Abstract
In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis function and its derivatives. The boundedness of the system state vector and actor and critic NN weights are shown through Lyapunov theory. Extension of the proposed approach to MNNs with more hidden layers is discussed. Simulation results are provided to illustrate the effectiveness of the proposed approach.
Recommended Citation
R. Moghadam et al., "Online Optimal Adaptive Control of a Class of Uncertain Nonlinear Discrete-time Systems," Proceedings of the International Joint Conference on Neural Networks, article no. 9206724, Institute of Electrical and Electronics Engineers, Jul 2020.
The definitive version is available at https://doi.org/10.1109/IJCNN48605.2020.9206724
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Discrete-time systems; Multi-layer neural network; Optimal adaptive control
International Standard Book Number (ISBN)
978-172816926-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jul 2020
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Medicine and Health Sciences Commons
Comments
Fulbright Association, Grant None