Precise trajectory control for an inspection class ROV

This work addresses the design, implementation and testing of a new precision guidance and control system for an inspection class remotely operated underwater vehicle (ROV). A new multi-input multi-output control law, composed of a model-based equivalent control signal and two adaptive signals, is presented. In the controller, the first adaptive signal is a PID signal with a novel adaptation law that enhances the controller performance and allows efficient fine tuning of the controller. The second adaptive signal continuously estimates the upper bound on the lumped uncertainty vector and acts as a corrective term for the equivalent control law. A Lyapunov based guidance algorithm is implemented that can tolerate significant uncertainties in the system kinematics. The effectiveness of the system on an ROV is demonstrated through field trials in sheltered waters. For the experimental work, an extended Kalman filter is used to for navigation, blending the on-board sensor measurements with a process model to produce an estimate of the vehicle dynamics. The collective guidance and navigation system are validated using high precision optical motion capture data. The system achieves decimetre-level precision, significantly extending the capabilities of the ROV for tasks requiring high precision position and velocity control.