Switching Economic Iterative Learning for Combined Path and Power Take-Off Control of a Drag-Powered Underwater Kite

Abstract

This brief presents the development of an online iterative learning technique for the optimization of both the flight path parameters and rotor control parameters for an underwater energy-harvesting kite. In this technique, a coupled control parameter space is explored through a switching exploration strategy, whereby only a subset of the total system parameters is adapted at each iteration. This formulation, termed switching economic iterative learning control (se-ILC), seeks to intelligently explore this coupled design space to rapidly converge near the global optimum. This work examines the high-level control of an underwater energy-harvesting kite as an application example of se-ILC. First, we establish generalized performance bounds for economic iterative learning control (e-ILC) in the presence of a time-varying environment subjected to parametric uncertainties. Leveraging an existing dynamic model, we then examine the performance of the se-ILC formulation applied to this specific application example, exploring the coupled path and rotor angular velocity parameter spaces. We demonstrate that the se-ILC strategy enables convergence to within tighter bounds (compared with standard e-ILC) of the true optimum in the presence of an imperfect performance characterization.