Set-based corral control in stochastic dynamical systems: Making almost invariant sets more invariant

Eric Forgoston, Montclair State University
Lora Billings, Montclair State University
Philip Yecko, Montclair State University
Ira Schwartz, US Naval Research Labratory


We consider the problem of stochastic prediction and control in a time-dependent stochastic environment, such as the ocean, where escape from an almost invariant region occurs due to random fluctuations. We determine high-probability control-actuation sets by computing regions of uncertainty, almost invariant sets, and Lagrangian coherent structures. The combination of geometric and probabilistic methods allows us to design regions of control, which provide an increase in loitering time while minimizing the amount of control actuation. We show how the loitering time in almost invariant sets scales exponentially with respect to the control actuation, causing an exponential increase in loitering times with only small changes in actuation force. The result is that the control actuation makes almost invariant sets more invariant. VC 2011 American Institute of Physics.