Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in
control systems, robotics, Machine Learning (ML) and optimization taking into account the …

We consider the problem of resilient control of networked control system (NCS) under denial-
of-service (DoS) attack via a unified game approach. The DoS attacks lead to extra …

The inverse optimal control for finite-horizon discrete-time linear quadratic regulators is
investigated in this paper, which is to estimate the parameters in the objective function using …

This article studies the robustness of policy iteration in the context of continuous-time infinite-
horizon linear quadratic regulator (LQR) problem. It is shown that Kleinman's policy iteration …

To enhance the machines' intelligence, it is important for them to learn how humans perform
tasks. In this article, the issue of online adaptive learning human behavior is addressed for a …

Plug-in hybrid electric vehicles (PHEVs) provide a good alternative in achieving better
performance and in the reduction of harmful gas emissions. The hybrid energy storage …

Recently it has been shown that the control energy required to control a dynamical complex
network is prohibitively large when there are only a few control inputs. Most methods to …

There have been broad ranges of human-in-the-loop (HiTL) control systems, like fly-by-wire
aircraft, share driving, and energy management. To develop advanced HiTL control systems …

With the growing interest in applications involving humans and robots teaming together, the
need to understand each other's intentions and behavior arises. This work presents a …