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Unmanned Underwater Vehicles (UUVs) are increasingly being used in advanced applications that require them to operate in tandem with human divers and around underwater infrastructure and other vehicles. These applications require precise control of the UUVs which is challenging due to the non-linear and time varying nature of the hydrodynamic forces, presence of external disturbances, uncertainties and unexpected changes that can occur within the UUV’s operating environment. Adaptive control has been identified as a promising solution to achieve desired control within such dynamic environments. Nevertheless, adaptive control in its basic form, such as Model Reference Adaptive Control (MRAC) has a trade-off between the adaptation rate and transient performance. Even though, higher adaptation rates produce better performance they can lead to instabilities and actuator fatigue due to high frequency oscillations in the control signal. Command Governor Adaptive Control (CGAC) is a possible solution to achieve better transient performance at low adaptation rates. In this study CGAC has been experimentally validated for depth control of a UUV, which is a unique challenge due to the unavailability of full state measurement and a greater thrust requirement. These in turn leads to additional noise from state estimation, time-delays from input noise filters, higher energy expenditure and susceptibility to saturation. Experimental results show that CGAC is more robust against noise and time-delays and has lower energy expenditure and thruster saturation. In addition, CGAC offers better tracking, disturbance rejection and tolerance to partial thruster failure compared to the MRAC.

Item Type:	Refereed Article
Keywords:	adaptive control, command governor adaptive control, external disturbances, measurement noise, thruster failure, time-delay, unmanned underwater vehicles, autonomous underwater vehicle, simulation and experiment
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Special vehicles
Objective Division:	Transport
Objective Group:	Water transport
Objective Field:	Autonomous water vehicles
UTAS Author:	Makavita, CD (Mr Charita Makavita)
UTAS Author:	Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage)
UTAS Author:	Nguyen, HD (Dr Hung Nguyen)
UTAS Author:	Ranmuthugala, D (Professor Dev Ranmuthugala)
ID Code:	150648
Year Published:	2019
Web of Science® Times Cited:	7
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2022-06-23
Last Modified:	2022-06-23
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