Multimedia applications such as video streaming and Voice over IP are becoming common today with the tremendous growth of the Internet. General purpose operating systems thus are required to support these applications. These multimedia applications have some timing constraints that need to be satisfied for good quality. For example, video streaming applications require that each video frame be decoded in time to be displayed every 33.3 milliseconds. In order to satisfy these timing requirements, general purpose operating systems need to have fine-grained scheduling. Current general purpose operating systems unfortunately are designed to maximize throughput to serve traditional data-oriented applications and have coarse-grained scheduling and timers. Time Sensitive Linux (TSL), designed by Goel, et al., solves this problem with fine-grained timers and schedulers. The scheduler for TSL is implemented at a very low level. The controller that implements the algorithm for resource allocation is implemented at a higher level. This controller can easily be modified to implement new control algorithms. Successful implementation of resource allocation to satisfy timing constraints of multimedia applications requires two problems to be addressed. First, the resources required by the application to satisfy the timing constraints should not exceed the total available resources in the system. Second, the controller must adapt to changing needs of the applications and allocate enough resources to satisfy the timing constraints of each application over time. The first problem has been addressed elsewhere using intelligent data dropping with TSL. We focus on the second problem in this thesis. We design a proportion-period controller in this thesis for allocating CPU to multimedia video applications with timing constraints. The challenges for the controller design include the coarse granularity of the time-stamp markings of the video frames, the unpredictable decoding completion times of the frames, the large variations in the decoding times of the frames, and the limit of the control actuation to positive values. We set up the problem in a state space. We design a predictive estimating controller to allocate the proportion of the CPU to a thread when its long term error is small. When the decoding process is running behind by more than a certain threshold, we switch to a different controller to drive the error back to a small value. This controller is the solution to a dynamic optimization LQR tracking problem. / Graduation date: 2003

Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications. / Graduation date: 1999

Thesis (M.Eng.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 107-116.

Precise machining of bearing rings is integral to finished bearing assembly quality. The output accuracy of center-based machining systems such as lathes or magnetic chuck grinders relates directly to the accuracy of part centering before machining. Traditional tooling and methods for centering on such machines are subject to wear, dimensional inaccuracy, setup time (hard tooling) and human error (manual centering).A flexible system for initial part centering is developed based on a single measurement system and actuator, whereby the part is placed by hand onto the machine table, rotated and measured to identify center of geometry offset from center of rotation, then moved by a series of controlled impacts or pushes to align the centers. The prototype centering system is developed as a demonstration platform for research in a number of mechanical engineering areas, particularly: Characterization of optimal state estimators through analysis of accuracy and computational efficiency; Distributed communication and control, efficient transfer of information in a real-time environment, and information sharing between processes; Modeling of sliding dynamics and the interaction of friction with compliant body dynamic models; Motion path planning through both deterministic geometric transforms and through frequency domain command manipulation.A vision is created for future work not only in the described areas, but also in the areas of advanced controller design incorporating multiple variables, derived machine diagnostic information, and application of the distributed communication architecture to information flow throughout the manufacturing organization. The guiding motivation for this research is reduction of manufacturing processing costs in the face of global competition. The technologies researched, developments made, and directions prescribed for future research aid in enabling this goal.

Combustion instabilities are a significant problem in combustion systems, particularly in Low NOx Gas Turbine combustors. These instabilities result in large-scale pressure oscillations in the combustor, leading to degraded combustor performance, shortened lifetime, and catastrophic combustor failure. The objective of this research was to develop a practical adaptive active control system that, coupled with an appropriate actuator, is capable of controlling the combustor pressure oscillations without a priori knowledge of the combustor design, operating conditions or instability characteristics. The adaptive controller utilizes an observer that determines the frequencies, phases and amplitudes of the dominant modes of the oscillations in real time. The research included development and testing of the adaptive controller on several combustors and on an unstable acoustic feedback system in order to analyze its performance. The research also included investigations of combustor controllability and combustor stability margin, which are critical issues for practical implementation of an active control system on an industrial combustor. The results of this research are directly applicable to a variety of combustors and can be implemented on full-scale industrial combustion systems.

Brains display very high-level parallel computation, fault-tolerance, and adaptability, all of which are what we struggle to recreate in engineered systems. The neurocomputer (an organic computer built from living neurons) seems possible and may lead to a new generation of computing device that can operate in a brain-like manner. Cultured neuronal networks on multi-electrode arrays (MEAs) are one of the best candidates for the neurocomputer for their controllability, accessibility, flexibility, and the ability to self-organize. I explored the possibility of the neurocomputer by studying whether we can show goal-directed learning, one of the most fascinating behavior of brains, in cultured networks. Inspired by the brain, which needs to be embodied in some way and interact with its surroundings in order to give a purpose to its activities, we have developed tools for closing the sensory-motor loop between a cultured network and a robot or an artificial animal (an animat), termed a ¡§hybrot¡¨. In order to efficiently find an effective closed-loop design among infinite potential options, I constructed a biologically-inspired simulated network. By using this simulated network, I designed: (1) a statistic that can effectively and efficiently decode network functional plasticity, and (2) feedback stimulations and an adaptive training algorithm to encode sensory information and to direct network plasticity. By closing the sensory-motor loop with these decoding and encoding designs, we successfully demonstrated a simple adaptive goal-directed behavior: learning to move in a user-defined direction, and further showed that multiple tasks could be learned simultaneously. These results suggest that even though a cultured network lacks the 3-D structure of the brain, it still can be functionally shaped and show meaningful behavior. To our knowledge, this is the first demonstration of goal-directed learning in embodied cultured networks. Extending from these findings, I further proposed a research plan to optimize closed-loop designs for evaluating the maximal learning capacity (or even true intelligence) of the cultured network. Knowledge gained from effective closed-loop designs provides insights about learning and memory in the nervous system, which could influence the design of neurocomputers, future artificial neural networks, and more effective neuroprosthetics.

This dissertation presents novel adaptive control laws in both state feedback and output feedback forms. In the setting of state feedback adaptive control K-modification provides a tunable stiffness term that results in a frequency dependent filtering effect, smoother transient responses, and time delay robustness in an adaptive system. K-modification is combined with the recently developed Kalman filter (KF) based adaptive control and derivative-free (DF) adaptive control. K-modification and its combinations with KF adaptive control and DF adaptive control preserve the advantages of each of these methods and can also be combined with other modification methods such as - and e-modification. An adaptive output feedback control law based on a state observer is also developed. The main idea behind this approach is to apply a parameter dependent Riccati equation to output feedback adaptive control. The adaptive output feedback approach assumes that a state observer is employed in the nominal controller design. The observer design is modified and employed in the adaptive part of the design in place of a reference model. This is combined with a novel adaptive weight update law. The weight update law ensures that estimated states follow both the reference model states and the true states so that both state estimation errors and state tracking errors are bounded. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controller uses the observer of the nominal controller in place of the reference model to generate an error signal. Thus the only components that are added by the adaptive controller are the realizations of the basis functions and the weight adaptation law. The realization is even less complex than that of implementing a model reference adaptive controller in the case of state feedback. The design procedure of output feedback adaptive control is illustrated with two examples: a simple wingrock dynamics model and a more complex aeroelastic aircraft transport model.

Fluid power technology has been widely used in industrial practice; however, its energy efficiency became a big concern in the recent years. Much progress has been made to improve fluid power energy efficiency from many aspects. Among these approaches, using a valve-less system to replace a traditional valve-controlled system showed eminent energy reduction. This thesis studies the valve-less solution-pump displacement controlled actuators- from the view of controls background. Singular perturbations have been applied to the fluid power to account for fluid stiffness; and a novel hydraulic circuit for single rod cylinder has been presented to increase the hydraulic circuit stabilities. Recursive Least Squares has been applied to account for measurement noise thus the parameters have fast convergence rate, square root algorithm has further applied to increase the controller's numerical stability and efficiency. It was showed that this technique is consistent with other techniques to increase controller's robustness. The developed algorithm is further extended to a hybrid adaptive control scheme to achieve desired trajectory tracking for general cases. A hardware test-bed using the invented hydraulic circuit was built up. The experimental results are presents and validated the proposed algorithms and the circuit itself. The end goal of this project is to develop control algorithms and hydraulic circuit suitable for industrial practice.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes MatLab code. Includes bibliography (p.43-44).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 125-133). Also available on the Internet.