We present a set of model-free, reduced-dimensional reinforcement learning (RL) based
optimal control designs for linear time-invariant singularly perturbed (SP) systems. We first …

This article delves into designing stabilizing feedback control gains for continuous-time
linear systems with unknown state matrix, in which the control gain is subjected to a …

In this paper, we consider consensus problems over a network of nodes, where the network
is divided into a number of clusters. We are interested in the case where the communication …

In this article, by using singular perturbation theory, reinforcement learning (RL), and Takagi–
Sugeno (T‐S) fuzzy methods, a RL‐fuzzy‐based composite suboptimal control method is …

This paper proposes a data-driven methodology to place the closed-loop poles in desired
convex regions in the complex plane with sufficient robustness constraints. We considered …

The primary objective of this paper is to bridge the gap between reinforcement learning (RL)
and nonlinear output-feedback control by develo** a novel solution to direct adaptive …

String stability can be employed to evaluate the formation stability of vehicular platoons by
checking if the velocity in the upstream direction of the vehicle platoon decreases, which is …

This paper delves into designing stabilizing feedback control gains for continuous linear
systems with unknown state matrix, in which the control is subject to a general structural …

Distributed learning can enable scalable and effective decision making in numerous
complex cyber-physical systems such as smart transportation, robotics swarm, power …

This paper proposes a composite control approach for a class of semi-coupled two-time-
scale systems, whose model parameters are completely unknown. In the framework of …