Ivan Zajic

Modelling of an Air Handling Unit: A Hammerstein-bilinear Model Identification Approach

The paper focuses on modelling and system identification of an air handling unit (AHU), which is part of larger heating ventilation and air conditioning (HVAC) system dedicated for clean room manufacturing. The aim is to use the model for subsequent control optimisation and further for control system design. The underlying physical relations of an AHU are investigated through a white-box model. Based on the white-box model considerations a discrete-time bilinear black-box model is proposed. The AHU cooling/heating capacity is altered by means of water valves. The valve introduces a Hammerstein nonlinearity on the system input, which needs to be additionally identified together with the dynamic bilinear model representing the heating/cooling coil of the AHU. The discrete-time model coefficients are then estimated based on real measurements.

Energy Consumption Analysis of HVAC System with Respect to Zone Temperature and Humidity Set-Point

Abstract The general aim is to increase the energy efficiency of heating ventilation and air conditioning (HVAC) systems by achieving highly stable control performance allowing for operation close to the specification limits, where the highest profitability can be obtained. Considering the nonlinear behaviour of HVAC systems the use of advanced control techniques is a necessity in achieving this goal. One of the key questions arises here: are the economical benefits gained from the implementation of advanced control techniques higher than the installation costs? In this paper, the steady state characteristics between control set-points and HVAC system energy usage are estimated, in order to help to answer this question.

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data–driven and physically meaningful nonlinear continuous–time model, which represents a test–bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model– based and data–driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte– Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.

Identification of Fractional Order Models: Application to 1D Solid Diffusion System Model of Lithium Ion Cell

In this paper the simplified refined instrumental variable method for fractional order transfer function model identification is applied to a simulated diffusion system of concentration of the lithium ions in a battery cell. The diffusion process is represented by a 1D spherical diffusion partial differential equation of concentration and is solved numerically by finite volume method (FVM) in spatial and temporal domains. The fractional transfer function (FTF) model order is selected and model parameters are identified based on sampled input-output data. The main advantage of using FTF model for simulation purposes is the increased accuracy, as compared to FVM, while retaining simulation simplicity of having a reduced order model.

Modelling and data-based identification of heating element in continuous-time domain

A unique data-based and physically meaningful nonlinear continuous-time model of heating element is presented. The model is considered to be of low complexity yet achieving high simulation performance. The physical meaningfulness of the model provides enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based control designs for tight temperature regulation. The second contribution presented in this work is the parameter estimation of the derived nonlinear model in continuous-time domain itself. For this purpose the application of refined instrumental variable methods has been found to be particularly suitable.

Temperature Model of an Industrial Air Handling Unit and Manufacturing Zone

Abstract The paper focuses on system identification of a temperature model of an industrial air handling unit in connection with an air conditioned manufacturing zone. The models obtained are intended to be used for a subsequent control analysis and tuning of the presently installed temperature control system. Model structures are predetermined based on simplified first principles modelling approach. A bilinear parametrisation method in tandem with a simplified instrumental variable technique is applied for the parameter estimation of a discrete-time Hammerstein-bilinear model representing the air handling unit. Whilst a simplified instrumental variable method for continuous-time system identification is utilised for estimating the parameters of the linear manufacturing zone model.

Operating point adjustment procedure for a class of continuous-time bilinear models

This paper presents a proposal of a novel parameter adjustment procedure for a class of continuous-time bilinear models. This procedure allows the user to adjust the operating point dependent dynamic characteristics of the considered bilinear model to match the dynamic characteristics of the same model however operated at different working point. This procedure finds its application in system identification and simulation of bilinear models as well as in the area of bilinear model based control.

Properties of input-output Hammerstein-bilinear structure with application to an industrial air handling unit

When developing mathematical models, especially for control, the practical interest lies in relatively simple extensions of linear structures that offer improved modelling capabilities. In this paper a discrete-time input-output Hammerstein-bilinear structure is introduced and its properties are discussed in detail. It consists of a cascade connection of a static nonlinearity followed by a dynamic bilinear system. By combining advantages of constituent subsystems the Hammerstein-bilinear structure allows for both an input dependant dynamic behaviour (particular property of bilinear systems) and an increased flexibility of the steady-state characteristic (particular property of Hammerstein models) to be obtained simultaneously. Modelling capabilities of such structure are evaluated on an air-handling unit that is a part of an industrial heating, ventilation and air-conditioning system.

Control Analysis and Tuning of an Industrial Temperature Control System

Abstract The paper focuses on control analysis and tuning of an already installed temperature control system in a manufacturing plant. The dynamics of the investigated heating ventilation and air conditioning system are inherently nonlinear throughout the operational region. Therefore, it is challenging to tune the adopted proportional-integral controller. For this reason, a nonlinear temperature model, whose structure is predetermined based on simplified first principles assumptions, is derived. The corresponding parameters are estimated based on measured data using identification techniques. The obtained temperature model is used for off-line control analysis and subsequent control tuning.