903 research outputs found
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a state-feedback control law which minimizes a "worst-case" infinite horizon objective function, subject to constraints on the control input and plant output. Using standard techniques, the problem of minimizing an upper bound on the "worst-case" objective function, subject to input and output constraints, is reduced to a convex optimization involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants under consideration. Several extensions, such as application to systems with time-delays and problems involving constant set-point tracking, trajectory tracking and disturbance rejection, which follow naturally from our formulation, are discussed. The controller design procedure is illustrated with two examples. Finally, conclusions are presented
Anti-Windup Design for Internal Model Control
This paper considers linear control design for systems with input magnitude saturation. A general anti-windup scheme which optimizes nonlinear performance, applicable to MIMO systems, is developed. Several examples, including an ill-conditioned plant, show that the scheme provides graceful degradation of performance. The attractive features of this scheme are its simplicity and effectiveness
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Our Fear of AI: Exploring Its Creators and Creations in Fiction
The idea of technological creation has proliferated across fiction for the last century.
As the world becomes increasingly technologically advanced, these fears have become more
tangible. With the rise of Artificial Intelligence particularly, from Alexa to self-driving cars,
comes a rise in the fear of what intelligent creations might lead to. In order for AI to
continue growing and adding value to society, experts must contend with the apprehension
surrounding AI. While these conversations are already occurring, they generally focus on the
fear of the machine itself. This thesis argues that the fear of the creators and regulators of AI,
not just the machine, heavily influences the fear of AI as a field. It examines three different
AI takeover narratives, "With Folded Hands", Do Androids Dream of Electric Sheep?, and
Ex Machina, in order to analyze the fears surrounding technology creators in conjunction
with the influence of societal events and systems of the times.Plan II Honors Progra
A Unified Framework for the Study of Anti-Windup Designs
We present a unified framework for the study of linear time-invariant (LTI) systems subject to control input nonlinearities. The framework is based on the following two-step design paradigm: "Design the linear controller ignoring control input nonlinearities and then add anti-windup bumpless transfer (AWBT) compensation to minimize the adverse eflects of any control input nonlinearities on closed loop performance". The resulting AWBT compensation is applicable to multivariable controllers of arbitrary structure and order. All known LTI anti-windup and/or bumpless transfer compensation schemes are shown to be special cases of this framework. It is shown how this framework can handle standard issues such as the analysis of stability and performance with or without uncertainties in the plant model. The actual analysis of stability and performance, and robustness issues are problems in their own right and hence not detailed here. The main result is the unification of existing schemes for AWBT compensation under a general framework
Enhanced Multi-Objective A* with Partial Expansion
The Multi-Objective Shortest Path Problem (MO-SPP), typically posed on a
graph, determines a set of paths from a start vertex to a destination vertex
while optimizing multiple objectives. In general, there does not exist a single
solution path that can simultaneously optimize all the objectives and the
problem thus seeks to find a set of so-called Pareto-optimal solutions. To
address this problem, several Multi-Objective A* (MOA*) algorithms were
recently developed to quickly compute solutions with quality guarantees.
However, these MOA* algorithms often suffer from high memory usage, especially
when the branching factor (i.e. the number of neighbors of any vertex) of the
graph is large. This work thus aims at reducing the high memory consumption of
MOA* with little increase in the runtime. By generalizing and unifying several
single- and multi-objective search algorithms, we develop the Runtime and
Memory Efficient MOA* (RME-MOA*) approach, which can balance between runtime
and memory efficiency by tuning two user-defined hyper-parameters.Comment: 8 pages, 4 figure
Abnormal Speech Motor Control in Individuals with 16p11.2 Deletions.
Speech and motor deficits are highly prevalent (>70%) in individuals with the 600 kb BP4-BP5 16p11.2 deletion; however, the mechanisms that drive these deficits are unclear, limiting our ability to target interventions and advance treatment. This study examined fundamental aspects of speech motor control in participants with the 16p11.2 deletion. To assess capacity for control of voice, we examined how accurately and quickly subjects changed the pitch of their voice within a trial to correct for a transient perturbation of the pitch of their auditory feedback. When compared to controls, 16p11.2 deletion carriers show an over-exaggerated pitch compensation response to unpredictable mid-vocalization pitch perturbations. We also examined sensorimotor adaptation of speech by assessing how subjects learned to adapt their sustained productions of formants (speech spectral peak frequencies important for vowel identity), in response to consistent changes in their auditory feedback during vowel production. Deletion carriers show reduced sensorimotor adaptation to sustained vowel identity changes in auditory feedback. These results together suggest that 16p11.2 deletion carriers have fundamental impairments in the basic mechanisms of speech motor control and these impairments may partially explain the deficits in speech and language in these individuals
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The Role of Phenytoin in the Treatment of Localization Related Epilepsy: An International Internet-Based Survey of Neurologists and Epileptologists
Phenytoin (PHT) has been the most widely used medication to treat both partial and generalized seizures. However, over the past twenty years, a variety of new compounds have been released with comparable efficacy, fewer adverse effects, and more predictable pharmacokinetic properties. We surveyed neurologists and epileptologists to determine current practice patterns relating to the use of PHT using an online survey instrument. A total of 200 responses were obtained though response rates for each survey question varied. Of the respondents, 78.1% were epilepsy specialists; 60% were adult practitioners; and the remainder saw either, only children or both adults and children. For new onset partial seizures only 10 respondents said PHT would be their first or second choice, while 45% reported that they would not consider PHT. This study shows that in the era of newer medications, the role of PHT has been placed in the category of a reserve medication in intractable epilepsy
Data Driven Control of Vagus Nerve Stimulation for the Cardiovascular System: An in Silico Computational Study
Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, cardiac arrhythmia and hypertension. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiovascular physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiovascular system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show that the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiovascular system
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