Adaptive model predictive process control using neural networks
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
- Research Organization:
- Univ. of California (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- Assignee:
- Univ. of California Office of Technology Transfer, Alemeda, CA (United States)
- Patent Number(s):
- US 5,659,667/A/
- Application Number:
- PAN: 8-373,736
- OSTI ID:
- 527745
- Resource Relation:
- Other Information: PBD: 19 Aug 1997
- Country of Publication:
- United States
- Language:
- English
Similar Records
Sensor validation in power plants using adaptive backpropagation neural network
Sensor validation in power plants using adaptive backpropagation neural network