  We present ResilienC, a framework for resilient control of Cyber-Physical
Systems subject to STL-based requirements. ResilienC utilizes a recently
developed formalism for specifying CPS resiliency in terms of sets of
$(\mathit{rec},\mathit{dur})$ real-valued pairs, where $\mathit{rec}$
represents the system's capability to rapidly recover from a property violation
(recoverability), and $\mathit{dur}$ is reflective of its ability to avoid
violations post-recovery (durability). We define the resilient STL control
problem as one of multi-objective optimization, where the recoverability and
durability of the desired STL specification are maximized. When neither
objective is prioritized over the other, the solution to the problem is a set
of Pareto-optimal system trajectories. We present a precise solution method to
the resilient STL control problem using a mixed-integer linear programming
encoding and an a posteriori $\epsilon$-constraint approach for efficiently
retrieving the complete set of optimally resilient solutions. In ResilienC, at
each time-step, the optimal control action selected from the set of
Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model
Predictive Control. We demonstrate the practical utility of the ResilienC
framework on two significant case studies: autonomous vehicle lane keeping and
deadline-driven, multi-region package delivery.
