Robust Temporal Logic Model Predictive Control

TL;DR

This paper presents a robust model predictive control framework for linear systems with disturbances, ensuring STL specifications are met or minimally violated, using mixed integer programming for efficient synthesis.

Contribution

It introduces a conservative, computationally efficient control synthesis method that guarantees robustness to disturbances and optimality with respect to a cost function.

Findings

Controllers satisfy STL specifications under disturbances

The framework handles infeasible constraints with minimal violation

Case study demonstrates practical effectiveness

Abstract

Control synthesis from temporal logic specifications has gained popularity in recent years. In this paper, we use a model predictive approach to control discrete time linear systems with additive bounded disturbances subject to constraints given as formulas of signal temporal logic (STL). We introduce a (conservative) computationally efficient framework to synthesize control strategies based on mixed integer programs. The designed controllers satisfy the temporal logic requirements, are robust to all possible realizations of the disturbances, and optimal with respect to a cost function. In case the temporal logic constraint is infeasible, the controller satisfies a relaxed, minimally violating constraint. An illustrative case study is included.