Frequency Sampling Filter Model Research Articles

Experimental Evaluation of Automatic Tuning of PID Controllers for an Electro-Mechanical System

Integrating processes are widely encountered in electro-mechanical systems and other areas. Automatic tuning of PID controllers for this type of system requires special consideration since the underlying system is unstable. Using relay feedback control in a closed-loop setting, an auto-tuner is designed to systematically find the controller parameters using a frequency sampling filter model. The performance of this auto-tuner is evaluated on an electro-mechanical experimental test facility that can be configured to operate in either as a single-input, single-output or multiple-input, multiple-output system. The experimental results demonstrate that when the interaction the between control loops is weak, the auto-tuner produces closed-loop performance that matches the specification, but this performance gradually decays as the level of interaction increases. The experimental results can also serve as experimental benchmarks to compare competing designs.

Control Oriented System Identification for Performance Management in Virtualized Software System

Capability to manage the performance of a shared resources environment relies on the model estimation of all dynamics in the system. The main challenge is to capture the nonlinear characteristic which inherently exists in software system applications. Hammerstein-Wiener block structural model is widely regarded as a basis for description of nonlinear systems. This paper extends the existing work in system identification of software systems using Hammerstein-Wiener block structural model. As part of the estimation, the linear element is represented in terms of Frequency Sampling Filter model and the inverse static output non-linearity is estimated using B-Spline curve functions. Experimental studies show that the static nonlinearities estimated when using the B-Spline functions have less oscillations and the overall model has a better performance than the case when using polynomial functions.

Identification of step response estimates utilizing continuous time subspace approach

Direct identification procedures using raw data seem to face difficulties especially when the data is corrupted with noise or the data acquisition leads to huge amount of data to be processed. This will lead to complexity in obtaining the accurate model of the system and the increase of computational load and time may also arise. In this paper, we present 2-stage identification, in which, the first stage involves a process to obtain step response estimates. A multi input multi output frequency sampling filter model is used to simulate the estimates. With the aid of finite impulse response model, maximum likelihood method and the predicted sum of square statistics, this procedure able to clean the noise that occurred at high frequency region, compressed the data into the reduced amount and obtained only meaningful parameter that describes the system. Next, at the second stage the continuous time subspace model identification is conducted using the step response estimates obtained from the first stage. Here, three continuous time subspace methods will be observed to develop a state space mathematical model; those are the MOESP, CCA and ORT methods. A Monte Carlo simulation is performed as to see the efficacy and robustness of those models in identifying the step response estimates of the observed system. Comparative analysis with respect to two-stage identification and direct identification procedure is also conducted. This is to show the significant contribution of having MIMO FSF in the overall identification procedure. From results, the developed MIMO FSF is able to compress raw MIMO data into fewer numbers, and produce cleaned and unbiased step response estimates. When it is implemented to MIMO continuous-time subspace identification, MOESP method has demonstrated good performance based on the accuracy and robustness of the developed model.

Control relevant identification for robust optimal control

This paper presents an approach to designing the input signal for an identification experiment, in which the process model estimate is to be used to formulate and solve for a robust (in a worst case sense) optimal controller. The input signal is designed to contain the information that is relevant for the end use of the model, that is for control purposes. The proposed approach uses sensitivity analysis to determine the input signal frequencies that are important with respect to a certain measure of achievable controller performance in conjunction with a frequency sampling filter model of the process. Based on the sensitivity analysis, an iterative experimental design methodology is suggested.

Identification of time-varying pH processes using sinusoidal signals

This paper presents an approach to the identification of time-varying, nonlinear pH processes based on the Wiener model structure. The algorithm produces an on-line estimate of the titration curve, where the shape of this static nonlinearity changes as a result of changes in the weak-species concentration and/or composition of the process feed stream. The identification method is based on the recursive least-squares algorithm, a frequency sampling filter model of the linear dynamics and a polynomial representation of the inverse static nonlinearity. A sinusoidal signal for the control reagent flow rate is used to generate the input–output data along with a method for automatically adjusting the input mean level to ensure that the titration curve is identified in the pH operating region of interest. Experimental results obtained from a pH process are presented to illustrate the performance of the proposed approach. An application of these results to a pH control problem is outlined.

Application of the Frequency Sampling Filter Model in Process Identification: An Industrial Cast Study

Abstract This paper examines the application of the frequency sampling filter (FSF) model in process identification. The study has been performed on a refinery distillation train with the objective being to obtain step response models from plant dynamic test data for model predictive controller design. The results focus on: (a) selection of the FSF model-based multivariable system structure using the sum of squared true prediction errors (PRESS): (b) development of noise models for use in prefiltering to remove the effect of any feedback on the step response estimates and to produce white residuals for estimation of the covariance matrix; and (c) presentation of statistical confidence bounds for the step response models. The FSF results are compared with those obtained using the finite impulse response (FIR) model structure and with results obtained from a commercially available identification software package.

Frequency-sampling filters: An improved model structure for step-response identification

This paper presents a new methodology for step-response identification using the frequency-sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite-impulse-response (FIR) model are (i) the number of parameters to be estimated is independent of the choice of sampling interval, and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (ii) the general conditioning of the correlation matrix is better with the FSF model because of the reduction in the number of parameters that need to be estimated and to the narrow-bandpass nature of the frequency sampling filters.

Development of quality bounds for time and frequency domain models: application to the Shell distillation column

Statistical confidence bounds are developed for frequency response and step response models estimated from process input-output data using the frequency sampling filter model structure. The frequency domain bound is an extension of earlier work by the authors (Arifin et al., Journal of Process Control, 1995, 5, 71–76) and the time domain bound is a new result. These bounds provide the engineer with a measure of the quality of the estimated model, and therefore an indication of which aspects of the model are reliable and which aspects require further improvement. The Shell distillation column benchmark problem introduced by Cott (Journal of Process Control, 1995, 5, 60–70) is used for illustrative purposes.

Frequency Sampling Filters: An Improved Model Structure for Step Response Identification

This paper presents a new methodology for step response identification using the frequency sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite impulse response (FIR) model are: (1) the number of parameters to be estimated is independent of the choice of sampling interval and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (2) the general conditioning of the correlation matrix is better with the FSF model due to the reduction in the number of parameters that need to be estimated and to the narrow bandpass nature of the frequency sampling filters.

A new approach to the identification of pH processes based on the Wiener model

This paper presents a new approach to the identification of pH processes based on the Wiener model structure (a dynamic linear element in series with a static nonlinearity). A frequency sampling filter model is used to represent the dynamic linear element and a simple least-squares algorithm is applied to simultaneously estimate the parameters of the linear subsystem and the inverse static nonlinearity. Experimental results obtained from a pilot-scale pH process are presented to illustrate the performance of this new scheme. A key benefit of using this approach is that an estimate of the pH titration curve can be obtained in significantly less time as compared to more traditional approaches.

Identification of the Shell distillation column using the frequency sampling filter model

Abstract This paper illustrates the application of the frequency sampling filter model to the identification of the Shell distillation column. The approach presented here produces an estimate of the process frequency response matrix along with probabilistic confidence bounds on the modelling error, and also an estimate of the process step responses. The role of disturbance models in the design of the identification experiment is emphasized.

Robust frequency domain identification

In this paper, a robust frequency domain identification technique is presented. The method is based on a frequency sampling filter (FSF) representation of the process. The FSF model has been combined with a standard least-squares estimator to directly obtain an estimate of the process frequency response. The identification technique is robust in the sense that a bound on the modelling error in the frequency domain is also provided using only input—output process data and prior knowledge of the distribution of the process disturbances. Simulation and experimental results using a pilot-plant heat exchanger are presented to illustrate the method.