Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

This paper reviews the identification and optimal control problems using recurrent neural
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …

This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …

In this article, we propose a way to enhance the learning framework for zero-sum games
with dynamics evolving in continuous time. In contrast to the conventional centralized actor …

This article studies the decentralized event-triggered control problem for a class of
constrained nonlinear interconnected systems. By assigning a specific cost function for each …

For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

This article investigates a neural network (NN)‐based control problem for unknown discrete‐
time nonlinear systems with a denial‐of‐service (DoS) attack and an adaptive event …

In this paper, adaptive impedance control is developed for an-link robotic manipulator with
input saturation by employing neural networks. Both uncertainties and input saturation are …

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each
other when performing intelligent optimization. They are both regarded as promising …

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control
design methods to multi-agent systems on communication graphs. It develops Riccati design …