This thesis describes a novel approach to model and estimate systems of unknown delay. The a-priori knowledge available about the systems is fully utilized so that the number of parameters to be estimated equals the number of unknowns in the systems. Existing methods represent the single unknown system delay by a large number of unknown parameters in the system model. The purpose of this thesis is to develop new methods of modelling the systems so that the unknowns are directly estimated. The Variable Regression Estimation technique is developed to provide direct delay estimation. The delay estimation requires minimum excitation and is robust, bounded, and it converges to the true value for first-order and second-order systems. The delay estimation provides a good model approximation for high-order systems and the model is always stable and matches the frequency response of the system at any given frequency. The new delay estimation method is coupled with the Pole Placement, Dahlin and the Generalized Predictive Controller (GPC) design and adaptive versions of these controllers result. The new adaptive GPC has the same closed-loop performance for different values of system delay. This was not achievable in the original adaptive GPC. The adaptive controllers with direct delay estimation can regulate systems with dominant time delay with minimum parameters in the controller and the system model. The delay does not lose identifiability in closed-loop estimation. Experiments on the delay estimation show excellent agreement with the theoretical analysis of the proposed methods. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

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M.Ing. (Mechanical Engineering) / This dissertation describes the performance of an Adaptive controller and a Neural Network controller for water level control in a steam drum, and compares them to the performance of a conventional PIO controller in the same application. The control problem is in essence the regulation of the speed of a boiler feed pump in order to maintain a constant level in the drum of a small model of a boiler. As a starting point, the hydraulics and dynamics of the system are analyzed and the system is shown to be nonlinear. A nonlinear computer simulation is created and its response is compared with that of the real plant. The simulation proves to be a close representation of the real plant and it is used as an aid in creating a linear mathematical model. A set of control specifications are drawn up and a PIO controller is designed for the plant. With the aid of a root locus diagram it is shown that the plant cannot be controlled within specifications under PID control. This shortcoming is then demonstrated on both the linear mathematical model and the nonlinear plant. Consequently, advanced control techniques are investigated in an attempt to control the plant within specifications. Different methods of adaptive control are discussed and a direct model reference adaptive controller is designed. The least squares algorithm for parameter adjustment is discarded in favour of the slower gradient algorithm when it becomes apparent that the wave motion inside the drum has an adverse effect on the former algorithm. The control results obtained with both the linear model and the real plant proves adaptive control to be superior to PIO control in this application. Additionally, the application of neural networks in control systems is discussed. An adaptive neural network controller is designed but is discarded due to instability caused by imperfect modelling of the system...

Vita. / Digitized by Kansas State University Libraries

Adaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.

Adaptive routing and flow control methods are necessary in computer networks due to the nonstationary conditions that exist in such networks. In this dissertation three distinct yet complementary approaches to modeling computer networks for performance evaluation and control under nonstationary conditions are presented namely: queueing theory, discrete event simulation, and state variable modeling. The application of these techniques to the design and performance evaluation of adaptive routing and flow control methods is demonstrated with the development of a new two-level hierarchical adaptive buffer management scheme and a dynamic virtual circuit routing policy.

This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection