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 …
D Wang, M Ha, M Zhao - Artificial Intelligence Review, 2022 - Springer
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 …
M Ha, D Wang, D Liu - IEEE/CAA Journal of Automatica Sinica, 2022 - ieeexplore.ieee.org
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 …
H Li, Y Wu, M Chen, R Lu - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent …
In this paper, we investigate the learning-based adaptive optimal output regulation problem with convergence rate requirement for disturbed linear continuous-time systems. An …
D Wang, N Gao, D Liu, J Li… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
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 considers the robust optimal control problem for a class of nonlinear systems in the presence of unmodeled dynamics. An adaptive optimal controller is designed using the …
X Yu, W He, H Li, J Sun - IEEE Transactions on Systems, Man …, 2020 - ieeexplore.ieee.org
This article focuses on the tracking control issue of robotic systems with dynamic uncertainties. To enhance tracking accuracy in a robotic manipulator with uncertainties, an …
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of applied sciences, having encountered many applications in Structural Dynamics and …