CN-121995955-A - Aircraft track optimization method and device

CN121995955ACN 121995955 ACN121995955 ACN 121995955ACN-121995955-A

Abstract

The invention provides an aircraft track optimization method and device, which relate to the technical field of aircraft control and comprise the following steps: based on the relative motion state data, interference estimation data and feedback control data are determined, linear superposition and overload limiting processing are carried out, and a composite guidance instruction is determined to control the aircraft. The method and the device provided by the invention have the advantages that the three-dimensional nonlinear relative motion model is constructed, the feedforward interference compensation signal and the feedback optimal control signal are cooperatively fused, so that the interference influence is eliminated from the source, the optimality of the flight track is ensured, and the interception precision and response speed of the aircraft to a high maneuvering target under a high dynamic and strong interference environment are obviously improved through the cooperative action of a plurality of links such as model construction, interference estimation, optimal guidance law design, instruction fusion, track control and the like, so that the method and the device have extremely high engineering application value.

Inventors

WEI QINGLAI
DU YANBIN
NI DONGDONG
CHENG XIANG
WANG YUDONG
LI HONGYANG
JIAO SHANSHAN

Assignees

中国科学院自动化研究所

Dates

Publication Date: 20260508
Application Date: 20260127

Claims (10)

1. A method of aircraft trajectory optimization, comprising: acquiring relative motion state data of an aircraft and a tracking target; Inputting the relative motion state data into a nonlinear interference observer to determine interference estimation data, wherein the nonlinear interference observer is constructed based on a relative motion model of the aircraft and the tracking target; Constructing a cost function estimation network, and inputting the relative motion state data into the cost function estimation network to determine feedback control data; And determining a composite guidance instruction based on the interference estimation data and the feedback control data so as to control the aircraft based on the composite guidance instruction.
2. The aircraft trajectory optimization method of claim 1, wherein the nonlinear disturbance observer comprises an internal state variable and an observer gain matrix, and wherein the structure and parameters of the observer gain matrix are determined based on Lyapunov stability criteria.
3. The method of claim 2, wherein said inputting the relative motion state data into a nonlinear disturbance observer determines disturbance estimation data, comprising: Updating the internal state variables in the non-linear disturbance observer based on the relative motion state data; The disturbance estimation data is output based on a linear combination of the internal state variable and the relative motion state data.
4. The aircraft trajectory optimization method of claim 1, wherein the constructing a cost function estimation network, inputting the relative motion state data into the cost function estimation network to determine feedback control data, comprises: constructing the cost function estimation network based on a single hidden layer neural network; inputting the relative motion state data into the cost function estimation network to obtain a cost function estimation value; and determining the feedback control data based on the value of the value function estimation and by combining a weight matrix of the control input energy consumption item in the performance index function and a control input matrix of the relative motion model.
5. The aircraft trajectory optimization method of claim 1, wherein the weight update law includes an error feedback term; The error feedback item is determined by a performance index function and an error function determined by the cost function estimation network; the error feedback term is used for dynamically adjusting the update rate of the weight of the cost function estimation network.
6. The aircraft trajectory optimization method of claim 1, wherein the determining a composite guidance command based on the disturbance estimation data and the feedback control data comprises: Linearly superposing the interference estimation data and the feedback control data to generate an initial composite guidance instruction; And performing amplitude limiting processing on the initial composite guidance instruction based on a preset maximum acceleration threshold value of the aircraft to obtain the composite guidance instruction.
7. An aircraft trajectory optimization device, comprising: the acquisition module is used for acquiring relative motion state data of the aircraft and the tracking target; The feedforward module is used for inputting the relative motion state data into a nonlinear interference observer to determine interference estimation data, wherein the nonlinear interference observer is constructed based on a relative motion model of the aircraft and the tracking target; The feedback module is used for constructing a cost function estimation network, inputting the relative motion state data into the cost function estimation network to determine feedback control data, wherein the cost function estimation network adopts a weight update law to adjust the weight of the cost function estimation network; and the control module is used for determining a composite guidance instruction based on the interference estimation data and the feedback control data so as to control the aircraft based on the composite guidance instruction.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the aircraft trajectory optimization method according to any one of claims 1 to 6 when executing the computer program.
9. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the aircraft trajectory optimization method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the aircraft trajectory optimization method according to any one of claims 1 to 6.

Description

Aircraft track optimization method and device Technical Field The invention relates to the technical field of aircraft control, in particular to an aircraft track optimization method and device. Background In modern war, the track control performance of the accurate guided weapon directly determines the success rate of target interception, and particularly when facing targets with high maneuverability and rapid orbit transfer characteristics, the method has extremely high requirements on the robustness, the real-time performance and the optimality of the aircraft track optimization method. The motion state of a high maneuvering target has strong nonlinearity, time variability and uncertainty, and maneuvering acceleration of the high maneuvering target is used as unknown interference, so that the track tracking precision of an aircraft can be seriously damaged, and even an interception task is failed. The traditional aircraft track optimization method mainly comprises a proportional guidance law, a sliding mode control method, a self-adaptive control method and the like, and has the problems of insufficient robustness, low track tracking precision, low convergence speed, easy overload saturation and the like when intercepting a high maneuvering target. Therefore, how to achieve accurate, rapid and stable interception of high maneuvering targets in three-dimensional space becomes a technical problem to be solved in the industry. Disclosure of Invention The invention provides an aircraft track optimization method and device, which are used for solving the defects of insufficient robustness, low track tracking precision, low convergence speed and easy overload saturation during the interception of a high maneuvering target in the prior art, realizing the accurate, rapid and stable interception of the high maneuvering target in a three-dimensional space and improving the engineering practicability and reliability of the aircraft track optimization. The invention provides an aircraft track optimization method, which comprises the following steps: acquiring relative motion state data of an aircraft and a tracking target; Inputting the relative motion state data into a nonlinear interference observer to determine interference estimation data, wherein the nonlinear interference observer is constructed based on a relative motion model of the aircraft and the tracking target; Constructing a cost function estimation network, and inputting the relative motion state data into the cost function estimation network to determine feedback control data; And determining a composite guidance instruction based on the interference estimation data and the feedback control data so as to control the aircraft based on the composite guidance instruction. In some embodiments, the nonlinear disturbance observer comprises an internal state variable and an observer gain matrix, the structure and parameters of which are determined based on Lyapunov stability criteria. In some embodiments, said inputting the relative motion state data into a nonlinear disturbance observer determines disturbance estimation data, comprising: Updating the internal state variables in the non-linear disturbance observer based on the relative motion state data; The disturbance estimation data is output based on a linear combination of the internal state variable and the relative motion state data. In some embodiments, the constructing a cost function estimation network, inputting the relative motion state data into the cost function estimation network to determine feedback control data, comprises: constructing the cost function estimation network based on a single hidden layer neural network; inputting the relative motion state data into the cost function estimation network to obtain a cost function estimation value; and determining the feedback control data based on the value of the value function estimation and by combining a weight matrix of the control input energy consumption item in the performance index function and a control input matrix of the relative motion model. In some embodiments, the weight update law includes an error feedback term; The error feedback item is determined by a performance index function and an error function determined by the cost function estimation network; the error feedback term is used for dynamically adjusting the update rate of the weight of the cost function estimation network. In some embodiments, the determining a composite guidance command based on the disturbance estimation data and the feedback control data includes: Linearly superposing the interference estimation data and the feedback control data to generate an initial composite guidance instruction; And performing amplitude limiting processing on the initial composite guidance instruction based on a preset maximum acceleration threshold value of the aircraft to obtain the composite guidance instruction. The invention provides an aircraft trajectory optimization devi