Optimization-based iterative learning for precise quadrocopter trajectory tracking
نویسندگان
چکیده
منابع مشابه
3D Trajectory Control for Quadrocopter
The demand for unattended aerial systems capable of fulfilling e.g. surveillance tasks in contaminated or inaccessible areas without any assistance of a human pilot is the motivation for the investigation of a 3D trajectory control. Hence, this paper deals with the development of such a control algorithm able to follow any kind of 3D trajectory within the quadrocopter's capabilities. In this pa...
متن کاملIterative Learning Control for Non-uniform Trajectory Tracking Problems
In this work Iterative Learning Control is extended to non-uniform trajectory tracking problems for certain classes of nonlinear uncertain systems. The proposed ILC scheme can learn from different motion patterns and guarantee the asymptotic convergence even if the target trajectory varies at every iteration. The concept of Composite Energy Function (CEF) is adopted to facilitate the convergenc...
متن کاملIterative learning neural network control for nonlinear system trajectory tracking
This paper presents a neural network controller for nonlinear system trajectory tracking, which works in an iterative learning manner. The controller is composed of many local neural networks and every point along the desired trajectory has its own one for approximating nonlinearity only nearby. This makes that every local neural network can be possessed of a simple structure and less neurons. ...
متن کاملB-spline network-based iterative learning control for trajectory tracking of a piezoelectric actuator
This paper presents the trajectory tracking approach of a piezoelectric actuator using an iterative learning control (ILC) scheme based on B-spline network (BSN) filtering. The ILC scheme adopts a state-compensated iterative learning formula, which compensates for the state difference between two consecutive iterations in order that the iterative learning can learn from the tracking errors of t...
متن کاملRobust Trajectory Optimization Under Frictional Contact with Iterative Learning
Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible traject...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Autonomous Robots
سال: 2012
ISSN: 0929-5593,1573-7527
DOI: 10.1007/s10514-012-9283-2