Acquisition of dynamic control knowledge for a robotic manipulator
Proceedings of the seventh international conference (1990) on Machine learning
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Convex Optimization
Model-based iterative learning control with a quadratic criterion for time-varying linear systems
Automatica (Journal of IFAC)
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers
Automatica (Journal of IFAC)
Quadrocopter control using an on-board video system with off-board processing
Robotics and Autonomous Systems
Adaptive fast open-loop maneuvers for quadrocopters
Autonomous Robots
Performance benchmarking of quadrotor systems using time-optimal control
Autonomous Robots
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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.