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Modelling and Control of a Model UAV with Simulink

von ı, ş, Akcay Calı
Über Modelling and Control of a Model UAV with Simulink

DEVELOPMENT OF A MODEL UNMANNED AERIAL VEHICLE WITH SIMULINK : MODELLING AND CONTROL // The nonlinear simulation model of an unmanned aerial vehicle (UAV) for Piper J3-Cub in MATLAB/Simulink® environment is developed by taking into consideration all the possible major system components such as actuators, gravity, engine, atmosphere, wind-turbulence models, as well as the aerodynamics components in the 6 DOF equations of motion. The model is validated by comparing with a similar UAV data in terms of open loop dynamic stability characteristics. Using two main approaches; namely, fuzzy based on PID which is the obtained value using genetic algorithm and adaptive neural fuzzy inference system, linear controllers are designed. The controllers are designed for control of roll, heading with coordinated turn, flight path, attitude command in pitch, altitude in achieving desired, i.e., for the achievement of control functions. These linear controllers are integrated into the nonlinear model, by gain scheduling with respect to altitude, controller input in terms of longitudinal and lateral linearization regarding the perturbed states and control.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783659566363
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 200
  • Veröffentlicht:
  • 5. Februar 2019
  • Abmessungen:
  • 229x152x12 mm.
  • Gewicht:
  • 299 g.
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Beschreibung von Modelling and Control of a Model UAV with Simulink

DEVELOPMENT OF A MODEL UNMANNED AERIAL VEHICLE WITH SIMULINK : MODELLING AND CONTROL // The nonlinear simulation model of an unmanned aerial vehicle (UAV) for Piper J3-Cub in MATLAB/Simulink® environment is developed by taking into consideration all the possible major system components such as actuators, gravity, engine, atmosphere, wind-turbulence models, as well as the aerodynamics components in the 6 DOF equations of motion. The model is validated by comparing with a similar UAV data in terms of open loop dynamic stability characteristics. Using two main approaches; namely, fuzzy based on PID which is the obtained value using genetic algorithm and adaptive neural fuzzy inference system, linear controllers are designed. The controllers are designed for control of roll, heading with coordinated turn, flight path, attitude command in pitch, altitude in achieving desired, i.e., for the achievement of control functions. These linear controllers are integrated into the nonlinear model, by gain scheduling with respect to altitude, controller input in terms of longitudinal and lateral linearization regarding the perturbed states and control.

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