Identification and optimal control of nonlinear systems using recurrent neural networks and reinforcement learning: An overview
A Perrusquía, W Yu - Neurocomputing, 2021 - Elsevier
This paper reviews the identification and optimal control problems using recurrent neural
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …
networks and reinforcement learning for nonlinear systems both in discrete-and continuous …
Non-Fragile H∞ Synchronization for Markov Jump Singularly Perturbed Coupled Neural Networks Subject to Double-Layer Switching Regulation
This work explores the synchronization issue for singularly perturbed coupled neural
networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a …
networks (SPCNNs) affected by both nonlinear constraints and gain uncertainties, in which a …
Artificial neural networks for controlling wind–PV power systems: A review
K Karabacak, N Cetin - Renewable and Sustainable Energy Reviews, 2014 - Elsevier
Nowadays, renewable energy systems are taking place than the conventional energy
systems. Especially, PV systems and wind energy conversion systems (WECS) are taking a …
systems. Especially, PV systems and wind energy conversion systems (WECS) are taking a …
Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
In this brief, adaptive neural control is presented for a class of output feedback nonlinear
systems in the presence of unknown functions. The unknown functions are handled via on …
systems in the presence of unknown functions. The unknown functions are handled via on …
A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systems
An online adaptive reinforcement learning-based solution is developed for the infinite-
horizon optimal control problem for continuous-time uncertain nonlinear systems. A novel …
horizon optimal control problem for continuous-time uncertain nonlinear systems. A novel …
Concurrent learning for convergence in adaptive control without persistency of excitation
G Chowdhary, E Johnson - 49th IEEE Conference on Decision …, 2010 - ieeexplore.ieee.org
We show that for an adaptive controller that uses recorded and instantaneous data
concurrently for adaptation, a verifiable condition on linear independence of the recorded …
concurrently for adaptation, a verifiable condition on linear independence of the recorded …
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
Following the idea of using first-order time derivatives, this paper presents a general
recurrent neural network (RNN) model for online inversion of time-varying matrices. Different …
recurrent neural network (RNN) model for online inversion of time-varying matrices. Different …
Theory and flight-test validation of a concurrent-learning adaptive controller
GV Chowdhary, EN Johnson - Journal of Guidance, Control, and …, 2011 - arc.aiaa.org
ADAPTIVE control has been extensively studied for aerospace applications. Many active
research directions exist: for example, Lewis [1], Kim and Lewis [2], and Patiño et al.[3] have …
research directions exist: for example, Lewis [1], Kim and Lewis [2], and Patiño et al.[3] have …
Recurrent neural networks and ARIMA models for euro/dollar exchange rate forecasting
P Escudero, W Alcocer, J Paredes - Applied Sciences, 2021 - mdpi.com
Analyzing the future behaviors of currency pairs represents a priority for governments,
financial institutions, and investors, who use this type of analysis to understand the …
financial institutions, and investors, who use this type of analysis to understand the …
Takagi–Sugeno dynamic neuro-fuzzy controller of uncertain nonlinear systems
The identification problem incorporated in feedback control of uncertain nonlinear systems
exhibiting complex behavior has been solved in different ways. Some of these solutions …
exhibiting complex behavior has been solved in different ways. Some of these solutions …