Controlled Autoregressive Moving Average Research Articles

Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data

Difficulties of identification for multivariable controlled autoregressive moving average (ARMA) systems lie in that there exist unknown noise terms in the information vector, and the iterative identification can be used for the system with unknown terms in the information vector. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a least squares based iterative algorithm is proposed for multivariable controlled ARMA systems. The simulation results indicate that the proposed algorithm is effective.

The residual based interactive least squares algorithms and simulation studies

This paper presents a two-stage least squares based iterative algorithm, a residual based interactive least squares algorithm and a residual based recursive least squares algorithm for identifying controlled autoregressive moving average (C-ARMA) models. The simulation studies indicate that the proposed algorithms can effectively estimate the parameters of the C-ARMA models.

Modeling and self-tuning pressure regulator design for pneumatic-pressure–load systems

This paper presents a dynamic model and a design method for an accurate self-tuning pressure regulator for pneumatic-pressure–load systems that have some special characteristics such as being nonlinear and time-varying. A mathematical model is derived, which consists of a chamber continuity equation, an orifice flow equation and a force balance equation of the spool. Based on a theoretical analysis of the system dynamics, a three-order controlled auto-regressive moving average (CARMA) model is used to describe the practical pressure–load systems. Then a linear quadratic Gaussian self-tuning pressure regulator is designed to realize an adaptive control of pressure in the chamber. Because the system parameters are time-varying and the system states are difficult to detect, the recursive forgetting factor least-squares algorithm and the Kalman filtering method are adopted to estimate the system parameters and the system states. Experimental results show that the proposed self-tuning pressure regulator can be adapted to parameters which vary with such factors as the volume of the chamber and the setting pressure and that better dynamic and static performances can be obtained.

The Effect of the Tuning Parameters on the Performance of the Parametric and Nonparametric Model Based Control Methods for Growth Medium Temperature of Baker's Yeast Production

In this study, nonparametric and parametric model based control methods were applied in the control line in order to control the growth medium temperature of aerobic baker's yeast production in a batch bioreactor, and the performance was compared experimentally and theoretically. For a non-parametric model, an experimental reaction curve was found by performing the open-loop step test. The reaction curve was presented by the first order dead time model to represent the dynamic behaviour of the reactor. An Internal Model Control (IMC) system based on a non-parametric model, has been used to track the temperature of the reactor mixture. The simulation program is used to calculate the parameters of the parametric Controlled Auto Regressive Moving Average (CARMA) model for Self-Tuning PID (STPID) control. The parameters of the CARMA model are calculated using Bierman algorithm by applying Pseudo Random Binary Sequence (PRBS). In the first part of the control work, a simulation program was used to observe the performance of DVIC and STPID control systems by calculating the Integral Square Error (ISE) values. Two different kinds of operating conditions, such as load and set point effects were applied to test the control performance for servo and regulatory behaviours. The filter time τ f for IMC and the first parameter of Tailoring polynomial for STPID were used as tuning parameters. In addition, PID control system performance was compared with both parametric and non-parametric control systems. In the second part of the control work, the growth medium temperature of aerobic baker's yeast production was controlled experimentally by using IMC and STPID systems in an on-line computer controlled batch bioreactor. Non-parametric and parametric models for both control systems were tested by considering the reaction heat as a load effect. The heat input given from the immersed heater was chosen as the manipulated variable. It was observed that IMC was more effective than STPID and PID methods.

Simulations Studies on the Adaptive Control Feasibility of Diabetic Blood Glucose Concentration Under Artificial Pancreas Monitoring

Our concern in this paper is the adaptive control of the blood glucose concentration of diabetics subject under artificial pancreas monitoring. We begin first by a structure determination from real data of a model represented by a diabetic patient in order to allow us to set bounds on the degree of different polynomial of the ARMA model used in the adaptive scheme as well as defining minimal bound on the time dealy of the glyceamia in response of the various inputs.The aim of the adaptive scheme is to control the fluctuation of the glyceamia rate in relation with the injected flow of insulin and glucose. In the identification step we first look for a structure which represents a relation betweenThe inputs-The insulin, considered as the main input-The glucose, a measurable disturbance.-The food intake, a measurable disturbance.The output-The glyceamia blood rate.The model is chosen to be a multi-input single-output CARMA (Controlled Auto-Regressive Moving Average) model which is easy to handle and fits to many identification algorithms.The structure determination is based upon the evaluation of various norms (Young et al. 1980 and Ahmed. 1985) such as the normalized residual and output error rvorms. The combination of these various norms and the parameter estimates convergence enables the selection of the structure, which has a low residual enroc and low parameter esunxition error.The model obtained in the structure determination step is used as a “mean” model in the digital simulation based on Ackerman's mathematical model. The simulation of various adaptative control algorithms such as the generated minimum variance and especially the generalized predictive control (Clarke et al, 1987a, 1987b). which are shown through numerical simulations to have the capability of compensating, in on line manner, parameter variations of the model with the desired glucose profile specified by physicians.The simulation results are very encouraging and we hope that we could apply these methods in a real experiment very soon.