This thesis investigated modifying the original Bio-Inspired approach, used initially to eliminate control signal jumps due to initial non-zero errors, to improve its convergence speed using adaptation. The proposed modifications demonstrated improved omnidirectional mobile robot (ODMR) model performance. Specifically, the pose error's Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) decreased by up to 26% and 49%, respectively. The sum of peak control signals decreased by at least 3% to 7% of the original method. The control effort decreased from 2% to 8%, depending on the presence of disturbances. The proposed controller with the novel update function consistently arrived at the pose error median value of 0.004 m the fastest, approximately 65% faster than the Bio-inspired method.
The Learning-based Machine Health Monitoring approach demonstrated the ability to detect severe loss of effectiveness and bias faults, although the sensitivity to more minor magnitude faults was lower. The benefit of using Temporal Convolutional Autoencoders could not be identified in terms of F1 scores. However, experiments show that the presence of a time-varying loss of effectiveness signal inside the training data generally increases the precision and reduces the recall scores.

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