Continuous time state-space model identification with application to magnetic bearing systems

TL;DR: In this paper, a multi input multi output frequency sampling filter model is used to simulate the step response estimates and a Monte Carlo simulation is performed as to see the efficacy and robustness of those models in identifying the observed system.

TL;DR: In this paper, a system identification of multi input multi output (MIMO) magnetic bearing system comprises of two steps, the first step employs a MIMO frequency sampling filters, in which, the raw data will be analyzed in order to obtain a meaningful multivariable parameters that describe the empirical model of the analyzed data.

TL;DR: In this article, the authors conducted an analysis to obtain underwater thruster model using system identification approach and further analyzed performance of the developed model is further analyzed by simulating it with PID control system.

TL;DR: The developed MIMO frequency sampling filters are demonstrated by compressing simulation data of a pH neutralization process in a stirring tank and data from model of a steam generator at Abbott Power Plant, demonstrating the efficacy of the developed filters.

TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.

TL;DR: In this article, the authors focus on concepts with a blend between illustrations using real data sets and mathematical and conceptual development and emphasize applications with examples that illustrate nearly all the techniques discussed, including simultaneous influence, maximum likelihood estimation of parameters, and the plotting of residuals.

TL;DR: This volume focuses on the design of computer-controlled systems, featuring computational tools that can be applied directly and are explained with simple paper-and-pencil calculations.