No abstract

No abstract

No abstract

No abstract

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results.

Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two position estimation methods are developed in this thesis, one using a single optical sensor and a second using two optical sensors. The first method can accurately estimate position under ideal conditions and when wheel slip perpendicular to the axis of the wheel occurs. A second method can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the optical sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, the method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where wheel slip occurs.

No abstract

This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.