of the present invention further provides an online optimal guidance device based on secondary rectification, comprising:

[0035] A building unit is used to build a dynamic model of the aircraft to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance;

[0036] a conversion unit, configured to convert the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectification function;

[0037] A discrete unit, used for discretizing the second optimal control problem by using the Euler method to obtain the second optimal control problem in a discrete form;

[0038] An acquisition and calculation unit, used to acquire a control quantity guess value corresponding to the control quantity in the second optimal control problem in the discrete form, and calculate a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form;

[0039] a calculation and judgment unit, configured to calculate a deviation between the final terminal output value and the expected terminal output value, and to judge whether the deviation is less than a preset threshold;

[0040] an updating unit, configured to update the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in analytical iterative form to obtain an updated control quantity guess value if it is not less than the preset threshold, wherein the control quantity update formula is obtained by solving the second optimal control problem through a model prediction static programming method;

[0041] A return execution unit is used to use the updated control quantity guess value as the control quantity guess value, return to execute the step of calculating discrete state quantities and final terminal output values ​​according to the control quantity guess value and the second optimal control problem in discrete form, until the deviation is less than the preset threshold, and use the control quantity guess value corresponding to the deviation being less than the preset threshold as the optimal guidance instruction.

[0042] Furthermore, the device is also used for:

[0043] If the deviation is smaller than the preset threshold, the control amount guess value is used as the optimal guidance instruction.

[0044] In a third aspect, an embodiment of the present invention further provides 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 steps of any one of the methods described in the first aspect when executing the computer program.

[0045] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to execute any method described in the first aspect above.

[0046] In an embodiment of the present invention, an online optimal guidance method based on secondary rectification is provided, comprising: constructing an aircraft dynamics model to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance; converting the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented using a secondary rectification function; discretizing the second optimal control problem using the Euler method to obtain a second optimal control problem in a discrete form; obtaining a control quantity guess value corresponding to the control quantity in the discrete form of the second optimal control problem, and calculating the control quantity guess value according to the control quantity guess value and the second optimal control problem in the discrete form; The method comprises the steps of calculating the discrete state quantity and the final terminal output value; calculating the deviation between the final terminal output value and the expected terminal output value, and judging whether the deviation is less than a preset threshold value; if not less than the preset threshold value, updating the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in the form of analytical iteration to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem by the model prediction static programming method; taking the updated control quantity guess value as the control quantity guess value, returning to execute the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form, until the deviation is less than the preset threshold value, and taking the control quantity guess value corresponding to the deviation less than the preset threshold value as the optimal guidance instruction. It can be seen from the above description that in the online optimal guidance method based on secondary rectification of the present invention, the first optimal control problem containing process constraints can be converted into a second optimal control problem without process constraints, and then the second optimal control problem is solved by the model prediction static programming method to obtain an analytical iterative form of control quantity update formula, and the control quantity guess value is iteratively corrected based on the above control quantity update formula to obtain the optimal guidance instruction, that is, a general solution algorithm with an analytical iterative form is realized, the form is concise, and since it is an analytical iterative form of control quantity update formula, a single iteration takes very little time, and the total time consumption is less, which greatly improves the solution efficiency. In addition, the above-mentioned method of converting the first optimal control problem containing process constraints into a second optimal control problem without process constraints, and then solving the second optimal control problem by the model prediction static programming method improves the accuracy of the solution, has good versatility, and alleviates the technical problems of poor accuracy, low efficiency and poor versatility of the existing solution methods for optimal control problems with process constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A flowchart of an online optimal guidance method based on secondary rectification provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the lift coefficient of an electromagnetic booster rocket provided in an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the drag coefficient of an electromagnetic booster rocket provided in an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of a trajectory curve provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of a trajectory inclination angle-time curve provided by an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of a speed-time curve provided by an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of a control amount-time curve provided by an embodiment of the present invention;

[0055] Figure 8 A schematic diagram of an angle of attack-time curve provided by an embodiment of the present invention;

[0056] Fig. 9 A schematic diagram of a dynamic pressure-time curve provided by an embodiment of the present invention;

[0057] Fig.10 A schematic diagram of an angle of attack-time curve under different simulation conditions provided by an embodiment of the present invention;

[0058] Fig.11 A schematic diagram for comparing control quantity-time curves with and without process constraints provided in an embodiment of the present invention;

[0059] Fig.12 A schematic diagram of a control amount-time curve under different iteration times provided by an embodiment of the present invention;

[0060] Fig.13 A schematic diagram of terminal error under different numbers of iterations provided by an embodiment of the present invention;

[0061] Fig.14 A schematic diagram of an online optimal guidance device based on secondary rectification provided by an embodiment of the present invention;

[0062] Fig.15 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The current solution methods for optimal control problems with process constraints have poor accuracy, low efficiency and poor versatility.

[0065] Based on this, in the online optimal guidance method based on secondary rectification of the present invention, the first optimal control problem containing process constraints can be converted into a second optimal control problem without process constraints, and then the second optimal control problem is solved by the model prediction static programming method to obtain a control quantity update formula in an analytical iterative form, and the control quantity guessed value is iteratively corrected based on the above control quantity update formula to obtain the optimal guidance instruction, that is, a general solution algorithm with an analytical iterative form is realized, the form is simple, and since it is a control quantity update formula in an analytical iterative form, a single iteration takes very little time, and the total time consumption is less, which greatly improves the solution efficiency, and the above-mentioned method of converting the first optimal control problem containing process constraints into a second optimal control problem without process constraints, and then solving the second optimal control problem by the model prediction static programming method improves the accuracy of the solution and has good versatility.

[0066] To facilitate understanding of this embodiment, firstly, an online optimal guidance method based on secondary rectification disclosed in an embodiment of the present invention is introduced in detail.

[0067] Embodiment 1:

[0068] According to an embodiment of the present invention, an embodiment of an online optimal guidance method based on secondary rectification is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0069] Figure 1 is a flow chart of an online optimal guidance method based on secondary rectification according to an embodiment of the present invention, such as Figure 1As shown, the method comprises the following steps:

[0070] Step S102, constructing an aircraft dynamics model to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance;

[0071] Specifically, the aircraft dynamics model can be expressed as: Among them, x(t) represents the state quantity, u(t) represents the control quantity, and t represents time.

[0072] The first optimal control problem to be solved for online guidance is expressed as:

[0073] C(x(t),(t))≤0

[0074] ψ(x(t f ), t f )=0

[0075] , where J represents the performance index, u(t) represents the control amount, and Q represents the control amount weight matrix. represents the nonlinear dynamics equation, x(t) represents the state quantity, C(x(t),u(t))≤0 represents the process constraint, ψ(x(t f ), t f )=0 indicates terminal constraint.

[0076] Step S104, transforming the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectifier function;

[0077] Specifically, the so-called state augmentation refers to expanding the dimension of the state quantity, and the terminal constraint augmentation refers to expanding the dimension of the terminal constraint. The process will be described in detail below and will not be repeated here.

[0078] Step S106, using the Euler method to discretize the second optimal control problem to obtain the second optimal control problem in a discrete form;

[0079] Step S108, obtaining a control quantity guess value corresponding to the control quantity in the second optimal control problem in a discrete form, and calculating a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in a discrete form;

[0080] Step S110, calculating the deviation between the final terminal output value and the expected terminal output value, and determining whether the deviation is less than a preset threshold;

[0081] Specifically, the final terminal output value is expressed as YN , the expected terminal output value is given and is expressed as

[0082] Step S112: if it is not less than the preset threshold, the control quantity guess value is updated based on the discrete state quantity, deviation and the control quantity update formula in analytical iteration form to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem through the model prediction static programming method;

[0083] Step S114, taking the updated guessed control value as the guessed control value, returning to execute the step of calculating the discrete state quantity and the final terminal output value according to the guessed control value and the second optimal control problem in discrete form, until the deviation is less than the preset threshold, and taking the guessed control value corresponding to the deviation less than the preset threshold as the optimal guidance instruction.

[0084] In an embodiment of the present invention, an online optimal guidance method based on secondary rectification is provided, comprising: constructing an aircraft dynamics model to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance; converting the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented using a secondary rectification function; discretizing the second optimal control problem using the Euler method to obtain a second optimal control problem in a discrete form; obtaining a control quantity guess value corresponding to the control quantity in the discrete form of the second optimal control problem, and calculating the control quantity guess value according to the control quantity guess value and the second optimal control problem in the discrete form; The method comprises the steps of calculating the discrete state quantity and the final terminal output value; calculating the deviation between the final terminal output value and the expected terminal output value, and judging whether the deviation is less than a preset threshold value; if not less than the preset threshold value, updating the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in the form of analytical iteration to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem by the model prediction static programming method; taking the updated control quantity guess value as the control quantity guess value, returning to execute the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form, until the deviation is less than the preset threshold value, and taking the control quantity guess value corresponding to the deviation less than the preset threshold value as the optimal guidance instruction. It can be seen from the above description that in the online optimal guidance method based on secondary rectification of the present invention, the first optimal control problem containing process constraints can be converted into a second optimal control problem without process constraints, and then the second optimal control problem is solved by the model prediction static programming method to obtain an analytical iterative form of control quantity update formula, and the control quantity guess value is iteratively corrected based on the above control quantity update formula to obtain the optimal guidance instruction, that is, a general solution algorithm with an analytical iterative form is realized, the form is concise, and since it is an analytical iterative form of control quantity update formula, a single iteration takes very little time, and the total time consumption is less, which greatly improves the solution efficiency. In addition, the above-mentioned method of converting the first optimal control problem containing process constraints into a second optimal control problem without process constraints, and then solving the second optimal control problem by the model prediction static programming method improves the accuracy of the solution, has good versatility, and alleviates the technical problems of poor accuracy, low efficiency and poor versatility of the existing solution methods for optimal control problems with process constraints.

[0085] The above content briefly introduces the online optimal guidance method based on secondary rectification of the present invention. The specific contents involved therein are described in detail below.

[0086] In an optional embodiment of the present invention, the method further comprises the following steps:

[0087] If the deviation is less than the preset threshold, the guessed value of the control amount is used as the optimal guidance instruction.

[0088] In an optional embodiment of the present invention, the first optimal control problem is transformed into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, which specifically includes the following steps:

[0089] (1) If the process constraint of the first optimal control problem is C(x(t), u(t))≤0, the state x(t) has n dimensions, the process constraint C(x(t), u(t)) has m dimensions, and the terminal constraint ψ(x(t)) f ), t f ) has l dimensions, then delete the process constraints and add a new m-dimensional state quantity x n+i (t), i = 1, 2, ..., m, and x n+i (t 0 )=0, and at the same time, a new m-dimensional terminal constraint ψ is added i (x(t f ), t f ), i=1, 2,...,m, and ψ i (x(t f ), t f )=x n+i (t f )=0, where ReQU(x) represents the secondary rectification function, u(t) represents the control quantity;

[0090] Specifically, the state augmentation process is expressed as follows: if the state quantity x(t) of the first optimal control problem has n dimensions, the process constraint C(x(t), u(t)) has m dimensions, and the terminal constraint ψ(x(t f ), t f ) has l dimensions, then delete the process constraints and add a new m-dimensional state quantity x n+i (t), i = 1, 2, ..., m, we have: x n+i (t 0 )=0, where ReQU(x) represents the secondary rectification function, u(t) represents the control quantity;

[0091] Add a new m-dimensional terminal constraint ψ i (x(t f ), t f ), i=1, 2,...,m, and ψ i (x(t f ), tf )=x n+i (t f )=0.

[0092]

[0093] The above x n+i (t 0 )=0 and ψ i (x(t f ), t f )=x n+i (t f )=0 is equivalent to the process constraint C(x(t),u(t))≤0, so the newly added m-dimensional state quantity and the newly added m-dimensional terminal constraint can be used to replace the m-dimensional process constraint in the first optimal control problem.

[0094] (2) The newly added m-dimensional state quantity and the newly added m-dimensional terminal constraint are used to replace the m-dimensional process constraint in the first optimal control problem to obtain the second optimal control problem, where the second optimal control problem is: ψ * (x * (t f ), t f )=0, where the augmented state quantity x * (t) has n+m dimensions, augmented terminal constraint Ψ * (x * (t f ), t f ) has l+m dimensions, is the performance indicator, is the augmented nonlinear dynamics equation.

[0095] Specifically, * indicates augmentation.

[0096] In an optional embodiment of the present invention, the control amount guess value is updated based on the discrete state quantity, deviation and the control amount update formula in the analytical iterative form, which specifically includes the following steps:

[0097] Update the formula according to the control quantity Update the guessed value of the control amount to obtain the updated guessed value of the control amount, where U k represents the updated guess value of the control amount, Q k Represents the weight matrix of the control amount, B k Represents the sensitivity matrix calculated based on discrete state quantities, dY N Indicates deviation, Indicates the guess value of the control amount.

[0098] In an optional embodiment of the present invention, the process of solving the control amount update formula specifically includes the following steps:

[0099] (1) The second optimal control problem is discretized by using the Euler method to obtain the second optimal control problem in a discrete form;

[0100] Specifically, the second optimal control problem is ψ * (x * (t f ), t f )=0, the second optimal control problem is discretized by Euler method to obtain the second optimal control problem in discrete form, whose state equation (obtained from the above-mentioned augmented nonlinear dynamic equation), output equation (obtained from the above-mentioned augmented terminal constraint) and performance index are as follows:

[0101] X k+1 =F k (X k , U k )

[0102] Y k =h(X k )

[0103] Among them, the state quantity is X∈R n+m (R represents the real number domain), the control quantity is U∈R p , the output variable (i.e. terminal output) is Y∈R l+m , k = 1, 2, ..., N is the time step, Q k Represents the discrete control weight matrix.

[0104] (2) linearizing the final terminal output in the second optimal control problem in discrete form to obtain a linearized terminal output error;

[0105] Specifically, the output at the terminal moment (i.e., the final terminal output) Y is expanded using the Taylor series. N Expanding and ignoring high-order small quantities, we can get the linearized terminal output error as follows:

[0106] Among them, Y N Represents the final terminal output value, Indicates the expected terminal output value, dY N Represents the terminal output error (i.e., deviation, terminal output deviation), It represents the terminal output error of the linear representation (that is, the linearized terminal output error, specifically, the expression of the linearized terminal output error).

[0107] (3) linearizing the state equation in the second optimal control problem in discrete form to obtain the linearized state error;

[0108] Specifically, according to the state equation X k+1 =F k (X k , U k ), we can get the state error of the k+1th time step, that is, the linearized state error (obtained by Taylor expansion of the state equation):

[0109]

[0110] Let k = N-1, then the linear state error is expressed as:

[0111]

[0112] (4) obtaining a new linearized terminal output error according to the linearized terminal output error and the linearized state quantity error;

[0113] Specifically, the terminal output error can be expressed as a linear combination of the initial state quantity deviation and the control quantity deviation of each step (i.e., the new linearized terminal output error) through iteration, as shown below:

[0114] dY N =AdX 1 +B 1 Ud 1 +B 2 Ud 2 +…+B N-1 Ud N-1

[0115] ,in,

[0116] Since the initial conditions are specified, there is no error in the initial value of the state quantity, that is, dX 1 =0, the above new linear terminal output error can be simplified to:

[0117] B k Represents the sensitivity matrix, which can be calculated recursively as shown below, thus greatly saving the calculation time.

[0118]

[0119] (5) Determine the augmented performance index based on the new linear terminal output error and performance index;

[0120] Specifically, it can be seen that The number of unknowns in is greater than the number of constraints, and the solution of the equation is not unique. Therefore, we can As the constraint condition of the system, the augmented performance index shown in the following formula is obtained (the original performance index is: ):

[0121] in, Indicates the guess value of the control amount, dU k represents the control deviation (k represents the time step), represents the updated guess value of the control amount, and λ represents the Lagrange multiplier.

[0122] (6) The augmented performance index is derived based on the first-order necessary conditions to obtain the control quantity deviation formula;

[0123] Specifically, the augmented performance index is derived according to the first-order necessary conditions, and we get:

[0124]

[0125] From this, we can get the control deviation formula, that is, the expression for the control deviation (where only the Lagrange multiplier λ is unknown), as shown below:

[0126] (7) The control quantity update formula is derived based on the control quantity deviation formula and the new linear terminal output error.

[0127] Specifically, in order to obtain the Lagrange multiplier λ, the control deviation formula is Substitute the new linear terminal output error You can get:

[0128] ,in,

[0129] Assumption A λ is non-singular, then the expression of the Lagrange multiplier can be obtained as:

[0130] Then the expression of Lagrange multiplier is Substitute the control quantity deviation formula into You can get:.

[0131] Then the control quantity update formula is:

[0132] In an optional embodiment of the present invention, the discrete state quantity and the final terminal output value are calculated according to the control quantity guess value and the second optimal control problem in discrete form, which specifically includes the following steps:

[0133] Substitute the guessed value of the control quantity into the second optimal control problem in discrete form for iterative calculation to obtain the discrete state quantity and the final terminal output value.

[0134] Specifically, the second optimal control problem in discrete form is expressed as: k+1 =F k (X k , U k )Y k =h(X k )

[0135] The current control amount guess value U k and the current state X k Known, then we can use X k+1 =F k (X k , U k ) Iterate to get the state quantity X of the next time step k+1 , because k = 1, 2, ..., N, so we can get multiple discrete state quantities, and at the same time, we can use Y k =h(X k ) to get the final terminal output value Y n .

[0136] The online optimal guidance method based on secondary rectification of the present invention realizes a universal solution algorithm with an analytical iterative form, which is concise in form and has significant effect; the algorithm is insensitive to initial values ​​and can be corrected from zero control to optimal control without the need for adjacent standard control; the algorithm has high calculation accuracy and fast convergence speed; the algorithm consumes very little time for a single iteration and the total time consumption is relatively low.

[0137] The present invention is further described in detail below by taking the first-stage online trajectory planning process of an electromagnetic booster rocket as an example:

[0138] Under the assumption of a flat earth, the rotational motion of the electromagnetic booster rocket around the center of mass is ignored, and it is regarded as a controllable particle. Only its motion in the longitudinal plane is considered, and its dynamic model is expressed as follows:

[0139]

[0140] Among them, h is the height, x is the range, V is the speed, θ is the ballistic inclination, α is the angle of attack, u is the control quantity, m is the mass, T is the engine thrust, D and L are the aerodynamic drag and lift respectively, L = qSC L , D = qSC D, m = 15000-126.8t, T = 325572, where q is the dynamic pressure, S is the reference area of ​​the electromagnetic booster rocket, which is 1.1304m 2 , C L and C D are the lift coefficient and drag coefficient of the electromagnetic booster rocket, which can be obtained by polynomial fitting, as shown in the attached figure. Figure 2 and Figure 3 As shown, v is the local atmospheric density, which is a function of the height h, as shown in the following formula, where ρ 0 is the atmospheric density at sea level, taken as 1.225 kg / m 3 , b is a constant, taken as -0.00013889.

[0141] ρ=ρ 0 e bh .

[0142] The optimal control problem to be solved for the online trajectory planning of the first stage of an electromagnetic booster rocket with limited attack angle and dynamic pressure (i.e., process constraints) is expressed as:

[0143]

[0144] |α|≤α max

[0145] q≤q max .

[0146] The simulation parameter settings in this example are described in the following table (the subscript f indicates the end time).

[0147]

[0148]

[0149] All simulation programs are completed in a personal computer with a CPU main frequency of 2.3GHz and a MATLAB 2022b simulation environment. A more efficient compilation environment will improve computing efficiency.

[0150] The method proposed in this invention (i.e., the online optimal guidance method based on secondary rectification, referred to as RQMPC) is used to solve the example, and the optimal solution is calculated by the optimal control software GPOPSⅡ (an authoritative and accurate toolkit for solving optimal guidance instructions offline). The simulation results are as follows: Figure 4-Figure 9 As shown. It can be seen that the solution result (RQMPC) of the algorithm is consistent with the calculation result of GPOPSⅡ and meets the optimality. Among them, the control quantity curve is highly nonlinear, the attack angle modulus satisfies the constraint of less than 10 degrees, and the dynamic pressure satisfies the constraint of less than 120 kPa.

[0151] Set different simulation conditions as shown in the table, the simulation results are as follows Fig.10 As shown, it can be seen that the algorithm solution results are consistent with the optimal solution results calculated by GPOPSⅡ.

[0152] parameter Case 0 Case 1 Case 2 <![CDATA[θ 0 ]]> 30deg 29deg 28deg

[0153] Compared with the optimal solution obtained after removing the process constraints, the simulation results are as follows Fig.11 As shown, we can see that there is a big difference between the two, which reflects the necessity of algorithms with process constraints.

[0154] Output the results of different iteration times. The simulation results are as follows: Fig.12 As shown, we can see that the algorithm is not sensitive to the initial value and can be corrected from zero control to optimal control without the need for adjacent standard control.

[0155] Output the terminal output error at different iteration times, such as Fig.13 As shown, we can see that the algorithm has high calculation accuracy (each parameter can reach the corresponding error value) and fast convergence speed (it can converge in about 20 iterations).

[0156] Since the algorithm has an analytical iterative form, it does not need to solve the optimization problem in each iteration, so the average single iteration time is extremely small, only 2.8ms in this example. In terms of total solution time, the algorithm takes 0.119s, while GPOPSⅡ takes 15.29s to solve the same example, which is only 0.778% of its solution time.

[0157] Embodiment 2:

[0158] An embodiment of the present invention also provides an online optimal guidance device based on secondary rectification. The online optimal guidance device based on secondary rectification is mainly used to execute the online optimal guidance method based on secondary rectification provided in Embodiment 1 of the present invention. The online optimal guidance device based on secondary rectification provided in the embodiment of the present invention is specifically introduced below.

[0159] Fig.14 is a schematic diagram of an online optimal guidance device based on secondary rectification according to an embodiment of the present invention, such as Fig.14 As shown, the device mainly includes: a building unit 10, a conversion unit 20, a discrete unit 30, an acquisition and calculation unit 40, a calculation and judgment unit 50, an update unit 60 and a return execution unit 70, wherein:

[0160] A building unit is used to build a dynamic model of the aircraft to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance;

[0161] A conversion unit, used for converting the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectification function;

[0162] A discrete unit is used to discretize the second optimal control problem by using the Euler method to obtain the second optimal control problem in a discrete form;

[0163] An acquisition and calculation unit, used to acquire a control quantity guess value corresponding to the control quantity in the second optimal control problem in a discrete form, and calculate a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in a discrete form;

[0164] A calculation and judgment unit, used to calculate the deviation between the final terminal output value and the expected terminal output value, and to judge whether the deviation is less than a preset threshold;

[0165] An updating unit is used to update the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in the form of analytical iteration to obtain an updated control quantity guess value if it is not less than a preset threshold, wherein the control quantity update formula is obtained by solving the second optimal control problem through a model prediction static programming method;

[0166] The return execution unit is used to use the updated control quantity guess value as the control quantity guess value, return to execute the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in discrete form, until the deviation is less than a preset threshold, and use the control quantity guess value corresponding to the deviation less than the preset threshold as the optimal guidance instruction.

[0167] In an embodiment of the present invention, an online optimal guidance method based on secondary rectification is provided, comprising: constructing an aircraft dynamics model to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance; converting the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, performance indicators and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented using a secondary rectification function; discretizing the second optimal control problem using the Euler method to obtain a second optimal control problem in a discrete form; obtaining a control quantity guess value corresponding to the control quantity in the discrete form of the second optimal control problem, and calculating the control quantity guess value according to the control quantity guess value and the second optimal control problem in the discrete form; The method comprises the steps of calculating the discrete state quantity and the final terminal output value; calculating the deviation between the final terminal output value and the expected terminal output value, and judging whether the deviation is less than a preset threshold value; if not less than the preset threshold value, updating the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in the form of analytical iteration to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem by the model prediction static programming method; taking the updated control quantity guess value as the control quantity guess value, returning to execute the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form, until the deviation is less than the preset threshold value, and taking the control quantity guess value corresponding to the deviation less than the preset threshold value as the optimal guidance instruction. It can be seen from the above description that in the online optimal guidance method based on secondary rectification of the present invention, the first optimal control problem containing process constraints can be converted into a second optimal control problem without process constraints, and then the second optimal control problem is solved by the model prediction static programming method to obtain an analytical iterative form of control quantity update formula, and the control quantity guess value is iteratively corrected based on the above control quantity update formula to obtain the optimal guidance instruction, that is, a general solution algorithm with an analytical iterative form is realized, the form is concise, and since it is an analytical iterative form of control quantity update formula, a single iteration takes very little time, and the total time consumption is less, which greatly improves the solution efficiency. In addition, the above-mentioned method of converting the first optimal control problem containing process constraints into a second optimal control problem without process constraints, and then solving the second optimal control problem by the model prediction static programming method improves the accuracy of the solution, has good versatility, and alleviates the technical problems of poor accuracy, low efficiency and poor versatility of the existing solution methods for optimal control problems with process constraints.

[0168] Optionally, the device is also used to: if the deviation is smaller than a preset threshold, use the guessed value of the control amount as the optimal guidance instruction.

[0169] Optionally, the conversion unit is further used for: if the process constraint of the first optimal control problem is C(x(t), u(t))≤0, the state quantity x(t) has n dimensions, the process constraint C(x(t), u(t)) has m dimensions, and the terminal constraint ψ(x(t f ), t f ) has l dimensions, then delete the process constraints and add a new m-dimensional state quantity x n+i (t), i = 1, 2, ..., m, and x n+i (t 0 )=0, and at the same time, a new m-dimensional terminal constraint ψ is added i (x(t f ), t f ), i=1, 2,...,m, and ψ i (x(t f ), t f )=x n+i (t f )=0, where ReQU(x) represents the secondary rectification function, u(t) represents the control quantity; the newly added m-dimensional state quantity and the newly added m-dimensional terminal constraint are used to replace the m-dimensional process constraint in the first optimal control problem to obtain the second optimal control problem, where the second optimal control problem is:

[0170] ψ * (x * (t f ), t f )=0, where the augmented state quantity x * (t) has n+m dimensions, augmented terminal constraint Ψ * (x * (t f ), t f ) has l+m dimensions, is the performance indicator, is the augmented nonlinear dynamics equation.

[0171] Optionally, the updating unit is further used to: update the formula according to the control amount

[0172] Update the guessed value of the control amount to obtain the updated guessed value of the control amount, where U k represents the updated guess value of the control amount, Q k Represents the weight matrix of the control amount, B k Represents the sensitivity matrix calculated based on discrete state quantities, dY N Indicates deviation, Indicates the guess value of the control amount.

[0173] Optionally, the device is also used to: discretize the second optimal control problem using the Euler method to obtain the second optimal control problem in discrete form; linearize the final terminal output in the discrete form of the second optimal control problem to obtain a linearized terminal output error; linearize the state equation in the discrete form of the second optimal control problem to obtain a linearized state quantity error; obtain a new linearized terminal output error based on the linearized terminal output error and the linearized state quantity error; determine an augmented performance indicator based on the new linearized terminal output error and the performance indicator; derive the augmented performance indicator based on the first-order necessary condition to obtain a control quantity deviation formula; derive a control quantity update formula based on the control quantity deviation formula and the new linearized terminal output error.

[0174] Optionally, the acquisition and calculation unit is further used to: substitute the guessed value of the control quantity into a second optimal control problem in a discrete form for iterative calculation to obtain a discrete state quantity and a final terminal output value.

[0175] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0176] like Fig.15 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the online optimal guidance method based on secondary rectification as described above.

[0177] Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, the online optimal guidance method based on secondary rectification can be executed.

[0178] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the above method in combination with its hardware.

[0179] Corresponding to the above-mentioned online optimal guidance method based on secondary rectification, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned online optimal guidance method based on secondary rectification.

[0180] The online optimal guidance device based on secondary rectification provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0181] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0182] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0183] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0185] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the online optimal guidance method based on secondary rectification described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0186] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0187] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application, rather than to limit it. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solution recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiment of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An online optimal guidance method based on secondary rectification, characterized in that: include: Build a dynamic model of the aircraft to obtain the first optimal control problem that needs to be solved for online guidance, including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations; The first optimal control problem is converted into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations by state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectification function; The second optimal control problem is discretized by using the Euler method to obtain a second optimal control problem in a discrete form; Obtaining a control quantity guess value corresponding to the control quantity in the second optimal control problem in the discrete form, and calculating a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form; Calculating the deviation between the final terminal output value and the expected terminal output value, and determining whether the deviation is less than a preset threshold; If it is not less than the preset threshold, the control quantity guess value is updated based on the discrete state quantity, the deviation and the control quantity update formula in analytical iterative form to obtain an updated control quantity guess value, wherein the control quantity update formula is obtained by solving the second optimal control problem through a model prediction static programming method; The updated control quantity guess value is used as the control quantity guess value, and the step of calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in discrete form is returned to execute until the deviation is less than the preset threshold value, and the control quantity guess value corresponding to the deviation being less than the preset threshold value is used as the optimal guidance instruction.

2. The method according to claim 1, characterized in that The method further comprises: If the deviation is smaller than the preset threshold, the control amount guess value is used as the optimal guidance instruction.

3. The method according to claim 1, characterized in that The first optimal control problem is transformed into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, including: If the process constraint of the first optimal control problem is C(x(t), u(t))≤0, the state quantity x(t) has n dimensions, the process constraint C(x(t), u(t)) has m dimensions, and the terminal constraint ψ(x(t f ), t f ) has l dimensions, then delete the process constraints and add a new m-dimensional state quantity x n+i (t), i = 1, 2, ..., m, and x n+i (t0) = 0, and at the same time, a new m-dimensional terminal constraint ψ is added i (x(t f ), t f ), i=1, 2,...,m, and ψ i (x(t f ), t f )=x n+i (t f )=0, where ReQU(x) represents the secondary rectification function, u(t) represents the control quantity; The m-dimensional process constraint in the first optimal control problem is replaced by the newly added m-dimensional state quantity and the newly added m-dimensional terminal constraint to obtain the second optimal control problem, wherein the second optimal control problem is: ψ * (x * (t f ), t f )=0, where the augmented state quantity x * (t) has n+m dimensions, the augmented terminal constraint ψ * (x * (t f ), t f ) has l+m dimensions, For the performance index, is the augmented nonlinear dynamic equation.

4. The method according to claim 1, characterized in that: The control amount guess value is updated based on the discrete state quantity, the deviation and the control amount update formula in an analytical iterative form, including: Update the formula according to the control amount The control amount guess value is updated to obtain the updated control amount guess value, where U k represents the updated control amount guess value, Q k Represents the weight matrix of the control amount, B k represents the sensitivity matrix calculated based on the discrete state quantity, dY N represents the deviation, represents the estimated value of the control amount.

5. The method according to claim 1, characterized in that The process of solving the control quantity update formula includes: The second optimal control problem is discretized by using the Euler method to obtain a second optimal control problem in a discrete form; Linearizing the final terminal output in the second optimal control problem in the discrete form to obtain a linearized terminal output error; Linearizing the state equation in the second optimal control problem in the discrete form to obtain a linearized state quantity error; Obtaining a new linearized terminal output error according to the linearized terminal output error and the linearized state quantity error; Determining an augmented performance indicator according to the new linearized terminal output error and the performance indicator; The augmented performance index is derived according to the first-order necessary condition to obtain a control quantity deviation formula; The control amount update formula is derived according to the control amount deviation formula and the new linear terminal output error.

6. The method according to claim 1, characterized in that Calculating the discrete state quantity and the final terminal output value according to the control quantity guess value and the second optimal control problem in discrete form, including: Substitute the control quantity guess value into the second optimal control problem in discrete form for iterative calculation to obtain the discrete state quantity and the final terminal output value.

7. An online optimal guidance device based on secondary rectification, characterized in that: include: A building unit is used to build a dynamic model of the aircraft to obtain a first optimal control problem including process constraints, terminal constraints, performance indicators and nonlinear dynamic equations required for online guidance; a conversion unit, configured to convert the first optimal control problem into a second optimal control problem without process constraints and including augmented terminal constraints, the performance index and augmented nonlinear dynamic equations through state augmentation and terminal constraint augmentation, wherein the state augmentation is implemented by a quadratic rectification function; A discrete unit, used for discretizing the second optimal control problem by using the Euler method to obtain the second optimal control problem in a discrete form; An acquisition and calculation unit, used to acquire a control quantity guess value corresponding to the control quantity in the second optimal control problem in the discrete form, and calculate a discrete state quantity and a final terminal output value according to the control quantity guess value and the second optimal control problem in the discrete form; a calculation and judgment unit, configured to calculate a deviation between the final terminal output value and the expected terminal output value, and to judge whether the deviation is less than a preset threshold; an updating unit, configured to update the control quantity guess value based on the discrete state quantity, the deviation and the control quantity update formula in analytical iterative form to obtain an updated control quantity guess value if it is not less than the preset threshold, wherein the control quantity update formula is obtained by solving the second optimal control problem through a model prediction static programming method; A return execution unit is used to use the updated control quantity guess value as the control quantity guess value, return to execute the step of calculating discrete state quantities and final terminal output values ​​according to the control quantity guess value and the second optimal control problem in discrete form, until the deviation is less than the preset threshold, and use the control quantity guess value corresponding to the deviation being less than the preset threshold as the optimal guidance instruction.

8. The device according to claim 7, characterized in that The device is also used for: If the deviation is smaller than the preset threshold, the control amount guess value is used as the optimal guidance instruction.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to execute the method according to any one of claims 1 to 6.

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