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We present an approach to model the dynamic of a copper flotation process. The conventional approach of system identification is applied to model the dynamics. In this research, experiments are performed to collect process data of determined input and output variables. It is followed by data pre-processing to handle outliers and to remove high frequency disturbances. Simulation and validation responses of linear estimated models, which captured the dynamic of the process, are presented. The long term goal is to use estimated models to design a models-based control system.

The thesis has been conducted at Hetvägg AB and the aim is to develop a combined PID and Model Predictive Controller (MPC) controller for an air to water heat pump system that supplies domestic hot water (DHW) to the users. The current control system is PLC based but because of its big size and expensive maintenance it must be replaced with a robust controller for the heat pump. The main goal of this project has been to find a suitable improvement strategy. By constructing a model of the system, the control system has been evaluated. First a model of the system is derived using system identification techniques in Matlab-Simulink; since the system is nonlinear and dynamic a model of the system is needed before the controller is implemented. The data has been estimated and validated for the final selection of the model in system identification toolbox and then the controller is designed for the selected model. The combined PID and MPC controller utilizes the obtained model to predict the future behavior of the system and by changing the constraints an optimal control of the system is achieved. In this thesis work, first the PID and MPC controller are evaluated and their results are compared using transient and frequency response plots. It is seen that the MPC obtained better control action than the PID controller, after some tuning the MPC controller is capable of maintaining the outlet water temperature to the reference or set point value. Both the controllers are combined to remove the minor instabilities from the system and also to obtain a better output. From the transient response behavior it is seen that the combined MPC and PID controller delivered good output response with minimal overshoot, rise time and settling time.

This work presents three techniques for parameter identification for nonlinear systems. The methods presented are expanded from those presented in Adetola and Guay [3, 4, 5] and are intended to improve the performance of existing adaptive control systems. The first two methods exactly recover open-loop system parameters once a defined convergence condition is met. In either case, the true parameters are identified when the regressor matrix is of full rank and can be inverted. The third case uses a novel method developed in Adetola and Guay [5] to define a parameter uncertainty set. The uncertainty set is periodically updated to shrink around the true value of the parameters. Each method is shown to be applicable to a large class of linearly parameterized nonlinear discrete-time system. In each case, parameter convergence is guaranteed subject to an appropriate convergence condition, which has been related to a classical persistence of excitation condition. The effectiveness of the methods is demonstrated using a simulation example. The application of the uncertainty set technique to nonlinearly parameterized systems constitutes the main contribution of the thesis. The parameter uncertainty set method is generalized to the problem of adaptive estimation in nonlinearly parameterized systems, for both continuous-time and discrete-time cases. The method is demonstrated to perform well in simulation for a simplified model of a bioreactor operating under Monod kinetics. / Thesis (Master, Chemical Engineering) -- Queen's University, 2010-10-19 10:58:24.888

Thesis (Ph.D.)--Drexel University, 2007. / Includes abstract. Includes bibliographical references (leaves 220-231).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 52-59). Also available in electronic version.

In this parametric inverse problem, we consider a lossless dielectric slab excited by a transient plane wave. The scattered electric field from the slab is presented in the ray-optic and the complex-resonance forms. Our interest is to extract the complex-resonances of the system in order to identify the parameters that describe the scatterer. We review the signal processing procedure and the identification procedure employed to identity the poles of the system. We investigate the effect of noise on identification and determine the maximum amount of noise one can impose on the system. In addition, we study the effect of data truncation on our identification procedure. We also discuss the parameters that dictate the minimum record required for successful identification. Finally, we demonstrate some similarities in effect of noise and truncation on our identification process.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Renewable energy generation will play an important role in solving the climate change problem. With renewable electricity generation increasing, there will be some significant changes in electric power systems, notably through smaller generators embedded in the distribution network. Historically insignificant volumes of Embedded Generation (EG) mean that traditionally it has been treated by the transmission system operator as negative load, with its impact on the dynamic behaviour of power systems neglected. However, with the penetration level increasing, EG would start to influence the dynamics and stability of the transmission network. Hence the dynamic behaviour of distribution network cannot be neglected any more. In most cases, a detailed distribution network model is not always available or necessary for the study of transmission network dynamics and stability. Thus a dynamic equivalent model of the distribution network that keeps its essential dynamic behavior, is required. Most existing dynamic equivalencing methods are based on the assumption that the detailed information of the complete power system is known. Dynamic equivalencing methods based on coherency of the machines have been applied to transmission networks but cannot be applied to distribution networks due to their radial structure. Hence an alternative methodology has been developed in this project to derive the dynamic equivalent model of the distribution network using system identification, without the detailed information of the distribution network necessarily known. Case studies have been accomplished in PSS/E on a model of the Scottish transmission network with the distribution network in Dumfries and Galloway. Embedded generation with a certain penetration level in either conventional generation or DFIG wind generation has been added to the model of the distribution network. The dynamic equivalent models of the distribution network are compared with the original distribution network model using a series of indicators. A constant power model has also been involved in the comparison to illustrate the advantage of using the dynamic equivalent to represent the distribution network. The results suggest that a proper dynamic equivalent model derived using this methodology may have better agreement to the original power system dynamic response than constant power equivalent. A discussion on factors that influence the performance of the dynamic equivalent model, is given to indicate the proper way to use this methodology. The major advantage of the dynamic equivalencing methodology developed in this project is that it can potentially use the time series obtained from measurements to derive the dynamic equivalent models without knowing detailed information on the distribution network. The derived dynamic equivalent, in a simple spate-space form, can be implemented in commercial simulation tools, such as PSS/E.

A frequent problem in physics and engineering is that of determining a mathematical model for the dynamic performance of a system. It is particularly useful to be able to make measurements which enable such a model to follow changes in the system dynamics in the course of normal operation. Linear control theory, although now being replaced by a more general approach, can still form the basis for such system analysis. Cross correlating signals from a linear process can give a great deal of information about the process dynamics without injecting any test disturbances, or, when test signals are possible, cross correlation can be used to recover dynamic information in the presence of considerable background noise. The use of specially constructed test signals can make cross correlation a powerful technique in the identification of dynamic systems.