Year 2000 Solutions for Dummies
Year 2000 Solutions for Dummies
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Control of Robot Manipulators
Nonlinear control of underactuated mechanical systems with application to robotics and aerospace vehicles
Exact linearization and sliding mode observer for a quadrotor unmanned aerial vehicle
International Journal of Robotics and Automation
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Robust neuro-control for a micro quadrotor
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Comparative Results on Stabilization of the Quad-rotor Rotorcraft Using Bounded Feedback Controllers
Journal of Intelligent and Robotic Systems
Fuzzy sliding mode control with chattering elimination for a quadrotor helicopter in vertical flight
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A Quadrotor Test Bench for Six Degree of Freedom Flight
Journal of Intelligent and Robotic Systems
Modeling and Adaptive Tracking Control of a Quadrotor UAV
International Journal of Intelligent Mechatronics and Robotics
Stabilization of desired motion of a quadrotor helicopter
Journal of Computer and Systems Sciences International
Robust Backstepping Control Based on Integral Sliding Modes for Tracking of Quadrotors
Journal of Intelligent and Robotic Systems
Real-Time Implementation of Decoupled Controllers for Multirotor Aircrafts
Journal of Intelligent and Robotic Systems
Adaptive Nonlinear Stabilization Control for a Quadrotor UAV: Theory, Simulation and Experimentation
Journal of Intelligent and Robotic Systems
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The dynamics of a quadrotor are a simplified form of helicopter dynamics that exhibit the same basic problems of underactuation, strong coupling, multi-input/multi-output design, and unknown nonlinearities. Control design for the quadrotor is more tractable yet reveals corresponding approaches for helicopter and UAV control design. In this paper, a backstepping approach is used for quadrotor controller design. In contrast to most other approaches, we apply backstepping on the Lagrangian form of the dynamics, not the state space form. This is complicated by the fact that the Lagrangian form for the position dynamics is bilinear in the controls. We confront this problem by using an inverse kinematics solution akin to that used in robotics. In addition, two neural nets are introduced to estimate the aerodynamic components, one for aerodynamic forces and one for aerodynamic moments. The result is a controller of intuitively appealing structure having an outer kinematics loop for position control and an inner dynamics loop for attitude control. The control approach described in this paper is robust since it explicitly deals with unmodeled state-dependent disturbances and forces without needing any prior knowledge of the same. A simulation study validates the results obtained in the paper.