Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 146-151). Also available via the Internet.

Thesis (Master)--İzmir Institute of Technology, İzmir, 2004.

In order to control a system or process a PID-regulator is often used although they can be difficult to tune so that they give the system a preferred behaviour. The goal with this project is to tune a PID-controller that controls the temperature of a large water tank. The initial systems temperature fluctuates around the set temperature by +/- 1◦C and our goal is to reduce it to +/- 0.5◦C. The project will use the parametric method of a step response in order to evaluate the systems behaviour into a transfer function model that can be PID-tuned. The system was modeled into a a first order model that matches the heating and cooling separately although not together since they have different time constants. Therefore a second order model was made. This model did not match the collected data very good but is used to get a better understanding of the system and figure out what kind of parameters that are required in order to create a better matching model. The final conclusion is that in order to get a matching transfer function of the plant a second order model or higher must be uses and also that more data distributed along more parameters need to be logged in order to understand the entire system behaviour.

Boliden Aitik is the largest open-pit copper mine in Sweden, and during 2017, more than 39 million tonnes of ore was processed into concentrated copper, silver and gold. The distributed control system 800xA is provided by ABB, and this is what the operators use for process monitoring and control. Currently, the control of the mill discharge pumps operating to transport slurry from the mill circuit to the flotation system is not completely satisfactory. This leads to additional work for the operators, and can in worst case interfere with production.  This project has aimed to identify the problems with the existing control solution mainly from the operators and the engineers perspective, but also by looking at trend data. The discharge pump system has also been modeled, based on a combination of theoretical knowledge and real sensor data obtained from the process control system. The model has been implemented in a 800xA simulation environment, where alternative control solutions have been simulated and evaluated. The modeling procedure has been limited by the lack of pressure, density and flow sensors in slurry flows, and this has lead to an increased uncertainty, in particular for the dynamic characteristics of the pump system. The static characteristics of the discharge pumps have been modeled by regression analysis of a large amount of steady state data over several months, and the dynamic characteristics have been identified from a step response experiment. A cascade connection of proportional-integral (PI) controllers are used for pump control, where the primary controller uses parameter scheduling as a function of measured tank level. This leads to smooth control during normal mill circuit operation, which is a benefit for the flotation process, but it can also cause problems such as flooding, in particular at mill circuit starts or when switching between pumps. According to simulations, it seems like some modifications in the PI control should make it possible to use smooth control around the reference level, and to still avoid flooding at the situations that appear to be problematic today. The initial goal of this project was a live implementation of improved control, but this was not possible during the final weeks of this project. Instead, the simulation results should be used as support for future decision making and controller tuning.

Modelling a linear mathematical model of a radio controlled (RC) helicopter in hover is the main goal of this thesis. The thesis introduces a general description about how RC-helicopters work and different phenomenons that effect the behaviour of a RC-helicopter. These phenomenons play an important role in the modelling part. The model equations of the RC-helicopter are computed by deriving mathematical descriptions of different helicopter characteristics. The flapping motion of the main rotor and the flybar are modelled since they play major role in describing helicopter dynamics. The model is linearised by using stability and control derivatives and a model structure is presented. The method describes how the external forces and moments in the rigid body equations of motion can be expressed as continuous functions of the model states and inputs. The model is divided into multiple sub-models that describe the different dynamics of the RUAV. The prarameters of the model are estimated using system identification methods. The prediction error method proved itself successful and the achieved models can accurately estimate the pitch, roll and yaw rate of the helicopter. These models could be used for further development of control designs.

The aim of this Licentiate is to advance the field of cooperative visual coverage path planners for multiple Micro Aerial Vehicles (MAVs), while aiming for their real life adoption towards the tasks of aerial infrastructure inspection. The fields that will be addressed are focusing in: a) the collaborative perception of the environment, b) the collaborative visual inspection, and c) the optimization of the aerial missions based on the remaining flying battery, camera constraints, coverage constraints and other real life mission induced constraints. Towards this envisioned aim, this Licentiate will present the following main theoretical contributions: a) centralized and distributed Model Predictive Control (MPC) schemes for the cooperative motion control of MAVs focusing in the establishing of a formation control architecture to enable a dynamic visual sensor from monocular cameras towards a reconfigurable environmental perception, b) revisiting the Cooperative Coverage Path Planning (C-CPP) problem for the inspection of complex infrastructures, c) developing a holistic approach to the problems of 2-D area coverage with MAVs for polygon areas, while considering the camera footprint, and d) designing of a scheme to estimate the Remaining Useful Life (RUL) of the battery during a flight mission, a fact that directly effects the flying capabilities of the MAVs. The theoretical contributions of this thesis have been extensively evaluated in simulation and real life large scale field trials, a direction that adds another contribution of the suggested framework towards the massive insertion of the aerial platforms as aerial tools in the close future. In the first part of this Licentiate, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions in this Licentiate are presented in the annex.

No description available.

The effect of anesthetics in the human body is usually described by Wiener models. The high number of patient-dependent parameters in the standard models, the poor excitatory pattern of the input signals (administered anesthetics) and the small amount of available input-output data make application of system identification strategies difficult. The idea behind this thesis is that, by reducing the number of parameters to describe the system, improved results may be achieved when system identification algorithms and control strategies based on those models are designed. The choice of the appropriate number of parameters matches the parsimony principle of system identification. The three first papers in this thesis present Wiener models with a reduced number of parameters for the neuromuscular blockade and the depth of anesthesia. Batch and recursive system identification algorithms are presented. Taking advantage of the small number of continuous time model parameters, adaptive controllers are proposed in the two last papers. The controller structure combines an inversion of the static nonlinearity of the Wiener model with a linear controller for the exactly linearized system, using the parameter estimates obtained recursively by an extended Kalman filter. The performance of the adaptive nonlinear controllers is tested in a database of realistic patients with good results.

The stringent regulations on the emissions levels of heavy duty vehicles create a demand for new methods of reducing harmful emissions from the engine. In order to be able to follow these increasingly stricter legislations, complex aftertreatment systems are used. Achievement of optimal performance of these systems requires accurate models that can be used for control design. As a result, the interest in modelling and control of aftertreatment systems has increased. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using the selective catalyst as an aftertreatment system for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear since the chemical reactions involved are highly depending on the operating point. The momentary operating point is defined by the driving profile of the vehicle which, for example, includes cold and hot engine starts, highway and urban driving. The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control in mind. The first two papers contain the theoretical work of this thesis. The first paper deals with improvement of an existing recursive prediction error method (RPEM) where a more accurate discretisation algorithm was used to improve the accuracy of the estimated nonlinear model. The second paper deals with analysis of the convergence properties of the algorithm. For this analysis several conditions were formulated that link the global and local convergence properties of the algorithm to stability properties of an associated differential equation. Global convergence to a stationary point was shown. In the third paper, the RPEM is used for identification of the SCR system and finally the fourth paper a Hammerstein–Wiener model for identification of the SCR system is applied. In both these cases the black-box models could predict the NOx behaviour of the SCR system quite well. The nonlinear models were shown to describe the SCR system more accurately than linear models.

Nonlinear system identification methods is a topic that has been gaining interest over the last years. One reason is the many application areas in controller design and system development. However, the problem of modeling nonlinear systems is complex and finding a general method that can be used for many different applications is difficult. This thesis treats recursive identification methods for identification of systems that can be described by nonlinear ordinary differential equations. The general model structure enables application to a wide range of processes. It is also suitable for usage in combination with many nonlinear controller design methods. The first two papers of the thesis illustrates how a recursive prediction error method (RPEM) can be used for identification of an anaerobic digestion process and a solar heating system. In the former case the model complexity is significantly reduced compared to a semi-physical model of the system, without loosing much in model performance. In the latter case it is shown that it is possible to reach convergence even for a small data set, and that the resulting model is of comparable quality as a previously published grey-box model of the same system. The third paper consists of a convergence analysis of the studied RPEM. The analysis exploits averaging analysis using an associated ordinary differential equation, and formulates conditions for convergence to a minimum of the criterion function. Convergence to a true parameter set is also illustrated by an example. The fourth, and last, paper of this thesis addresses the problem of finding suitable initial parameters e.g. for the RPEM. With a potentially non-convex criterion function the choice of initial parameters becomes decisive for whether the algorithm converges to the global optimum, or a local one. The suggested initialization algorithm is a Kalman filter based method. Experiments using a simulated example show that the Kalman based method can, under beneficial circumstances, be used for initialization of the RPEM. The result is further supported by successful identification experiments of a laboratory scale cascaded tanks process, where the Kalman based method was used for initialization.