[go: up one dir, main page]

Nodland et al., 2013 - Google Patents

Neural network-based optimal adaptive output feedback control of a helicopter UAV

Nodland et al., 2013

View PDF
Document ID
13465312927873311672
Author
Nodland D
Zargarzadeh H
Jagannathan S
Publication year
Publication venue
IEEE transactions on neural networks and learning systems

External Links

Snippet

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an …
Continue reading at scholarsmine.mst.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • G05B13/042Adaptive 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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Nodland et al. Neural network-based optimal adaptive output feedback control of a helicopter UAV
Zuo et al. Unmanned aerial vehicles: Control methods and future challenges
Labbadi et al. Robust integral terminal sliding mode control for quadrotor UAV with external disturbances
Madebo et al. Robust tracking control for quadrotor UAV with external disturbances and uncertainties using neural network based MRAC
Ramirez-Rodriguez et al. Robust backstepping control based on integral sliding modes for tracking of quadrotors
Das et al. Backstepping approach for controlling a quadrotor using lagrange form dynamics
Islam et al. Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty
Shin Adaptive dynamic surface control for a hypersonic aircraft using neural networks
Raffo et al. Robust nonlinear control for path tracking of a quad‐rotor helicopter
Abdulkareem et al. Modeling and nonlinear control of a quadcopter for stabilization and trajectory tracking
Jiang et al. Enhanced LQR control for unmanned helicopter in hover
Jeong et al. Control System Design for a Ducted‐Fan Unmanned Aerial Vehicle Using Linear Quadratic Tracker
Chekakta et al. Model-free control applied for position control of quadrotor using ros
Ferdaus et al. A generic self-evolving neuro-fuzzy controller based high-performance hexacopter altitude control system
Muslimov et al. UAV formation flight using non-uniform vector field and fuzzy self-tuning PD-control
Li et al. Optimized neural network based sliding mode control for quadrotors with disturbances
Velagić et al. Design of LQR controller for 3D trajectory tracking of octocopter unmanned aerial vehicle
Cheng et al. Hover-to-cruise transition control for high-speed level flight of ducted fan UAV
Budiyono Advances in unmanned aerial vehicles technologies
Chen et al. Robust control of quadrotor MAV using self‐organizing interval type‐II fuzzy neural networks (SOIT‐IIFNNs) controller
Abdessameud et al. Formation control of VTOL UAVs without linear-velocity measurements
Zhang et al. Zero-shot sim-to-real transfer of robust and generic quadrotor controller by deep reinforcement learning
Valencia et al. Trajectory tracking control for multiple quadrotors based on a neurobiological-inspired system
Lara et al. Robust control design techniques using differential evolution algorithms applied to the pvtol
Housny et al. Robust sliding mode control for quadrotor UAV