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 machining with internally cooled smart cutting tools is a smart solution for industrial applications, which have stringent manufacturing requirements such as contamination free machining (CFM), high material removal rate, low tool wear and better surface integrity. The absence of cutting fluid in CFM causes the cutting tool and the workpiece subject to great thermal loads owing to higher friction and adhesion, and as a result may increase the levels of tool wear drastically. The increase in cutting temperature may influence the chip morphology which in return producing metal chips in unfavourable ribbon or snarl forms. CFM is difficult to be realized as contaminants can be in various forms in the machining operation and to avoid them totally requires a very tight controlled condition. However, the ecological, economical and technological demands compel the manufacturing practitioners to implement environmentally clean machining process (ECMP). Machining with innovative cooling techniques such as heat pipe, single-phase microduct, cryogenic or minimum quantity lubrication (MQL) has been intensely researched in recent years in order to reduce the cutting temperature in ECMP, thus enabling the part quality, the tool life and the material removal rate achieved in ECMP at least equate or surpass those obtained in conventional machining. On the other hand, the reduction of cutting temperature by using these techniques is often superfluous and is adverse to the produced surface roughness as the work material tends to inherent brittle and hard property at low temperature. Open cooling system means the machining requires a constant cooling supply and it does not provide a solution for process condition feedback as well.This Ph.D. project aims to investigate the design and analysis of internally cooled cutting tools and their implementation and application perspectives for smart adaptive machining in particular. Circulating the water based cooling fluid in a closed loop circuit contributes to sustainable manufacturing. The advantage of reducing cutting temperature from localized heat at the tool tip of an internally cooled cutting tool is enhanced with the smart features of the tool, which is trained by real experimental data, to cognitively vary the coolant flow rate, cutting feed rate or/and cutting speed to control the critical machining temperature as well as optimum machining conditions. Environmental friendly internal micro-cooling can avoid contamination of generated swarf which can also reduce the cutting temperature and thus reduce tool wear, increase machining accuracy and optimize machining economics. Design of the smart cutting tool with internal micro-cooling not only takes into account of the environmental aspects but also justifies with its ability to reduce the machining cost. Reduction of production cost can be achieved with the lower consumption of cooling fluid and improved machining resources/ energy efficiency. The models of structural, heat transfer, computational fluid dynamics (CFD) and tool life provide useful insight of the performance of the internally cooled smart cutting tool. Experimental validation using the smart cutting tool to machine titanium, steel and aluminium, indicates that the application of internally cooled smart cutting tools in adaptive machining can improve machining performance such as cutting temperature, cutting forces and surface quality generated. The useful tool life span is also extended significantly with internally cooled smart cutting tools in comparison to the tool life in conventional machining. The internally cooled smart cutting tool has important implications in the application to ECMP particularly by overcoming the stigma of high uncontrollable cutting temperature with the absence of cooling fluid.

Το ενεργειακό πρόβλημα αποτελεί ένα από τα πιο πολυσηζητημένα θέματα της ανθρωπότητας που απασχολεί και θα συνεχίσει να απασχολεί τον πλανήτη μας για τις επόμενες δεκαετίες. Μια λύση στο πρόβλημα της ηλεκτρικής ενέργειας είναι η εγκατάσταση και λειτουργία μικροδικτύων είτε σε οικιακή κλίμακα είτε σε επίπεδο εθνικού δικτύου ή ακόμα και σε επίπεδο μιας ολόκληρης ηπείρου. Για την επιτυχή και ευσταθή λειτουργία τέτοιων συστημάτων, κρίνεται απαραίτητος ο έλεγχος των επιμέρους στοιχείων τους ξεχωριστά. Στην παρούσα διπλωματική εργασία μοντελοποιήθηκαν και ελέγθηκαν δύο ηλεκτρονικοί μετατροπείς ισχύος, ο TCSC και ο BOOST, καθώς και η τριφασική ασύγχρονη μηχανή βραχυχκυκλωμένου κλωβού, κυρίαρχα στοιχεία ενός μικροδικτύου. Τα καταστατικά μοντέλα όμως, τόσο των μετατροπέων ισχύος όσο και της μηχανής περιγράφονται από μη γραμμικές διαφορικές εξισώσεις και συνεπώς ο έλεγχος τους δεν είναι καθόλου εύκολη υπόθεση. Συγκεκριμένα, χρησιμοποιήθηκαν μοντέρνες τεχνικές μη γραμμικού και προσαρμοστικού ελέγχου προκειμένου να ελεγχθούν τα συστήματα αυτά και να εξασφαλιστεί κάθε φορά η ευστάθεια του συστήματος στη μόνιμη κατάσταση λειτουργίας. Για τους μετατροπείς TCSC και BOOST σχεδιάστηκαν απευθείας οι μη γραμμικοί - προσαρμοστικοί ελεγκτές ενώ για την τριφασική ασύγχρονη μηχανή εφαρμόστηκε πρώτα έμμεσος διανυσματικός έλεγχος στο στατό πλαίσιο αναφοράς και στη συνέχεια σχεδιάστηκαν οι ελεγκτές και οι εκτιμητές της ροής, της ροπής και της αντίστασης. Τέλος όλα τα συστήματα προσομοιώθηκαν με τη χρήση του εργαλείου Simulink του Matlab και εξήχθησαν τα συμπεράσματα. / The energy problem is one of the most spoken issues of humanity that concerns and will continue to concern our planet for decades to come. One solution to the electrical energy problem is the installation and operation of microgrids either at household level or at the level of a national network or even at the level of an entire continent. For successful and stable operation of such systems, it is necessary to test their components separately. In this Thesis there have been modeled and tested two electronic power converters, the TCSC and BOOST, and the three-phase asynchronous machine of shortcircuited cage, dominant elements of a microgrid. The constitutive models, however, both the power converters and the machine are described by nonlinear differential equations and, therefore, their control is not at all easy. Specifically, modern techniques of nonlinear and adaptive control were used in order to check these systems so that the stability of the system in the steady state be ensured. For the inverters TCSC and BOOST the nonlinear - adaptive controllers were designed directly, while for the three-phase asynchronous machine there was applied firstly the indirect vector control in the stationary frame of reference and then the controllers and the flow, torque and resistance estimators were designed. Finally, all the systems were simulated using the Matlab Simulink tool and conclusions were drawn.

It is well known that typical welding processes such as laser welding are nonlinear although mostly they are treated as linear system. For the purpose of automatic control, Identification of nonlinear system, especially welding processes is a necessary and fundamental problem. The purpose of this research is to develop a simple and practical identification and control for welding processes. Many investigations have shown the possibility to represent physical processes by nonlinear models, such as Hammerstein structure, consisting of a nonlinearity and linear dynamics in series with each other. Motivated by the fact that typical welding processes do not have non-zeroes, a novel two-step nonlinear Hammerstein identification method is proposed for laser welding processes. The method can be realized both in continuous and discrete case. To study the relation among parameters influencing laser processing, a standard diode laser processing system is built as system prototype. Based on experimental study, a SISO and 2ISO nonlinear Hammerstein model structure are developed to approximate the diode laser welding process. Specific persistent excitation signals such as PRTS (Pseudo-random-ternary-series) to Step signal are used for identification. The model takes welding speed as input and the top surface molten weld pool width as output. A vision based sensor implemented with a Pulse-controlled-CCD camera is proposed and applied to acquire the images and the geometric data of the weld pool. The estimated model is then verified by comparing the simulation and experimental measurement. The verification shows that the model is reasonably correct and can be use to model the nonlinear process for further study. The two-step nonlinear identification method is proved valid and applicable to traditional welding processes and similar manufacturing processes. Based on the identified model, nonlinear control algorithms are also studied. Algorithms include simple linearization and backstepping based robust adaptive control algorithm are proposed and simulated.

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.

High fidelity, compliant robot control requires a sufficiently accurate dynamics model. Often though, it is not possible to obtain a dynamics model sufficiently accurately or at all using analytical methods. In such cases, an alternative is to learn the dynamics model from movement data. This thesis discusses the problems specific to dynamics learning for control under nonstationarity of the dynamics. We refer to the cause of the nonstationarity as the context of the dynamics. Contexts are, typically, not directly observable. For instance, the dynamics of a robot manipulator changes as the robot manipulates different objects and the physical properties of the load – the context of the dynamics – are not directly known by the controller. Other examples of contexts that affect the dynamics are changing force fields or liquids with different viscosity in which a manipulator has to operate. The learned dynamics model needs to be adapted whenever the context and therefore the dynamics changes. Inevitably, performance drops during the period of adaptation. The goal of this work, is to reuse and generalize the experience obtained by learning the dynamics of different contexts in order to adapt to changing contexts fast. We first examine the case that the dynamics may switch between a discrete, finite set of contexts and use multiple models and switching between them to adapt the controller fast. A probabilistic formulation of multiple models is used, where a discrete latent variable is used to represent the unobserved context and index the models. In comparison to previous multiple model approaches, the developed method is able to learn multiple models of nonlinear dynamics, using an appropriately modified EM algorithm. We also deal with the case when there exists a continuum of possible contexts that affect the dynamics and hence, it becomes essential to generalize from a set of experienced contexts to novel contexts. There is very little previous work on this direction and the developed methods are completely novel. We introduce a set of continuous latent variables to represent context and introduce a dynamics model that depends on this set of variables. We first examine learning and inference in such a model when there is strong prior knowledge on the relationship of these continuous latent variables to the modulation of the dynamics, e.g., when the load at the end effector changes. We also develop methods for the case that there is no such knowledge available. Finally, we formulate a dynamics model whose input is augmented with observed variables that convey contextual information indirectly, e.g., the information from tactile sensors at the interface between the load and the arm. This approach also allows generalization to not previously seen contexts and is applicable when the nature of the context is not known. In addition, we show that use of such a model is possible even when special sensory input is not available by using an instance of an autoregressive model. The developed methods are tested on realistic, full physics simulations of robot arm systems including a simplistic 3 degree of freedom (DOF) arm and a simulation of the 7 DOF DLR light weight robot arm. In the experiments, varying contexts are different manipulated objects. Nevertheless, the developed methods (with the exception of the methods that require prior knowledge on the relationship of the context to the modulation of the dynamics) are more generally applicable and could be used to deal with different context variation scenarios.

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

by John Tak Kuen Koo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 217-[220]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2 / Chapter 1.3 --- Adaptive Fuzzy Control --- p.4 / Chapter 1.4 --- Object of Study --- p.10 / Chapter 1.5 --- Scope of the Thesis --- p.13 / Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17 / Chapter 2.1 --- Adaptive control --- p.17 / Chapter 2.1.1 --- Model reference adaptive systems --- p.20 / Chapter 2.1.2 --- MIT Rule --- p.23 / Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24 / Chapter 2.2 --- Fuzzy Logic Control --- p.33 / Chapter 2.2.1 --- Fuzzy sets and logic --- p.33 / Chapter 2.2.2 --- Fuzzy Relation --- p.40 / Chapter 2.2.3 --- Inference Mechanisms --- p.43 / Chapter 2.2.4 --- Defuzzification --- p.49 / Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51 / Chapter 3.1 --- Introduction --- p.51 / Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53 / Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57 / Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65 / Chapter 3.5 --- B-Spline fuzzy controller --- p.68 / Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75 / Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79 / Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84 / Chapter 4.5 --- Simulation --- p.90 / Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97 / Chapter 5.1 --- Introduction --- p.98 / Chapter 5.2 --- Choice of Controller --- p.99 / Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102 / Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109 / Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112 / Chapter 6.1 --- Introduction --- p.113 / Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114 / Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118 / Chapter 6.4 --- Input-Output Linearization --- p.130 / Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132 / Chapter 6.6 --- Example --- p.136 / Chapter 7 --- Analysis of MRAFC System --- p.140 / Chapter 7.1 --- Averaging technique --- p.140 / Chapter 7.2 --- Parameter convergence --- p.143 / Chapter 7.3 --- Robustness --- p.152 / Chapter 7.4 --- Simulation --- p.157 / Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166 / Chapter 8.1 --- Introduction --- p.166 / Chapter 8.2 --- Robot Manipulator Control --- p.170 / Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173 / Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174 / Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182 / Chapter 8.4 --- Simulation --- p.186 / Chapter 9 --- Conclusion --- p.199 / Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203 / Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203 / Chapter A.2 --- Implementation Considerations --- p.211 / Chapter A.3 --- MRAFC System Design Procedure --- p.215 / Bibliography --- p.217

by Wan, Kwok Fai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 138-148). / Chapter Chapter 1. --- Adaptive signal processing and its applications --- p.1 / Chapter 1.1. --- Introduction --- p.1 / Chapter 1.2. --- Applications of adaptive system --- p.3 / Chapter 1.2.1. --- Adaptive noise cancellation --- p.3 / Chapter 1.2.2. --- Adaptive echo cancellation --- p.5 / Chapter 1.2.3. --- Adaptive line enhancement --- p.5 / Chapter 1.2.4. --- Adaptive linear prediction --- p.7 / Chapter 1.2.5. --- Adaptive system identification --- p.8 / Chapter 1.3. --- Algorithms for adaptive systems --- p.10 / Chapter 1.4. --- Transform domain adaptive filtering --- p.12 / Chapter 1.5 --- The motivation and organization of the thesis --- p.13 / Chapter Chapter 2. --- Time domain split-path adaptive filter --- p.16 / Chapter 2.1. --- Adaptive transversal filter and the LMS algorithm --- p.17 / Chapter 2.1.1. --- Wiener-Hopf solution --- p.17 / Chapter 2.1.2. --- The LMS adaptive algorithm --- p.20 / Chapter 2.2. --- Split structure adaptive filtering --- p.23 / Chapter 2.2.1. --- Split structure of an adaptive filter --- p.24 / Chapter 2.2.2. --- Split-path structure for a non-symmetric adaptive filter --- p.25 / Chapter 2.3. --- Split-path adaptive median filtering --- p.29 / Chapter 2.3.1. --- Median filtering and median LMS algorithm --- p.29 / Chapter 2.3.2. --- The split-path median LMS (SPMLMS) algorithm --- p.32 / Chapter 2.3.3. --- Convergence analysis of SPMLMS --- p.36 / Chapter 2.4. --- Computer simulation examples --- p.41 / Chapter 2.5. --- Summary --- p.45 / Chapter Chapter 3. --- Multi-stage split structure adaptive filtering --- p.46 / Chapter 3.1. --- Introduction --- p.46 / Chapter 3.2. --- Split structure for a symmetric or an anti-symmetric adaptive filter --- p.48 / Chapter 3.3. --- Multi-stage split structure for an FIR adaptive filter --- p.56 / Chapter 3.4. --- Properties of the split structure LMS algorithm --- p.59 / Chapter 3.5. --- Full split-path adaptive algorithm for system identification --- p.66 / Chapter 3.6. --- Summary --- p.71 / Chapter Chapter 4. --- Transform domain split-path adaptive algorithms --- p.72 / Chapter 4.1. --- Introduction --- p.73 / Chapter 4.2. --- general description of transforms --- p.74 / Chapter 4.2.1. --- Fast Karhunen-Loeve transform --- p.75 / Chapter 4.2.2. --- Symmetric cosine transform --- p.77 / Chapter 4.2.3. --- Discrete sine transform --- p.77 / Chapter 4.2.4. --- Discrete cosine transform --- p.78 / Chapter 4.2.5. --- Discrete Hartley transform --- p.78 / Chapter 4.2.6. --- Discrete Walsh transform --- p.79 / Chapter 4.3. --- Transform domain adaptive filters --- p.80 / Chapter 4.3.1. --- Structure of transform domain adaptive filters --- p.80 / Chapter 4.3.2. --- Properties of transform domain adaptive filters --- p.83 / Chapter 4.4. --- Transform domain split-path LMS adaptive predictor --- p.84 / Chapter 4.5. --- Performance analysis of the TRSPAF --- p.93 / Chapter 4.5.1. --- Optimum Wiener solution --- p.93 / Chapter 4.5.2. --- Steady state MSE and convergence speed --- p.94 / Chapter 4.6. --- Computer simulation examples --- p.96 / Chapter 4.7. --- Summary --- p.100 / Chapter Chapter 5. --- Tracking optimal convergence factor for transform domain split-path adaptive algorithm --- p.101 / Chapter 5.1. --- Introduction --- p.102 / Chapter 5.2. --- The optimal convergence factors of TRSPAF --- p.104 / Chapter 5.3. --- Tracking optimal convergence factors for TRSPAF --- p.110 / Chapter 5.3.1. --- Tracking optimal convergence factor for gradient-based algorithms --- p.111 / Chapter 5.3.2. --- Tracking optimal convergence factors for LMS algorithm --- p.112 / Chapter 5.4. --- Comparison of optimal convergence factor tracking method with self-orthogonalizing method --- p.114 / Chapter 5.5. --- Computer simulation results --- p.116 / Chapter 5.6. --- Summary --- p.121 / Chapter Chapter 6. --- A unification between split-path adaptive filtering and discrete Walsh transform adaptation --- p.122 / Chapter 6.1. --- Introduction --- p.122 / Chapter 6.2. --- A new ordering of the Walsh functions --- p.124 / Chapter 6.3. --- Relationship between SM-ordered Walsh function and other Walsh functions --- p.126 / Chapter 6.4. --- Computer simulation results --- p.132 / Chapter 6.5. --- Summary --- p.134 / Chapter Chapter 7. --- Conclusion --- p.135 / References --- p.138