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Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems.

Conventional ball-bearings in rotational applications can potentially be replaced by active magnetic bearings (AMBs). AMBs levitate the rotor via contact-free, actively controlled, electromagnetic forces. At the NorthWest University, AMBs are applied to a flywheel uninterrupted power supply (Fly-UPS) system. Regrettably, AMBs are inherently open-loop unstable because of the inverse displacement-force relationship, and for this reason requires closed-loop feedback control. Thus, the feasibility of multivariable H control for a Fly-UPS system is investigated. At present, the Fly-UPS system is being controlled by a number of decentralized single-input single-output (SISO), PD controllers. Ultimately, the combination of a multivariable plant, My sincere thanks to my supervisors, Dr. Pieter van Vuuren and Prof. George van Schoor. Your guidance, time, dedication and valuable inputs were and are priceless. Thank you for placing your trust in me and making it possible for me to further my studies. It has been an honour and a privilege working with you both. Furthermore, the financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. I would like to thank Stefan Myburgh and Jan Janse van Rensburg for helping, guiding and assisting me throughout this project (and allowing me to tinker with their flywheel system). My thanks to all my colleagues in the McTronX group, your teamwork and kinship are valuable to me. I would like to thank all my friends, Eugène, Jacques, Martin, Ryno, Luke and my brother and sister, who kept me sane at times when I got too 'academic'. And to all those I have not mentioned, I appreciate your support. Finally, to my parents, Martin and Ansa Steyn, for making my studies possible and teaching me that I can accomplish anything I put my mind to. I appreciate your love, wisdom and guidance.

Despite the many advantages run-time reconfiguration of FPGAs brings to the table, its usage is mostly limited to quasi-static applications. This is either due to the throughput of the reconfiguration process, or the time required to create new hardware. In order to optimise the former, the literature proposes a block RAM (BRAM)-based architecture in which a new configuration is stored in localised memory and reconfiguration is facilitated by a controller implemented in the FPGA fabric. The limitation of this architecture is that only a subset of configurations can be stored. When new hardware is required, the slow synthesis process (or a part thereof) has to be repeated for each new configuration. Various third-party tools aim to mitigate this overhead, but since the bitstream is shrouded in obscurity, all rely on a layer of abstraction that make them unusable in real-time. To address this issue, this paper presents a novel method to parse and analyse a Xilinx® FPGA bitstream to extract certain characteristics. It is shown how these characteristics could be used to design and implement a bitstream specialiser, capable of taking a bitstream and modifying the configuration bits of lookup tables in real-time.

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