Browsing by Subject "Optimal control"
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Item Brushless DC motor modeling and optimal control: a cardiovascular application(2016-08) Rapp, Ethan Stewart; Longoria, Raul G.; Chen, DongmeiThe increasing use of Ventricular Assist Devices (VADs) in patients with weak or failing hearts has driven a need for more thorough analysis of VAD design, control methods, and cardiovascular dynamic effects. In recent years, studies have shown the potential of applying formal optimization methods to VAD actuation in order to reduce power consumption or improve pump output. This thesis continues the use of formal optimization methods as well as digital analysis using the brushless DC (BLDC) motors within the TORVAD(TM), designed by Windmill Cardiovascular Systems, Inc. (WCS), as a basis. To begin the optimization, a parameterized model of the BLDC motor system has been developed and combined with a lumped parameter model of the cardiovascular system. The combined system is then digitally analyzed under varying rates of TORVAD(TM) motor controller frequencies to determine the minimum frequency at which the system will remain stable and minimize detrimental physiological effects. Formal optimization methods are then introduced and implemented on the combined motor and cardiovascular system model. The output of the optimization is a reference trajectory that minimizes average motor power consumption. This trajectory, along with the results from the digital analysis, provides a more robust examination on the combined motor and cardiovascular system.Item Controlled self-assembly of charged particles(2010-05) Shestopalov, Nikolay Vladimirovic; Rodin, G. J. (Gregory J.); Henkelman, GraemeSelf-assembly is a process of non-intrusive transformation of a system from a disordered to an ordered state. For engineering purposes, self-assembly of microscopic objects can benefit significantly from macroscopic guidance and control. This dissertation is concerned with controlling self-assembly in binary monolayers of electrically charged particles that follow basic laws of statistical mechanics. First, a simple macroscopic model is used to determine an optimal thermal control for self-assembly. The model assumes that a single rate-controlling mechanism is responsible for the formation of spatially ordered structures and that its rate follows an Arrhenius form. The model parameters are obtained using molecular dynamics simulations. The optimal control is derived in an analytical form using classical optimization methods. Two major lessons were learned from that work: (i) isothermal control was almost as effective as optimal time-dependent thermal control, and (ii) neither electrostatic interactions nor thermal control were particularly effective in eliminating voids formed during self-assembly. Accordingly, at the next stage, the focus is on temperature-pressure control under isothermal-isobaric conditions. In identifying optimal temperature and pressure conditions, several assumptions, that allow one to relate the optimal conditions to the phase diagram, are proposed. Instead of verifying the individual assumptions, the entire approach is verified using molecular dynamics simulations. It is estimated that under optimal isothermal-isobaric conditions the rate of self-assembly is about five time faster than that under optimal temperature control conditions. It is argued that the proposed approach of relating optimal conditions to the phase diagram is applicable to other systems. Further, the work reveals numerous and useful parallels between self-assembly and crystal physics, which are important to exploit for developing robust engineering self-assembly processes.Item Design and control of a variable ratio gearbox for distributed wind turbine systems(2012-08) Hall, John Francis, 1968-; Chen, Dongmei, Ph. D.; Longoria, Raul G.; Masada, Glenn Y.; Pratap, Siddharth B.; Traver, Alfred E.Wind is one of the most promising resources in the renewable energy portfolio. Still, the cost of electrical power produced by small wind turbines impedes the use of this technology, which can otherwise provide power to millions of homes in rural regions worldwide. To encourage their use, small wind turbines must convert wind energy more effectively while avoiding increased equipment costs. A variable ratio gearbox (VRG) can provide this capability to the simple low-cost fixed-speed wind turbine through discrete operating speeds. The VRG concept is based upon mature technology taken from the automotive industry and is characterized by low cost and high reliability. A 100 kW model characterizes the benefits of integrating a VRG into a fixed-speed stall-regulated wind turbine system. Simulation results suggest it improves the efficiency of the fixed-speed turbine in the partial-load region and has the ability to limit power in the full-load region where pitch control is often used. To maximize electrical production, mechanical braking is applied during the normal operation of the wind turbine. A strategy is used to select gear ratios that produce torque slightly above the maximum amount the generator can accept while simultaneously applying the mechanical brake, so that full-load production may be realized over greater ranges of the wind speed. Dynamic programming is used to establish the VRG ratios and an optimal control design. This optimization strategy maximizes the energy production while insuring that the brake pads maintain a predetermined service life. In the final step of the research, a decision-making algorithm is developed to find the gears that emulate the ratios found in the optimal control design. The objective is to match the energy level as closely as possible, minimize the mass of the gears, and insure that tooth failure does not occur over the design life of the VRG. Recorded wind data of various wind classes is used to quantify the benefit of using the VRG. The results suggest that an optimized VRG design can increase wind energy production by roughly 10% at all of the sites in the study.Item Evidence of intelligent neural control of human eyes(2011-05) Najemnik, Jiri; Geisler, Wilson S.; Cormack, Lawrence K.; Hayhoe, Mary; Huk, Alex; Bovik, Alan C.Nearly all imaginable human activities rest on a context-appropriate dynamic control of the flow of retinal data into the nervous system via eye movements. The brain’s task is to move the eyes so as to exert intelligent predictive control over the informational content of the retinal data stream. An intelligent oculomotor controller would first model future contingent upon each possible next action in the oculomotor repertoire, then rank-order the repertoire by assigning a value v(a,t) to each possible action a at each time t, and execute the oculomotor action with the highest predicted value each time. We present a striking evidence of such an intelligent neural control of human eyes in a laboratory task of visual search for a small target camouflaged by a natural-like stochastic texture, a task in which the value of fixating a given location naturally corresponds to the expected information gain about the unknown location of the target. Human searchers behave as if maintaining a map of beliefs (represented as probabilities) about the target location, updating their beliefs with visual data obtained on each fixation optimally using the Bayes Rule. On average, human eye movement patterns appear remarkably consistent with an intelligent strategy of moving eyes to maximize the expected information gain, but inconsistent with the strategy of always foveating the currently most likely location of the target (a prevalent intuition in the existing theories). We derive principled, simple, accurate, and robust mathematical formulas to compute belief and information value maps across the search area on each fixation (or time step). The formulas are exact expressions in the limiting cases of small amount of information extracted, which occurs when the number of potential target locations is infinite, or when the time step is vanishingly small (used for online control of fixation duration). Under these circumstances, the computation of information value map reduces to a linear filtering of beliefs on each time step, and beliefs can be maintained simply as running weighted averages. A model algorithm employing these simple computations captures many statistical properties of human eye movements in our search task.Item Fluid and queueing networks with Gurvich-type routing(2015-08) Sisbot, Emre Arda; Hasenbein, John J.; Bickel, James Eric; Cudina, Milica; Djurdjanovic, Dragan; Khajavirad, AidaQueueing networks have applications in a wide range of domains, from call center management to telecommunication networks. Motivated by a healthcare application, in this dissertation, we analyze a class of queueing and fluid networks with an additional routing option that we call Gurvich-type routing. The networks we consider include parallel buffers, each associated with a different class of entity, and Gurvich-type routing allows to control the assignment of an incoming entity to one of the classes. In addition to routing, scheduling of entities is also controlled as the classes of entities compete for service at the same station. A major theme in this work is the investigation of the interplay of this routing option with the scheduling decisions in networks with various topologies. The first part of this work focuses on a queueing network composed of two parallel buffers. We form a Markov decision process representation of this system and prove structural results on the optimal routing and scheduling controls. Via these results, we determine a near-optimal discrete policy by solving the associated fluid model along with perturbation expansions. In the second part, we analyze a single-station fluid network composed of N parallel buffers with an arbitrary N. For this network, along with structural proofs on the optimal scheduling policies, we show that the optimal routing policies are threshold-based. We then develop a numerical procedure to compute the optimal policy for any initial state. The final part of this work extends the analysis of the previous part to tandem fluid networks composed of two stations. For two different models, we provide results on the optimal scheduling and routing policies.Item Harnessing demand flexibility to minimize cost, facilitate renewable integration, and provide ancillary services(2014-08) Kefayati, Mahdi; Baldick, RossRenewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner.Item Lossless convexification of optimal control problems(2014-05) Harris, Matthew Wade; Açıkmeşe, BehçetThis dissertation begins with an introduction to finite-dimensional optimization and optimal control theory. It then proves lossless convexification for three problems: 1) a minimum time rendezvous using differential drag, 2) a maximum divert and landing, and 3) a general optimal control problem with linear state constraints and mixed convex and non-convex control constraints. Each is a unique contribution to the theory of lossless convexification. The first proves lossless convexification in the presence of singular controls and specifies a procedure for converting singular controls to the bang-bang type. The second is the first example of lossless convexification with state constraints. The third is the most general result to date. It says that lossless convexification holds when the state space is a strongly controllable subspace. This extends the controllability concepts used previously, and it recovers earlier results as a special case. Lastly, a few of the remaining research challenges are discussed.Item Model-based controller design and simulation of a marine chiller(2012-08) Salhotra, Gautam Vijay; Kiehne, Thomas M.; Longoria, Raul G.For the past decade, the US Navy has committed to fundamental research and technology development on its next generation of surface ships. The vision is that these warships will be dynamically reconfigurable, energy-efficient, and have state-of-the-art pulsed energy weapons and sensors onboard. These developments represent a significant increase in highly dynamic on-board electrical systems that will produce correspondingly large amounts of dynamic heat generation, which, if not managed properly, will likely produce significant thermal side effects. In previous work, a highly customizable simulation framework has been developed to address thermal management issues across both the mechanical and electrical domains. This software environment is called the Dynamic Thermal Modeling and Simulation (DTMS) framework. The purpose of the current work is to introduce modern control theory into DTMS, thus providing the framework with the ability to control large-scale system simulations. The research reported in this thesis uses control of a marine chiller as a simulation vehicle. Several control strategies were implemented. These included the well-established PID controller as well as a new controller based on optimal control theory. Results for chiller simulations in the case of no-control, PID control, and optimal control are presented here. The comparative effectiveness of these controls in bringing the chiller to startup equilibrium is investigated. Response of the chiller model and the optimal controller to highly dynamic, varying heat loads was tested. The PID controller in DTMS is modeled as a special case of the transfer function control scheme. A PID controller is simple to implement but responses are inherently local and multiple controls in a system or subsystem simulation can easily lead to conflicts. The optimal control problem has been modeled as an Infinite Horizon Linear Quadratic Regulator (LQR) problem. This formulation is not local and does not create undesirable effects in parts of the system that not controlled directly by controller inputs. Using the York 200-ton marine chiller as an example, specific steps required to formulate the LQR problem are documented in this report. Implementation of the LQR controller was demonstrated for the startup to steady-state function of the chiller at full load. Treatment of the optimal controller ends with simulation of the chiller and its LQR controller under the influence of varying dynamic heat loads in a chilled water loop. The heat load variation examined has highly transient characteristics that affect the temperature of the fresh water entering the chiller, as well as the refrigerant pressure and temperature in the evaporator. The LQR formulation is shown to actively adjust to these varying operating points in a smooth and responsive manner.Item Numerical optimal control of a wind turbine system(2015-08) Yan, Zeyu, Ph. D.; Chen, Dongmei, Ph. D.; Fahrenthold, Eric P.; Hall, Neal A.; Li, Wei; Seepersad, Carolyn C.With the development of wind turbine technology and the need for maximizing wind energy harvesting, more wind turbines operate in the partial load region. Among many control algorithms developed for this region, controllers based on feedback of the global maximum power coefficient have been widely used. These control schemes offer good performance with simple implementations, but they may not be suited for wind turbines with limited rotor speed ranges. In such cases, the controller is challenged because the main feature ---the global maximum power coefficient--- is not achievable due to the turbine speed constraint. It is necessary to develop a controller to seek the achievable maximum power coefficient that leads to optimal wind energy capture. In this dissertation, the development of an optimal control framework to maximize wind energy capture for wind turbines with constrained turbine speed is first presented. Numerical optimal control techniques are applied to search for the achievable maximum power coefficient, with proposed modifications to make this task more computationally feasible. Mitigating the turbine generator torque variation, thus reducing the fatigue loading on turbine generator shaft, is also important for the partial load region operation. Including this aspect in the optimal control is then discussed. Furthermore, an approach of incorporating time-varying weightings into developing the optimal controller is introduced to seek further improvement on turbine generator torque variation reduction, thus fatigue reduction. In addition, the power generated by the wind turbine varies due to variation in the wind speed. Depending on the load demand and the wind speed, the wind turbine's operation switches between two modes: a multi-input-single-output (MISO) mode and a single-input-single-output (SISO) mode. Due to the wind turbine changes its dynamic behavior during the switching process, applying the traditional control methods to each corresponding mode may not be capable of maximizing the overall wind energy capture throughout the entire turbine's operation. Therefore, the development of an optimal control framework to maximize the overall wind energy capture for a switched wind turbine system is subsequently presented.Item Optimal control for a modern wind turbine system(2012) Yan, Zeyu, master of science in engineering; Chen, DongmeiWind energy is the most abundant resource in the renewable energy portfolio. Increasing the wind capture capability improves the economic viability of this technology, and makes it more competitive with traditional fossil-fuel based supplies. Therefore, it is necessary to explore control strategies that maximize aerodynamic efficiency, thus, the wind energy capture. Several control algorithms are developed and compared during this research. A traditional feedback control is adapted as the benchmark approach, where the turbine torque and the blade pitch angle are used to control the wind turbine operation during partial and full load operations, correspondingly. Augmented feedback control algorithms are then developed to improve the wind energy harvesting. Optimal control methodologies are extensively explored to achieve maximal wind energy capture. Numerical optimization techniques, such as direct shooting optimization are employed. The direct shooting method convert the optimal control problem into a parameter optimization problem and use nonlinear programming algorithm to find the optimal solution. The dynamic programming, a global optimization approach over a time horizon, is also investigated. The dynamic programming finds the control inputs for the blade pitch angle and speed ratio to maximize the power coefficient, based on historical wind data. A dynamic wind turbine model has been developed to facilitate this process by characterizing the performance of the various possible input scenarios. Simulation results of each algorithm on real wind site data are presented to compare the wind energy capture under the proposed control algorithms with the traditional feedback control design. The result of the tradeoff analysis between the computation expense and the energy capture is also reported.Item Optimal control of wind turbines for distributed power generation(2015-08) Shaltout, Mohamed Lotfi Eid Nasr; Chen, Dongmei, Ph. D.; Longoria, Raul G.; Crawford, Richard H.; Deshpande, Ashish D.; Malikopoulos, Andreas A.; Pratap, Siddharth B.Wind energy represents one of the major renewable energy sources that can meet future energy demands to sustain our lifestyle. During the last few decades, the installation of wind turbines for power generation has grown rapidly worldwide. Besides utility scale wind farms, distributed wind energy systems contributes to the rise in wind energy penetration. However, the expansion of distributed wind energy systems is faced by major challenges such as the system’s reliability in addition to the environmental impacts. This work is intended to explore various control algorithms to enable the distributed wind energy systems to face the aforementioned challenges. First of all, a stall regulated fixed speed wind turbine augmented with a variable ratio gearbox has been proven to enhance the wind energy capture at a relatively low cost, and considered as an attractive design for small wind energy systems. However, the high reliability advantage of traditional fixed-speed wind turbines can be affected by the integration of the variable ratio gearbox. A portion of this work is intended to develop a control algorithm that extends the variable ratio gearbox service life, thus improves overall system reliability and reduces the expected operational cost. Secondly, a pitch regulated variable speed wind turbines dominates the wind energy industry as it represents a balance between cost and flexibility of operation. They can be used for midsized wind power generation. Optimizing its wind energy capture while maintain high system reliability has been the one of the main focuses of many researchers. Another portion of this work introduces a model predictive control framework that enhances the reliability of pitch regulated variable speed wind turbines, thus improves their operational cost. Finally, one of the major environmental challenges facing the continuous growth of wind energy industry is the noise emitted from wind turbines. The severity of the noise emission problem is more significant for small and medium sized wind turbines installed in the vicinity of residential areas for distributed power generation. Consequently, the last portion of this work is intended to investigate the potential of wind turbine control design to reduce noise emission in different operating conditions with minimal impact on power generationItem The development of a battery management system with special focus on capacity estimation and thermal management(2016-08) Anyaegbunam, Ifedioranma; Chen, Dongmei, Ph. D.; Beaman, JosephLithium ion batteries are instrumental in tackling the challenges of global warming. They have shown great utility in electric and hybrid vehicles. However, challenges with regard to performance and safety such as capacity fade and thermal runaway need to be accounted for in the implementation of these battery systems. One way is through battery management systems that monitor and control various aspects of the battery’s operation. At the heart of the battery management system is an analytical model of the battery. This thesis proposes a battery management system which uses a “lowuses a “low-order” physics- based battery model that estimates capacity and optimally manages the temperature of the battery. A capacity estimation methodology is proposed that uses the state of charge estimate from an extended kalman filter and the inverse of the coulomb counting equation to estimates the “instant” capacity of the battery. This instant value is then used in an averaging calculation that uses saturation limits and a time delay to obtain a value for the capacity that is representative of the battery. This value is then feedback into the kalman filter. The capacity estimate obtained through this method was between 2 and 8 % off of the true value. A thermal management system is also proposed that optimally controls a fan to cool a lithium ion battery. The thermal management system was developed and tested in a simulated environment. First, the fan model was integrated with the battery model and simulations were run to test the open loop temperature response of the battery to the fan cooling while varying the input voltage of the fan the current demanded of the battery. From this data an operating point was chosen, the system was linearized, and a linear quadratic controller was designed and implemented. The controller was sluggish when faced with a temperature perturbation in the absence of a current demand increase but drove the temperature change to zero. In the presence of a current change controller drove the state to a nonzero steady state value. The same result occurred when a disturbance rejection mechanism was applied to the controller.