Nonlinear Model Predictive Controller Design for Identified Nonlinear Parameter Varying Model
Abstract: In this paper, a
novel nonlinear model predictive controller (MPC) is proposed based on an
identified nonlinear parameter varying (NPV) model. First, an NPV model scheme
is present for process identification, which is featured by its nonlinear
hybrid Hammerstein model structure and varying model parameters. The hybrid
Hammerstein model combines a normalized static artificial neural network with a
linear transfer function to identify general nonlinear systems at each fixed
working point. Meanwhile, a model interpolating philosophy is utilized to
obtain the global model across the whole operation domain. The NPV model
considers both the nonlinearity of transition dynamics due to the variation of
the working-point and the nonlinear mapping from the input to the output at
fixed working points. Moreover, under the new NPV framework, the control action
is computed via a multistep linearization method aimed for nonlinear
optimization problems. In the proposed scheme, only low cost tests are needed
for system identification and the controller can achieve better output
performance than MPC methods based on linear parameter varying (LPV) models.
Numerical examples validate the effectiveness of the proposed approach.
Author: Jiangang Lu, Jie You
Jie You, Qinmin Yang
Journal Code: jptkomputergg120077
