On-line lower-order modeling via neural networks.
This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme.
Ho
H F
HF
The Hong Kong Polytechnic University, Department of Electrical Engineering, Hung Hom, Kowloon, Hong Kong.
Rad
A B
AB
Wong
Y K
YK
Lo
W L
WL
D000465:Algorithms; D001709:Biotechnology / Q000379:methods*; D003198:Computer Simulation; D005240:Feasibility Studies; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity