Structured Hammerstein-Wiener Model Learning for Model Predictive Control

Nonlinear Model Based Predictive Controller Using a Fuzzy-neural Wiener-hammerstein Model

It is presented in this paper a method for designing a nonlinear model predictive controller. The controller is based on a hybrid Wiener-Hammerstein fuzzy-neural predictive model and а simplified gradient optimization algorithm. The proposed approach is used to control the product temperature in a Lyophlization plant. The controller efficiency is tested and proved by simulation experiments in M...

Control Performance Assessment for Hammerstein-Wiener Models

Control performance assessment, or CPA, is an increasingly vital tool to quantify the performance of industrial control loops. While most of the research and commercial activity in CPA has been applied to linear systems to date, those researchers investigating nonlinear systems fall into one of two groups. The first group focussed on the diagnosis of a common specific nonlinearity, namely valve...

Identification of Wiener–Hammerstein benchmark model via rank minimization

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Learning Model Predictive Control for Iterative Tasks

A Learning Model Predictive Controller (LMPC) for iterative tasks is presented. The controller is referencefree and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nonincreasing performance at each iteration. The paper presents the control design approach, and shows how to r...