Sampled-data-based adaptive optimal output-feedback control of a 2-degree-of-freedom helicopter

This study addresses the adaptive and optimal control problem of a Quanser's 2-degree-of-freedom helicopter via output feedback. In order to satisfy the requirement of digital implementation of flight controller, this study distinguishes itself through proposing a novel sampled-data-based approximate/adaptive dynamic programming approach. A policy iteration algorithm is presented that yields to learn a near-optimal control gain iteratively by input/output data. The convergence of the proposed algorithm is theoretically ensured and the trade-off between the optimality and the sampling period is rigorously studied as well. Finally, the authors show the performance of the proposed algorithm under bounded model uncertainties.