A method for predicting a descent apex in a flight profile

Abstract

The present application belongs to the technical field of flight management, and particularly relates to a method for predicting a descent vertex in a flight profile. The method comprises initializing a predicted state of a descent end point according to a formulated flight plan, inversely constructing a descent path from the descent end point to a cruising altitude, then performing a predicted state recursion calculation according to an aircraft performance characteristic from a current flight position along the descent path to the descent end point, so as to obtain a weight prediction value at the descent end point. This cycle is repeated until the weight at the descent end point in the previous and subsequent iteration processes changes by less than a specified threshold. The method predicts the position of the descent vertex in the flight profile by means of a cyclic bisection iteration, and has the characteristics of high accuracy, fast calculation speed and strong versatility.

Description

Technical Field

[0001] This invention relates to the field of flight management technology, and in particular to a method for predicting the descent peak in a flight profile. Background Technology

[0002] Flight Management System (FMS) is a standard feature of modern aircraft, especially civil airliners. Based on a pre-defined flight plan, and while meeting flight plan constraints and aircraft performance constraints, the FMS constructs a predicted flight profile and guides the aircraft to fly automatically along this profile, thereby significantly reducing the operational workload of the crew during flight.

[0003] In the process of constructing a flight profile, predicting the descent peak is a crucial step. Descenting too early will result in the aircraft spending too much time flying at low altitudes, reducing flight efficiency; descenting too late will lead to insufficient descent distance, preventing the aircraft from completing the descent as planned. Since the descent peak cannot be directly obtained from the plan, most existing algorithms calculate the intersection point with the cruise altitude by recursively calculating from the final approach point according to aerodynamic principles, and use this point as the predicted descent peak. Summary of the Invention

[0004] The objective of this invention is to propose a method for predicting the descent vertex in a flight profile. This method constructs a descent path while satisfying flight plan constraints and aircraft performance constraints, and optimizes the predicted state of the descent vertex through iteration. The descent vertex state prediction is considered complete when the predicted weight change at the descent endpoint is less than a given threshold.

[0005] The technical solution of this invention: A method for predicting the descent peak in a flight profile is proposed, comprising the following steps:

[0006] S1, obtain flight plan constraint information and aircraft performance constraint information;

[0007] S2, parameter initialization settings; including initialization settings for descent end point weight, initialization settings for predicted weight iteration value at descent end point, initialization settings for upper limit of iteration weight, initialization settings for lower limit of iteration weight, and judgment threshold settings;

[0008] S3, starting from the end point of descent to the cruise altitude, construct a descent path that satisfies the flight plan constraints and aircraft performance constraints, and calculate the predicted descent peak state parameters in the flight profile;

[0009] S4. Based on the predicted descent peak state parameters obtained in step S3, predict the trajectory along the descent path until the descent end point, and calculate the predicted descent end point state parameters; the predicted descent end point state parameters include the predicted descent end point weight.

[0010] S5, calculate the ratio of the absolute value of the difference between the upper limit of the iteration weight and the lower limit of the iteration weight to the upper limit of the iteration weight, and determine whether it meets the judgment threshold requirement set in step S2. If it meets the threshold, proceed directly to S7; otherwise, execute S6.

[0011] S6: By comparing the predicted weight at the end of descent calculated in step S4 with the iterative value of the predicted weight at the end of descent, the upper limit or lower limit of the iterative weight is updated using the iterative value of the predicted weight at the end of descent; then, the iterative value of the predicted weight at the end of descent is updated using the updated upper limit and lower limit of the iterative weight; the updated iterative value of the predicted weight at the end of descent is then used to update the predicted weight at the end of descent; then, the process jumps to S3.

[0012] S7 outputs the predicted descent vertex state parameters.

[0013] In one possible embodiment, in step S1, the constraint information of the flight plan includes the segment type, segment start and end point positions, segment entry angle, segment exit angle, and the position, type, and value of altitude and speed constraints for each segment in the flight plan; the constraint information of the aircraft performance at the segment start and end point positions includes takeoff weight, takeoff speed, and approach speed.

[0014] In one possible embodiment, step S2 specifically includes:

[0015] S21, initialize the weight at the end of descent to the aircraft's takeoff weight;

[0016] S22, initialize the position of the descent end point to the position of the end point of the last flight segment;

[0017] S23, initialize the altitude of the descent end point to the altitude constraint value of the last flight segment;

[0018] S24, initialize the velocity at the end of descent to the approach velocity;

[0019] S25, initialize the iterative weight upper limit value to the aircraft's takeoff weight;

[0020] S26, initialize the iterative weight lower limit to half the aircraft's takeoff weight;

[0021] S27, initialize the predicted weight iteration value of the descent end point to the aircraft's takeoff weight;

[0022] S28, Set the threshold for determining the termination of the iteration.

[0023] In one possible embodiment, in step S3, the predicted descending vertex state parameters include the predicted descending vertex height, the predicted descending vertex velocity, the predicted descending vertex horizontal position, and the predicted descending vertex weight.

[0024] In one possible embodiment, in step S4, the predicted descent endpoint state parameters further include the predicted descent endpoint height, the predicted descent endpoint velocity, and the predicted descent endpoint horizontal position.

[0025] In one possible embodiment, step S5 specifically includes the following steps:

[0026] S51, calculate the difference between the upper limit of the iteration weight Mhigh and the lower limit of the iteration weight Mlow;

[0027] S52, if |Mhigh-Mlow| / Mhigh<ε, then the iteration terminates and jumps to step S7;

[0028] S53, if |Mhigh-Mlow| / Mhighε, then proceed to step S6.

[0029] In one possible embodiment, step S6 specifically includes the following steps:

[0030] S61, compare the predicted weight at the end of descent calculated in step S4 with the iterative value of the predicted weight at the end of descent; if the predicted weight at the end of descent is greater than the iterative value of the predicted weight at the end of descent, update the iterative value of the predicted weight at the end of descent to the lower limit of the iterative weight Mlow, keep the upper limit of the iterative weight Mhigh unchanged, and update the predicted weight at the end of descent to a*Mhigh+b*Mlow, where a is the upper limit coefficient of the iterative weight, b is the lower limit coefficient of the iterative weight, and a+b=1;

[0031] S62, if the predicted weight at the end of descent is less than or equal to the predicted weight iteration value at the end of descent, the predicted weight iteration value at the end of descent is updated and assigned to the upper limit of the iteration weight Mhigh, the lower limit of the iteration weight Mlow remains unchanged, and the predicted weight at the end of descent is updated to a*Mhigh+b*Mlow, where a is the upper limit coefficient of the iteration weight, b is the lower limit coefficient of the iteration weight, and a+b=1.

[0032] In one possible embodiment, in step S6, a = b = 1 / 2.

[0033] The advantages and effects of this invention can be:

[0034] (1) By iteratively calculating the descent vertex state, the prediction accuracy of the descent vertex in the flight profile can be improved;

[0035] (2) The algorithm is relatively simple to implement and converges quickly. In the test plan from Xi'an to Beijing, given the iterative convergence threshold ε = 0.001, it was found that the convergence state of the descent peak could be reached after 11 tests.

[0036] (3) The algorithm is universal and has little correlation with specific prediction algorithms. The algorithm can be embedded into different types of trajectory prediction and optimization algorithms. Attached Figure Description

[0037] To more clearly illustrate the technical solutions implemented in this invention, a simple explanation of the accompanying drawings used in the description of this invention will be provided below. Obviously, the drawings described below are merely some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0038] Figure 1 This is a flowchart illustrating the method for predicting the descent peak in a flight profile according to a preferred embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of a typical descent phase profile in the flight profile described in a preferred embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] The features of various aspects of the embodiments of the present invention will now be described in detail. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can also be practiced without these specific details. The following description of the embodiments is merely intended to provide a better understanding of the invention by illustrating examples. The invention is not limited to any specific setups and methods provided below, but covers all improvements, substitutions, etc., to product structures and methods without departing from the spirit of the invention. In the various drawings and the following description, well-known structures and techniques are not shown to avoid unnecessarily obscuring the invention.

[0042] It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other, and the various embodiments can be referenced and cited in each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] Figure 1 This is a flowchart illustrating a method for predicting the descent vertex in a flight profile according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of a typical descent phase profile in the flight profile described in this invention. As shown in

[11] , the cruise altitude in the flight profile is determined by the crew input; as shown in

[12] , the estimated position of the descent peak in the flight profile is shown; as shown in

[13] , the descent path segment predicted in reverse from the descent end point is shown; as shown in

[14] , the altitude constraint requirements defined in the flight plan are defined at a certain waypoint in the flight plan, representing the flight altitude requirements that need to be met at that waypoint; as shown in

[15] , the predicted position of the descent end point is shown; and as shown in

[16] , the location of the landing airport runway is shown.

[0045] like Figure 1 As shown, the method may include the following steps:

[0046] S101, Read the flight plan and extract segment constraint information, which requires extracting the altitude and speed constraints for the descent phase from the flight plan;

[0047] S102, determine the convergence threshold ε = 0.001;

[0048] S103, set the initial value of the descent end point weight iteration number i to 1;

[0049] S104, Set the upper limit value of iteration weight M high M is the aircraft's takeoff weight. TW Set the lower limit of the iteration weight to the aircraft takeoff weight M. TW Half of the predicted weight at the end of descent is set to M as the initial value for the iteration. EOD =M TW Meanwhile, the initial value of the predicted weight at the end of descent is set to M. i =M EOD ;

[0050] S105, calculate the descent end point state, where the descent end point weight is determined by

[104] , the descent end point position is determined by the position of the final approach point and its altitude constraints, and the descent end point speed is determined by the aircraft's approach speed;

[0051] S106, according to any disclosed method, such as using a flight management system conforming to ARINC 702A standards, input a flight plan and activate it, triggering its trajectory prediction function. Starting from the descent endpoint, construct the descent path backward to the cruise altitude. The constructed descent path satisfies the altitude and speed constraints in the flight plan, while also meeting the aircraft's flight performance limitations. The intersection of the backward-constructed descent path and the cruise altitude is taken as the descent peak prediction state S_TOD. i ;

[0052] S107, according to any disclosed method, such as using a flight management system that meets the ARINC 702A standard, input a flight plan and activate it, triggering its trajectory prediction function. Starting from the aircraft's current state, using the predicted descent peak position, predict the descent endpoint position along the constructed descent path to obtain the descent endpoint state, whose predicted weight is M. p It is worth noting that at this point, only the predicted position of the descent peak is used; the aircraft's operational status at the descent peak is calculated from the prediction.

[0053] S108, Calculate |M high -M low | / M high Determine |M high -M low | / M high If ε is true, execute S109; otherwise, execute S110.

[0054] S109, Descent vertex prediction calculation complete. Output descent vertex prediction state S_TOD. i ;

[0055] S110, Update the predicted weight value at the end of descent, Execute: M EOD =M p ;

[0056] S111, determine M EOD >M i If the condition is true, execute S112; otherwise, execute S113.

[0057] S112, update the iteration weight lower limit value, execute: M low =M i ;

[0058] S113, update the iterative weight limit value, execute: M high =M i ;

[0059] S114, update the weight iteration count i at the end of descent, execute: i = i + 1;

[0060] S115, Update the predicted weight iteration value at the descent endpoint, Execute: M i =(M high +M low ) / 2;

[0061] S116, Update M EOD Value: M EOD =M i .

[0062] It should be noted that the above process operations can be combined to varying degrees. For the sake of simplicity, the implementation methods of various combinations will not be elaborated further. Those skilled in the art can flexibly adjust or combine the order of the steps of the above method (or the position of the components of the product) according to the actual situation.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the descent peak in a flight profile, characterized in that: Includes the following steps: S1, Obtain flight plan constraint information and aircraft performance constraint information; In step S1, the flight plan constraint information includes the segment type, segment start and end point positions, segment entry angle, segment exit angle, and the position, type, and value of altitude and speed constraints for each segment in the flight plan; The aircraft performance constraint information in the segment start and end point positions includes takeoff weight, takeoff speed, and approach speed; S2, parameter initialization settings; This includes initial settings for the weight at the end of descent, the predicted weight iteration value at the end of descent, the upper limit of the iteration weight, the lower limit of the iteration weight, and the judgment threshold. S3, starting from the descent endpoint to the cruising altitude, construct a descent path that satisfies both flight plan constraints and aircraft performance constraints, and calculate the predicted descent peak state parameters in the flight profile; step S2 specifically includes: S21, initialize the weight at the end of descent to the aircraft's takeoff weight; S22, initialize the position of the descent end point to the position of the end point of the last flight segment; S23, initialize the altitude of the descent end point to the altitude constraint value of the last flight segment; S24, initialize the velocity at the end of descent to the approach velocity; S25, initialize the iterative weight upper limit value to the aircraft's takeoff weight; S26, initialize the iterative weight lower limit to half the aircraft's takeoff weight; S27, initialize the predicted weight iteration value of the descent end point to the aircraft's takeoff weight; S28, Set the threshold for determining iteration termination; S4. Based on the predicted descent peak state parameters obtained in step S3, predict the trajectory along the descent path until the descent end point, and calculate the predicted descent end point state parameters; the predicted descent end point state parameters include the predicted descent end point weight. S5, calculate the ratio of the absolute value of the difference between the upper limit of the iteration weight and the lower limit of the iteration weight to the upper limit of the iteration weight, and determine whether it meets the judgment threshold requirement set in step S2. If it meets the threshold, proceed directly to S7; otherwise, execute S6. S6: By comparing the predicted weight at the end of descent calculated in step S4 with the iterative value of the predicted weight at the end of descent, the upper limit or lower limit of the iterative weight is updated using the iterative value of the predicted weight at the end of descent; then, the iterative value of the predicted weight at the end of descent is updated using the updated upper limit and lower limit of the iterative weight; the updated iterative value of the predicted weight at the end of descent is then used to update the predicted weight at the end of descent; then, the process jumps to S3. Step S6 specifically includes the following steps: S61, compare the predicted descent endpoint weight calculated in step S4 with the iterative value of the predicted descent endpoint weight; if the predicted descent endpoint weight is greater than the iterative value of the predicted descent endpoint weight, update and assign the iterative value of the predicted descent endpoint weight to the lower limit M of the iterative weight. low Iteration weight limit M high The predicted weight iteration value at the descent endpoint remains unchanged and is updated to a*M. high +b*M low Where a is the upper limit coefficient of iteration weight and b is the lower limit coefficient of iteration weight, a+b=1; S62, if the predicted descent endpoint weight is less than or equal to the predicted descent endpoint weight iteration value, update and assign the predicted descent endpoint weight iteration value to the iteration weight upper limit M. high Iteration weight lower limit M low The predicted weight iteration value at the descent endpoint remains unchanged and is updated to a*M. high +b*M low Where a is the upper limit coefficient of iteration weight and b is the lower limit coefficient of iteration weight, and a+b=1; S7 outputs the predicted descent vertex state parameters.

2. The method for predicting the descent peak in a flight profile according to claim 1, characterized in that: In step S3, the predicted descent vertex state parameters include the predicted descent vertex height, the predicted descent vertex velocity, the predicted descent vertex horizontal position, and the predicted descent vertex weight.

3. The method for predicting the descent peak in a flight profile according to claim 1, characterized in that: In step S4, the predicted descent endpoint state parameters also include the predicted descent endpoint height, the predicted descent endpoint velocity, and the predicted descent endpoint horizontal position.

4. The method for predicting the descent peak in a flight profile according to claim 1, characterized in that: Step S5 specifically includes the following steps: S51, Calculate the upper limit of the iteration weight M high With the lower limit of iterative weight M low The difference; S52, if |M high -M low | / M high If ε < ε, the iteration terminates and proceeds to step S7. S53, if |M high -M low | / M high If ε, then proceed to step S6.

5. The method for predicting the descent peak in a flight profile according to claim 1, characterized in that: In step S6, a = b = 1 / 2.

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