In control theory, complicated dynamics such as systems of (nonlinear) differential equations
are controlled mostly to achieve stability. This fundamental property, which can be with …

In control theory, complex models of physical processes, such as systems of differential or
difference equations, are usually checked against simple specifications, such as stability …

We present an abstraction and refinement methodology for the automated controller
synthesis to enforce general predefined specifications. The designed controllers require …

We introduce SCOTS a software tool for the automatic controller synthesis for nonlinear
control systems based on symbolic models, also known as discrete abstractions. The tool …

In this paper we present a method for automatic planning of optimal paths for a group of
robots that satisfy a common high-level mission specification. The motion of each robot is …

In this paper, we develop a method to automatically generate a control policy for a
dynamical system modeled as a Markov Decision Process (MDP). The control specification …

While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …

We present an efficient computational procedure for finite abstraction of discrete-time mixed
monotone systems by considering a rectangular partition of the state space. Mixed …

In this paper we study how to formulate the optimal control problem for a piecewise-affine
dynamical system as a mixed-integer program. Problems of this form arise typically in hybrid …

While the identification of nonlinear dynamical systems is a fundamental building block of
model-based reinforcement learning and feedback control, its sample complexity is only …