Risk-Sensitive Control of Vibratory Energy Harvesters

Linear-quadratic-Gaussian (LQG) optimal control theory can be used to maximize the average electrical power generated by a vibratory energy harvester subjected to random disturbances. However, feedback controllers designed using the LQG framework often require large peak power flows for their successful implementation, which may be undesirable for several reasons. In this paper, we propose using a risk-sensitive performance measure to synthesize control laws for stochastic vibratory energy harvesters. The proposed methodology is applied in two examples, in which we show how the risk-sensitive parameter can be systematically tuned to maximize power generation and mitigate excessive power flows. The first example involves a simple single-degree-of-freedom oscillator subjected to a bandpass filtered noise excitation, and the second pertains to ocean wave energy harvesting.