Gao et al., 2017 - Google Patents

Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field

Gao et al., 2017

Document ID
17321484714215318197
Author
Gao C
Liu H
Publication year
Publication venue
Optimal Control Applications and Methods

External Links

Snippet

This paper studies small unmanned aerial vehicle dynamic soaring for conserving onboard energy to extend flight endurance performance. A novel dynamic soaring path planning approach is proposed using the Dubins path. It enjoys a significant improvement in …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • 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
    • 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
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Liang et al. Three-dimensional path following of an underactuated AUV based on fuzzy backstepping sliding mode control
Karimi et al. Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm
Lin Distributed UAV formation control using differential game approach
Doukhi et al. Neural network-based robust adaptive certainty equivalent controller for quadrotor UAV with unknown disturbances
Gao et al. Dubins path‐based dynamic soaring trajectory planning and tracking control in a gradient wind field
Davoudi et al. Quad-rotor flight simulation in realistic atmospheric conditions
Sarabakha et al. Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles
Fowler et al. Bézier curve path planning for parafoil terminal guidance
CN110750850B (en) Three-dimensional profile optimization design method, system and medium under strong constraint complex task condition
Wang et al. Nonlinear aeroelastic control of very flexible aircraft using model updating
Kayacan et al. Learning Control of Fixed‐Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks
Trapiello et al. Position‐heading quadrotor control using LPV techniques
CN110297500A (en) A kind of continuous path planning method giving unmanned plane under more way points
Kose et al. Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA
CN112947572A (en) Terrain following-based four-rotor aircraft self-adaptive motion planning method
Jeong et al. Control System Design for a Ducted‐Fan Unmanned Aerial Vehicle Using Linear Quadratic Tracker
Ferdaus et al. A generic self-evolving neuro-fuzzy controller based high-performance hexacopter altitude control system
Malaek et al. Novel minimum time trajectory planning in terrain following flights
García‐Beltrán et al. Passivity‐based control laws for an unmanned powered parachute aircraft
Fahimi Full formation control for autonomous helicopter groups
Almeida Tight formation flight with feasible model predictive control
Cowling Towards autonomy of a quadrotor UAV
González et al. Loop-Separation Control for Very Flexible Aircraft
Zhao et al. Dynamic modelling of parafoil system based on aerodynamic coefficients identification
Nemes Synopsis of soft computing techniques used in quadrotor UAV modelling and control