Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

This article reviews the recent development of adaptive dynamic programming (ADP) with
applications in control. First, its applications in optimal regulation are introduced, and some …

The idea of optimization can be regarded as an important basis of many disciplines and
hence is extremely useful for a large number of research fields, particularly for artificial …

This is a survey of neural network applications in the real-world scenario. It provides a
taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of …

The core task of tracking control is to make the controlled plant track a desired trajectory. The
traditional performance index used in previous studies cannot eliminate completely the …

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning
(RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …

Since the beginning of the 21st century, artificial intelligence (AI) has been resha** almost
all areas of human society, which has high potential to spark the fourth industrial revolution …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

In this paper, we investigate the learning-based adaptive optimal output regulation problem
with convergence rate requirement for disturbed linear continuous-time systems. An …

Optimal control problems are ubiquitous in practical engineering applications and social life
with the idea of cost or resource conservation. Based on the critic learning scheme, adaptive …