The design of robust multi-loop-cascaded hydro governors
Engineering with Computers
Nonlinear predictive control for a NNARX hydro plant model
Neural Computing and Applications
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
This paper presents the performance study of predictive control approach in application to hydro plant. The tracking on deviated power as reference signal for identified neural network nonlinear autoregressive with exogenous signal (NNARX) hydro plant model is studied. A detailed plant dynamics constituting various hydro-components and -structures are considered for identification of its corresponding NNARX model. In obtaining an appropriate NNARX model structure, the plant is simulated on random load disturbance variation with input as random gate position and output as deviated power. With the identified model, a nonlinear predictive control (NPC) strategy is applied using Levenberg-Marquardt (LM) and Quasi-Newton (QN) algorithms for optimization of control performance index (CPI). The study also describes a control approach involving extraction of linear model from the neural network (NN) model, based on instantaneous linearization theory. Its tracking performance is compared with NPC on different nature of reference signals. These control approaches are applied so as to cause model output track on various deviated power as a reference signal.