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.
ASJC Scopus subject areas
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Control and Optimization
- Electrical and Electronic Engineering