Lyapunov-based backward-horizon adaptive stabilization

摘要

In this paper we develop a discrete-time adaptive stabilization algorithm based on a one-step backward-horizon cost criterion. By optimizing the cost with respect to the update step size, we obtain a gain update law that guarantees convergence of the plant states. The convergence proof is based on a modified Lyapunov technique. We extend the algorithm to include integral control for rejecting constant disturbances and we present an experimental application to DC motor positioning system.