Abstract
The purpose of this paper is to present a solution to the minimization problem of the transient time to accomplish the switching between different levels of power in a nucleaz reseazch reactor satisfying the inverse period constraint and avoiding to use any physical model of the plant. The strategy here proposed consists of two stages: fast, the optimal trajectory which satisfies the constraint is calculated off-line; second, a control law based on a generalized Hopfield neural network is employed to assure that the reactor power follows this optimal trajectory. The boundedness for both the weights and the identification error is guaranteed by a new online learning law. Likewise, proposed control law guarantees an upper bound for the tracking error. The effectiveness of this procedure is illustrated by numeric simulation.
Original language | English |
---|---|
Pages (from-to) | 39-60 |
Number of pages | 22 |
Journal | Intelligent Automation and Soft Computing |
Volume | 16 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2010 |
Externally published | Yes |
Keywords
- Hopfield neural network
- Model-free control
- Nuclear research reactor