Unmanned vehicle safety prediction tracking control method in complex network environment

By adopting model prediction controller and linear matrix inequality technology in complex network environments, combining event trigger function and LPV system model, the security stability and trajectory tracking problems of unmanned vehicle systems in complex environments are solved, and the tracking and control effect with high precision and attack resistance is achieved.

CN119690106AActive Publication Date: 2025-03-25SICHUAN UNIV
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Patent Information

Application Number
CN202411872928.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-18
Publication Date
2025-03-25
Estimated Expiration
2044-12-18

AI Technical Summary

Technical Problem

In complex network environments, the security and stability of unmanned vehicle systems are easily affected, trajectory tracking does not match expectations, and traditional methods are difficult to effectively deal with longitudinal speed fluctuations, input and output constraints, potential network attacks and communication constraints.

Method used

A method of safety prediction and tracking control for unmanned vehicles in complex network environments is adopted. Through the model prediction controller combined with linear matrix inequality technology, optimization problems are solved online, the optimal control amount of unmanned vehicles is calculated, and multiple factors are comprehensively considered to achieve trajectory tracking through event trigger function and LPV system model.

Benefits of technology

It realizes accurate tracking and control of the expected trajectory by unmanned vehicles in complex network environments, improves tracking accuracy and smoothness of control volume, and improves anti-network attack capabilities and ride comfort.

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Abstract

The invention discloses an unmanned vehicle safety prediction tracking control method in a complex network environment. The method comprises the steps that S1, an expected track is sent to an unmanned vehicle; s2, whether the unmanned vehicle runs for the first time or not is judged, if yes, S3 is executed, and if not, S6 is executed; s3, calculating the optimal control quantity of the unmanned vehicle, and sending the optimal control quantity to a state estimator to estimate the estimated state of the next moment; s4, transmitting the optimal control quantity to the unmanned vehicle for execution through a channel containing potential network attacks; s5, judging whether the current moment is the last moment of the tracking control period, if so, ending the control method, and otherwise, entering S6; s6, non-triggered output is collected, and a function value of the non-triggered output is calculated through the event triggering function expression; s7, judging whether the function value is greater than zero or not, if so, entering S8, otherwise, keeping the triggered output unchanged, and returning to S3; and S8, the triggered output is updated to non-triggered output, an attacked signal received by the model prediction controller is calculated, and then the S3 is returned.
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