Ryozo Nagamune

Bearingless motors are an emerging class of electric machines that combines the functionality of an active magnetic bearing (AMB) and an electric motor into the same stator structure. This integration benefits from the advantages of AMBs while addressing their shortcomings, resulting in more compact magnetically levitated systems with reduced system complexity and cost. However, despite these advantages, the relatively low power density of bearingless motors has primarily limited their use to research settings and niche applications with low power demands. The operation of a bearingless motor necessitates the generation of two distinct field components in the airgap for torque and radial force generation. Traditionally, this was achieved using separate suspension and torque windings. However, this results in an inefficient winding volume utilization and reduced power density. Consequently, a shift has emerged towards combined windings, utilizing the same windings for both suspension and rotation. This is achievable through the use of multiphase windings, offering additional degrees of freedom to simultaneously generate torque and suspension forces. To support research on multiphase bearingless motors and exploit the advantages of combined windings, we develop a custom multiphase motor control platform that integrates the controller, sensor interface, and power electronics into a reconfigurable system. The design consists of a two-level twelve-leg inverter with isolated inline shunt-based phase current sensing and isolated DC bus voltage sensing. A 12-phase inverter is realized using GaNFET-based half-bridge power stages. The DC bus voltage and the phase currents are measured using isolated delta-sigma modulators. An FPGA is used to implement a sophisticated motor control algorithm with high temporal determinism. The performance of the motor drive is evaluated with a homopolar bearingless motor prototype. Development of the drive enables the use of custom control strategies, such as sensorless control, which allows the prototype motor to operate up to its rated speed (36000 r/min) as limited by the available DC link voltage (30 V). This represents an almost fourfold increase in the maximum no-load speed of 9500 r/min reached when using off-the-shelf power electronics and a DC link voltage of 48 V.

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The monitoring of machining process, tool damage and machine health are crucial to the part quality while avoiding damage to the machine tool. The cutting force provides important information about the state of the process and cutting tool because it correlates to the physics-based mathematical model of the process. This thesis presents methods for the estimation and prediction of cutting forces from the spindle and feed drive motor current data extracted from CNC. The spindle motor current is correlated to inertia, friction in the bearings, and cutting torque as a disturbance in closed-loop spindle speed controller. The Frequency response function (FRF) of the spindle servo drive is measured via a built-in CNC function. The state-space model of cutting torque disturbance of the spindle drive is modeled. Kalman Filter and Regularized Convolution (RD) are used to compensate the disturbance effects of electrical and mechanical dynamics to widen the bandwidth of cutting torque estimation from spindle motor current. Automatic tuning of Kalman Filter’s covariance and RD’s regularization factor is investigated by checking the Neural Network-based online tuning of Kalman Filter and the off-line L-curve tuning of both estimators. The proposed methods are experimentally illustrated in milling operations. It is shown that Kalman Filter can estimate the cutting forces on-line during machining. RD is used only in off-line estimation due to its computational cost, although it has higher accuracy due to its compensation at a wider frequency range. L-curve only achieves offline tuning due to its computational cost. Neural Network can auto-tune the estimator, but training the network requires high computational efforts. The feed drive motor current commands are used to predict the cutting forces in Cartesian coordinate system. The previous research illustrated that Kalman filter estimates the periodic cutting forces up to about 150 Hz bandwidth, provided that the FRF is not position-dependent, and friction is compensated. This thesis presents the use of average motor current to estimate the average cutting forces after friction compensation. The average forces are used to monitor the variation in cutting force coefficients which is used to calibrate the process simulation models in a digital twin environment.

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Bearingless motors, whose rotors are magnetically suspended without physical contact, show advantages in applications that require high speed and clean working environment. However, due to the natural instability of radial displacements, radial suspension control is essential for rotor operation. In this thesis, we improve the suspension control performance for our dipole interior permanent magnet bearingless slice motor by developing a new current-to-force model and re-designing suspension controller for eliminating self-excitation behaviour. The proposed suspension force model, derived using magnetic co-energy method and verified by finite element analysis, aims to reduce the radial displacements and increase stability of suspension comparing to the conventional model. Radial displacement measurements show that our proposed model significantly reduces radial displacements at high speed and increases the maximum speed of the rotor.We also investigate the undesired self-excitation behaviour that the rotor speed is maintained even if the torque command is zero. Stress tensor method and finite element analysis indicate that additional torque is generated due to rotor eccentricity. The self-excitation behaviour is eliminated by suppressing the peak of the suspension loop sensitivity function, so as to reduce the maximum rotor eccentricity during the rotation.

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Improving the efficiency of electricity generation is an important component of reducing the cost of electricity. When using wind turbines to generate power, grouping them together results in losses in efficiency due to the wake effect. In order to counter those losses, it has been proposed that wind turbines in a floating offshore wind farm be dynamically repositioned to reduce wake interactions between turbines. However, this relocation strategy has raised concerns that fatigue may be increased. In response to these concerns, this thesis investigates the changes in lifetime fatigue at the tower base caused by adding position control to a Floating Offshore Wind Turbine (FOWT). The primary goal of this thesis is to answer the questions of how much the fatigue damage changes and how much the fatigue can be reduced.Building on previous works, a position controller is designed as a Proportional-Derivative (PD) controller which adjusts nacelle yaw in order to position the turbine in the crosswind direction. Then, the controller is tested with simulations in order to assess how the fatigue changes with its inclusion. Based on these results, a fatigue reducing controller is designed. This controller uses the Linear Quadratic Regulator (LQR) structure with the states in the model chosen based on the fatigue results of the position controller. Finally, the system with both position control and fatigue reduction is tested using the same environmental conditions as the position controller.When analyzing the effectiveness of the controllers with respect to fatigue, the results are compared to a baseline controller that has been designed for power capture. The results indicate that further tuning of the position controller is needed to improve station keeping, however, the controller is still reasonably successful at maintaining steady-state position. The position control goal also does not have a substantial impact on fatigue, but increases were noted in the side-to-side direction at the tower base. The fatigue controller shows success in reducing the increase in side-to-side fatigue. As well, the fatigue controller reduced lifetime fore-aft fatigue at the tower base by between 37 and 43 percent compared to the baseline.

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This thesis presents a combined low-fidelity control-oriented dynamic parametric wake model and wind turbine platform motion model for floating offshore wind farms, named FLOw Redirection and Induction Dynamics for Floating turbines (FLORIDynFloat). This model builds on the existing FLOw Redirection and Induction Dynamics (FLORIDyn) model by adding effects arising from wind turbine translation on the sea surface and incorporating horizontal turbine platform motion simulation based on aerodynamic, hydrodynamic, and mooring line forces. These changes make the model suitable for use in floating wind farms where turbines are tethered to the sea floor.FLORIDynFloat models the relationship between turbine control inputs, incoming wind speed, and turbine positions and velocities; and the power generated by each turbine. The parametrised wake generated by a moving turbine is transformed between a local reference frame in which the turbine is stationary, and the global reference frame in which the wake's propagation and effects on other turbines can be calculated. The velocity of the moving turbine results in a correction to the amount of power generated, due to a changed relative wind speed. Based on current and previous values of turbine positions, velocities, and control inputs (turbine yaw and axial induction factor), the model outputs an estimate for the amount of power generated by each turbine, taking into account the aerodynamic coupling effects in a wind farm. In addition, the wake model is coupled to a previously published model for the dynamic motion of a floating wind turbine platform resulting from aerodynamic, hydrodynamic, and mooring line forces, to predict future turbine positions.Due to a lack of reference comparison, the model is validated by comparing its output to a modified static wake model in which the wake propagates at the relative wind speed in the local frame. The simulations performed, both with pre-defined and force-defined turbine motion trajectories, had realistic results. The model has sufficiently low computational complexity for use with a model-based controller such as model predictive control, and can serve to estimate the effects of control inputs on the overall power production of a floating wind farm.

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In this thesis, Nonlinear Model Predictive Control (NMPC) is used in conjunction with an Agent-Based Model (ABM) in order to find optimal control measures in containing the Coronavirus Disease 2019 (COVID-19) pandemic. The control measures are designed so that the hospital can have some level of available beds and at the same time the economy is minimally adversely impacted by the restrictive measures imposed by the government. To achieve this goal, it is crucial to predict the effects of control measures and tuning them adaptively. The ABM and NMPC are presented as solutions to these issues.The ABM is capable of predicting how control measures affect people's disease status by using various traits of individual agents such as symptom levels, latent periods (i.e., the time during which people are infected but not infectious), infectious periods (i.e., the time during which people remain infectious), and mobilities. Based on these predictions, NMPC can be employed to tune the control measures adaptively by taking into account the upcoming changes.Computer simulations demonstrate the superiority of NMPC in keeping the number of hospitalized patients below the hospital bed threshold with the least economic cost compared to on-off control and Proportional (P) control using the same ABM. According to the 100-triple simulations among NMPC, on-off control, and P-control, NMPC is shown to be the best control technique for 95 out of 100 times.

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In this thesis, an Iterative Learning (IL) approach to disturbance prediction that uses intelligent iteration grouping is proposed for Economic Model Predictive Control (EMPC), and applied to an Integrated Solar Thermal System (ISTS) in order to improve controller performance. An ISTS consists of a Solar Thermal Collector (STC) which collects energy from the sun, a Thermal Storage Tank (TST) which stores this energy for later use, and an auxiliary Heat Pump (HP) which acts as the actuator for the system, providing additional energy as required. The disturbance in the system is then the user hot water demand. In order to optimize the control performance of an ISTS with EMPC, it is important to be able to accurately predict this hot water demand before it happens. To solve this problem, a novel IL-based approach to disturbance prediction for EMPC is presented. This approach involves separating long-term disturbance data, which in this case is user hot water demand, into a number of 24 hour iterations. These iterations are then further divided into groups using unsupervised learning based on the individual iteration profiles. Following the grouping of iterations, each iteration is given features such as the day of the week it occurs on, and a supervised learning classi fier is trained to map from features to groups in order to predict the group of future iterations. Finally, IL is applied to learn patterns within each group iteratively and predict the actual hot water demand trajectory for future iterations. A simulation of an ISTS using real world hot water demand data then demonstrates the effectiveness of the proposed approach to disturbance prediction, achieving higher performance EMPC than can be attained with existing disturbance prediction methods. Specifically, the EMPC implementation using the IL-based disturbance prediction algorithm is shown to prevent constraint violations within the ISTS more effectively than all other EMPC implementations while decreasing the average daily system cost by over 6%.

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This thesis proposes a new concept of an actively-controlled wave energy converter for suppressing the pitch and roll motions of the floating offshore wind turbines. The wave energy converter consists of several floating bodies which receive the wave energy, actuators which convert the wave energy into electrical energy and generate the mechanical forces, and rigid bars which connect the floating bodies and the wind turbine platform and deliver the actuator forces to the platform. The rotational torques to minimize the platform pitch and roll motions are determined by the linear quadratic regulator, while the determined torques are realized by the actuator forces that maximize the wave power capture. The performance of the proposedwave energy converter in simultaneously suppressing the platform pitch and roll motions and extracting the wave energy is validated in simulations.

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This thesis describes a hand-held confocal optical scanner for cellular imaging. In current clinical practice, the detection of disease like colorectal cancer is performed using endoscopy and biopsy. Random biopsies and oversampling are usually required to reduce false results, so potentials exist for non-invasive optical diagnostic techniques. Optical microscopy techniques such as confocal laser scanning microscopy provide images for biological tissues at a micrometer level. The challenge is to miniaturize the system into a form of hand-held devices or catheters. The developed system in this thesis consists of a portable scanner probe and a reflectance confocal imaging unit. The imaging unit was constructed in an all-fiber configuration for convenient packaging. Confocal scanning was performed at 785 nm laser illumination with a piezoelectric fiber scanner probe. The fiber-based probe was constructed by mounting a fiber directly on a two-dimensional piezoelectric bender. Horizontal image scanning has been successfully achieved, and the developed device can provide image resolution of 1.16-1.41 μm in the lateral direction and can resolve cell structures. The operating scanning speed is 1.25 frames per second with 88 × 88 μm² field of view, potentially applicable to real-time imaging. Image results were presented with onion epidermis and optical paper samples in comparison to galvanometer mirrors based confocal laser scanning unit.

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In this thesis, a new control strategy is proposed for an Integrated Solar Thermal Hydronic System (ISTHS) to optimize the system performance. The ISTHS utilizes two sources of energy which are solar and electrical to provide the domestic hot water. The ISTHS performance can be optimized by reducing the consumed electricity and retaining the hot water demand temperature under disturbances such as solar radiation, ambient temperature, and how water demand flow rate. For the performance optimization, the proposed control strategy employs three techniques that are optimization, feedback control, and feedforward control.Required for designing the proposed controller, the ISTHS model is obtained by applying heat transfer and state-space modeling techniques. Using the state-space model of the ISTHS, the control structure can be designed. The control structure consists of four sub-controllers described as off-line, STC-Side, feedback, and robust feedforward controllers. By a combination of logic based switches and four sub-controllers, the final control inputs are robust against the predicted disturbances (Off-line), the actual disturbances (STC-Side and robust feedforward), and the model uncertainties (feedback). The off-line controller applies an optimization method to compute the control inputs one day ahead. The STC-Side controller performs an optimization method to manage some of the control inputs which affect the stored solar energy. The feedback controller keeps the hot water temperature within an allowable range. By using the robust feedforward controller, the consumed electricity is reduced by adjusting the control inputs which affects the amount of the transformed electricity to the thermal energy. For examining the effectiveness of the proposed robust feedforward controller, another controller named simple feedforward controller is developed and separately added to the overall controller. Both controllers are designed such that the impacts of deviated disturbances from predicted values on the system’s output are eliminated. Unlike the robust feedforward controller, the simple feedforward controller does not reduce the consumed electricity. Finally, by making some comparisons through simulations, the effectiveness of the proposed control structure is demonstrated.

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Floating offshore wind farms have a potential in capturing wind energy in a cost-effective manner, with advantages of consistent and strong wind over the ocean, and of little noise and visual impacts on humans. However, a wind farm may lose its efficiency due to the aerodynamic wake, which is the turbulence passed from the upstream turbines to the downstream ones. The wake is undesirable because it can not only reduce the total power of the wind farm but also increase the structural loading of the downwind turbines. This wake effect can be mitigated by optimizing the layout of the wind farm in real time according to the wind speed and direction, as well as power output of each turbine.In this thesis, for a 5 MW floating offshore wind turbine with a semi-submersible platform, an H∞ state-feedback controller design method is proposed to achieve four objectives simultaneously. The objectives are (1) to relocate its position to a specified target location, (2) to regulate its position there by rejecting wind and wave disturbances, (3) to maintain the harvested power to a target level, and (4) to reduce the angular motion of the floating platform. The target location of the floating wind turbine and the target level of the generated power are assumed to be provided by high-level real-time wind farm optimization. For the controller design, a physics-based control-oriented nonlinear model which was previously developed is adopted. The H∞ controller design problem is formulated as minimization of the position deviations from the target, of the generator speed fluctuation, and of platform oscillations. The designed controller is validated using the medium-fidelity software Fatigue-Aerodynamic-Structure-Turbulence (FAST). The simulation results demonstrate that the H∞ state-feedback controller outperforms the linear quadratic regulator with an integrator in various tested scenarios.The research outcome of this thesis will improve the wind farm efficiency, thereby reducing the wind energy cost and increasing the wind energy utilization.

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The past few decades have seen an increasing interest toward wind energy since it has the potential to become the main global power source in the near future. Particularly, researchers are looking towards the development of offshore wind turbines since they have the potential to be way more efficient and have a higher rated power in response to stronger and steadier offshore winds. However, the harsh marine environment places challenges on the path to making offshore wind turbines an economically viable source of energy. Specifically, wind and wave disturbances act on the wind turbines inducing harmful loads on their major components, shortening their useful life and increasing maintenance cost.In this thesis, we propose a control method that rejects both wind and wave disturbances, thus reducing fatigue loads on the wind turbine structure while also maximizing power regulation. Based on a simplified control-oriented modeling technique, both wind and wave disturbance matrices are obtained from linearized state-space systems, and used to design a disturbance rejection H∞ controller. Furthermore, the proposed control method demonstrates the usefulness of including the wave disturbance matrix in the control design process, something that has been attempted in the past but not achieved yet in offshore wind turbine research. Finally, the performance of the designed H∞ controller is validated and compared against a baseline controller using Fatigue, Aerodynamics, Structures, and Turbulence (FAST), an open-source software package capable of modeling wind turbine systems and simulating their physical dynamics with high accuracy. This comparison demonstrates the effectiveness of the proposed method.

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As a renewable and environmental-friendly energy source, wind energy has experienced a significant increase in its popularity. To take advantage of the stronger and steadier offshore wind resource, utility-scale floating wind turbine systems need to be deployed in offshore wind farms. To maximize the overall power production, the distribution layout of wind turbines in the wind farm should be optimized and updated in response of real-time wind direction changes. To achieve this goal, it requires both a wind farm level layout optimization algorithm for computing the optimal layout and a wind turbine level position controller for ensuring successfullyposition transfers.In this thesis, we first propose a mechanism to move the floating wind turbine by passively utilizing the aerodynamic thrust force from the wind. Advantages of this actuation mechanism include its general applicability and easy implementation to modern floating wind turbine systems, without the need for energy consumptionand hardware modifications.Secondly, we introduce the concept of the movable range of a floating wind turbine which describes the feasible range of its equilibrium position under the constraints of the power requirement and safety limits. This movable range information is critical in the wind farm layout optimization algorithm as it determines the set of feasible layouts from which the optimal solution should be sought. A numerical algorithm is proposed to obtain the movable range of a wind turbine system, and the computed result is validated in widely-adopted wind turbine simulation software. In addition, the influences of wind speed and direction, required power, and catenary line length on the movable range are analyzed.Finally, three position controllers for the floating wind turbine, that is, a model-free controller, a model-based open-loop controller and a model-based state feedback controller, are designed. Their simulated control performances are compared in terms of position transfer, power regulation and vibration suppression. We demonstrate that it is possible to achieve the desired position transfer while maintaining a smooth and uninterrupted wind turbine operation.

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Cameras in mobile phones are the most popular due to their availability and portability; however, image blur caused by involuntary hand-shakes of the photographer degrades their image quality as mobile phones become lighter, smaller, and high-resolution. The optical image stabilizer (OIS) is a hardware-based alternative to conventional software-based de-bluring algorithms that offer superior de-blur; however, they are set back for mobile phone applications by cost, size, and power limitations. The magnetically-actuated lens-tilting OIS is a novel miniaturizable and low-power conceptual design which is suitable for low-cost micro manufacturing methods; however, significant product variabilities caused by these methods, along with the strict performance requirements to outperform software-based algorithms, and the limited controller implementation capabilities of mobile phone devices pose a challenging control problem that is solved by the modeling and controller design method proposed in this thesis. To solve the problem, practical manufacturing tolerances are simulated through computer-aided design and analyzed by finite-element methods to obtain the structure of the dynamics of OIS and uncertainties in dynamics. A dynamic uncertainty model is developed based on the analysis results and the robust H∞ control theory is applied to guarantee the closed-loop stability and optimize the closed-loop performance against uncertainties with constrained controller order. The proposed method is demonstrated in two steps. First, it is applied to a set of large-scale OIS prototypes to demonstrate its feasibility in an experimental setting and its capability to deal with physical product variabilities. Then, it is applied to a set of small-scale OIS prototypes containing mass-produced parts to verify its applicability to real OISs. In both cases, the experimental results suggest that the robust H∞ controller outperforms the conventional nominal controllers and the μ-synthesis controller. By dealing with control challenges of the magnetically-actuated lens-tilting OIS, the application of this conceptual design to mobile phone cameras is expanded. Substitution of the conventional post-processing algorithms in mobile phone cameras with OIS has significant impact on their image quality.

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As offshore wind technology advances, floating wind turbines are becoming larger and moving further offshore, where wind is stronger and more consistent. Despite the increased potential for energy capture, wind turbines in these environments are susceptible to large platform motions, which in turn can lead to fatigue loading and shortened life, as well as harmful power fluctuations. To minimize these ill effects, it is possible to use advanced, multi-objective control schemes to minimize harmful motions, reject disturbances, and maximize power capture. Synthesis of such controllers requires simple but accurate models that reflect all of the pertinent dynamics of the system, while maintaining a reasonably low degree of complexity.In this thesis, we present a simplified, control-oriented model for floating offshore wind turbines that contains as many as six platform degrees of freedom, and two drivetrain degrees of freedom. The model is derived from first principles and, as such, can be manipulated by its real physical parameters while maintaining accuracy across the highly non-linear operating range of floating wind turbine systems. We validate the proposed model against advanced simulation software FAST, and show that it is extremely accurate at predicting major dynamics of the floating wind turbine system.Furthermore, the proposed model can be used to generate equilibrium points and linear state-space models at any operating point. Included in the linear model is the wave disturbance matrix, which can be used to accommodate for wave disturbance in advanced control schemes either through disturbance rejection or feedforward techniques. The linear model is compared to other available linear models and shows drastically improved accuracy, due to the presence of the wave disturbance matrix.Finally, using the linear model, we develop four different controllers of increasing complexity, including a multi-objective PID controller, an LQR controller, a disturbance-rejecting H∞ controller, and a feedforward H∞ controller. We show through simulation that the controllers that use the wave disturbance information reduce harmful motions and regulate power better than those that do not, and reinforce the notion that multi-objective control is necessary for the success of floating offshore wind turbines.

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Modern automotive engines are mechanically mature technologies that have been significantly optimized to reduce harmful emissions, reduce fuel consumption, and improve driveablilty. Spark-ignition (SI) engines must operate under a stoichiometric air-to-fuel ratio that allows the three-way catalytic converter to effectively mitigate harmful tail-pipe emissions. Electronic throttle control (ETC) is the primary means of regulating the amount of charge-air entering the cylinders. This research studies the application of switching linear parameter-varying (LPV) feedback control techniques to the ETC problem. Electronic throttle valves are highly nonlinear systems due to their packaging, cost, and reliability constraints. The plant's dynamics vary considerably with respect to slip-stick friction, limp-home springs, and unmodeled disturbances. The complete plant model encapsulates the aforementioned nonlinear dynamics as a linear model that changes affinely with parameter-varying slip-stick friction and voltage fluctuation. The ETC synthesis problem is formulated as a linear matrix inequality as a non-convex optimization problem and solved via iterative methods. In previous literature pertaining to LPV controllers, large variations throughout the electronic throttle valve's operating region have led to conservative results. In this research, to reduce conservatism, the operating region has been partitioned into smaller subregions. Switching events between subregions are based on the scheduling-parameters of the LPV system, which are related to slip-stick friction and voltage fluctuation. The devised switching LPV controller is tuned and validated and its performance is experimentally compared with popular control solutions to the ETC problem. The baseline controllers include classic proportional-integral-derivative control, sliding-mode control, and gain-scheduling LPV control. Experimental results reveal that the switching LPV controller outperforms the baseline controllers throughout all the prescribed operating regions.

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This thesis proposes an algorithm to designswitching surfaces for the switching linear parameter-varying(LPV) controller with hysteresis switching. The switching surfacesare sought for to optimize the bound of the closed-loopL2-gain performance. An optimization problem is formulatedwith respect to parameters characterizing Lyapunov matrixvariables, local controller matrix variables, and locations ofthe switching surfaces. Since the problem turns out to benon-convex in terms of these characterizing parameters, anumerical algorithm is given to guarantee the decrease of thecost function value after each iteration, which consists of two steps: direction selection and line search. A hybrid method whichis a combination of the steepest descent method and Newton'smethod is employed in the direction selection step to decide the orientation of proceeding. A numerical algorithm is used to compute the most appropriate length of the proceeding along the selected direction which generates the most decrease in the cost function. To demonstrate the efficiency and usefulness of the proposed algorithm, it will be applied to three examples in control applications: a tracking problem for a mass-spring-damper system, a vibration suppression problem for a magnetically-actuated optical image stabilizer, and an air-fuel-ratio control problem for automotive engines. In these examples, it will be shown that the proposed optimization approach to the design of the switching surfaces and the switching LPV controller is superior to heuristic approaches in closed-loop performances, at the price of higher computational costs. Additionally, it will be shown that the algorithm can be applied to the general n-parameters case.

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This thesis studies the application of gain-scheduling (GS) control techniques to floating offshore wind turbines on barge platforms. Modelling, control objectives, controller design and performance evaluations are presented for both low wind speed and high wind speed cases. Special emphasis is placed on the dynamics variation of the wind turbine system caused by plant nonlinearity with respect to wind speed.The dynamics variation is represented by a linear parameter-varying (LPV) model. The LPV model for wind turbines is derived by linearizing the nonlinear dynamics at various operating wind speeds and by interpolating the linearized models.In low wind speed, to achieve control objectives of maximizing power capture and minimizing platform movements, for the LPV model, the LPV GS designtechnique is explored. In this region, the advantage of making use of blade pitch angle as a control input is also investigated. In high wind speed, to achieve control objectives of regulating power capture and minimizing platform movements, both LQR and LPV GS design techniques are explored.To evaluate the designed controllers, simulation studies are conducted with a realistic 5 MW wind turbine model developed at National Renewable Energy Laboratory, and realistic wind and wave profiles. The average and root mean square values of power capture and platform pitch movement are adopted as performance measures, and compared among designed GS controllers and conventional controllers. The comparisons demonstrate the performance improvement achieved by GS controltechniques.

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The Three-Way Catalytic Converter (TWC) is a critical component for the mitigation of tailpipe emissions of modern Internal Combustion (IC) engines. Because the TWC operates effectively only when a stoichiometric ratio of air and fuel is combusted in the engine, accurate control of the air-fuel ratio is required. To track the desired ratio, a switching Linear Parameter Varying (LPV) air-fuel ratio feedback controller, scheduled based on engine speed and air flow, and providing guaranteed L2 performance, is introduced. The controller measures the air-fuel ratio in the exhaust flow using a Universal Exhaust Gas Oxygen (UEGO) sensor and adjusts the amount of fuel injected accordingly. A detailed model of the air-fuel ratio control problem is developed to demonstrate the non-linear and parameter-dependent nature of the plant, as well as the presence of pure delays. The model’s dynamics vary considerably with engine speed and air flow. A simplified model, widely used in literature and known as a First Order Plus Dead Time (FOPDT) model, is then derived. It effectively captures the control problem using a model which is linear but parameter-varying with engine speed and air flow. Large variation of the FOPDT model across the engine’s operating range has led to conservative LPV controllers in previous literature. For this reason, the operating range is divided into smaller subregions, and an individual LPV controller is designed for each subregion. The LPV controllers are then switched based on the current engine speed and air flow and are collectively referred to as a switching LPV controller. The controller design problem is expressed as a Linear Matrix Inequality (LMI ) convex optimization problem which can be efficiently solved using available LMI techniques. Simulations are performed and the air-fuel ratio tracking performance of the switching LPV controller is compared with that of conventional controllers including, H∞ and LPV, as well as a novel adaptive controller. The switching LPV controller achieves improved performance over the complete operating range of the engine.

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