Nonlinear Model Predictive Controller Design for Identified Nonlinear Parameter Varying Model

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

Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function

This paper is concerned with the identification of linear parameter varying (LPV) systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning) to further validate the effectiveness of the proposed approach

A taxonomic study of diatom plants in Yading Nature Reserve of Sichuan

In August 2015, diatom diversity of the Yading Nature Reserve of Sichuan was investigated. Diatom samples were observed using light microscope and scanning electron microscope. Totally, 81 species (including varieties) were identified, belonging to 3 classes and 19 families and 31 genera, of which 2 species were newly recorded from China:Cocconeis placentula var. klinorahis Geitler and Psammothidium daonense (Lange-Bertalot) Lange-Bertalot. For two newly reported species, morphological characters, habits and the distributions were described in detail