Ryozo Nagamune


Research Interests

control engineering
data-driven modeling and control
robust and linear parameter-varying control
modeling and control of floating offshore wind turbines and wind farms
modeling and control of COVID-19 outbreak
modeling and control of solar themal systems
control of automotive engines

Relevant Degree Programs

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.

Research Methodology

theoretical development
computer simulations
experimental validations
small scale wind turbine
integrated solar thermal experimental setup


Master's students
Doctoral students
Postdoctoral Fellows

Floating offshore wind farm control and optimization.

Modeling and control of COVID-19 outbreak.

Control of an integrated solar thermal system.

Modeling and control of metal additive manufacturing processes

Mechanical, electrical, mechatronics, control engineering background

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - April 2022)
Gain-scheduling and preview control of selective catalytic reduction systems in diesel engines (2021)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Machine learning modeling of a direct-injected dual-fuel engine based on low density experimental data (2021)

Automotive systems are constantly increasing in complexity, requiring advanced modeling methods with large data sets to analyze these systems. This work proposes a machine learning approach to rapidly developing, steady state, control oriented, engine models that use optimization methods and engineering knowledge to reduce the burden of data collection and improve model performance and reliability. Data is collected from a pilot ignited direct injection natural gas engine using a full factorial approach for a high density data set and a design of experiments approach for a low density training data set with randomized validation data. An optimization approach for selecting hyperparameters for neural network and Gaussian process regression models is proposed. Models for emissions and performance metrics are created and compared to response surface models. The hyperparameter optimized models show an improvement in robustness and model performance, reducing the normalized root mean square error by 26% compared to other hyperparameter configurations. Gaussian process regression hyperparameter optimization shows the lowest error, 46% lower than response surface models. The Gaussian process regression hyperparameter optimized models are further improved using multi-region modeling, sensitivity analysis based input reduction, layered modeling, and hybrid layered modeling. The sensitivity based input reduction reduces the normalized root mean square error for all models by an average of 8% and up to 19%. The layered models reduce the normalized root mean square error for the CO by 52%, NOₓ by 30%, and particulate matter by 33%. The multi-region models reduce the normalized root mean square error for the O₂ by 40% and thermal efficiency by 16%. Using the best techniques for each output, the error is reduced by 19%, compared to hyperparameter optimization alone and 45% compared to typical Gaussian process regression models. These results show that hyperparameter optimization combined with the other techniques presented here significantly reduce model error. Using these techniques, it is possible to reduce the reliance on data for engine modeling. Future research in energy conversion technologies can use these techniques to rapidly develop new technologies without the cost in time and funding typically reserved for extensive data collection.

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Theoretical foundations for optimal control of floating offshore wind farms (2021)

Due to a phenomenon termed the wake effect, wind turbines that are placed in close proximity within wind farms interact aerodynamically. In short, each turbine generates a wake within which wind speeds are reduced, and these wakes overlap with the rotors of machines located downstream. This interaction diminishes power production in wind farms by up to 60%. Using a process referred to as wind farm control, individual wind turbines may be operated in a manner that increases power production from the collective. This thesis investigates the potential of a wind farm control strategy named yaw and induction-based turbine repositioning (YITuR) that is specifically compatible with floating offshore wind farms. Since floating platforms are anchored to the seabed using slack mooring line cables, each turbine may be repositioned in real-time using the aerodynamic forces exerted on its rotor. By relocating floating platforms accordingly, the overlap area between the wakes generated by upstream turbines and the rotors of downstream machines may be reduced; leading to an increase in wind farm efficiency. The potential of YITuR is assessed through several steps. First, a steady-state model of floating offshore wind farms is constructed and stationary optimization studies are carried out to determine the potential of YITuR under idealized steady wind conditions. Major findings from this study are that wind farm efficiency may increase by more than 40% using YITuR over traditional wind farm operation; however, these benefits are strongly influenced by mooring system designs. Second, a dynamic floating wind farm model is developed to evaluate the performance of real-time control systems. Third, due to the non-convexity of the YITuR control problem, novel distributed economic model predictive control (DEMPC) theory is developed to guarantee power maximization. Existing DEMPC algorithms do not offer such a guarantee in the presence of non-convex objective functions. Finally, the DEMPC algorithm is evaluated using the dynamic simulation tool. Neural networks are used to estimate the dynamics of floating platforms in order to expedite decision-making in DEMPC. Simulation results indicate gains of 20% in energy production when YITuR replaces traditional wind farm operation.

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Robust and optimal switching linear parameter-varying control (2018)

Linear parameter-varying (LPV) control is a systematic way for gain-scheduling control of a nonlinear or time-varying system that has parameter-dependent dynamics variations in its operating region. However, when the dynamics variations are large, LPV control may give the conservative performance. One way to reduce the conservatism is switching LPV (SLPV) control, in which we partition the operating region into sub-regions, design one local LPV controller for each sub-region, and switch among those local controllers according to some switching rules. On the one hand, this thesis makes three theoretical contributions to the SLPV control theory. Firstly, this thesis proposes a new approach to designing SLPV controllers with guaranteed stability and performance even when the scheduling parameters cannot be exactly measured. Secondly, this thesis presents two algorithms to optimize the switching surfaces (SSs) that can further improve the performance of an SLPV controller. One algorithm is based on sequentially optimizing the SSs and the SLPV controller for the state-feedback case. The other one is based on particle swarm optimization and can be used for both state-feedback and output-feedback cases. Finally, this thesis introduces a novel approach to designing SLPV controllers that could yield significantly improved local performance in some sub-regions without much sacrifice of the worst-case performance. This is different from the traditional approach that often leads to similar performance in all the sub-regions.On the other hand, this thesis addresses two practical problems using the theoretic approaches developed in this thesis. One is control of miniaturized optical image stabilizers with product variations. Specifically, multiple parameter-dependent robust (MPDR) controllers are designed to adapt to the product variations, while being robust against the uncertainties in measurement of the scheduling parameters that characterize the dynamics variation. Experimental results validate the advantages of the proposed MPDR controllers over a conventional robust mu-synthesis controller. The other application is control of a floating offshore wind turbine on a semi-submersible platform. SLPV controllers are designed for regulating the power and the generator speed and reducing the platform motion. The superior performance of the SLPV controllers is demonstrated in high-fidelity simulations.

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Linear Parameter-Varying Control of CNC Machine Tool Feed-Drives with Dynamic Variations (2015)

This thesis presents new approaches to feed-drive control of computer numerical control (CNC) machine tools machine tools with a significant range of dynamic variations during machining operations. Several sources which can cause dynamic variations of feed-drive systems are considered, such as the change of table position, the reduction of workpiece mass, and the variations of tool-path orientation. Feed-drive systems having the dynamic variations are modeled as linear parameter varying (LPV) models. For the LPV models, three control methods are proposed to achieve satisfactory control performance of feed-drive systems.In the first method, we propose a parallel structure of an LPV gain-scheduled controller which aims at both tracking control and the vibration suppression by taking into account the resonant modes' variations which are peculiar to ball-screw drives. In the second method, instead of designing one LPV controller, a set of gain-scheduled controllers are designed to compensate for a wide range of dynamic variations. In this method, switching between two adjacent controllers may result in a transient jump of control signal at switching instants. In the third method, to ensure a smooth control signal, we present a novel method to design a smooth switching gain-scheduled LPV controller. The moving region of the gain-scheduling variables is divided into a specified number of local subregions as well as subregions for the smooth controller switching. Then, one gain-scheduled LPV controller is assigned to each of the local subregions, while for each switching subregion, a function interpolating local LPV controllers associated with its neighbourhood subregions is designed. This interpolating function imposes the constraint of smooth transition on controller system matrices.The smooth switching controller design problem amounts to solving a feasibility problem which involves non-linear matrix inequalities that are solvable by a proposed iterative descent algorithm. The developed smooth switching controller is applied to control problems in both parallel and serial CNC machine tool mechanisms. Finally, for the multi-axis CNC machine tools, a multi-input-multi-output (MIMO) LPV feedback controller is designed to directly minimize contouring error in the task coordinate frame system.

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Modeling of linear systems with parameter variations: applications in hard disk and ball screw drives (2011)

This thesis considers variations in the parameters of the dynamics of linear systems, and tackles modeling of Linear Time-Invariant (LTI) and Linear Parameter Varying (LPV) plants. The variations in the dynamics make the controller design challenging, and to successfully overcome this challenge, two methods are proposed in this thesis. One method generates a connected model set. The idea of the multidimensional principal curves methodology is employed to detect the nonlinear correlations between parameters of the given set of system dynamics. The connected model set is simple and tight, leading to both nonconservatism and reduced computational complexity in subsequent controller design, and hence, to improve the controller performance. The other method is developed to derive a family of discrete model sets for a given set of system response data. A relaxed version of the normalized cut methodology is developed and used in an algorithm to divide a given set of system responses into the smallest possible number of partitions in such a way that a desired performance objective is satisfied for all partitions by designing one controller for each partition. Using the proposed method, a tight uncertainty model is derived for Hard Disk Drive (HDD) systems, and an H∞ controller is synthesized. The dynamics of HDDs is studied from a controller design point of view. Especially, the variations in the dynamics due to the change in temperatures and limited precision in the production line are examined. Also, the variations in the dynamics of Ball Screw Drive (BSD) systems due to the structural flexibility, runout, and workpiece mass variation are studied. These three factors are explicitly incorporated in LPV models. To build the LPV models, it is determined how the system parameters are affected by two variables, namely, the measurable table position and the uncertain mass of the table. We design robust gain scheduling controllers which are scheduled by the table position and are robust over the table mass.

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Master's Student Supervision (2010 - 2021)
FLORIDynFloat: a dynamic parametric wake model and motion simulator for floating offshore wind farms (2021)

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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Nonlinear model predictive control for the suppression of the COVID-19 pandemic based on an agent-based model (2021)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Control of an integrated solar thermal system based on intelligent iterative learning for hot water demand prediction (2020)

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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Floating wind turbine motion suppression using an active wave energy converter (2020)

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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A piezoelectric fiber scanner for reflectance confocal imaging of biological tissues (2019)

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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An optimal control strategy for an integrated solar thermal hydronic system with a heat pump (2018)

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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H-infinity position control of a 5-MW offshore wind turbine with a semi-submersible platform (2018)

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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Wind and wave disturbance rejection control of floating offshore wind turbines (2018)

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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Position control of a floating offshore wind turbine system using aerodynamic force (2017)

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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Robust Control of Miniaturized Optical Image Stabilizers for Mobile Phone Cameras (2016)

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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Physics-Based Control-Oriented Modelling for Floating Offshore Wind Turbines (2015)

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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A numerical optimization approach to switching surface design for switching linear parameter-varying control (2014)

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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Air-Fuel Ratio Control in Spark Ignition Internal Combustion Engines Using Switching LPV Techniques (2011)

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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