In a paper by Willems et al., it was shown that persistently exciting data can be used to
represent the input-output behavior of a linear system. Based on this fundamental result, we …

Direct policy gradient methods for reinforcement learning and continuous control problems
are a popular approach for a variety of reasons: 1) they are easy to implement without …

A central notion in systems theory is dissipativity, which was introduced by Jan Willems with
the explicit goal of arriving at a fundamental understanding of the stability properties of …

In recent years, linear matrix inequalities (LMI's) have emerged as a powerful tool to
approach control problems that appear hard if not impossible to solve in an analytic fashion …

Model-free reinforcement learning attempts to find an optimal control action for an unknown
dynamical system by directly searching over the parameter space of controllers. The …

We consider the Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-
valued matrix function over the set of feedback gains. Such a setup facilitates examining the …

The celebrated Kalman-Yakubovic/spl caron/-Popov (KYP) lemma establishes the
equivalence between a frequency domain inequality (FDI) and a linear matrix inequality …

We study the problem of controlling linear time-invariant systems with known noisy dynamics
and adversarially chosen quadratic losses. We present the first efficient online learning …

At publication, The Control Handbook immediately became the definitive resource that
engineers working with modern control systems required. Among its many accolades, that …

The integral quadratic constraints (IQC) approach facilitates a systematic and efficient
analysis of robust stability and performance for uncertain dynamical systems based on linear …