Breaking perceived limits of performance for nanoscale interrogation & transport systems

Abstract

Scientific instruments for nano-interrogation, in particular optical field based probing instruments, typically do not leverage modern control paradigm, thereby constraining themselves to false limits of performance. The first part of my reserach is on developing a novel disturbance estimation paradigm built upon LMI based mixed objective synthesis, which is geared towards systems requiring regulation of a certain system variable against an external disturbance while simultaneously providing a real-time estimate of the disturbance. Examples of such systems include optical traps, scanning probe microscopy, microfluidic sensors, high density data storage systems etc. In general this disturbance is corrupted by process noise (which for nano-scale systems is primarily thermal noise) and the disturbance estimation scheme has to mitigate the effect of such noise to provide any meaningful estimate. In the particular context of the optical field based probing and manipulation, I have experimentally demonstrated more than an order of magnitude improvement in bandwidth over previous state-of-the-art using the aforementioned paradigm. This optimal force clamp will enable biologists to study motor proteins at in-vivo speeds which is not currently possible. The later part of my research is on control of Brownian ratchet based stochastic transport mechanisms where I have used physical insights to reduce the model complexity in order to analytically derive the approximate evolution of the probability density function of the system state. This allowed for obtaining design parameters for optimal performance, which was missing from the previous literature. I will also demonstrate the advantages of using dynamic programming based multi-objective optimization techniques to obtain transport strategies that strike an optimal velocity-efficiency trade-off. Here a key insight obtained is that maximizing velocity of transport can significantly compromise efficiency of transport; an aspect not realized/emphasized by researchers in the area. Extensive Monte Carlo simulations demonstrates up to $35\%$ increase in efficiency from other closed loop strategies and more importantly, being an optimal strategy, provides a benchmark of comparison for other heuristic strategies in the domain.