We study the problem of receding horizon control of stochastic discrete-time systems with bounded control inputs and incomplete state information. Given a suitable choice of causal control policies, we first present a slight extension of the Kalman filter to estimate the state optimally in mean-square sense. We then show how to augment the underlying optimization problem with a negative drift-like constraint, yielding a second-order cone program to be solved periodically online. Finally, we prove that the receding horizon implementation of the resulting control policies renders the state of the overall system mean-square bounded under mild assumptions.
Stochastic receding horizon control with output feedback and bounded control inputs
RAMPONI, Federico Alessandro;
2010-01-01
Abstract
We study the problem of receding horizon control of stochastic discrete-time systems with bounded control inputs and incomplete state information. Given a suitable choice of causal control policies, we first present a slight extension of the Kalman filter to estimate the state optimally in mean-square sense. We then show how to augment the underlying optimization problem with a negative drift-like constraint, yielding a second-order cone program to be solved periodically online. Finally, we prove that the receding horizon implementation of the resulting control policies renders the state of the overall system mean-square bounded under mild assumptions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.