Article ID Journal Published Year Pages File Type
412616 Robotics and Autonomous Systems 2011 11 Pages PDF
Abstract

This paper presents an algorithm to iteratively perform an aggressive maneuver, i.e. drive a system quickly from one state to another. A simple model which captures the essential features of the system is used to compute the reference trajectory as the solution of an optimal control problem. Based on a lifted domain description of that same model an iterative learning controller is synthesized by solving a linear least-squares problem. The controller adjusts a feedforward signal using the results of experiments with the system. The non-causality of the approach makes it possible to anticipate recurring disturbances. Computational requirements are modest, allowing controller update in real-time. The experience gained from successful maneuvers can be used to adjust the model, which significantly reduces transients when performing similar motions. The algorithm is successfully applied to a real quadrotor unmanned aerial vehicle. The results are presented and discussed.

Research highlights► Data-based algorithm to control non-linear system. ► Simple model captures essential system properties. ► Iterative learning controller synthesized in lifted domain. ► Experiments performed on real quadrotor UAV. ► Extension of maneuver after nonlinear parameter adaptation.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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