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.

<p>The last years a TC-sensing module called HSS-440 has been developed at AppliedSensor. The sensor is used in hydrogen powered cars to detect H2-leakages. TC-sensing is a technique that uses small changes in thermal conductivity when H2 is present to determine concentrations. Today these small changes are estimated with a prediction model that uses several hundreds of parameters.</p><p>A sensor substrate from a new manufacturer is now introduced. This means an opportunity to look over the current solution. The task for this thesis is to investigate system properties and new solutions regarding a prediction model with minimal need for calibration.</p><p>System properties are investigated and relations for heat flow and influence of H2 are established. In the process an earlier not known nonlinearity are proved to exist. From this, a new open loop nonlinear greybox model is estimated and the nonlinearity are concluded to improve the model. The model is then closed with an earlier implemented PI-regulator and concluded to be useful for H2-predictions. The new model also utilizes 11 parameters instead of hundreds which is a big improvement.</p> / <p>Sista åren har en sensor, med beteckningen HSS-440, mätandes Termisk konduktivitet utvecklats på AppliedSensor. Sensorn används för att upptäcka läckage av H2-gas i vätgasdrivna bilar. Vid Termisk Konduktivitets mätning används små förändringar av den termiska konduktiviteten, då H2 är närvarande i det omgivande mediumet, som ett mått på koncentrationen. Idag änvänder prediktionsmodellen flera hundra parametrar för att skatta denna koncentration.</p><p>Nu introduceras ett sensorsubstrat från en ny tillverkare, vilket innebär ett bra tillfälle att se över den gamla lösningen. Syftet med examensarbetet är därför att undersöka nya systemegenskaper i och med introduktionen av det nya sensorsubstratet samt nya lösningar på en prediktionsmodel med ett minimalt behov av kalibrering.</p><p>Systemegenskaperna undersöks och samband för värmeflöden och H2's påverkan på systemet fastställs. Vid denna undersökning upptäcks en tidigare okänd olinjäritet. Utifrån detta bestämms en ny olinjär greybox modell där den nyfunna olinjäriteten bevisas förbättra modellen. Modellen sluts med en tidigare implementerade PI-regulator och bevisas vara användbar vid H2-prediktion. Den nya modellen använder även bara 11 parametrar istället för flera hundra vilket är en stor förbättring.</p>

Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Romano, Marcello ; Bevilacqua, Riccardo. "December 2009." Description based on title screen as viewed on January 27, 2010. Author(s) subject terms: spacecraft, cubesat, nanosat, TINYSCOPE, simulator, test bed, control, system identification, least squares, adaptive mass balancing, mass balancing, three axis simulator, NACL, TAS, CubeTAS, ADCS. Includes bibliographical references (p. 77-82). Also available in print.

The last years a TC-sensing module called HSS-440 has been developed at AppliedSensor. The sensor is used in hydrogen powered cars to detect H2-leakages. TC-sensing is a technique that uses small changes in thermal conductivity when H2 is present to determine concentrations. Today these small changes are estimated with a prediction model that uses several hundreds of parameters. A sensor substrate from a new manufacturer is now introduced. This means an opportunity to look over the current solution. The task for this thesis is to investigate system properties and new solutions regarding a prediction model with minimal need for calibration. System properties are investigated and relations for heat flow and influence of H2 are established. In the process an earlier not known nonlinearity are proved to exist. From this, a new open loop nonlinear greybox model is estimated and the nonlinearity are concluded to improve the model. The model is then closed with an earlier implemented PI-regulator and concluded to be useful for H2-predictions. The new model also utilizes 11 parameters instead of hundreds which is a big improvement. / Sista åren har en sensor, med beteckningen HSS-440, mätandes Termisk konduktivitet utvecklats på AppliedSensor. Sensorn används för att upptäcka läckage av H2-gas i vätgasdrivna bilar. Vid Termisk Konduktivitets mätning används små förändringar av den termiska konduktiviteten, då H2 är närvarande i det omgivande mediumet, som ett mått på koncentrationen. Idag änvänder prediktionsmodellen flera hundra parametrar för att skatta denna koncentration. Nu introduceras ett sensorsubstrat från en ny tillverkare, vilket innebär ett bra tillfälle att se över den gamla lösningen. Syftet med examensarbetet är därför att undersöka nya systemegenskaper i och med introduktionen av det nya sensorsubstratet samt nya lösningar på en prediktionsmodel med ett minimalt behov av kalibrering. Systemegenskaperna undersöks och samband för värmeflöden och H2's påverkan på systemet fastställs. Vid denna undersökning upptäcks en tidigare okänd olinjäritet. Utifrån detta bestämms en ny olinjär greybox modell där den nyfunna olinjäriteten bevisas förbättra modellen. Modellen sluts med en tidigare implementerade PI-regulator och bevisas vara användbar vid H2-prediktion. Den nya modellen använder även bara 11 parametrar istället för flera hundra vilket är en stor förbättring.

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.

The School of Electrical, Electronic and Computer Engineering at the North-West University in Potchefstroom has established an active magnetic bearing (AMB) research group called McTronX. This group provides extensive knowledge and experience in the theory and application of AMBs. By making use of the expertise contained within McTronX and the rest of the control engineering community, an adaptive controller for an AMB flywheel system is implemented. The adaptive controller is faced with many challenges because AMB systems are multivariable, nonlinear, dynamic and inherently unstable systems. It is no wonder that existing AMB models are poor approximations of reality. This modelling problem is avoided because the adaptive controller is based on an indirect adaptive control law. Online system identification is performed by a neural network to obtain a better model of the AMB flywheel system. More specifically, a nonlinear autoregressive with exogenous inputs (NARX) neural network is implemented as an online observer. Changes in the AMB flywheel system’s environment also add uncertainty to the control problem. The adaptive controller adjusts to these changes as opposed to a robust controller which operates despite the changes. Making use of reinforcement learning because no online training data can be obtained, an adaptive critic model is applied. The adaptive controller consists of three neural networks: a critic, an actor and an observer. It is called an observer-based adaptive critic neural controller (ACNC). Genetic algorithms are used as global optimization tools to obtain values for the parameters of the observer, critic and actor. These parameters include the number of neurons and the learning rate for each neural network. Since the observer uses a different error signal than the actor and critic, its parameters are optimized separately. When the actor and critic parameters are optimized by minimizing the tracking error, the observer parameters are kept constant. The chosen adaptive control design boasts analytical proofs of stability using Lyapunov stability analysis methods. These proofs clearly confirm that the design ensures stable simultaneous identification and tracking of the AMB flywheel system. Performance verification is achieved by step response, robustness and stability analysis. The final adaptive control system remains stable in the presence of severe cross-coupling effects whereas the original decentralized PD control system destabilizes. This study provides the justification for further research into adaptive control using artificial intelligence techniques as applied to the AMB flywheel system. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.

The School of Electrical, Electronic and Computer Engineering at the North-West University in Potchefstroom has established an active magnetic bearing (AMB) research group called McTronX. This group provides extensive knowledge and experience in the theory and application of AMBs. By making use of the expertise contained within McTronX and the rest of the control engineering community, an adaptive controller for an AMB flywheel system is implemented. The adaptive controller is faced with many challenges because AMB systems are multivariable, nonlinear, dynamic and inherently unstable systems. It is no wonder that existing AMB models are poor approximations of reality. This modelling problem is avoided because the adaptive controller is based on an indirect adaptive control law. Online system identification is performed by a neural network to obtain a better model of the AMB flywheel system. More specifically, a nonlinear autoregressive with exogenous inputs (NARX) neural network is implemented as an online observer. Changes in the AMB flywheel system’s environment also add uncertainty to the control problem. The adaptive controller adjusts to these changes as opposed to a robust controller which operates despite the changes. Making use of reinforcement learning because no online training data can be obtained, an adaptive critic model is applied. The adaptive controller consists of three neural networks: a critic, an actor and an observer. It is called an observer-based adaptive critic neural controller (ACNC). Genetic algorithms are used as global optimization tools to obtain values for the parameters of the observer, critic and actor. These parameters include the number of neurons and the learning rate for each neural network. Since the observer uses a different error signal than the actor and critic, its parameters are optimized separately. When the actor and critic parameters are optimized by minimizing the tracking error, the observer parameters are kept constant. The chosen adaptive control design boasts analytical proofs of stability using Lyapunov stability analysis methods. These proofs clearly confirm that the design ensures stable simultaneous identification and tracking of the AMB flywheel system. Performance verification is achieved by step response, robustness and stability analysis. The final adaptive control system remains stable in the presence of severe cross-coupling effects whereas the original decentralized PD control system destabilizes. This study provides the justification for further research into adaptive control using artificial intelligence techniques as applied to the AMB flywheel system. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.

Tuned mass dampers (TMDs) are auxiliary damping devices installed within tall structures to reduce undesirable wind-induced vibrations and to enhance the overall system damping and hence, the dissipative capacity. The design of TMDs involves the selection of optimal auxiliary mass, frequency, and damping, based on the main structure's mass, natural frequency and damping properties. TMDs are inherently susceptible to detuning, where the auxiliary parameters are no longer optimal due to deterioration or changes within the system, resulting in a degradation in their performance. In order to correct for this detuning, it is necessary to perform a condition assessment while the TMDs are in service. The main goal of this thesis is to present a methodology to conduct condition assessment while the TMDs are in service. The proposed methodology does not involve either restraining the TMD or providing controlled external excitation to the structure, and relies on ambient measurements only. The first phase in the condition assessment is to estimate the bare structure's modal properties using acceleration measurements obtained from the structure while the TMDs are unrestrained. The present work accomplishes this goal within the framework of parametric identification using Kalman filtering, where the unknown parameters (bare modal properties) are appended to the state vector and estimated. Unlike most of the literature on this subject, the noise statistics for the filter are not assumed to be known a priori. They are estimated from the measurements and incorporated into the filter equations. This filter involves direct feedthrough of the process noise in the measurement equation and the appropriate filter is derived and used following the noise covariance estimation step. In the next phase, criteria to assess the condition of the TMD are developed. They include optimal tuning parameters established using simulated experiments and measured equivalent viscous damping. The research considered pendulum tuned mass dampers (PTMDs), which presently account for a large fraction of full-scale applications. Results were demonstrated using numerical investigations, a bench-scale model equipped with an adaptive mechanism for adjusting auxiliary damper parameters, and a full-scale PTMD-equipped structure. The main contributions of this thesis are: (a) a broader understanding of the coupled biaxial behaviour of PTMDs has been developed; (b) a systematic procedure for estimating the underlying modal characteristics of the structure from ambient vibration measurements within the framework of Kalman filtering has been achieved; (c) a comprehensive framework to undertake condition assessment of TMDs has been presented, integrating parametric identification from measured response data and performance prediction for design period wind events using boundary layer wind tunnel studies. The work provided new insight into the design and behaviour of PTMDs and presented a comprehensive approach to quantify their performance. The Kalman filtering framework also provides an efficient platform to build adaptive passive tuned mass dampers that can be tuned in place and adjusted to correct for detuning and accommodate various operating conditions.