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Critically ill patients are known to experience stress-induced hyperglycemia. Inhibiting the physiological response to increased glycaemic levels in these patients are factors such as increased insulin resistance, increased dextrose input, absolute or relative insulin deficiency, and drug therapy. Although hyperglycemia can be a marker for severity of illness, it can also worsen outcomes, leading to an increased risk of further complications. Recent studies have shown that tight control can reduce mortality up to 43%. Metabolic modelling has been used to study physiological behaviour and/or to control glycaemia for a long time and many successful approximate system models have been developed. Due to the malfunction of medical equipments, clinical measurements obtained usually come with noise. In addition, the few such systems currently available can have errors in excess of 20-30%. Therefore, to fully simulate the clinical data, the system model also needs to couple with a successful noise model. This research has developed a new noise model that better fits the current available statistical description of the noise profile and therefore can be applied to achieve better simulation results. The research also designed a filter algorithm that is capable of reducing the sensor measurement error down to an acceptable value. Achieving such a goal is a significant step towards fully automated adaptive control of hyperglycaemia in critically ill patients and would therefore reduce mortality.

In adaptive control the goal is to deal with systems that have unknown and/or time-varying parameters. Adaptive control techniques have been developed since 1950’s and most results were proven in the cases when the time-variations were non-existent or slow. However the results pertaining to systems with fast time-variations are still limited, in particular, when it comes to plants with unstable zero dynamics. In this work we adopt the controller design technique from the area of gain scheduling, where the time-varying parameter is assumed to be measurable. We propose the design of a nonlinear periodic controller, where in each period the state and parameter values are estimated and an appropriate stabilizing control signal is applied. It is shown that the closed loop system is stable under fast parameter variations with persistent jumps: the trajectory of the closed loop state in response to the initial condition is bounded by a decaying exponential plus a gain times the size of the noise. Our approach imposes several constraints on the plant; however, we show that there exists at least one interesting class of systems, which includes plants with unstable zero dynamics, that can be stabilized by our controller.

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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