Distributed Model Predictive Control for Rendezvous Problem
Abstract: This thesis investigates the potential advantages and disadvantages of using adistributed control approach to land an autonomous drone on an autonomousboat. The expected advantages include better utilisation of computational resources,as well as increased robustness towards communication delays. Inthis context, distributed control means that separate computers on the droneand boat are both involved in computing the control inputs to the system. Thisstands in contrast to an existing centralised algorithm where all computationsfor finding the control input are performed on the drone. Two new algorithmsare proposed, one using distributed model predictive control (DMPC) and oneusing a combination of DMPC with linear state-space feedback. The followingproperties of all the algorithms are tested: what the longest possible predictionhorizon with sufficiently short solution time is, how long it takes to solve optimisationproblems for the algorithms, and how quickly and safely each algorithmcan land the drone. Finally, the DMPC algorithm is shown to in certainscenarios possess improved robustness towards communication delays.
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