Tag Archives: Kalman Filter

Design and Implementation of LQG Strategies for Temperature Control under Greenhouse (Published)

In order to introduce dynamic model based to climate controlling in real-time an environmental control computer system integration witch electronic communication and software interfaces was developed. This paper presents greenhouses control problem of internal temperature which a solution through an optimal control methodology was introduced. So we begin our study by given a state space model using N4SID numerical algorithms for subspace identification algorithm model that allows estimating KALMAN state and Linear Quadratic LQR gain. This evaluates parameters permits to control the inside temperature in real time by Linear Quadratic Gaussian LQG controller. LQG/LTR-based controllers for a heater and for a ventilator have been presented and the stability of switched system will be approved by a good performances management. This controller will be developed by a blocks of software SIMULINK/MATLAB.

Keywords: Data Acquisition, Identification, Kalman Filter, LQG Controller, N4SID Algorithm

Estimation and Forecasting Age Groups wise Survival of CABG Patients (Kalman Filter Smoothing Approach) (Published)

In this paper we present a new approach (Kalman Filter Smoothing) to estimate and forecast, Age Groups wise, survival of Coronary Artery Bypass Graft Surgery (CABG) patients. Survival proportions of the patients are obtained from a lifetime representing parametric model (Weibull distribution with Kalman Filter approach). Moreover, an approach of complete population (CP) from its incomplete population (IP) of the patients with 12 years observations/ follow-up is used for their survival analysis [23]. The survival proportions of the CP obtained from Kaplan Meier method are used as observed values at time t (input) for Kalman Filter Smoothing process to update time varying parameters. In case of CP, the term representing censored observations may be dropped from likelihood function of the distribution. Maximum likelihood method, in-conjunction with Davidon-Fletcher-Powell (DFP) optimization method [8] and Cubic Interpolation method is used in estimation of the survivor’s proportions. The estimated and forecasted, Age Groups wise survival proportions of CP of the CABG patients from the Kalman Filter Smoothing approach are presented in terms of statistics, survival curves, discussion and conclusion.

Keywords: CABG Patients, Complete and Incomplete populations, DFP method, Estimation and Forecasting of Survivor’s Proportions., Kalman Filter, Maximum Likelihood method, Weibull Distribution

AN EMPIRICAL ANALYSIS OF PETROLEUM PRODUCTS DEMAND IN NIGERIA: A RANDOM TREND APPROACH (Published)

The main objective of this study is to estimate petroleum products demand using a random trend approach with aim of deriving improved and more robust estimates of price and income elasticities. The study specifies the random trend model of petroleum products demand as a two-step stochastic process. The estimates of model parameters for each petroleum products, in Nigeria are obtained by applying maximum likelihood in conjunction with Kalman filter. The study revealed that the introduction of random trend reduces the estimate of the coefficient of the lagged dependent variable in the three petroleum products relative to no trend model. As a result, price and income elasticities of petroleum product demands are higher in the short run and long run relative to constant intercept model. The introduction of random trend leads to improvement in the mean square errors of within sample forecasts

Keywords: Elasticity, Kalman Filter, Maximum Likelihood And Energy Demand

Design and Implementation of Lqg Strategies for Temperature Control under Greenhouse (Review Completed - Accepted)

In order to introduce dynamic model based to climate controlling in real-time an environmental control computer system integration witch electronic communication and software interfaces was developed. This paper presents greenhouses control problem of internal temperature which a solution through an optimal control methodology was introduced. So we begin our study by given a state space model using N4SID numerical algorithms for subspace identification algorithm model that allows estimating KALMAN state and Linear Quadratic LQR gain. This evaluates parameters permits to control the inside temperature in real time by Linear Quadratic Gaussian LQG controller. LQG/LTR-based controllers for a heater and for a ventilator have been presented and the stability of switched system will be approved by a good performances management. This controller will be developed by a blocks of software SIMULINK/MATLAB

Keywords: Data Acquisition, Identification, Kalman Filter, LQG Controller, N4SID Algorithm