Self-tuning Predictor Research Articles

Modeling and Simulation of Single Screw Extruder Control System

This article is mainly talking about a pressure control system designed for single screw extruder. In order to achieve constant pressure control of the head, we used fuzzy PID self-tuning techniques and Smith Predictor to control extrusion system and then we used Matlab Simulink toolbox to simulated control result. Simulation results show that the method achieve good control effect and meet the design requirements.

Multiple model self-tuning predictor applied to power demand forecasting

A multiple model self-tuning predictor is presented which does not require the assumption or knowing the parameter vectors in the finite set. The parameters of the submodes are recursively estimated in real-time with a parallel model identification method. The simulation example shows the satisfactory performance of the proposed algorithm. The algorithm is applied to power demand forecasting and gives the significant improved results in comparison with a self-tuning predictor, although the Markov dynamic model assumption is not exactly satisfied.

Application of an Adaptive Forecast Algorithm to the River Västerdalälven

During the last two decades, advances in electronic engineering, hydrological modelling and systems theory have given considerable benefits to the hydrological forecast developments. Today several powerful adaptive techniques are available, which can improve the reliability of hydrological forecasting. One of these techniques is the self-tuning predictor based on an ARMA type model using direct parameter estimation by recursive least square algorithm. The selftuning predictor has been tested on the River Västerdalälven in Sweden.

Application of a multivariable self-tuning predictor

A multivariable predictor algorithm is presented which can produce multistep prediction of a stochastic time series. The algorithm is used to predict product quality in an iron-ore sintering process. The application uses a modified low-pass filtering technique involving less time lag than conventional filters.

Prediction of railway power demand

The short-term prediction problem is considered for power demand at railway energy supply stations of the Dutch national railway company. A model and an adaptive prediction algorithm are presented. The algorithm is based on a self-tuning predictor for the output of a Gaussian system.

Self-tuning prediction and control

An attempt is made to summarize the vast amount of self -tuning algorithms that have recently appeared in the control literature. This is done by correlating the different methods into a generalized self-tuning predictor. This unifying algorithm contains several typos of self-tuners as special members using appropriate design filters. This generalized algorithm can be used to implement the different strategies. The presented method is based essentially on the prediction point of view. The self-tuning aspect is entirely embodied in the predictor while a fixed parameter controller is used. The controller consists of feedforward from the reference input and feedback from the predicted system output (instead of the real system output). The coupling of the self-tuning predictor with the fixed parameter controller makes the whole control system self-adaptive. The self-tuning system does not suffer from opening or closing of the control loop during its operation nor from alternating between several controllers. ...

Typical Application Possibilities for Self-Tuning Predictive Control

Control based on self-tuning prediction has some typical application possibilities. Two such realizations are described in this paper.The first one deals with dead-time control in a heat transport process. A predicted value of the process output is fed back, leading to a compensation of the time-delay. This allows for higher loop gains, resulting in a better control performance as is demonstrated by the comparison with a PI-regulator.The second implementation of self-tuning predictive control is in a decision-making strategy used in an electrical servo. This application shows that very satisfactory control performance can be obtained with rather primitive control tools (on-off) combined with a self-tuning predictor. The results of the strategy are compared to those of well-known control methods, among which a PD-controller.

Self-Tuning Predictor for a Class of Distributed Parameter Systems

The paper deals with the possible extensions of the existing self-tuning prediction methods for a class of distributed parameter systems (DPS). The distributed output signal of the process is transformed to a single output variable using the spatial weighted average (SWA) transformation and then finite number of local measurements are taken into consideration. Several predictor algorithms are presented for different DPS-s. Simulation results are also given.

Self-Tuning Load-Frequency Control of the Hungarian Power System

A combined regulator based on a self-tuning predictor of the area requirement and the RAFT control strategy for the ACE is described for the load-frequency control of the Hungarian Power System. Real-time experiments with the implemented adaptive regulator are also shown.

Self-Tuning Fly-By-Wire Control System

Several self-tuning control algorithms are applied to control the lateral motion dynamics of an aircraft. A comparison is made with the results obtained with a classical three-term controller. The aircraft dynamics are strongly time-variant due to the dependence on the aerodynamic proper-ties of the air which change with altitude and velocity. Consequently this system is an excellent test-medium for the evaluation of adaptive controllers. The self-tuning controllers are obtained with one algorithm in which a number of design parameters can be altered. The algorithm is based on the combination of a self-tuning predictor and a feedforward - feedback regulator with feedforward from the pilot input and feedback from the predicted system output. This leads to an adaptive controller with direct estimation of the control parameters.

A self-tuning predictor for a class of multivariable stochastic processes

Abstract This paper concerns the prediction of multivariable stochastic time series. An algorithm for a self-tuning predictor is constructed and some of its properties are discussed. Possible modifications of the algorithm for practical applications are also presented. A simulated example further illustrates the behaviour of the predictor.

The Global Error Approach to the Convergence and Accuracy of Complex Recursive Identification Algorithms

The global error is the identification error expressed expiIcItly as a function of all unknown system parameters, model parameters and external system inputs. It is used to transform the difficult problem of mean-square convergence and accuracy testing for recursive identification algorithms into a relatively simple problem of determining the properties of the solution for a polynomial equation. The method is introduced for the simple problem of closed-loop identification of time-discrete systems. Its full power is demonstrated for the case of the self-tuning regulator and self-tuning predictor.

A Self-tuning Predictor as Operator Guide

A self-tuning multistep predictor is presented. It predicts the output of a stochastic process with unknown, possibly tiIre-varying, parameters over a range of several sampling periods in the future. At each sampling instant it is tuned by using a recursive least-squares parameter estimator in real time. By doing this, the combination predictor-estimator converges fast to the optimal predictor for processes with known parameters (self-tuning property). The method seems to have powerful caIXiliilities as an aid in controlling complex industrial processes which are until now only operated under manual control. The predictor can be used by the operator in selecting an appropriate control action. A typical application, the control of a blast furnace, is extensively dealt with in the paper.The paper opens new perspectives in the donain of self-tuning controllers, and it has practical importance as is indicated by the blast-furnace experiment.

Some interpretations of the self-tuning controller

The self-tuning controller is extended to include rational transfer function (as opposed to polynomial) terms in the associated cost function. Two interpretations of the self-tuning controller are examined in some detail: a model reference adaptive control, and a self-tuning least-squares predictor in conjunction with conventional compensation. The former version is shown to give not only prespecified set point response, but also a closed-loop disturbance with largely prespecified spectral density. The latter version is compared with the method of O.J.M. Smith, and is shown to be less sensitive to uncertainty in some system parameters. Examples are given which illustrate the continuous-time performance of these discrete-time control laws.

A self-tuning predictor

An adaptive predictor for discrete time stochastic processes with constant but unknown parameters is described. The predictor which in real time tunes its parameters using the method of least squares is called a self-tuning predictor. The predictor has attractive asymptotic properties. If the parameter estimation converges and if the predictor contains parameters enough, then it will converge to the minimum square error predictor that could be obtained if the parameters of the process were known. The computations to be carried out at each sampling interval are very moderate and the algorithm is well suited for real-time applications. The self-tuning predictor can be used to predict processes which contain trends or periodic disturbances. Further, the algorithm can easily be modified in order to make it possible to predict processes with slowly time-varying parameters.