An adaptive autopilot design for an uninhabited surface vehicle
Andy SK Annamalai
The work described herein concerns the development of an innovative approach to the
design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of
autonomous missions, uninhabited surface vehicles must be able to operate with a minimum
of external intervention. Existing strategies are limited by their dependence on a fixed
model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect
on performance. This thesis presents an approach based on an adaptive model predictive
control that is capable of retaining full functionality even in the face of sudden changes in
dynamics.
In the first part of this work recent developments in the field of uninhabited surface vehicles
and trends in marine control are discussed. Historical developments and different strategies
for model predictive control as applicable to surface vehicles are also explored. This thesis
also presents innovative work done to improve the hardware on existing Springer
uninhabited surface vehicle to serve as an effective test and research platform. Advanced
controllers such as a model predictive controller are reliant on the accuracy of the model to
accomplish the missions successfully. Hence, different techniques to obtain the model of
Springer are investigated. Data obtained from experiments at Roadford Reservoir, United
Kingdom are utilised to derive a generalised model of Springer by employing an innovative
hybrid modelling technique that incorporates the different forward speeds and variable
payload on-board the vehicle. Waypoint line of sight guidance provides the reference
trajectory essential to complete missions successfully.
The performances of traditional autopilots such as proportional integral and derivative
controllers when applied to Springer are analysed. Autopilots based on modern controllers
such as linear quadratic Gaussian and its innovative variants are integrated with the
navigation and guidance systems on-board Springer. The modified linear quadratic
Gaussian is obtained by combining various state estimators based on the Interval Kalman
filter and the weighted Interval Kalman filter.
Change in system dynamics is a challenge faced by uninhabited surface vehicles that result
in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms
are analysed and an innovative, adaptive autopilot based on model predictive control is
designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that
is obtained by combining the advances made to weighted least squares during this research
and is used in conjunction with model predictive control. Successful experimentation is
undertaken to validate the performance and autonomous mission capabilities of the adaptive
autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council
