Partially Observed Non-linear Risk-sensitive Optimal Stopping Control for Non-linear Discrete-time Systems

In this paper we introduce and solve the partially observed optimal stopping non-linear risk-sensitive stochastic control problem for discrete-time non-linear systems. The presented results are closely related to previous results for finite horizon partially observed risk-sensitive stochastic control problem. An information state approach is used and a new (three-way) separation principle established that leads to a forward dynamic programming equation and a backward dynamic programming inequality equation (both infinite dimensional). A verification theorem is given that establishes the optimal control and optimal stopping time. The risk-neutral optimal stopping stochastic control problem is also discussed

Practical stability with respect to model mismatch of approximate discrete-time output feedback control

This paper establishes a practical stability result for discrete-time output feedback control involving mismatch between the exact system to be stabilised and the approximating system used to design the controller. The practical stability is in the sense of an asymptotic bound on the amount of error bias introduced by the model approximation, and is established using local consistency properties of the systems. Importantly, the practical stability established here does not require the approximating system to be of the same model type as the exact system. Examples are presented to illustrate the nature of our practical stability result

Optimal stopping and hard terminal constraints applied to a missile guidance problem

This paper describes two new types of deterministic optimal stopping control problems: optimal stopping control with hard terminal constraints only and optimal stopping control with both minimum control effort And hard termind constraints. Both problems are initially formulated in continuous-time (a discretetime formulation is given towards the end of the paper) and soIutions given via dynamic programming. A numeric solution to the continuous-time dynamic programming equations is then briefly discussed. The optimal stopping with terminal constraints problem in continuous-time is a natural description of a particular type of missile guidance problem. This missile guidance appiication is introduced and the presented solutions used in missile engagements against targets

Exploring Rovibrational States of Floppy Molecules Using Diffusion Monte Carlo

In this work, diffusion Monte Carlo (DMC) methodology was extended to allow for the calculation of rotationally excited states by expansion into a functional space. This new methodology was used to study CH5+ and its deuterated isotopologues. Previous results regarding the localization of deuterium atoms within the H3 subunit are corroborated, and new results regarding the lack of change in the wavefunction upon rotational excitation up to J = 10 are shown. The method was then tested concurrently with the previously established fixed node DMC method on H2D+ and HD2+ , to determine its efficacy in capturing rovibrational coupling. This mixed method was found to produce errors up to 20 cm−1 for states with J = 2 and νasym = 1. Group theory was then used to analyze the cause of the error, and showed the exclusion of Coriolis coupling terms to likely be at fault.College of Arts and Sciences Undergraduate Research ScholarshipDivision of Natural and Mathematical Sciences Mayers Summer Research ScholarshipNo embargoAcademic Major: Chemistr

Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons

In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal control that has a computationally efficient online form in which pairs of states and controls from given state and control trajectories are processed sequentially without being stored or processed in batches. We establish conditions guaranteeing the uniqueness of the objective-function parameters computed by our proposed method from trajectories with active control constraints. We illustrate our proposed method in simulation.Comment: 10 pages, 4 figures, Accepted for publication in Automatic

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

Commercial operation of unmanned aerial vehicles (UAVs) would benefit from an onboard ability to sense and avoid (SAA) potential mid-air collision threats. In this paper we present a new approach for detection of aircraft below the horizon. We address some of the challenges faced by existing vision-based SAA methods such as detecting stationary aircraft (that have no relative motion to the background), rejecting moving ground vehicles, and simultaneous detection of multiple aircraft. We propose a multi-stage, vision-based aircraft detection system which utilises deep learning to produce candidate aircraft that we track over time. We evaluate the performance of our proposed system on real flight data where we demonstrate detection ranges comparable to the state of the art with the additional capability of detecting stationary aircraft, rejecting moving ground vehicles, and tracking multiple aircraft

Analysis of Rotationally Excited States of Deuterated CH5+ Using Diffusion Monte Carlo

Mathematical and Physical Sciences: 3rd Place (The Ohio State University Denman Undergraduate Research Forum)The chemistry that occurs in space is very different from that on Earth, but it is necessary to understand these differences in order to comprehend many astronomical processes. In order to study the astrochemistry that is taking place, radioastronomy can be used to observe transitions between rotational energy levels of the molecules and ions. The resulting information, the spectrum, acts as a molecular fingerprint and can be utilized to determine some of the properties of the molecules and ions that are present. Understanding and predicting spectra is quite difficult for floppy molecules, those that exhibit large amplitude vibrational motions in their ground state. Diffusion Monte Carlo (DMC) is a statistical approach to solving the Schrödinger equation which has been successfully used in the past to describe floppy systems. The McCoy research group and I have recently extended the DMC methodology to simultaneously treat the ground state and multiple rotationally excited states of floppy molecules. This newly developed technique has been applied to the deuterated isotopologues of CH5+ to determine rotational energies and similarities in structure.College of Arts and Sciences - Undergraduate Research ScholarshipAcademic Major: Chemistr