Linear Parameter-Varying Control of a Ducted Fan Engine

Parameter-dependent control techniques are applied to a vectored thrust, ducted fan engine. The synthesis technique is based on the solution of Linear Matrix Inequalities and produces a controller which achieves specified performance against the worst-case time variation of measurable parameters entering the plant in a linear fractional manner. Thus the plant can have widely varying dynamics over the operating range. The controller designed performs extremely well, and is compared to an ℋ∞ controller

Dubious decision evidence and criterion flexibility in recognition memory.

When old-new recognition judgments must be based on ambiguous memory evidence, a proper criterion for responding "old" can substantially improve accuracy, but participants are typically suboptimal in their placement of decision criteria. Various accounts of suboptimal criterion placement have been proposed. The most parsimonious, however, is that subjects simply over-rely on memory evidence - however faulty - as a basis for decisions. We tested this account with a novel recognition paradigm in which old-new discrimination was minimal and critical errors were avoided by adopting highly liberal or conservative biases. In Experiment 1, criterion shifts were necessary to adapt to changing target probabilities or, in a "security patrol" scenario, to avoid either letting dangerous people go free (misses) or harming innocent people (false alarms). Experiment 2 added a condition in which financial incentives drove criterion shifts. Critical errors were frequent, similar across sources of motivation, and only moderately reduced by feedback. In Experiment 3, critical errors were only modestly reduced in a version of the security patrol with no study phase. These findings indicate that participants use even transparently non-probative information as an alternative to heavy reliance on a decision rule, a strategy that precludes optimal criterion placement

Electrodynamic Tethers for Novel LEO Missions

The exponential increase of launch system size - and cost - with deltaV makes missions requiring large total impulse cost prohibitive. Northrop Grumman and partners have matured a fundamentally different method for generating propulsion using electrodynamic tethers (EDTs) that escapes the limitations of the rocket equation. With essentially unlimited delta V, we can perform new classes of missions that are currently unaffordable or unfeasible

An experimental comparison of controllers for a vectored thrust, ducted fan engine

Experimental comparisons between four different control design methodologies are applied to a small vectored thrust engine. Each controller is applied to three trajectories of varying aggressiveness. The control strategies considered are LQR, ℋ∞, gain scheduling, and feedback linearization. The experiments show that gain scheduling is essential to achieving good performance. The strengths and weaknesses of each methodology are also examined

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

Robust simulation and analysis of nonlinear systems

NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract is included in .pdf document. For linear systems, robust analysis techniques are well developed. For non-linear systems, they are not. Most nonlinear analysis techniques use extensive simulation to examine system performance. However, these simulations do not give guarantees, they only describe local performance. This thesis presents a simulation technique, called robust simulation, that answers the nonlinear robust analysis question. For an uncertain nonlinear system and a set of initial conditions, robust simulation calculates the set of all possible trajectories. By applying a measure to the set of all trajectories, a performance guarantee is obtained. To allow efficient robust simulation, only discrete time piecewise linear systems are considered. This class of systems admits a wide variety of nonlinearities and can approximate generic nonlinear systems to any degree of accuracy. To measure performance, a generalized [...] norm is used. As in the linear case, the robust nonlinear analysis question cannot be answered exactly. Instead, upper and lower bounds are calculated. Many techniques, including traditional simulation, exist for finding lower bounds. Robust simulation provides efficient methods for calculating an upper bound. Robust simulation also supports simulation when multiple models exist for a single system. When modeling a physical system, any amount of complexity is possible. Traditional simulation of these models with different levels of detail yields different individual trajectories. Which is correct? By explicitly quantifying the uncertainty as noise, robust simulation calculates sets of possible trajectories. For each model the result is guaranteed to contain the true output. More detailed models yield smaller sets of possible trajectories. To test the algorithms, robust simulation is applied to a variety of examples. Algorithm performance is generally very good. Three other applications of robust simulation are also presented. In addition to measuring robust non-linear performance, robust simulation also generates lower bounds for model predictive control optimizations, verifies the stability of piecewise linear systems, and analyzes gain scheduled systems