This paper considers receding horizon control of finite deterministic
systems, which must satisfy a high level, rich specification expressed as a
linear temporal logic formula. Under the assumption that time-varying rewards
are associated with states of the system and they can be observed in real-time,
the control objective is to maximize the collected reward while satisfying the
high level task specification. In order to properly react to the changing
rewards, a controller synthesis framework inspired by model predictive control
is proposed, where the rewards are locally optimized at each time-step over a
finite horizon, and the immediate optimal control is applied. By enforcing
appropriate constraints, the infinite trajectory produced by the controller is
guaranteed to satisfy the desired temporal logic formula. Simulation results
demonstrate the effectiveness of the approach.Comment: Technical report accompanying a paper to be presented at ACC 201