The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures.

Meeting Name

International Joint Conference on Neural Networks, 2003


Electrical and Computer Engineering

Keywords and Phrases

Adaptive Critic Design; Control System Synthesis; Dynamic Programming; Electric Power Grid; Heuristic Programming; Learning (Artificial Intelligence); Multilayer Perceptron Neural Network; Multilayer Perceptrons; Neural Net Architecture; Neural Network Architecture; Neurocontrollers; Nonlinear Optimal Neurocontrollers; Optimal Control; Power System Control; Radial Basis Function Networks; Radial Basis Function Neural Network; Synchronous Generator

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2003