The goal of this project is to apply system identification techniques to model people's perception of emotion in music as a function of time. Emotional appraisals of six selections of classical music are measured from volunteers who continuously quantify emotion using the dimensions valence and arousal. Also, features that communicate emotion are extracted from the music as a function of time. By treating the features as inputs to a system and the emotional appraisals as outputs of that system, linear models of the emotional appraisals are created. The models are validated by predicting a listener's emotional appraisals of a musical selection (song) unfamiliar to the system. The results of this project show that system identification provides a means to improve previous models for individual songs by allowing them to generalize emotional appraisals for a genre of music. The average <i>R</i>² statistic of the best model structure in this project is 7. 7% for valence and 75. 1% for arousal, which is comparable to the <i>R</i>² statistics for models of individual songs.

Inom processindustrin finns en etablerad reglerhierarki där basreglering sker med PID-regulatorer och där avancerad, multivariabel styrning sköts av MPC-programvara. Steget mellan dessa två nivåer kan upplevas som stort. För mindre och snabba multivariabla processer undvikes helst en multivariabel ansats, med försämrad reglering som följd. På Preem AB har detta upplevts som ett problem. Syftet med examensarbetet har varit att utveckla en alternativ, multivariabel styrstrategi för en process med ett mindre antal reglerstorheter som interagerar. Detta har gjorts genom en utveckling av en LQG-regulator i styrsystemet DeltaV. För att implementera en regulator i ett styrsystem måste hänsyn tas till en rad faktorer, såsom hantering av olika körlägen, bortfall av signaler, integratoruppvridning, kommunikation med slavregulatorer och inte minst operatörernas gränssnitt för hantering av regulatorn. Att sedan utveckla en regulator för en process kräver bland annat stegförsök, analys och anpassning av stegtestdata, modellidentifiering, framtagning av trimningskonstanter, testning av styrstrategi i simulerad miljö och idrifttagning. Den typen av frågeställningar addresseras i rapporten. Examensarbetet visar att det finns en plats för LQG-regulatorn i processindustrin för en viss typ av problem. Den utvecklade regulatorn har implementerats på en avsvavlingsprocess på Preems oljeraffenaderi i Lysekil med lyckat resultat. Oscillationer i processen, som tidvis påverkat produktionen av propen, har kunnat reduceras. / Process control in process industry is done in a hierarchy in which PID controllers are used for basic control and MPC software is used for advanced, multivariable process control. The implementation of multivariable control using MPC software is a major undertaking and development of such controllers for small and fast multivariable processes is therefore avoided. To achieve better control for such processes, a simpler approach to multivariable control is often sought. The purpose of this masters thesis is to develop an alternative, multivariable control strategy for processes with a smaller number of interacting control variables. This is achieved by developing an LQG-controller in the DCS DeltaV at Preem AB. Implementation of such a controller in a DCS requires that consideration is given to a number of factors, including handling of different modes, loss of signals, reset windup, communication with slave controllers and construction of operator interface. To develop a controller for a specific process also requires step testing, model identification, tuning of the controller parameters, simulation of the control strategy and commissioning. Solutions to such issues are addressed in this report. The thesis shows that  LQG-controllers can be useful in process industry for some niche applications. The LQG-controller has successfully been applied to a desulphurisation process at Preem's oil refinery in Lysekil, where oscillations affecting the production of propylene have been reduced.

Disturbing sound sometimes should be cancelled when music has been recorded. In this thesis, MATLAB was used as a tool. System identification was a main method used to find the unknown system. By subtracting the simulated output, disturbing sound was cancelled. Two different systems were identified with both linear (ARX) model and nonlinear (Parallel Hammerstein) model. The quality of these models was measured and compared using different methods. Possibility to implement this work on hardware was also discussed.

When doing recording work in the studio, disturbing sound must be removed. In this thesis, the purpose of this thesis is to formulate a mathematical equation, by using MATLAB to identify a system, then using the system to do cancellation of disturbing sound. The method of doing cancellation is to subtract the simulated output by the actual output, and then the disturbing sound was cancelled. The main thesis work will focus on the system identification, which is the process of determining the characteristic of an unknown system. Three systems were identified with the same model structure, which is linear (ARX) model. After finding out the model, the model quality must be evaluated. If the model is valid, there is a discussion if it is possible to run the mathematical equation in the real application, and how is the market today.

Large marine vessels carry their loads all over the world. It can be a container ship carrying over 10 000 containers filled with foods, textiles and electronics or a bulk freighter carrying 400 000 tons of coal. Vessels usually have a ballast system that pumps water into ballast tanks to stabilize the vessel. The ballast system can be used to change the vessel’s trim and list angles. Trim and list are the ship equivalents of pitch and roll. By changing the trim angle the water resistance can be reduced and thus also the fuel consumption. Since the vessel is consuming a couple of hundred tons of fuel per day, a small reduction in fuel consumption can save a considerable amount of money, and it is good for the environment. In this thesis, the ship’s power consumption has been estimated using an artificial neural network, which is a mathematical model based on data. The name refers to certain structural similarities with the neural synapse system in animals. The idea with neural networks has been to create brain-like systems. For applications such as learning to interpret sensor data, artificial neural networks are an effective learning method. The goal is to estimate the ship power using a artificial neural network and then use it to calculate the trim angle, to be able to save fuel. The data used in the artificial neural network come from sensor systems mounted on a container ship sailing between Europe and Asia. The sensor data have been thoroughly preprocessed and this includes for example removing the parts when the ship is docked in harbour, data patching and synchronisation and outlier detection based on a Kalman filter. A physical model of a marine craft including wind, wave, hydrodynamic and hydrostatic effects, has also been introduced to help analyse the performance and behaviour of the artificial neural network. The artificial neural network developed in this thesis could successfully estimate the power consumption of the ship. Based on the developed networks it can be seen that the fuel consumption is reduced by trimming the ship by bow, i.e., the ship is angled so the bow is closer to the water line than the stern. The method introduced here could also be applied on other marine vessels, such as bulk freighters or tank ships.

Haptic applications often employ devices with many degrees of freedom in order to allow the user to have natural movement during human-machine interaction. From the development point of view, the complexity in mechanical dynamics imposes a lot of challenges in modelling the behaviour of the device. Traditional system identification methods for nonlinear systems are often computationally expensive. Moreover, current research on using neural network approaches disconnect the physical device dynamics with the identification process. This thesis proposes a different approach to system identification of complex haptic devices when analytical models are formulated. It organizes the unknowns to be identified based on the governing dynamic equations of the device and reduces the cost of computation. All the experimental work is done with the Freedom 6S, a haptic device with input and feedback in positions and velocities for all 6 degrees of freedom . <br /><br /> Once a symbolic model is developed, a subset of the overall dynamic equations describing selected joint(s) of the haptic robot can be obtained. The advantage of being able to describe the selected joint(s) is that when other non-selected joints are physically fixed or locked up, it mathematically simplifies the subset dynamic equation. Hence, a reduced set of unknowns (e. g. mass, centroid location, inertia, friction, etc) resulting from the simplified subset equation describes the dynamic of the selected joint(s) at a given mechanical orientation of the robot. By studying the subset equations describing the joints, a locking sequence of joints can be determined to minimize the number of unknowns to be determined at a time. All the unknowns of the system can be systematically determined by locking selected joint(s) of the device following this locking sequence. Two system identification methods are proposed: Method of Isolated Joint and Method of Coupling Joints. Simulation results confirm that the latter approach is able to successfully identify the system unknowns of Freedom 6S. Both open-loop experimental tests and close-loop verification comparison between the measured and simulated results are presented. <br /><br /> Once the haptic device is modelled, fuzzy logic is used to address chattering phenomenon common to strong virtual effects. In this work, a virtual wall is used to demonstrate this approach. The fuzzy controller design is discussed and experimental comparison between the performance of using a proportional-derivative gain controller and the designed fuzzy controller is presented. The fuzzy controller is able to outperform the traditional controller, eliminating the need for hardware upgrades for improved haptic performance. Summary of results and conclusions are included along with suggested future work to be done.

The goal of this project is to apply system identification techniques to model people's perception of emotion in music as a function of time. Emotional appraisals of six selections of classical music are measured from volunteers who continuously quantify emotion using the dimensions valence and arousal. Also, features that communicate emotion are extracted from the music as a function of time. By treating the features as inputs to a system and the emotional appraisals as outputs of that system, linear models of the emotional appraisals are created. The models are validated by predicting a listener's emotional appraisals of a musical selection (song) unfamiliar to the system. The results of this project show that system identification provides a means to improve previous models for individual songs by allowing them to generalize emotional appraisals for a genre of music. The average <i>R</i>² statistic of the best model structure in this project is 7. 7% for valence and 75. 1% for arousal, which is comparable to the <i>R</i>² statistics for models of individual songs.

Magnetically levitated microrobotic systems have shown a great deal of promise for micromanipulation tasks. A new large-gap magnetic suspension system has recently been developed at the University of Waterloo in order to develop microrobotic systems for various applications. In order to achieve motion with the system, a model is needed in order to facilitate the design of various aspects of the system, such as the microrobot and the controller. In order to derive equations of motion for the system attempts were made to characterize the force produced by the magnetic drive unit in terms of a simple analytical equation. The force produced by the magnetic drive unit was estimated with the aid of a finite element model. The derived equations were able to predict the general trend of the force curves, and with sufficient parameter tweaking the error between the force estimated by the finite element model and the force estimated by the analytical equation could be minimized. System models describing the motion of the system in the horizontal and vertical directions are identified and compared to the actual system response. The vertical position response is identified through a least squares parameter estimate of the closed loop response combined with a partial reconstruction of the root locus diagram, with the model structure based on the known dynamics of a simplified form of magnetic levitation. This model was able to provide a reasonable prediction of the system response for a variety of PID controllers under a variety of input conditions. The horizontal models are identified using a least-squares parameter estimate of the open loop characteristics of the system. The horizontal models are able to provide a reasonable prediction of the system response under PD and PID control. Full spatial motion of a microrobot prototype is demonstrated over a working range of 20x22x30 mm³, with PID controller parameters and reference trajectories adjusted to minimize disturbances. The RMS error at steady state is on the order of 0. 020 mm for vertical positioning and 0. 008 mm for horizontal positioning. A linear quadratic regulator implemented for vertical position control was able to reduce the vertical position RMS error to 0. 014 mm.

In this thesis we propose a robust PID controller to regulate the hydrogen flow of proton exchange membrane fuel cells. The controller allows the so-called hydrogen excess ratio to track a desired value rapidly in order to achieve saving hydrogen and to avoid damage of the fuel cell when the power output of the fuel cell varies from one level to another. The fuel cell system is governed by a set of complicated nonlinear dynamical equations. To ease the control design task, we model the system, at each operating point, as a feedback interconnection of a linear time-invariant nominal part with a norm-bounded perturbation. We use the technique of system identification to acquire the transfer function representation of the nominal part and the size of the perturbation. To do this, the chirp signal is adopted to excite the system and the observed response is analyzed using spectral analysis to obtain the model. Based on the model, a $H_{infty}$ PID controller is designed for the fuel cell system. The design is tested on an experimental platform. The experimental results verify that the proposed controller can regulate the hydrogen excess ratio rapidly under load variation, and effectively reject the influence of external disturbances.

Smart materials have been widely used for control actuation. A robotic hand can be equipped with artificial tendons and sensors for the operation of its various joints mimicking human-hand motions. The motors in the robotic hand could be replaced with novel electroactive-polymer (EAP) actuators. In the three-finger gripper proposed in this paper, each finger can be actuated individually so that dexterous handling is possible, allowing precise manipulation. In this dissertation, a microscale position-control system using a novel EAP is presented. A third-order model was developed based on the system identification of the EAP actuator with an AutoRegresive Moving Average with eXogenous input (ARMAX) method using a chirp signal input from 0.01 Hz to 1 Hz limited to 7 ± V. With the developed plant model, a digital PID (proportional-integral-derivative) controller was designed with an integrator anti-windup scheme. Test results on macro (0.8-mm) and micro (50-μm) step responses of the EAP actuator are provided in this dissertation and its position tracking capability is demonstrated. The overshoot decreased from 79.7% to 37.1%, and the control effort decreased by 16.3%. The settling time decreased from 1.79 s to 1.61 s. The controller with the anti-windup scheme effectively reduced the degradation in the system performance due to actuator saturation. EAP microgrippers based on the control scheme presented in this paper will have significant applications including picking-and-placing micro-sized objects or as medical instruments. To develop model-based control laws, we introduced an approximated linear model that represents the electromechanical behavior of the gripper fingers. Several chirp voltage signal inputs were applied to excite the IPMC (ionic polymer metal composite) fingers in the interesting frequency range of [0.01 Hz, 5 Hz] for 40 s at a sampling frequency of 250 Hz. The approximated linear Box-Jenkins (BJ) model was well matched with the model obtained using a stochastic power-spectral method. With feedback control, the large overshoot, rise time, and settling time associated with the inherent material properties were reduced. The motions of the IPMC fingers in the microgripper were coordinated to pick, move, and release a macro- or micro-part. The precise manipulation of this three-finger gripper was successfully demonstrated with experimental closed-loop responses.