A methodology for representing and analyzing manufacturing systems in a hybrid systems framework for control reconfiguration purposes in the presence of defects and failures at the product and system levels is presented. At the top level, a supervisory Petri net directs parts/jobs through the manufacturing system. An object-based hybrid systems model that incorporates both Petri nets at the event-driven level and differential equations at the time-driven level describes the subsystems. Rerouting capabilities utilizing this model at the product and operation levels were explained. Simulations were performed on a testbed model for optimal time and mode transition cost to determine the route for parts. The product level reconfiguration architecture utilizes an adaptive network-based fuzzy inference system (ANFIS) to map histogram comparison metrics to set-point adjustments when product defects were detected. Tests were performed on good and defective plastic parts from a plastic injection molding machine. In addition, a mode identification architecture was described that incorporates both time- and event-driven information to determine the operating mode of a system from measured sensor signals. Simulated data representing the measured process signals from a Navy ship chiller system were used to verify that the appropriate operating modes were detected.

Hybrid neural-microelectronic systems, systems composed of biological neural networks and neuronal models, have great potential for the treatment of neural injury and disease. The utility of such systems will be ultimately determined by the ability of the engineered component to correctly replicate the function of biological neural networks. These models can take the form of mechanistic models, which reproduce neural function by describing the physiologic mechanisms that produce neural activity, and empirical models, which reproduce neural function through more simplified mathematical expressions. We present our research into the role of model complexity in creating robust and flexible behaviors in hybrid systems. Beginning with a complex mechanistic model of a leech heartbeat interneuron, we create a series of three systematically reduced models that incorporate both mechanistic and empirical components. We then evaluate the robustness of these models to parameter variation, and assess the flexibility of the models activities. The modeling studies are validated by incorporating both mechanistic and semi-empirical models in hybrid systems with a living leech heartbeat interneuron. Our results indicate that model complexity serves to increase both the robustness of the system and the ability of the system to produce flexible outputs.

Inference of regulatory relations in dynamical systems is a promising active research area. Recently, most of the investigations in this field have been stimulated by the researches in functional genomics. In this thesis, the inferential modeling problem for switching hybrid systems is studied. The hybrid systems refers to dynamical systems in which discrete and continuous variables regulate each other, in other words the jumps and flows are interrelated. In this study, piecewise linear approximations are used for modeling purposes and it is shown that piecewise linear models are capable of displaying the evolutionary characteristics of switching hybrid systems approxi- mately. For the mentioned systems, detection of switching instances and inference of locally linear parameters from empirical data provides a solid understanding about the system dynamics. Thus, the inference methodology is based on these issues. The primary difference of the inference algorithm is the idea of transforming the switch- ing detection problem into a jump detection problem by derivative estimation from discrete data. The jump detection problem has been studied extensively in signal processing literature. So, related techniques in the literature has been analyzed care- fully and suitable ones adopted in this thesis. The primary advantage of proposed method would be its robustness in switching detection and derivative estimation. The theoretical background of this robustness claim and the importance of robustness for real world applications are explained in detail.

Regulatory processes and history dependent behavior appear in many dynamical systems in nature and technology. For modeling regulatory processes, hybrid systems offer several advances. From this point of view, to observe the capability of hybrid systems in a history dependent system is a strong motivation. In this thesis, we developed functional hybrid systems which exhibit memory dependent behavior such that the dynamics of the system is determined by both the location of the state vector and the memory. This property was explained by various examples. We used the hybrid system with memory in modeling the gene regulatory network of human immune response to Influenza A virus infection. We investigated the sensitivity of the piecewise linear model with memory. We introduced how the model can be developed in future.

Many countries around the world integrated Renewable Energy Systems (RES) in their future energy plans in order to reduce negative impacts of fossil fuel consumption on the environment. However, RES may as well cause various environmental problems which are mostly related with the geographic locations of these facilities. The aim of this thesis is to create a Geographic Information System-based methodology for evaluating alternative locations for wind, solar and hybrid power plants by using fuzzy multi-criteria decision making. Environmental objectives and economical feasibility criteria for wind and solar systems are identified through Turkish legislations, previous studies, and interviews with General Directorate of Electrical Power Resources Survey and Development. Individual satisfaction degrees for each alternative location with respect to the identified environmental objectives and economical feasibility criteria are calculated using fuzzy set theory tools. Then these individual satisfaction degrees are aggregated into overall performance indexes which are used to determine priority maps for wind and solar energy generation facilities. Finally, maps of priority sites for wind and solar energy systems are overlaid to identify suitable locations for hybrid wind-solar energy systems. The proposed methodology is applied on a case study area composed of USak, Aydin, Denizli, Mugla, and Burdur provinces.

Transient regimes experienced by dynamic systems may have severe impacts on the operation of the aircraft. They are often regulated by dynamic constraints, requiring the dynamic signals to remain within bounds whose values vary with time. The verification of these peculiar types of constraints, which generally requires high-fidelity time-domain simulation, intervenes late in the system development process, thus potentially causing costly design iterations. The research objective of this thesis is to develop a methodology that integrates the verification of dynamic constraints in the early specification of dynamic systems. In order to circumvent the inefficiencies of time-domain simulation, multivariate dynamic surrogate models of the original time-domain simulation models are generated using wavelet neural networks (or wavenets). Concurrently, an alternate approach is formulated, in which the envelope of the dynamic response, extracted via a wavelet-based multiresolution analysis scheme, is subject to transient constraints. Dynamic surrogate models using sigmoid-based neural networks are generated to emulate the transient behavior of the envelope of the time-domain response. The run-time efficiency of the resulting dynamic surrogate models enables the implementation of a data farming approach, in which the full design space is sampled through a Monte-Carlo Simulation. An interactive visualization environment, enabling what-if analyses, is developed; the user can thereby instantaneously comprehend the transient response of the system (or its envelope) and its sensitivities to design and operation variables, as well as filter the design space to have it exhibit only the design scenarios verifying the dynamic constraints. The proposed methodology, along with its foundational hypotheses, is tested on the design and optimization of a 350VDC network, where a generator and its control system are concurrently designed in order to minimize the electrical losses, while ensuring that the transient undervoltage induced by peak demands in the consumption of a motor does not violate transient power quality constraints.

<p>We consider stochastic hybrid systems that stem from evolution equations with right-hand sides that stochastically switch between a given set of right-hand sides. To begin our study, we consider a linear ordinary differential equation whose right-hand side stochastically switches between a collection of different matrices. Despite its apparent simplicity, we prove that this system can exhibit surprising behavior.</p><p>Next, we construct mathematical machinery for analyzing general stochastic hybrid systems. This machinery combines techniques from various fields of mathematics to prove convergence to a steady state distribution and to analyze its structure.</p><p>Finally, we apply the tools from our general framework to partial differential equations with randomly switching boundary conditions. There, we see that these tools yield explicit formulae for statistics of the process and make seemingly intractable problems amenable to analysis.</p> / Dissertation

As control systems become more integrated with high-end engineering systems as well as consumer products, they are expected to achieve specifications that may include logic rules, safety constraints, startup procedures, and so forth. Control design for such complex specifications is a relatively unexplored research area. One possible design approach is based on partitioning the state space into polytopic regions, and then formulating a certain control problem on each polytope, with the intention that the set of all controllers so obtained would collectively achieve the specification. The control problem which must be solved for each polytope is called the reach control problem, and it has been identified as turnkey to the further development of this approach. The reach control problem (RCP) is to find a state feedback to make the closed-loop trajectories of an affine (or linear) control system defined on a polytope reach and exit a prescribed facet of the polytope in finite time. This dissertation studies a number of aspects of the reach control problem, and it uses tools from convex analysis, nonsmooth analysis, and computational geometry for this study. The dissertation has three main themes. First, we formulate and solve a variant of RCP in which trajectories exit the polytope in a monotonic sense; this provides a triangulation-independent solution of RCP. Second, we develop a Lyapunov-like theory for verifying if RCP is solved using a given candidate controller. This involves the introduction of the notion of generalized flow functions, a LaSalle Principle for RCP, and several converse theorems on existence of generalized flow functions. Third, we study the relationship between affine feedbacks and continuous state feedbacks for RCP on simplices. Although the two feedback classes have been shown to be equivalent under an assumption on the triangulation of the state space, we show by a counterexample that the equivalence is no longer true under arbitrary triangulations. Then we provide for single-input systems a constructive method for the synthesis of multi-affine feedbacks for RCP on simplices.

The global market for membrane separation technologies is forecast to reach $16 billion by the year 2017 due to wide adoption of the membrane technology across various end-use markets. With the growth in demand for high quality products, stringent regulations, environmental concerns, and exhausting natural resources, membrane separation technologies are forecast to witness significant growth over the long term (Global Industry Analysts Inc., 2011). The future of membrane technology promises to be equally exciting as new membrane materials, processes and innovations make their way to the marketplace. The current trend in membrane gas separation industry is, however, to develop robust membranes, which exhibit superior separation performance, and are reliable and durable for particular applications. Process simulation allows the investigation of operating and design variables in the process, and in new process configurations. An optimal operating condition and/or process configuration could possibly yield a better separation performance as well as cost savings. Moreover, with the development of new process concepts, new membrane applications will emerge. The thesis addresses developing models that can be used to help in the design and operation of CO2 capture processes. A mathematical model for the dynamic performance of gas separation with high flux, asymmetric hollow fiber membranes was developed considering the permeate pressure build-up inside the fiber bore and cross flow pattern with respect to the membrane skin. The solution technique is advantageous since it requires minimal computational effort and provides improved solution stability. The model predictions and the robustness of the numerical technique were validated with experimental data for several membrane systems with different flow configurations. The model and solution technique were applied to investigate the performance of several membrane module configurations for air separation and methane recovery from biogas (landfill gas or digester gas). Recycle ratio plays a crucial role, and optimum recycle ratios vital for the retentate recycle to permeate and permeate recycle to feed operation were found. From the concept of two recycle operations, complexities involved in the design and operation of continuous membrane column were simplified. Membrane permselectivity required for a targeted separation to produce pipeline quality natural gas by methane-selective or nitrogen-selective membranes was calculated. The study demonstrates that the new solution technique can conveniently handle the high-flux hollow fiber membrane problems with different module configurations. A section of the study was aimed at rectifying some commonly believed perceptions about pressure build-up in hollow fiber membranes. It is a general intuition that operating at higher pressures permeates more gases, and therefore sometimes the membrane module is tested or characterized at lower pressures to save gas consumption. It is also perceived that higher pressure build-up occurs at higher feed pressures, and membrane performance deteriorates at higher feed pressures. The apparent and intrinsic permeances of H2 and N2 for asymmetric cellulose acetate-based hollow fiber membranes were evaluated from pure gas permeation experiments and numerical analysis, respectively. It was shown that though the pressure build-up increases as feed pressure increases, the effect of pressure build-up on membrane performance is actually minimized at higher feed pressures. Membrane performs close to its actual separation properties if it is operated at high feed pressures, under which conditions the effect of pressure build-up on the membrane performance is minimized. The pressure build-up effect was further investigated by calculating the average loss and percentage loss in the driving force due to pressure build-up, and it was found that percentage loss in driving force is less at high feed pressures than that at low feed pressures. It is true that unsteady state cyclic permeation process can potentially compete with the most selective polymers available to date, both in terms selectivity and productivity. A novel process mode of gas separation by means of cyclic pressure-vacuum swings for feed pressurization and permeate evacuation using a single pump was evaluated for CO2 separation from flue gas. Unlike transient permeation processes reported in the literature which were based on the differences in sorption uptake rates or desorption falloff rates, this process was based on the selective permeability of the membrane for separations. The process was analyzed to elucidate the working principle, and a parametric study was carried out to evaluate the effects of design and operating parameters on the separation performance. It was shown that improved separation efficiency (i.e., product purity and throughput) better than that of conventional steady-state permeation could be obtained by means of pressure-vacuum swing permeation. The effectiveness of membrane processes and feasibility of hybrid processes combining membrane permeation and conventional amine absorption process were investigated for post-combustion CO2 capture. Traditional MEA process uses a substantial amount of energy at the stripper reboiler when CO2 concentration increases. Several single stage and multi-stage membrane process configurations were simulated for a target design specification aiming at possible application in enhanced oil recovery. It was shown that membrane processes offer the lowest energy penalty for post-combustion CO2 capture and likely to expand as more and more CO2 selective membranes are developed. Membrane processes can save up to 20~45% energy compared to the stand-alone MEA capture processes. A comparison of energy perspective for the CO2 capture processes studied was drawn, and it was shown that the energy requirements of the hybrid processes are less than conventional MEA processes. The total energy penalty of the hybrid processes decreases as more and more CO2 is removed by the membranes.

The global market for membrane separation technologies is forecast to reach $16 billion by the year 2017 due to wide adoption of the membrane technology across various end-use markets. With the growth in demand for high quality products, stringent regulations, environmental concerns, and exhausting natural resources, membrane separation technologies are forecast to witness significant growth over the long term (Global Industry Analysts Inc., 2011). The future of membrane technology promises to be equally exciting as new membrane materials, processes and innovations make their way to the marketplace. The current trend in membrane gas separation industry is, however, to develop robust membranes, which exhibit superior separation performance, and are reliable and durable for particular applications. Process simulation allows the investigation of operating and design variables in the process, and in new process configurations. An optimal operating condition and/or process configuration could possibly yield a better separation performance as well as cost savings. Moreover, with the development of new process concepts, new membrane applications will emerge. The thesis addresses developing models that can be used to help in the design and operation of CO2 capture processes. A mathematical model for the dynamic performance of gas separation with high flux, asymmetric hollow fiber membranes was developed considering the permeate pressure build-up inside the fiber bore and cross flow pattern with respect to the membrane skin. The solution technique is advantageous since it requires minimal computational effort and provides improved solution stability. The model predictions and the robustness of the numerical technique were validated with experimental data for several membrane systems with different flow configurations. The model and solution technique were applied to investigate the performance of several membrane module configurations for air separation and methane recovery from biogas (landfill gas or digester gas). Recycle ratio plays a crucial role, and optimum recycle ratios vital for the retentate recycle to permeate and permeate recycle to feed operation were found. From the concept of two recycle operations, complexities involved in the design and operation of continuous membrane column were simplified. Membrane permselectivity required for a targeted separation to produce pipeline quality natural gas by methane-selective or nitrogen-selective membranes was calculated. The study demonstrates that the new solution technique can conveniently handle the high-flux hollow fiber membrane problems with different module configurations. A section of the study was aimed at rectifying some commonly believed perceptions about pressure build-up in hollow fiber membranes. It is a general intuition that operating at higher pressures permeates more gases, and therefore sometimes the membrane module is tested or characterized at lower pressures to save gas consumption. It is also perceived that higher pressure build-up occurs at higher feed pressures, and membrane performance deteriorates at higher feed pressures. The apparent and intrinsic permeances of H2 and N2 for asymmetric cellulose acetate-based hollow fiber membranes were evaluated from pure gas permeation experiments and numerical analysis, respectively. It was shown that though the pressure build-up increases as feed pressure increases, the effect of pressure build-up on membrane performance is actually minimized at higher feed pressures. Membrane performs close to its actual separation properties if it is operated at high feed pressures, under which conditions the effect of pressure build-up on the membrane performance is minimized. The pressure build-up effect was further investigated by calculating the average loss and percentage loss in the driving force due to pressure build-up, and it was found that percentage loss in driving force is less at high feed pressures than that at low feed pressures. It is true that unsteady state cyclic permeation process can potentially compete with the most selective polymers available to date, both in terms selectivity and productivity. A novel process mode of gas separation by means of cyclic pressure-vacuum swings for feed pressurization and permeate evacuation using a single pump was evaluated for CO2 separation from flue gas. Unlike transient permeation processes reported in the literature which were based on the differences in sorption uptake rates or desorption falloff rates, this process was based on the selective permeability of the membrane for separations. The process was analyzed to elucidate the working principle, and a parametric study was carried out to evaluate the effects of design and operating parameters on the separation performance. It was shown that improved separation efficiency (i.e., product purity and throughput) better than that of conventional steady-state permeation could be obtained by means of pressure-vacuum swing permeation. The effectiveness of membrane processes and feasibility of hybrid processes combining membrane permeation and conventional amine absorption process were investigated for post-combustion CO2 capture. Traditional MEA process uses a substantial amount of energy at the stripper reboiler when CO2 concentration increases. Several single stage and multi-stage membrane process configurations were simulated for a target design specification aiming at possible application in enhanced oil recovery. It was shown that membrane processes offer the lowest energy penalty for post-combustion CO2 capture and likely to expand as more and more CO2 selective membranes are developed. Membrane processes can save up to 20~45% energy compared to the stand-alone MEA capture processes. A comparison of energy perspective for the CO2 capture processes studied was drawn, and it was shown that the energy requirements of the hybrid processes are less than conventional MEA processes. The total energy penalty of the hybrid processes decreases as more and more CO2 is removed by the membranes.