A comparison of software-based approaches to identifying FOPDT and SOPDT model parameters from process step response data

Abstract

a b s t r a c tSystem identification is the experimental approach to deriving process models, which can take many forms depending upon their intended use. In the work described in this paper, the ultimate aim is to use them in the design of controllers for regulating engineering processes. Modelling always involves approximations since all real systems are to some extent non-linear, time-varying, and distributed. Thus, it is highly improbable that any set of models will contain the 'true' system structure. A more realistic aim is therefore to identify a model that provides an acceptable approximation, in the context of the application in which it is used. In controller design, a first step is often to determine the model using step and frequency response data. This paper compares different modern software approaches that exploit step response data, where the aim is to determine either a first-or second-order-plus-dead-time (FOPDT or SOPDT) transfer function. They include an integral equation method, an algorithm available in the MATLAB Optimization Toolbox, and recently developed in-house software that uses a particle swarm optimisation (PSO) approach.

Research on Identification Algorithm of Cascade Control System

“…In addition to the least-squares method, there are Newton iteration method, particle swarm method, auxiliary variable method, etc. [17][18][19], but the above methods have diferent defects and are not suitable for practical applications. For example, Newton's iteration method [20,21], duelist algorithm [22], bargaining game theory-based approach [23], biquad flters [24], and other similar algorithms [25] are too computationally expensive, particle swarm method iterations are too many, and so on.…”

Tuning proportional-integral controllers based on new analytical methods for finding centroid of stability locus for stable/unstable first-order plus dead-time processes

“…8 In fact, a first-order plus dead-time (FOPDT) model is still commonly and successfully used for approximating the behavior of actual industrial processes, especially those with single-input single-output. 9,10…”

Nonsquare Multivariable Chemical Processes: A Hybrid Centralized Control Proposal

“…These models are regularly assumed to be continuous-time transfer functions. The first-order-plus-dead-time (FOPDT) or second order-plus-dead-time (SOPDT) models are the most common, because their responses exhibit a good approximation of the monotonic, nonovershooting, continuous-time step response of many industrial processes and subprocesses. , …”

“…The first-order-plusdead-time (FOPDT) or second order-plus-dead-time (SOPDT) models are the most common, because their responses exhibit a good approximation of the monotonic, nonovershooting, continuous-time step response of many industrial processes and subprocesses. 5,6 The main objective of controlling multivariable systems is to obtain the required performance of several outputs by manipulating several inputs. Industrial processes usually involve various operations and numerous pieces of equipment operating at different concentrations, temperatures, and pressures, which often result in complex and large plants.…”