The School of Electrical, Electronic and Computer Engineering at the North-West University in Potchefstroom has established an active magnetic bearing (AMB) research group called McTronX. This group provides extensive knowledge and experience in the theory and application of AMBs. By making use of the expertise contained within McTronX and the rest of the control engineering community, an adaptive controller for an AMB flywheel system is implemented. The adaptive controller is faced with many challenges because AMB systems are multivariable, nonlinear, dynamic and inherently unstable systems. It is no wonder that existing AMB models are poor approximations of reality. This modelling problem is avoided because the adaptive controller is based on an indirect adaptive control law. Online system identification is performed by a neural network to obtain a better model of the AMB flywheel system. More specifically, a nonlinear autoregressive with exogenous inputs (NARX) neural network is implemented as an online observer. Changes in the AMB flywheel system’s environment also add uncertainty to the control problem. The adaptive controller adjusts to these changes as opposed to a robust controller which operates despite the changes. Making use of reinforcement learning because no online training data can be obtained, an adaptive critic model is applied. The adaptive controller consists of three neural networks: a critic, an actor and an observer. It is called an observer-based adaptive critic neural controller (ACNC). Genetic algorithms are used as global optimization tools to obtain values for the parameters of the observer, critic and actor. These parameters include the number of neurons and the learning rate for each neural network. Since the observer uses a different error signal than the actor and critic, its parameters are optimized separately. When the actor and critic parameters are optimized by minimizing the tracking error, the observer parameters are kept constant. The chosen adaptive control design boasts analytical proofs of stability using Lyapunov stability analysis methods. These proofs clearly confirm that the design ensures stable simultaneous identification and tracking of the AMB flywheel system. Performance verification is achieved by step response, robustness and stability analysis. The final adaptive control system remains stable in the presence of severe cross-coupling effects whereas the original decentralized PD control system destabilizes. This study provides the justification for further research into adaptive control using artificial intelligence techniques as applied to the AMB flywheel system. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.

The School of Electrical, Electronic and Computer Engineering at the North-West University in Potchefstroom has established an active magnetic bearing (AMB) research group called McTronX. This group provides extensive knowledge and experience in the theory and application of AMBs. By making use of the expertise contained within McTronX and the rest of the control engineering community, an adaptive controller for an AMB flywheel system is implemented. The adaptive controller is faced with many challenges because AMB systems are multivariable, nonlinear, dynamic and inherently unstable systems. It is no wonder that existing AMB models are poor approximations of reality. This modelling problem is avoided because the adaptive controller is based on an indirect adaptive control law. Online system identification is performed by a neural network to obtain a better model of the AMB flywheel system. More specifically, a nonlinear autoregressive with exogenous inputs (NARX) neural network is implemented as an online observer. Changes in the AMB flywheel system’s environment also add uncertainty to the control problem. The adaptive controller adjusts to these changes as opposed to a robust controller which operates despite the changes. Making use of reinforcement learning because no online training data can be obtained, an adaptive critic model is applied. The adaptive controller consists of three neural networks: a critic, an actor and an observer. It is called an observer-based adaptive critic neural controller (ACNC). Genetic algorithms are used as global optimization tools to obtain values for the parameters of the observer, critic and actor. These parameters include the number of neurons and the learning rate for each neural network. Since the observer uses a different error signal than the actor and critic, its parameters are optimized separately. When the actor and critic parameters are optimized by minimizing the tracking error, the observer parameters are kept constant. The chosen adaptive control design boasts analytical proofs of stability using Lyapunov stability analysis methods. These proofs clearly confirm that the design ensures stable simultaneous identification and tracking of the AMB flywheel system. Performance verification is achieved by step response, robustness and stability analysis. The final adaptive control system remains stable in the presence of severe cross-coupling effects whereas the original decentralized PD control system destabilizes. This study provides the justification for further research into adaptive control using artificial intelligence techniques as applied to the AMB flywheel system. / Thesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2011.

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Researchers at Uppsala University are developing a flywheel energy storage device intended to be used in electrical vehicles. Kinetic energy storage technology has potential to make purely electric powertrain both more effective and efficient. While deployment of the third prototype is approaching there has been a request for a more precise and noise-immune circuitry to power active magnetic bearings that hold and stabilise the rotor. A similar circuit designed for powering electromagnets was recently developed at the Uppsala University’s Electricity department and is used as a template in development of the new active magnetic bearing driver circuit. Current measurement integration technique is tested and implemented as a way to increase circuit’s control feedback loop performance. To further boost precision and noise-immunity 0-20 mA current loop signals are adapted as the standard for output signals. Results of this project include a thorough analysis of the electromagnet driver circuit development, implementation of a new current sensing technique including an experimental self-inductance measurement, printed circuit board layout design and a full list of components necessary to power and control two sets of active magnetic bearings consisting of 8 individual electromagnets.

Orientador: Luiz de Paula do Nascimento / Banca: Vicente Lopes Junior / Banca: Domingos Alves Rade / Resumo: Este trabalho apresenta uma análise teórica do desempenho de um sistema de controle ativo de vibração de rotores utilizando mancais magnéticos. O esquema de controle ativo proposto utiliza a estratégia de controle ativo feedforward sobreposta ao sistema de controle feedback dos mancais magnéticos. O desempenho desse sistema de controle foi analisado em função da flexibilidade dos rotores considerando o impacto do número e da localização dos atuadores e dos sensores de erro sobre a redução dos níveis de vibração desses rotores, tanto em termos de vibração global quanto em termos de vibração local. O sistema de controle foi aplicado em um modelo teórico de rotor desenvolvido através do método da matriz de impedância. / Abstract: This work presents a theoretical analysis of the performance of a system of active control of rotor vibrations using magnetic bearings. The proposed scheme of active control uses a feedforward active control strategy superimposed on the feedback control system of the magnetic bearings. The performance of this control system was analyzed as a function of the rotor flexibility considering the impact and optimization of the actuators and error sensors placement on the reduction of vibration levels of these rotors, in terms of global vibration as well as in terms of local vibration of the rotor. The control system was applied to a theoretical rotor model developed by the matrix impedance method. / Mestre

In this study, a novel concept for a downhole flywheel energy storage module to be embedded in a bottom-hole-assembly (BHA) is presented and modeled, as an alternative power source to existing lithium-ion battery packs currently deployed in measurement-while-drilling (MWD) or logging-while-drilling (LWD) operations. Lithium-ion batteries disadvantages include deteriorated performance in high temperature, limited lifetime that necessitates frequent replacement which elevates operational costs, and environmental disposal. Extreme and harsh downhole conditions necessitate that the flywheel module withstands temperatures and pressures exceeding 300 ?F and 20 kpsi, respectively, as well as violent vibrations encountered during drilling. Moreover, the flywheel module should adhere to the geometric constraints of the wellbore and its corresponding BHA. Hence, a flywheel sizing procedure was developed that takes into consideration the required energy to be stored, the surrounding environmental conditions, and the geometric constraints. A five-axis magnetic levitation control system was implemented and tuned to maintain continuous suspension of the flywheel under the harsh lateral, axial and torsional drilling vibrations of the BHA. Thus, an integrated finite element model was developed that included the rotordynamic behavior of the flywheel and the BHA, the component dynamics of the magnetic levitation control system, and the cutting dynamics of the drillbit for both PDC and tricone types. The model also included a newly developed coupling between lateral, axial and torsional vibrations. It was demonstrated through simulations conducted by numerical integration that the flywheel maintains levitation due to all different types of external vibration as well as its own lateral vibration due to mass unbalance. Moreover, a passive proof-mass-damper (PPMD) was developed that suppresses axial bit-bounce vibrations as well as torsional vibrations, and was extended to also mitigate lateral vibrations. Optimized values of the mass, stiffness and damping values of the PPMD were obtained by the hybrid analytical-numerical Chebyshev spectral method that was superior in computational efficiency to iterative numerical integration. This also enabled the fine-plotting of an operating stability chart indicating stability regions where bit-bounce and stick-slip are avoided. The proof-mass-damping concept was extended to the flywheel to be an active proof-mass-damper (APMD) where simulations indicated functionality for a light-weight BHA.

This thesis discuss the possibility to position control a rotor levitated with active magnetic bearings. The controller type considered is model predictive control which is an online strategy that solves an optimization problem in every sample, making the model predictive controller computation-intense. Since the sampling time must be short to capture the dynamics of the rotor, very little time is left for the controller to perform the optimization. Different quadratic programming strategies are investigated to see if the problem can be solved in realtime. Additionally, the impact of the choices of prediction horizon, control horizon and terminal cost is discussed. Simulations showing the characteristics of these choises are made and the result is shown. / Det här examensarbetet diskuterar möjligheten att positionsreglera en rotor som leviteras på aktiva magnetlager. Reglerstrategin som används är modellbaserad prediktionsreglering vilket är en online-metod där ett optimeringsproblem löses i varje sampel. Detta gör att regulatorn blir mycket beräkningskrävande. Samplingstiden för systemet är mycket kort för att fånga dynamiken hos rotorn. Det betyder att regulatorn inte ges mycket tid att lösa optimeringsproblemet. Olika metoder för att lösa QP-problem betraktas för att se om det är möjligt att köra regulatorn i realtid. Dessutom diskuteras hur valet av prediktionshorisont, reglerhorisont och straff på sluttillståndet påverkar regleringen. Simuleringar som visar karakteristiken av dessa val har utförts.

Thesis deals with active magnetic bearing (AMB) levitation control design. Its prototype was done in FSI collaboration with FEKT VUT Brno. The research is focused on communication tools and mathematical model making as well as general AMB issues. Based on this the control design is done an experimentally verified.

The PhD thesis describes control design of active magnetic bearing. Active magnetic bearing is nonlinear unstable system. This means it is not possible to use classic methods of control design for linear time invariant systems. Also methods of nonlinear control design are not universal and theirs application is not easy task. The thesis describes usage of simple nonlinear compensation which linearizes response of active magnetic bearing and allows usage of classic methods of control design for linear time invariant systems. It is shown that CARLA method can significantly improve parameters of designed controller. First part of thesis describes derivation of model of controlled active magnetic bearing and nonlinear compensation which linearizes response of controlled active magnetic bearing on input signal. Following part contains description of methods of state control design methods, selected methods of robust control design and most common methods of artificial intelligence used for control design and implementation. Next part describes hardware of used experimental device and its parameters. It also contains experimental derivation of model of electromagnetic force because the parameters are not available from manufacturer. Last part describes control design of active magnetic bearing. Several different approaches are described here. The approaches vary from completely experimental approach, through using Ziegler-Nichols method, state control design to methods for robust control design. During design is heavily used CARLA method which is very suitable for usage for online learning in real controller due its principle.

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