Flying car take-off path tracking method based on model predictive control
Through a model prediction control method, aerial and ground models of quadrotor flight vehicles are established, and the increments are optimized in real time, which solves the problem of path tracking errors during takeoff during travel, and achieves high-precision path tracking.
CN119717521AActive Publication Date: 2025-03-28HAINAN UNIV
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Patent Information
- Application Number
- CN202411855886.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-16
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2044-12-16
AI Technical Summary
Technical Problem
When existing quadrotor flying vehicles take off while traveling, there is an error in the actual flight path and the reference path, resulting in the inability to effectively track the reference path.
Method used
Using a model prediction control (MPC)-based method, the MPC prediction model is constructed by establishing aerial model and ground model of a quadrotor flying vehicle, and the control increments are optimized in real time to minimize the error between the reference path and the actual path.
Benefits of technology
It realizes that when a quadrotor flying vehicle takes off while taking off, accurately tracking the reference path, improving the flexibility of the aircraft and the accuracy of path tracking.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses an aerocar take-off path tracking method based on model predictive control. The method comprises the following steps: S1, acquiring a reference path corresponding to a four-rotor aerocar; s2, establishing an air model and a ground model of the four-rotor flying vehicle based on the kinetic model of the four-rotor flying vehicle and the kinetic model of the vehicle, and establishing a relation between the air model and the ground model; s3, acquiring a state quantity and a control quantity at the current moment, establishing an MPC prediction model based on an air model and a ground model, inputting the reference path and the state quantity and the control quantity at the current moment into the MPC prediction model to obtain a control increment at the current moment, and predicting a state quantity at the next moment; performing real-time optimization by using a target function, and minimizing an error between a reference path and an actual path of the four-rotor flying vehicle; and S4, acting the control increment at the current moment on the four-rotor flying vehicle so as to ensure that the four-rotor flying vehicle can accurately track the reference path.
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