Real-Time Nonlinear Optimal Control using Neural Networks
In this paper, a neural network based controller which optimizes a finite horizon quadratic cost function is developed for a class of nonlinear systems. The controller converges to its optimal value real-time eliminating the need for a priori knowledge of the nonlinearity and the initial conditions. The method makes use of the optimality conditions obtained from the Hamiltonian directly. These conditions are realized by a series of neural networks which converge to the optimal control iteratively in real-time. A nonlinear system to demonstrate its applicability is also included.
J. K. Antony and L. Acar, "Real-Time Nonlinear Optimal Control using Neural Networks," Proceedings of the American Control Conference (1994, Baltimore, MD), vol. 3, pp. 2926-2930, Institute of Electrical and Electronics Engineers (IEEE), Jun 1994.
The definitive version is available at https://doi.org/10.1109/ACC.1994.735104
American Control Conference (1994: Jun. 29-Jul. 1, Baltimore, MD)
Electrical and Computer Engineering
National Science Foundation (U.S.)
United States. Army Research Office
University of Missouri--Rolla. Intelligent Systems Center
Keywords and Phrases
Computer simulation; Control nonlinearities; Control theory; Finite element method; Mathematical models; Nonlinear control systems; Optimal control systems; Optimization; Real time systems; Dynamical systems; Finite horizon quaratic cost function; Optimality conditions; Real time nonlinear optimal control; Neural networks
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jun 1994