Abstract
This paper presents a novel neural network (NN) - based self-tuning controller for the optimal regulation of affine nonlinear continuous-time systems. Knowledge of the internal system dynamics is not required whereas the control coefficient matrix is considered to be available. the proposed nonlinear optimal regulator tunes itself in order to simultaneously learn the optimal control input, optimal cost function, and the system internal dynamics using a single NN. a novel NN weight tuning algorithm is derived which ensures the internal system dynamics are learned while simultaneously minimizing a predefined cost function. an initial stabilizing controller is not required. Lyapunov methods are used to show that all signals are uniformly ultimately bounded (UUB). in the absence of NN reconstruction errors, the approximated control input is shown to converge to the optimal control asymptotically for the regulator design, and simulation results illustrate the effectiveness of the approach. © 2012 IEEE.
Recommended Citation
T. Dierks and S. Jagannathan, "A Self-tuning Optimal Controller for Affine Nonlinear Continuous-time Systems with Unknown Internal Dynamics," Proceedings of the IEEE Conference on Decision and Control, pp. 5392 - 5397, article no. 6425986, Institute of Electrical and Electronics Engineers, Jan 2012.
The definitive version is available at https://doi.org/10.1109/CDC.2012.6425986
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Serial Number (ISSN)
2576-2370; 0743-1546
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 Jan 2012
