Linear regression techniques for state-space models with application to biomedical/biochemical example

In this paper a novel approach to estimate parameters in an LTI continuous-time statespace model is proposed. Essentially, the approach is based on a so-called pqR-decomposition of the numerator and denominator polynomials of the system’s transfer function. This approach allows the physical knowledge of the system to be preserved. As an illustrative example, a biomedical/biochemical process with two compartments in parallel and with first-order reaction is used.First, the process is approximated by a discrete-time state-space model. Next, after deriving the corresponding discrete-time transfer function, the rational transfer function is decomposed into pqR form and then reparametrized to obtain a set of linear regressive equations. Subsequently, the unknown linear regression parameters, which are a polynomial function of the original physical parameters, are uniquely estimated from real data of the biomedical/biochemical process using the ordinary least-squares method. This approach is favourable when there is a need to preserve physical interpretations in the parameters. Furthermore, by taking into account the original model structure, a smaller number of parameters than in the case of direct transfer function estimation may result and the identifiability property naturally appears

Identification-based Diagnosis of Rainfall ¿Stream Flow Data: the Tinderry Catchment

System identification tools, such as transfer function (TF) model structure identification, recursive estimation, time-varying parameter (TVP) estimation and assessment of data information, are used to evaluate the quality of rainfall-stream flow data from the Tinderry catchment (ACT, Australia) and the timevarying behaviour of the rainfall-stream flow dynamics. For the catchment, given the wide range and the abrupt changes of the single input-single output transfer functions describing different periods or events, we conclude that further investigation of (i) local rainfall effects, (ii) time-varying time delays (travelling time), (iii) time-varying residence times related to the base flow and (iv) occurrence of negative residues is needed. Periods with high and low data information content, for further use in effective parameter estimation procedures, are clearly indicated by the analysis

Climate control of a bulk storage room for foodstuffs

A storage room contains a bulk of potatoes that produce heat due to respiration. A ventilator blows cooled air around to keep the potatoes cool and prevent spoilage. The aim is to design a control law such that the product temperature is kept at a constant, desired level. This physical system is modelled by a set of nonlinear coupled partial differential equations (pde's) with nonlinear input. Due to their complex form, standard control design will not be adequate. A novel modelling procedure is proposed. The input is considered to attain only discrete values. Analysis of the transfer functions of the system in the frequency domain leads to a simplification of the model into a set of static ordinary differential equations ode's). The desired control law is now the optimal time to switch between the discrete input values on an intermediate time interval. The switching time can be written as a symbolic expression of all physical parameters of the system. Finally, a dynamic controller can be designed that regulates the air temperature on a large time interval, by means of adjustment of the switching time

On Feedback Identification of Unknown Biochemical Characteristics in an Artificial Lake

The problem of dynamical identification of unknown characteristics (states/parameters) in a biochemical model of an artificial lake with only inflow and given observations of some states is considered. An algorithm that solves this simultaneous state and parameter estimation problem and that is stable with respect to bounded informational noises and computational errors is presented. The algorithm is based on the principle of auxiliary models with adaptive controls. Convergence of the algorithm is proven and a convergence rate is derived. The performance of the algorithm is illustrated to a typical single-species environmental example

Low impact urban design by closing the urban water cycle

Abstract Current fast urbanization and increasing quality of life result in increments on resources’ demand. Increasing resources demand implies as well increments on waste production. However, limited availability of resources such us: oil, fresh water, phosphorus, metals (Boyle et al., 2010, Gordon et al., 2006; Rockström et al., 2009) and limited earth’s productive and carrying capacity (Rees, 1999) are potential restrictions to urban growth and urban sustainability. These pressures, however, are drivers towards more efficient resource use. In a world of cities, urban systems play a key role to find solutions for these global pollution and depletion problems (Xu et al., 2010). To alleviate these pressures, it is needed to minimize demand and to shift from linear to circular metabolism, in which recycling and reusing are key activities (Girardet, 2003)

Improving Local Weather Forecasts for Agricultural Applications

For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems usually the forecasts of the nearest meteorological station are used whereas measurements are taken from the systems location. The objective of this study is to evaluate the reduction of the forecast error for a specific agricultural system. Three weather variables , that are most relevant for greenhouse systems are studied: temperature, wind speed, and global radiation. Two procedures are used consecutively: diurnal bias correction and local adaptive forecasting. For each of the variables both bias and standard deviation were reduced. In general, if local measurements are reliable, forecast errors can be reduced considerably