Yu et al., 2022 - Google Patents
Wind-field identification for parafoils based on deep Q-learning iterative inversionYu et al., 2022
- Document ID
- 7885805769082340127
- Author
- Yu Z
- Sun H
- Sun Q
- Tao J
- Chen Z
- Publication year
- Publication venue
- Information Sciences
External Links
Snippet
Powered parafoils are flexible aircraft with high load capacities and low costs and are often used in long-term surveillance and airdrop missions. As a flexible aircraft, powered parafoil flights are easily affected by winds. However, wind-field identification is difficult for low-to …
- 230000010006 flight 0 abstract description 93
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
- G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
- G05D1/0825—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using mathematical models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/04—Control of altitude or depth
- G05D1/042—Control of altitude or depth specially adapted for aircraft
- G05D1/046—Control of altitude or depth specially adapted for aircraft to counteract a perturbation, e.g. gust of wind
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ma et al. | Deep reinforcement learning of UAV tracking control under wind disturbances environments | |
González-Rocha et al. | Sensing wind from quadrotor motion | |
Tian et al. | Wind sensing and estimation using small fixed-wing unmanned aerial vehicles: A survey | |
Sankaralingam et al. | A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs | |
Luo et al. | On decoupling trajectory tracking control of unmanned powered parafoil using ADRC-based coupling analysis and dynamic feedforward compensation | |
Dussart et al. | Identification of in-flight wingtip folding effects on the roll characteristics of a flexible aircraft | |
Zhou et al. | Modeling and PID control of quadrotor UAV based on machine learning | |
Nguyen et al. | Development of a new hybrid drone and software-in-the-loop simulation using px4 code | |
Yu et al. | Wind-field identification for parafoils based on deep Q-learning iterative inversion | |
Xu et al. | Real-time parameter identification method for a novel blended-wing-body tiltrotor UAV | |
Korsun et al. | Direct method for forming the optimal open loop control of aerial vehicles | |
Zimmerman et al. | Wind estimation by multirotor dynamic state measurement and machine learning models | |
Jianhong et al. | Synthesis cascade estimation for aircraft system identification | |
Zhao et al. | Dynamic modelling of parafoil system based on aerodynamic coefficients identification | |
Zhou et al. | Nonlinear system identification and trajectory tracking control for a flybarless unmanned helicopter: theory and experiment | |
Zhao et al. | Aerodynamic modeling using an end-to-end learning attitude dynamics network for flight control | |
Miaolei et al. | A real-time H∞ cubature Kalman filter based on SVD and its application to a small unmanned helicopter | |
Zhen et al. | Aircraft control method based on deep reinforcement learning | |
Liu et al. | A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope | |
Yan et al. | Aerodynamic structural design and control for a new miniature coaxial dual-rotor unmanned aerial vehicle | |
Zheng et al. | Auto-landing of moving-mass actuated unmanned aerial vehicles based on linear active disturbance rejection control | |
You et al. | Research on attitude detection and flight experiment of coaxial twin-rotor UAV | |
Zhou et al. | An interpretable machine learning model for trajectory prediction based on nonlinear dynamics mechanism constraints: applications for HVs | |
Zimmerman et al. | Wind estimation by multirotor drone state using machine learning with data rotation and reduction | |
Wang et al. | High-performance attitude control design of supersonic tailless aircraft: a cascaded disturbance rejection approach |