Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants

We present a sound and automated approach to synthesize safe digital feedback controllers for physical plants represented as linear, time-invariant models. Models are given as dynamical equations with inputs, evolving over a continuous state space and accounting for errors due to the digitization of signals by the controller. Our counterexample guided inductive synthesis (CEGIS) approach has two phases: We synthesize a static feedback controller that stabilizes the system but that may not be safe for all initial conditions. Safety is then verified either via BMC or abstract acceleration; if the verification step fails, a counterexample is provided to the synthesis engine and the process iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for intricate physical plant models from the digital control literature