This paper traces the development of neural-network (NN)-based feedback controllers that are derived from the principle of adaptive/approximate dynamic programming (ADP) and discusses their closed-loop stability. Different versions of NN structures in the literature, which embed mathematical mappings related to solutions of the ADP-formulated problems called “adaptive critics” or “action-critic” networks, are discussed. Distinction between the two classes of ADP applications is pointed out. Furthermore, papers in “model-free” development and model-based neurocontrollers are reviewed in terms of their contributions to stability issues. Recent literature suggests that work in ADP-based feedback controllers with assured stability is growing in...
Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering an...
This paper presents a discussion of the applicability of neural networks in the identification and c...
In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired ...
Abstract—A constrained approximate dynamic programming (ADP) approach is presented for designing ada...
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond th...
Dynamic programming is an exact method of determining optimal control for a discretized system. Unfo...
An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperat...
Recent research shows that supervised learning can be an effective tool for designing near-optimal f...
Stability analysis and controller design are among the most important issues in feedback control pro...
This dissertation includes three papers on power system stabilization using neural network based con...
This report is devoted to the problem of controlling a class of linear time-invariant dynamic system...
Utilizing the universal approximation property of neural networks, we develop several novel approac...
In this dissertation, neural networks (NN) approximate unknown nonlinear functions in the system equ...
Recent research has shown that supervised learning can be an effective tool for designing optimal fe...
Adaptive critic (AC) neural network solutions to optimal control designs using dynamic programming h...
Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering an...
This paper presents a discussion of the applicability of neural networks in the identification and c...
In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired ...
Abstract—A constrained approximate dynamic programming (ADP) approach is presented for designing ada...
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond th...
Dynamic programming is an exact method of determining optimal control for a discretized system. Unfo...
An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperat...
Recent research shows that supervised learning can be an effective tool for designing near-optimal f...
Stability analysis and controller design are among the most important issues in feedback control pro...
This dissertation includes three papers on power system stabilization using neural network based con...
This report is devoted to the problem of controlling a class of linear time-invariant dynamic system...
Utilizing the universal approximation property of neural networks, we develop several novel approac...
In this dissertation, neural networks (NN) approximate unknown nonlinear functions in the system equ...
Recent research has shown that supervised learning can be an effective tool for designing optimal fe...
Adaptive critic (AC) neural network solutions to optimal control designs using dynamic programming h...
Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering an...
This paper presents a discussion of the applicability of neural networks in the identification and c...
In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired ...