[...]... perturbing the process input and observing the resulting response in the process output variable A process model describing this dynamic input-output relationship can then be identified directly from the data iself In a process control context, the end-use of such a model would typically be for controller design The step response test is one of the simplest identification experiments to perform The... models relating each input to each output These models fall in a class which we will refer to as inputonly models where the process output is expressed as a function of only past values of the process input The FIR/FSR models are popular because they fit very naturally into the predictive control algorithms and also because the types of multivariable processes on which these controllers are typically... process dynamics Despite these advantages, there are a few widely recognized problems associated with the identification of these FIR/FSR models from process input-output data The first problem is their high dimensionality The order of these models is equal to the settling time of the process (the time required for the process output to reach a new steady state after a change has been made in the process. .. continue 6 Introduction to strive to find relatively simple ways to design these controllers in order to improve closed-loop performance However, it is safe to say that not one method in over 50 years has been able to replace the Ziegler-Nichols (1942) tuning methods in terms of familiarity and ease of use More recent developments in the area of PID controller tuning fall into three categories: Model-Based... (1984) This experiment was suggested as a means to automate the Ziegler-Nichols scheme for determining ultimate gain and frequency information about a process Their approach followed directly from a describing function approximation (DFA) to the nonlinear relay element The objective was to use the obtained process information for automatic tuning of PID controllers Astrom and Hagglund's work (1984) has... the step response data to improve the model accuracy In Section 2.7, the modelling algorithm Modelling using Laguerre Functions 10 is applied to step response data obtained from a pilot-scale polymerization reactor Port ions of this chapter have been reprinted from Chemical Engineering Science 50, L Wang and W.R Cluett, "Building transfer function models from noisy step response data using the Laguerre... permission from Elsevier Science, and from IEEE Transactions on Automatic Control 39, L Wang and W.R Cluett, "Optimal choice of time-scaling factor for linear system approximations using Laguerre models", pp 1463-1467, 1994, with permission from IEEE 2.2 PROCESS REPRESENTATION USING LAGUERRE MODELS This section introduces the Laguerre model for representing the process transfer function The basic idea is to. .. area of process identification, where the objective is to obtain a more complete and accurate model of the process from data generated under relay feedback Fitting a more complete process model (i.e a transfer function model) normally requires knowledge of several points on the process Nyquist curve Given that the standard relay experiment combined with the DFA identification technique is able to identify... approach may produce process models with significant errors In this case, other types of input signals, such as a random binary input signal or a periodic input signal, should be used to enable the effect of the disturbances on the process output to be separated from the process response due to the input variable Introduction 1.2 3 USE OF PRESS FOR MODEL STRUCTURE SELECTION IN PROCESS IDENTIFICATION... hence the number of FSF model parameters to be estimated at a modest level, say 11 or 13, for a large class of systems 1.4 PID CONTROLLER DESIGN: A NEW FREQUENCY DOMAIN APPROACH The PID controller continues to be the most common type of single-loop feedback regulator used in the process industries However, the tuning of these controllers is still not widely understood and, in fact, many still operate . x0 y0 w0 h1" alt="" From Plant Data to Process Control Also in the Systems and Control Series Advances in Intelligent Control, edited by C. J. Harris Intelligent Control in Biomechanics,. Duality of IdentiJication and Control . , by S. Veres and D. Wall. Series Editors E. Rogers and J. O'Reilly From Plant Data to Process Control Ideas for process identification and. theory with applications ranging from the ubiquitous PID controller, widely encountered in the process industries, through to high-performance fidelity controllers typical of aerospace applications.