Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

The electric power system is witnessing a shift in the technology of generation. Conventional
thermal generation based on synchronous machines is gradually being replaced by power …

Policy gradients methods apply to complex, poorly understood, control problems by
performing stochastic gradient descent over a parameterized class of polices. Unfortunately …

This paper describes the development of a new state-of-the-art large wind tunnel model for
active flutter suppression studies as well as the supporting techniques used in tests focused …

We design sparse and block sparse feedback gains that minimize the variance amplification
(ie, the H 2 norm) of distributed systems. Our approach consists of two steps. First, we …

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 …

Policy optimization (PO) is a key ingredient for modern reinforcement learning (RL). For
control design, certain constraints are usually enforced on the policies to optimize …

The linear quadratic regulator is the fundamental problem of optimal control. Its state
feedback version was set and solved in the early 1960s. However, the static output feedback …

We consider the design of optimal state feedback gains subject to structural constraints on
the distributed controllers. These constraints are in the form of sparsity requirements for the …

The applications of direct policy search in reinforcement learning and continuous control
have received increasing attention. In this work, we present novel theoretical results on the …