|dc.description.abstract||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.||en_US