Optimal Receding Horizon Control for Finite Deterministic Systems with Temporal Logic Constraints
This work addresses control synthesis for systems with temporal logic specifications, which is incremental as it builds on existing methods for deterministic systems with local penalty sensing.
The paper tackles the problem of optimal control for finite deterministic systems with temporal logic constraints, developing a receding horizon strategy that minimizes expected average cumulative penalties while guaranteeing satisfaction of Linear Temporal Logic formulas, as demonstrated in a persistent surveillance robotics application.
In this paper, we develop a provably correct optimal control strategy for a finite deterministic transition system. By assuming that penalties with known probabilities of occurrence and dynamics can be sensed locally at the states of the system, we derive a receding horizon strategy that minimizes the expected average cumulative penalty incurred between two consecutive satisfactions of a desired property. At the same time, we guarantee the satisfaction of correctness specifications expressed as Linear Temporal Logic formulas. We illustrate the approach with a persistent surveillance robotics application.