Reinforcement learning is bedeviled by the curse of dimensionality: the number of
parameters to be learned grows exponentially with the size of any compact encoding of a …

This paper introduces the concept of a hybrid system and some of the challenges associated
with the stability of such systems, including the issues of guaranteeing stability of switched …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

Deeper neural networks are more difficult to train. We present a residual learning framework
to ease the training of networks that are substantially deeper than those used previously. We …

l\lany systems encountered in practice involve a coupling between contin uous dynamics
and discrete events. Systems in which these two kinds of dynamics coexist and interact are …

After going through all the previous chapters, it would be natural for readers to conclude that
DES are inherently complex and hard to analyze, regardless of the modeling framework …

Robust stability and control for systems that combine continuous-time and discrete-time
dynamics. This article is a tutorial on modeling the dynamics of hybrid systems, on the …

This paper proposes a framework for modeling and controlling systems described by
interdependent physical laws, logic rules, and operating constraints, denoted as mixed …

Switched linear systems have a long history in the control literature but-along with hybrid
systems more generally-they have enjoyed a particular growth in interest since the 1990s …

Modeling and simulation techniques are today extensively used both in industry and
science. Parts of larger systems are, however, typically modeled and simulated by different …