Reinforcement learning (RL) has been successfully employed as a powerful tool in
designing adaptive optimal controllers. Recently, off-policy learning has emerged to design …

Reinforcement learning methods such as Q-learning have shown promising results in the
model-free design of linear quadratic regulator (LQR) controllers for linear time-invariant …

Although robust control has been studied for decades, the output-feedback robust control
design is still challenging in the control field. This article proposes a new approach to …

Abstract Learning a stable Linear Dynamical System (LDS) from data involves creating
models that both minimize reconstruction error and enforce stability of the learned …

This brief presents a novel direct adaptive optimal controller design for uncertain continuous-
time linear time-invariant systems. The optimal gain parameter, obtained from the Riccati …

A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous
linear time‐invariant systems is proposed. Such an approach is based on the direct self …

This paper proposes a memory-efficient approx-imate/adaptive optimal control (AOC) design
of completely unknown continuous-time (CT) linear time invariant (LTI) systems, without …

In this paper, adaptive linear quadratic regulator (LQR) is proposed for continuous-time
systems with uncertain dynamics. The dynamic state-feedback controller uses input-output …

This paper proposes a novel memory-efficient double-filtered policy iteration (PI) algorithm
for adaptive optimal control of continuous-time (CT) linear time invariant (LTI) systems. The …

This paper proposes a novel indirect adaptive optimal controller (AOC) for completely
unknown continuous-time (CT) linear time invariant (LTI) systems using the policy iteration …