Study of Two Instrumental Variable Methods for Closed-Loop Multivariable System Identification.

New control strategies are based on the model of the process and it is thus necessary to identify the systems to be controlled. It is also often necessary to identify them during closed-loop operation in order to maintain efficient operation and product quality. Some results of multivariable closed-loop identification carried out on a simulated 2 x 2 linear time-invariant system, using two new versions of instrumental variable methods called IV4D and IV4UP as the identification methods, are presented. In each case pseudorandom binary signals (PRBS), or dithers, are applied to the outputs of the feedback controllers. Algorithms IV4D and IV4UP are created in a four step environment where iterations are performed to obtain the best possible estimated model. For IV4D only the dither is used as part of the instrument. For IV4UP only the part of the input that comes from the dither is used for the instrument. This is obtained with the estimated model and with the description of the controllers using the closed-loop transfer function between the dither and the input to the process. The implementation is made to be run in MatLab and it uses several of the functions defined in its System Identification Toolbox (Ljung, 1991). Both instrumental variable (IV) algorithms perform very well identifying closed-loop multivariable systems under the influence of white noise and correlated noise disturbances. The two new instrumental variable methods are compared with the prediction error method, PEM, and with IV4, the regular instrumental variable open-loop algorithm, both of them are obtained from the MatLab System Identification Toolbox. IV4 does not perform well in closed-loop operation. From the simulated results, the performances of the new IV algorithms are the best but, PEM\u27s performance is very close. Finally, real plant data are analyzed with IV4D and its results are compared with the results of other identification methods, PEM and Dynamic Matrix Identification (DMI) (Cutler and Yocum, 1991). For this closed-loop real plant data PEM is the best that performs followed by IV4D, while DMI does not perform well

Design and Implementation of Smart Sensors with Capabilities of Process Fault Detection and Variable Prediction

A typical sensor consists of a sensing element and a transmitter. The major functions of a transmitter are limited to data acquisition and communication. The recently developed transmitters with ‘smart’ functions have been focused on easy setup/maintenance of the transmitter itself such as self-calibration and self-configuration. Recognizing the growing computational capabilities of microcontroller units (MCUs) used in these transmitters and underutilized computational resources, this thesis investigates the feasibility of adding additional functionalities to a transmitter to make it ‘smart’ without modifying its foot-print, nor adding supplementary hardware. Hence, a smart sensor is defined as sensing elements combined with a smart transmitter. The added functionalities enhance a smart sensor with respect to performing process fault detection and variable prediction. This thesis starts with literature review to identify the state-of-the-arts in this field and also determine potential industry needs for the added functionalities. Particular attentions have been paid to an existing commercial temperature transmitter named NCS-TT105 from Microcyber Corporation. Detailed examination has been made in its internal hardware architecture, software execution environment, and additional computational resources available for accommodating additional functions. Furthermore, the schemes of the algorithms for realizing process fault detection and variable prediction have been examined from both theoretical and feasibility perspectives to incorporate onboard NCS-TT105. An important body of the thesis is to implement additional functions in the MCUs of NCS-TT105 by allocating real-time execution of different tasks with assigned priorities in the real-time operating system (RTOS). The enhanced NCS-TT105 has gone through extensive evaluation on a physical process control test facility under various normal/fault conditions. The test results are satisfactory and design specifications have been achieved. To the best knowledge of the author, this is the first time that process fault detection and variable prediction have been implemented right onboard of a commercial transmitter. The enhanced smart transmitter is capable of providing the information of incipient faults in the process and future changes of critical process variables. It is believed that this is an initial step towards the realization of distributed intelligence in process control, where important decisions regarding the process can be made at a sensor level

The Effect of Controller Parameters on Closed-loop System Identification of Multiple-Input Multiple-Output (MIMO) Systems

In the industries today, less attention has been put on the effect of the controller parameters on closed loop system identification of a Multiple-Input Multiple-Output (MIMO) system. This paper studies the effect of Proportional controller and Proportional-Integral controller on a MIMO system using ARX model. The paper focuses on the effect of the controller parameters of a MIMO system on closed loop system identification when both loops are closed and one loop is open. It is observed that different parameters give different effects on the closed loop system identification. Generally, it is better to keep the gain lower if only Proportional controller is used. For a distillation column, a MIMO system in this project, neither too high or too low gain give a better model accuracy for both closed loop and one loop open when Proportional-Integral controller is used

Refined instrumental variable estimation: maximum likelihood optimization of a unified Box–Jenkins model

For many years, various methods for the identification and estimation of parameters in linear, discretetime transfer functions have been available and implemented in widely available Toolboxes for MatlabTM. This paper considers a unified Refined Instrumental Variable (RIV) approach to the estimation of discrete and continuous-time transfer functions characterized by a unified operator that can be interpreted in terms of backward shift, derivative or delta operators. The estimation is based on the formulation of a pseudo-linear regression relationship involving optimal prefilters that is derived from an appropriately unified Box–Jenkins transfer function model. The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box–Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms