Developing a flexible receiver, which can be reconfigured to multiple standards, is the key to solving the problem of embedding numerous and ever-changing functionalities in mobile handsets. Difficulty in efficiently reconfiguring analog blocks of a receiver chain to multiple standards calls for moving the ADC as close to the antenna as possible so that most of the processing is done in DSP. Different standards are sampled at different frequencies and a programmable anti-aliasing filtering is needed here. Windowed integration samplers have an inherent sinc filtering which creates nulls at multiples of fs. The attenuation provided by sinc filtering for a bandwidth B is directly proportional to the sampling frequency fs and, in order to meet the anti-aliasing specifications, a high sampling rate is needed. ADCs operating at such a high oversampling rate dissipate power for no good use. Hence, there is a need to develop a programmable discrete-time down-sampling circuit with high inherent anti-aliasing capabilities. Currently existing topologies use large numbers of switches and capacitors which occupy a lot of area.A novel technique in reducing die area on a discrete-time sinc2 ↓2 filter for charge sampling is proposed. An SNR comparison of the conventional and the proposed topology reveals that the new technique saves 25 percent die area occupied by the sampling capacitors of the filter. The proposed idea is also extended to implement higher downsampling factors and a greater percentage of area is saved as the down-sampling factor is increased. The proposed filter also has the topological advantage over previously reported works of allowing the designers to use active integration to charge the capacitance, which is critical in obtaining high linearity. A novel technique to implement a discrete-time sinc3 ↓2 filter for windowed integration samplers is also proposed. The topology reduces the idle time of the integration capacitors at the expense of a small complexity overhead in the clock generation, thereby saving 33 percent of the die area on the capacitors compared to the currently existing topology. Circuit Level simulations in 45 nm CMOS technlogy show a good agreement with the predicted behaviour obtained from the analaysis.

The dissertation addresses direct adaptive control frameworks for Lyapunov stabilization of the MIMO nonlinear uncertain systems for both uncertain discrete-time and continuous-time systems. For system theory, the development of continuous-time theory always comes along with its discrete-time counterpart. However, for direct adaptive control frameworks we find relative few Lyapunov-based results published, which is mainly due to difficulty to find feasible Lyapunov candidates and to prove negative definiteness of the Lyapunov difference. Furthermore, digital computer is widely used in all fields. Most of time, we have to deal with the direct source of discrete-time signals, even the discrete-time signals arise from continuous-time settings as results of measurement or data collection process. These motivate our study in this field. For discrete-time systems, we have investigated the results with trajectory dependent hypothesis, where the Lyapunov candidate function V combines the information from the current state k and one step ahead k-1 along the track x(k), for k≥0. The proposed frameworks guarantee partial stability of the closed-loop systems, such that the feedback gains stabilize the closed-loop system without the knowledge of the system parameters. In addition, our results show that the adaptive feedback laws can be characterized by Kronecker calculus. Later, we release this trajectory dependent hypothesis for normal discrete-time nonlinear systems. At the same time, the continuous-time cases are also studied when system with matched disturbances, where the disturbances can be characterized by known continuous function matrix and unknown parameters. Here, the trajectory dependent Lyapunov candidates (tdLC), so long as the time step |t(k)-t(k-1) | ≤ £_ and the corresponding track |x(k)-x(k-1)| ≤ £` are sufficiently small, only exist in discrete-time case. In addition, we have extended the above control designs to systems with exogenous disturbances and £d2 disturbances. Finally, we develop a robust direct adaptive control framework for linear uncertain MIMO systems under the variance of unknow system matrix from given stable solution is bounded, that is |A-Ac| ¡Ý |B Kg| ≤ |£GA|. In general, through Lyapunov-based design we can obtain the global solutions and direct adaptive control design can simultaneously achieve parameter estimation and closed-loop stability.

In this thesis, we consider a discrete time/cost trade-off problem with a single nonrenewable resource. We assume the resource is released at some prespecified time points and at some prespecified quantities. We also assume that the costs due to the activities are incurred at their completions. Our aim is to minimize total project completion time. We formulate the problem as a pure integer programming model. We show that the problem is strongly NP-hard. We find lower bounds by pure linear programming and mixed integer linear programming relaxations of the model. We develop three heuristic procedures using the optimal solutions of mixed integer linear program and pure linear program. The results of our computational study reveal the satisfactory performance of our heuristic procedures.

This thesis presents strict LMI conditions for the bounded real lemma of discrete descriptor systems. Compared with existing nonstrict LMI conditions, the proposed new conditions are more tractable and reliable in numerical computations, in the sense that they can be tested easily by using the LMI Control Toolbox of Matlab. Based on the strict LMI conditions, the state feedback design for H-infinite control problem is also addressed. A sufficient LMI condition is derived so that the constructed feedback gain matrix from its solution will meet the design criteria of the closed-loop systems. Furthermore, we can probe into the problems of robust H-infinite control and pole-clustering in a disk for uncertain discrete descriptor systems subject to time-invariant norm-bounded uncertainty and convex polytopic uncertainty in the state matrix, respectively. Some sufficient LMI conditions are derived for analysis and design of these problems as well. Numerical examples are included to illustrate the results.

Multi-core architectures are becoming more common and core counts continue to increase. There are six- and eight-core chips currently in production, such as Intel Gulftown, and many-core chips with dozens of cores, such as the Intel Teraflops 80-core chip, are projected in the next five years. However, adding more cores often does not improve the performance of applications. It would be desirable to take advantage of the multi-core environment to speed up parallel discrete event simulation. The current bottleneck for many parallel simulations is time synchronization. This is especially true for simulations of wireless networks and on-chip networks, which have low lookahead. Message passing is also a common simulation bottleneck. In order to address the issue of time synchronization, we have designed hardware at a functional level that performs the time synchronization for parallel discrete event simulation asynchronously and in just a few clock cycles, eliminating the need for global communication with message passing or lock contention for shared memory. This hardware, the Global Synchronization Unit, consists of 3 register files, each the size of the number of cores, and is accessed using 5 new atomic instructions. In order to reduce the simulation overhead from message passing, we have also designed two independent pieces of hardware at a functional level, the Atomic Shared Heap and Atomic Message Passing, which can be used to perform lock-free, zero-copy message passing on a multi-core system. The impact of these specialized hardware units on the performance of parallel discrete event simulation is assessed and compared to traditional shared-memory techniques.

The US Navy is shifting to power projection from the sea which stresses the capabilities of its current fleet and exposes a need for a new surface connector. The design of complex systems in the presence of changing requirements, rapidly evolving technologies, and operational uncertainty continues to be a challenge. Furthermore, the design of future naval platforms must take into account the interoperability of a variety of heterogeneous systems and their role in a larger system-of-systems context. To date, methodologies to address these complex interactions and optimize the system at the macro-level have lacked a clear direction and structure and have largely been conducted in an ad-hoc fashion. Traditional optimization has centered around individual vehicles with little regard for the impact on the overall system. A key enabler in designing a future connector is the ability to rapidly analyze technologies and perform trade studies using a system-of-systems level approach. The objective of this work is a process that can quantitatively assess the impacts of new capabilities and vessels at the systems-of-systems level. This new methodology must be able to investigate diverse, disruptive technologies acting on multiple elements within the system-of-systems architecture. Illustrated through a test case for a Medium Exploratory Connector (MEC), the method must be capable of capturing the complex interactions between elements and the architecture and must be able to assess the impacts of new systems). Following a review of current methods, six gaps were identified, including the need to break the problem into subproblems in order to incorporate a heterogeneous, interacting fleet, dynamic loading, and dynamic routing. For the robust selection of design requirements, analysis must be performed across multiple scenarios, which requires the method to include parametric scenario definition. The identified gaps are investigated and methods recommended to address these gaps to enable overall operational analysis across scenarios. Scenarios are fully defined by a scheduled set of demands, distances between locations, and physical characteristics that can be treated as input variables. Introducing matrix manipulation into discrete event simulations enables the abstraction of sub-processes at an object level and reduces the effort required to integrate new assets. Incorporating these linear algebra principles enables resource management for individual elements and abstraction of decision processes. Although the run time is slightly greater than traditional if-then formulations, the gain in data handling abilities enables the abstraction of loading and routing algorithms. The loading and routing problems are abstracted and solution options are developed and compared. Realistic loading of vessels and other assets is needed to capture the cargo delivery capability of the modeled mission. The dynamic loading algorithm is based on the traditional knapsack formulation where a linear program is formulated using the lift and area of the connector as constraints. The schedule of demands from the scenarios represents additional constraints and the reward equation. Cargo available is distributed between cargo sources thus an assignment problem formulation is added to the linear program, requiring the cargo selected to load on a single connector to be available from a single load point. Dynamic routing allows a reconfigurable supply chain to maintain a robust and flexible operation in response to changing customer demands and operating environment. Algorithms based on vehicle routing and computer packet routing are compared across five operational scenarios, testing the algorithms ability to route connectors without introducing additional wait time. Predicting the wait times of interfaces based on connectors en route and incorporating reconsideration of interface to use upon arrival performed consistently, especially when stochastic load times are introduced, is expandable to a large scale application. This algorithm selects the quickest load-unload location pairing based on the connectors routed to those locations and the interfaces selected for those connectors. A future connector could have the ability to unload at multiple locations if a single load exceeds the demand at an unload location. The capability for multiple unload locations is considered a special case in the calculation of the unload location in the routing. To determine the unload location to visit, a traveling salesman formulation is added to the dynamic loading algorithm. Using the cost to travel and unload at locations balanced against the additional cargo that could be delivered, the order and locations to visit are selected. Predicting the workload at load and unload locations to route vessels with reconsideration to handle disturbances can include multiple unload locations and creates a robust and flexible routing algorithm. The incorporation of matrix manipulation, dynamic loading, and dynamic routing enables the robust investigation of the design requirements for a new connector. The robust process will use shortfall, capturing the delay and lack of cargo delivered, and fuel usage as measures of performance. The design parameters for the MEC, including the number available and vessel characteristics such as speed and size were analyzed across four ways of testing the noise space. The four testing methods are: a single scenario, a selected number of scenarios, full coverage of the noise space, and feasible noise space. The feasible noise space is defined using uncertainty around scenarios of interest. The number available, maximum lift, maximum area, and SES speed were consistently design drivers. There was a trade-off in the number available and size along with speed. When looking at the feasible space, the relationship between size and number available was strong enough to reverse the number available, to desiring fewer and larger ships. The secondary design impacts come from factors that directly impacted the time per trip, such as the time between repairs and time to repair. As the noise sampling moved from four scenario to full coverage to feasible space, the option to use interfaces were replaced with the time to load at these locations and the time to unload at the beach gained importance. The change in impact can be attributed to the reduction in the number of needed trips with the feasible space. The four scenarios had higher average demand than the feasible space sampling, leading to loading options being more important. The selection of the noise sampling had an impact of the design requirements selected for the MEC, indicating the importance of developing a method to investigate the future Naval assets across multiple scenarios at a system-of-systems level.

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This thesis studies the indirect adaptive control for discrete linear time invariant systems. The adaptive control strategy is based on the linear quadratic regulator that places the closed loop poles such that an infinite stage quadratic cost function is minimized. The plant parameters are identified recursively using a projection algorithm. / First, we study the effect of the model over-parametrization. For this purpose, we introduce an algorithm to generate the controller parameters recursively. This asymptotic reformulation is shown to overcome situations in which the pole-zero cancellation is a limit point of the identification algorithm. We also show that the algorithm will generate a unique control sequence that converges asymptotically to the solution of the Diophantine (pole assignment) equation. / Next, we study the stability of the proposed adaptive scheme in both deterministic and stochastic cases. We show that the global stability of the resulting adaptive scheme is obtained with no implicit assumptions about parameter convergence or the nature of the external input. Then the global convergence of the adaptive algorithm is obtained if the external input is "persistently exciting". By convergence we mean that the adaptive control will converge to the optimal control of the system. / The performance of the adaptive algorithm in the presence of deterministic disturbances is also considered, where we show that the adaptive controller performs relatively well if the model order is high enough to include a description of the disturbances.

This thesis focuses on decentralized deadbeat output regulation of discrete-time nonlinear plants that are composed of multiple agents. These agents interact, via scalar-valued signals, in a known structured way represented with a graph. This work is motivated by applications where it is infeasible and/or undesirable to introduce control action within each plant agent; instead, control agents are introduced to interact with certain plant agents, where each control agent focuses on regulating a specific plant agent, called its target. Then, two analyses are carried out to determine if regulation is achieved: targeting analysis is used to determine if control laws can be found to regulate all target agents, then growing analysis is used to determine the effect of those control laws on non-target plant agents. The strength of this novel approach is the intuitively-appealing notion of each control agent focusing on the regulation of just one plant agent. This work goes beyond previous research by generalizing the class of allowable plant dynamics, considering not only arbitrary propagation times through plant agents, but also allowing for non-symmetrical influence between the agents. Moreover, new necessary and sufficient algebraic conditions are derived to determine when targeting succeeds. The main contribution of this work, however, is the development of new easily-verifiable conditions necessary for targeting and/or growing to succeed. These new conditions are valuable due to their simplicity and scalability to large systems. They concern the positioning of control agents and targets as well as the propagation time of signals through the plant, and they help significantly with design decisions. Various graph structures (such as queues, grids, spiders, rings, etc.) are considered and for each, these conditions are used to develop a control scheme with the minimum number of control agents needed.

In this study, we present the implementation and experimental analysis of a modeling approach for analyzing tactical level land combat to generate information for weapon and ammunition planning. The discrete-time stochastic model (DSM), which can handle small and moderately large force levels, is based on single shot kill probabilities. Forces are assumed to be heterogeneous on both sides, and both directed and area fire types are modeled by means of combinatorial analysis. DSM considers overkills and can handle noncombat loss and engagement processes, discrete reinforcements, force combinations and divisions. In addition to experimenting with DSM, we estimate attrition rate coefficients used in Lanchester combat models, such that the two models will yield similar figures for force levels throughout the combat.