Abstract
In this paper, we address a neural-network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state-feedback control law for the nonlinear system based on the certainty equivalence principle. The control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure.
Original language | English (US) |
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Pages (from-to) | 1422-1429 |
Number of pages | 8 |
Journal | IEEE Transactions on Neural Networks |
Volume | 9 |
Issue number | 6 |
DOIs | |
State | Published - 1998 |
Keywords
- Linear matrix inequality
- Multilayer perceptron
- Nonlinear control
- Stability analysis
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence