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Power converters are electronic circuits for conversion, control and regulation of electric power for various applications, such as from tablet computers in milliwatts to electric power systems at megawatts range. There are three basic types of power converters: buck (output voltage less than the input voltage), boost (output voltage higher than the input voltage) and buck-boost converters. The reliability of the power converters has become an essential focus of industrial applications. This research presents modeling and control of DC/DC boost converter using several control methods, such as Proportional-Integral (PI), Linear Quadratic Regulator (LQR) control, and nonlinear control concepts. Based on standard circuit laws, a mathematical model of the boost converter is derived which is expressed as a bilinear system. First a small signal model of the converter is derived to analyze the small deviations around the steady-state operating point which is used to develop closed loop control using the PI and the LQR methods. Simulation results show that the performance of the converter is good for operation around the operating state, however is unacceptable if there are large variations in the load or the reference input. To improve the performance of the closed loop system, the nonlinear control concept is used which shows excellent closed loop performance under large variations of load or setpoint. Comparative simulation results are presented for closed loop performance under various types of disturbances including random variations in load. / Electrical and Computer Engineering

High performance current control is critical to the success of the switched reluctance motor (SRM). Yet high motor phase nonlinearities in the SRM place extra burden on the current controller, rendering it the weakest link in SRM control. In contrast to linear motor control techniques that respond to current error, the deadbeat controller calculates the control voltage by the current command, phase current, rotor position and applied phase voltage. The deadbeat controller has demonstrated superior response in three-phase inverter current control, PM motor current control, and other relatively linear control applications. This study will investigate the viability and performance of a deadbeat controller for the highly nonlinear SRM. The need for an accurate deadbeat control model first motivates the investigation of experimental inductance measurement techniques. A deadbeat control law is then proposed through multiple revisions to demonstrate the benefit of the numerical method chosen to derive the controller and a current predictor that accounts for processor latency and PWM delay. The practical problems of loop delay, feedback noise, feedback filtering, and deadbeat controller parameter sensitivity are investigated by linear analysis, simulation, experimental implementation and nonlinear model analysis. Simulation and implementation verify deadbeat performance and various measures of transient performance are presented. To address the problem of SRM model error the study ends with a brief discussion of adaptive deadbeat control modifications for possible future research. / Master of Science

This thesis aims to address the real-time planning and nonlinear control of quadrupedal locomotion such that the resulting gaits are robust to various kinds of disturbances. Specifically, this work addresses two scenarios. Namely, a quasi-static formulation in which an inertial appendage (i.e., a tail) is used to assist the quadruped in negating external push disturbances, and an agile formulation which is derived in a manner such that an appendage could easily be added in future work to examine the affect of tails on agile and high-speed motions. Initially, this work presents a unified method in which bio-inspired articulated serpentine robotic tails may be integrated with walking robots, specifically quadrupeds, in order to produce stable and highly robust locomotion. The design and analysis of a holonomically constrained 2 degree of freedom (DOF) tail is shown and its accompanying nonlinear dynamic model is presented. The model created is used to develop a hierarchical control scheme which consists of a high-level path planner and a full-order nonlinear low-level controller. The high-level controller is based on model predictive control (MPC) and acts on a linear inverted pendulum (LIP) model which has been extended to include the forces produced by the tail by augmenting the LIP model with linearized tail dynamics. The MPC is used to generate center of mass (COM) and tail trajectories and is subject to the net ground reaction forces of the system, tail shape, and torque saturation of the tail in order to ensure overall feasibility of locomotion. At the lower level, a full-order nonlinear controller is implemented to track the generated trajectories using quadratic program (QP) based input-output (I-O) feedback linearization which acts on virtual constraints. The analytical results of the proposed approach are verified numerically through simulations using a full-order nonlinear model for the quadrupedal robot, Vision60, augmented with a tail, totaling at 20 DOF. The simulations include a variety of disturbances to show the robustness of the presented hierarchical control scheme. The aforementioned control scheme is then extended in the latter portion of this thesis to achieve more dynamic, agile, and robust locomotion. In particular, we examine the use of a single rigid body model as the template model for the real-time high-level MPC, which is linearized using variational based linearization (VBL) and is solved at 200 Hz as opposed to an event-based manner. The previously defined virtual constraints controller is also extended so as to include a control Lyapunov function (CLF) which contributes to both numerical stability of the QP and aids in stability of the output dynamics. This new hierarchical scheme is validated on the A1 robot, with a total of 18 DOF, through extensive simulations to display agility and robustness to ground height variations and external disturbances. The low-level controller is then further validated through a series of experiments displaying the ability for this algorithm to be readily transferred to hardware platforms. / Master of Science / This thesis aims to address the real-time planning and nonlinear control of four legged walking robots such that the resulting gaits are robust to various kinds of disturbances. Initially, this work presents a method in which a robotic tail can be integrated with legged robots to produce very stable walking patterns. A model is subsequently created to develop a multi-layer control scheme which consists of a high-level path planner, based on a reduced-order model and model predictive control techniques, that determines the trajectory for the quadruped and tail, followed by a low-level controller that considers the full-order dynamics of the robot and tail for robust tracking of the planned trajectory. The reduced-order model considered here enforces quasi-static motions which are slow but generally stable. This formulation is validated numerically through extensive full-order simulations of the Vision60 robot. This work then proceeds to develop an agile formulation using a similar multi-layer structure, but uses a reduced-order model which is more amenable to dynamic walking patterns. The low-level controller is also augmented slightly to provide additional robustness and theoretical guarantees. The latter control algorithm is extensively numerically validated in simulation using the A1 robot to show the large increase in robustness compared to the quasi-static formulation. Finally, this work presents experimental validation of the low-level controller formulated in the latter half of this work.

No description available.

Control Designs for Low-Loss Active Magnetic Bearings: Theory and Implementation Brian C. D. Wilson 327 Pages Directed by Dr. Panagiotis Tsiotras and Dr. Bonnie Heck-Ferri Active Magnetic Bearings (AMB) have been proposed for use in Electromechanical Flywheel Batteries. In these devices, kinetic energy is stored in a magnetically levitated flywheel which spins in a vacuum. The AMB eliminates all mechanical losses, however, electrical loss, hich is proportional to the square of the magnetic flux, is still significant. For fficient operation, the flux bias, which is typically introduced into the electromagnets to improve the AMB stiffness, must be reduced, preferably to zero. This zero-bias (ZB) mode of operation cripples the classical control techniques which are customarily used and nonlinear control is required. As a compromise between AMB stiffness and efficiency, a new flux bias scheme is proposed called the generalized complementary flux condition(gcfc). A flux-bias dependent trade-off exists between AMB stiffness, power consumption, and power loss. This work theoretically develops and experimentally verifies new low-loss AMB control designs which employ the gcfc condition. Particular attention is paid to the removal of the singularity present in the standard nonlinear control techniques when operating in ZB. Experimental verification is conduced on a 6-DOF AMB reaction wheel. Practical aspects of the gcfc implementation such as flux measurement and flux-bias implementation with voltage mode amplifiers using IR compensation are investigated. Comparisons are made between the gcfc bias technique and the standard constant-flux-sum (cfs) bias method. Under typical operating circumstances, theoretical analysis and experimental data show that the new gcfc bias scheme is more efficient in producing the control flux required for rotor stabilization than the ordinary cfs bias strategy.

Thesis (MEng)--University of Stellenbosch, 2000. / ENGLISH ABSTRACT: The growth in intelligent control has primarily been a reaction to the realisation that nonlinear control theory has been unable to provide practical solutions to present day control challenges. Consequently the chemical industry may be cited for numerous instances of overdesign, which result as an attempt to avoiding operation near or within complex (often more economically viable) operating regimes. Within these complex operating regimes robust control system performance may prove difficult to achieve using conventional (algorithmic) control methodologies. Biological neuronal control mechanisms demonstrate a remarkable ability to make accurate generalisations from sparse environmental information. Neural networks, with their ability to learn and their inherent massive parallel processing ability, introduce numerous opportunities for developing superior control structures for complex nonlinear systems. To facilitate neural network learning, reinforcement learning techniques provide a framework which allows for learning from direct interactions with a dynamic environment. lts promise as a means of automating the knowledge acquisition process is beguiling, as it provides a means of developing control strategies from cause and effect (reward and punishment) interaction information, without needing to specify how the goal is to be achieved. This study aims to establish evolutionary reinforcement learning as a powerful tool for developing robust neurocontrollers for application in highly nonlinear process systems. A novel evolutionary algorithm; Symbiotic, Adaptive Neuro-Evolution (SANE), is utilised to facilitate neurocontroller development. This study also aims to introduce SANE as a means of integrating the process design and process control development functions, to obtain a single comprehensive calculation step for maximum economic benefit. This approach thus provides a tool with which to limit the occurrence of overdesign in the process industry. To investigate the feasibility of evolutionary reinforcement learning in achieving these aims, the SANE algorithm is implemented in an event-driven software environment (developed in Delphi 4.0), which may be applied for both simulation and real world control problems. Four highly nonlinear reactor arrangements are considered in simulation studies. As a real world application, a novel batch distillation pilot plant, a Multi-Effect Batch Distillation (MEBAD) column, was constructed and commissioned. The neurocontrollers developed using SANE in the complex simulation studies, were found to exhibit excellent robustness and generalisation capabilities. In comparison with model predictive control implementations, the neurocontrollers proved far less sensitive to model parameter uncertainties, removing the need for model mismatch compensation to eliminate steady state off-set. The SANE algorithm also proved highly effective in discovering the operating region of greatest economic return, while simultaneously developing a neurocontroller for this optimal operating point. SANE, however, demonstrated limited success in learning an effective control policy for the MEBAD pilot plant (poor generalisation), possibly due to limiting the algorithm's search to a too small region of the state space and the disruptive effects of sensor noise on the evaluation process. For industrial applications, starting the evolutionary process from a random initial genetic algorithm population may prove too costly in terms of time and financial considerations. Pretraining the genetic algorithm population on approximate simulation models of the real process, may result in an acceptable search duration for the optimal control policy. The application of this neurocontrol development approach from a plantwide perspective should also have significant benefits, as individual controller interactions are so doing implicitly eliminated. / AFRIKAANSE OPSOMMING: The huidige groei in intelligente beheerstelsels is primêr 'n reaksie op die besef dat nie-liniêre beheerstelsel teorie nie instaat is daartoe om praktiese oplossings te bied vir huidige beheer kwelkwessies nie. Gevolglik kan talle insidente van oorontwerp in die chemiese nywerhede aangevoer word, wat voortvloei uit 'n poging om bedryf in of naby komplekse bedryfsgebiede (dikwels meer ekonomies vatbaar) te vermy. Die ontwikkeling van robuuste beheerstelsels, met konvensionele (algoritmiese ) beheertegnieke, in die komplekse bedryfsgebiede mag problematies wees. Biologiese neurobeheer megamsmes vertoon 'n merkwaardige vermoë om te veralgemeen vanaf yl omgewingsdata. Neurale netwerke, met hulle vermoë om te leer en hulle inherente paralleie verwerkingsvermoë, bied talle geleenthede vir die ontwikkeling van meer doeltreffende beheerstelsels vir gebruik in komplekse nieliniêre sisteme. Versterkingsleer bied a raamwerk waarbinne 'n neurale netwerk leer deur direkte interaksie met 'n dinamiese omgewing. Versterkingsleer hou belofte in vir die inwin van kennis, deur die ontwikkeling van beheerstrategieë vanaf aksie en reaksie (loon en straf) interaksies - sonder om te spesifiseer hoe die taak voltooi moet word. Hierdie studie beaam om evolutionêre versterkingsleer as 'n kragtige strategie vir die ontwikkeling van robuuste neurobeheerders in nie-liniêre prosesomgewings, te vestig. 'n Nuwe evolutionêre algoritme; Simbiotiese, Aanpasbare, Neuro-Evolusie (SANE), word aangewend vir die onwikkeling van die neurobeheerders. Hierdie studie beoog ook die daarstelling van SANE as 'n weg om prosesontwerp en prosesbeheer ontwikkeling vir maksimale ekonomiese uitkering, te integreer. Hierdie benadering bied dus 'n strategie waardeur die insidente van oorontwerp beperk kan word. Om die haalbaarheid van hierdie doelwitte, deur die gebruik van evolusionêre versterkingsleer te ondersoek, is die SANE algoritme aangewend in 'n Windows omgewing (ontwikkel in Delphi 4.0). Die Delphi programmatuur geniet toepassing in beide die simulasie en werklike beheer probleme. Vier nie-liniêre reaktore ontwerpe is oorweeg in die simulasie studies. As 'n werklike beheer toepassing, is 'n nuwe enkelladingsdistillasie kolom, 'n Multi-Effek Enkelladingskolom (MEBAD) gebou en in bedryf gestel. Die neurobeheerders vir die komplekse simulasie studies, wat deur SANE ontwikkel is, het uitstekende robuustheid en veralgemeningsvermoë ten toon gestel. In vergelyking met model voorspellingsbeheer implementasies, is gevind dat die neurobeheerders heelwat minder sensitief is vir model parameter onsekerheid. Die noodsaak na modelonsekerheid kompensasie om gestadigde toestand afset te elimineer, word gevolglik verwyder. The SANE algoritme is ook hoogs effektief vir die soek na die mees ekonomies bedryfstoestand, terwyl 'n effektiewe neurobeheerder gelyktydig vir hierdie ekonomies optimumgebied ontwikkel word. SANE het egter beperkte sukses in die leer van 'n effektiewe beheerstrategie vanaf die MEBAD toetsaanleg getoon (swak veralgemening). Die swak veralgemening kan toegeskryf word aan 'n te klein bedryfsgebied waarin die algoritme moes soek en die negatiewe effek van sensor geraas op die evaluasie proses. Vir industriële applikasies blyk dit dat die uitvoer van die evolutionêre proses vanaf 'n wisselkeurige begintoestand nie koste effektief is in terme van tyd en finansies nie. Deur die genetiese algoritme populasie vooraf op 'n benaderde modelop te lei, kan die soek tydperk na 'n optimale beheerstrategie aansienlik verkort word. Die aanwending van die neurobeheer ontwikkelingstrategie vanuit 'n aanlegwye oogpunt mag aanleiding gee tot aansienlike voordele, aaangesien individuele beheerder interaksies sodoende implisiet uitgeskakel word.

GMAW (Gas Metal Arc Welding) is one of the most important arc welding processes being adopted in modern manufacturing industry due to its advantages in productivity, energy efficiency and automation. By monitoring and improving some of the important properties of GMAW such as production rate, metal transfer and base metal heat input, researchers could bring the process efficiency and stability to a new level. In recent years, some innovative modifications of GMAW such as Twins, Tandem and laser-MIG hybrid welding have been adopted into many industrial applications for better productivity. In this dissertation, a novel GMAW called DB-GMAW (Dual Bypass Gas Metal Arc Welding) using two GTAW torches and one GMAW torch to construct a welding system, is proposed and developed. In DB-GMAW, two GTAW torches perform the bypass system which decouples the total welding current into base metal current and bypass current after the melt down of filler wire. Compared to conventional GMAW, DB-GMAW has many advantages in droplet formation, base metal heat input and penetration achievement due to its unique characteristics in welding arc and current flow. In the first place of the research, experimental system of DB-GMAW is constructed. Then, sufficient experiments under different parameters are performed to provide us a good understanding of the behaviors and characteristics of this novel GMAW process. Observation about metal transfer formation and base metal heat input is studied to verify its theoretical analysis. Full penetration of work piece via DB-GMAW is achieved based on a series of parameter testing experiments. Moreover, image processing techniques are applied to DB-GMAW to monitor the welding process and construct a feedback system for control. Considering the importance of maintaining stable full penetration during many welding applications, a nonlinear model of DB-GMAW full penetration is developed in this dissertation. To do that, we use machine vision techniques to monitor the welding profile of the work piece. A control algorithm based on the nonlinear model using adaptive control technique is also designed. The achievement of this dissertation provides a fundamental knowledge of a novel welding process: DB-GMAW, and a good guidance for further studies about DBGMAW.

The subject of this thesis is the motion planning algorithm known as the continuation method. To solve motion planning problems, the continuation method proceeds by lifting curves in state space to curves in control space; the lifted curves are the solutions of special initial value problems called path-lifting equations. To validate this procedure, three distinct obstructions must be overcome. The first obstruction is that the endpoint maps of the control system under study must be twice continuously differentiable. By extending a result of A. Margheri, we show that this differentiability property is satisfied by an inclusive class of time-varying fully nonlinear control systems. The second obstruction is the existence of singular controls, which are simply the singular points of a fixed endpoint map. Rather than attempting to completely characterize such controls, we demonstrate how to isolate control systems for which no controls are singular. To this end, we build on the work of S. A. Vakhrameev to obtain a necessary and sufficient condition. In particular, this result accommodates time-varying fully nonlinear control systems. The final obstruction is that the solutions of path-lifting equations may not exist globally. To study this problem, we work under the standing assumption that the control system under study is control-affine. By extending a result of Y. Chitour, we show that the question of global existence can be resolved by examining Lie bracket configurations and momentum functions. Finally, we show that if the control system under study is completely unobstructed with respect to a fixed motion planning problem, then its corresponding endpoint map is a fiber bundle. In this sense, we obtain a necessary condition for unobstructed motion planning by the continuation method. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2012-12-18 20:53:43.272

Modern power systems are characterized by an increasing penetration of renewable energy generating units. These aim to reduce the carbon emissions in the environment by replacing conventional energy generating units which rely on fossil fuels. In this new power systems composition, wind generators (WGs) dominate, being one of the largest and fastest-growing sources of renewable energy production. Nevertheless, their unpredictable and highly volatile power output hinders their efficient and secure large-scale deployment, and poses challenges for the transient stability of power systems. Given that, we identify two challenges in the operation of modern power systems: rendering WGs capable of reguating their power output while securing transient stabilization of conventional synchronous generators (SGs). This dissertation makes several contributions for effectively dealing with these major challenges by introducing new distributed control techniques for SGs, storage devices and state-of-the-art (SoA) WGs. Initially, this dissertation introduces a novel nonlinear control design which is able to coordinate a storage device and a SG to attain transient stabilization and concurrent voltage regulation on their terminal bus. Thereafter, it proposes control designs that SoA WGs can adopt to effectively regulate their power out- put to meet local or group objectives. In this context, the rst control design is a decentralized nonlinear energy-based control design, that can be employed by a wind double-fed induction generator (DFIG) with an incorporated energy storage device (namely a SoA WG) to regulate its power output by harnessing stored energy, with guaranteed performance for a wide-range of operating conditions. Recognizing that, today, albeit wind farms (WFs) are comprised of numerous WGs which are sparsely located in large geographical areas, they are required to respond rapidly and provide services to the grid in an efficient, reliable and timely fashion. To this end, this dissertation proposes distributed control methods for power output regulation of WFs comprised of SoA WGs. In particular, a novel distributed control design is proposed, which can be adopted by SoA WGs to continuously, dynamically and distributively self-organize and control their power outputs by leveraging limited peer-to-peer communication. By employing the proposed control design, WGs can exploit their storage devices in a fair load-sharing manner so that their total power output tracks a total power reference under highly dynamical conditions. Finally, this dissertation proposes a distributed control design for wind DFIGs without a storage device, the most common type of WGs deployed today. With this control design, wind DFIGs can dynamically, distributively and fairly self-dispatch and adjust the power they extract from the wind for the purpose of their total power tracking a dynamic reference. The effectiveness of the control designs proposed in this dissertation is illustrated through several case studies on a 3-bus power system and the IEEE 24-bus Reliability Test System.