<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.

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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.

System identification has importance in modeling and control of industrial systems. The main task of system identification is to build a suitable model that represents the relationship between input, output and disturbances of a real system. The thesis presents identification and discrete time linear modeling of a hydraulic actuator. This thesis demonstrates how to formulate hydraulic functions for both normal and faulty conditions with internal leakage using both offline and on-line measurements. Least square and recursive least square methods are used to estimate the model parameters based on the Auto Regressive technique with Exogenous input (ARX) model. For the offline case, square and sine wave signals are used as input control signals. For the online case, random input control signal is applied. Prediction error criterion is used for model validation based on experimental data. It is shown that the ARX model is capable of representing a valve-controlled hydraulic system dynamics.

The aim of this study was to develop a load prognosis model for Uppsala district heating system to be used as a tool for heat production optimization. The methodwas to build three models for the different customer types; housing, industry andoffices and then scale them for the total system using data from Uppsala districtheating system. The heat load consists of two parts, one that is temperaturedependent and one that is dependent of the social behavior of the customers. Thetemperature part was modelled with an ARX model using an outdoor temperatureprognosis as input signal. The social behavior part was modelled using the mean ofthe social behavior from some days before and additionally by distinguishing betweenweekdays and weekends. The outcome was a model that would produce a prognosisfor the heat load for each customer type. The total model for the whole districtheating system was less accurate, but still usable. All models developed are howeverrelying on the quality of the available weather prognosis. The benefit of a precise loadprognosis is to facilitate production planning and optimization. Accurate predictions ofthe heat demand, especially in the case of peak load, will result in better productionplanning and thus cost efficiency.

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.

System identification has importance in modeling and control of industrial systems. The main task of system identification is to build a suitable model that represents the relationship between input, output and disturbances of a real system. The thesis presents identification and discrete time linear modeling of a hydraulic actuator. This thesis demonstrates how to formulate hydraulic functions for both normal and faulty conditions with internal leakage using both offline and on-line measurements. Least square and recursive least square methods are used to estimate the model parameters based on the Auto Regressive technique with Exogenous input (ARX) model. For the offline case, square and sine wave signals are used as input control signals. For the online case, random input control signal is applied. Prediction error criterion is used for model validation based on experimental data. It is shown that the ARX model is capable of representing a valve-controlled hydraulic system dynamics.

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.