44,558 research outputs found
DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control
Precise arbitrary trajectory tracking for quadrotors is challenging due to
unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To
tackle these challenges, we present Deep Adaptive Trajectory Tracking (DATT), a
learning-based approach that can precisely track arbitrary, potentially
infeasible trajectories in the presence of large disturbances in the real
world. DATT builds on a novel feedforward-feedback-adaptive control structure
trained in simulation using reinforcement learning. When deployed on real
hardware, DATT is augmented with a disturbance estimator using L1 adaptive
control in closed-loop, without any fine-tuning. DATT significantly outperforms
competitive adaptive nonlinear and model predictive controllers for both
feasible smooth and infeasible trajectories in unsteady wind fields, including
challenging scenarios where baselines completely fail. Moreover, DATT can
efficiently run online with an inference time less than 3.2 ms, less than 1/4
of the adaptive nonlinear model predictive control baselin
Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement
The goal of many early visual filtering processes is to remove noise while at the same time sharpening contrast. An historical succession of approaches to this problem, starting with the use of simple derivative and smoothing operators, and the subsequent realization of the relationship between scale-space and the isotropic dfffusion equation, has recently resulted in the development of "geometry-driven" dfffusion. Nonlinear and anisotropic diffusion methods, as well as image-driven nonlinear filtering, have provided improved performance relative to the older isotropic and linear diffusion techniques. These techniques, which either explicitly or implicitly make use of kernels whose shape and center are functions of local image structure are too computationally expensive for use in real-time vision applications. In this paper, we show that results which are largely equivalent to those obtained from geometry-driven diffusion can be achieved by a process which is conceptually separated info two very different functions. The first involves the construction of a vector~field of "offsets", defined on a subset of the original image, at which to apply a filter. The offsets are used to displace filters away from boundaries to prevent edge blurring and destruction. The second is the (straightforward) application of the filter itself. The former function is a kind generalized image skeletonization; the latter is conventional image filtering. This formulation leads to results which are qualitatively similar to contemporary nonlinear diffusion methods, but at computation times that are roughly two orders of magnitude faster; allowing applications of this technique to real-time imaging. An additional advantage of this formulation is that it allows existing filter hardware and software implementations to be applied with no modification, since the offset step reduces to an image pixel permutation, or look-up table operation, after application of the filter
Hardware Coupled Nonliear Oscillators as a Model of Retina
An electronic circuit consisting of coupled nonlinear oscillatorsâŽïŒâ” simulates the spatiotemporal processing in retina. Complex behavior recorded in vivo from ganglion cells in the cat retina 6 in response to flickering light spots is matched by setting the coupling parameters in the hardware oscillators. An electronic neuron (c-neuron) is composed of four coupled oscillators: three representing the light driven generator potential of the ganglion cell, the other representing membrane spiking. A 1-D ring of e-neurons reflects the connectivity in the retina: strong neighborhood excitation, and wider inhibition. E-neurons, like retinal ganglion cells, exhibit spontaneous spiking. Driving more than one e-neuron with a sinusoidally modulated input increases regularity in the e-neurons responses, as is found in the retina. We encoded c-neuron activity into single-bit spike trains and found chaotic spontaneous oscillations using close return histograms. The model's behavior gives a new understanding of neurophysiological findings.Whitehall (S93-24); Air Force Office of Scientific Research (F49620-92-J-0499, F49620-92-J-0334); Office of Naval Research (N00014-89-J-1377, N00014-95-I-0409); MIT Undergraduate Research Oppurtunities Progra
PID control system analysis, design, and technology
Designing and tuning a proportional-integral-derivative
(PID) controller appears to be conceptually intuitive, but can
be hard in practice, if multiple (and often conflicting) objectives
such as short transient and high stability are to be achieved.
Usually, initial designs obtained by all means need to be adjusted
repeatedly through computer simulations until the closed-loop
system performs or compromises as desired. This stimulates
the development of "intelligent" tools that can assist engineers
to achieve the best overall PID control for the entire operating
envelope. This development has further led to the incorporation
of some advanced tuning algorithms into PID hardware modules.
Corresponding to these developments, this paper presents a
modern overview of functionalities and tuning methods in patents,
software packages and commercial hardware modules. It is seen
that many PID variants have been developed in order to improve
transient performance, but standardising and modularising PID
control are desired, although challenging. The inclusion of system
identification and "intelligent" techniques in software based PID
systems helps automate the entire design and tuning process to
a useful degree. This should also assist future development of
"plug-and-play" PID controllers that are widely applicable and
can be set up easily and operate optimally for enhanced productivity,
improved quality and reduced maintenance requirements
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed
properties, such as stability or safety, and have been successfully applied to
the domain of robotics. However, model uncertainty remains a persistent
challenge, weakening theoretical guarantees and causing implementation failures
on physical systems. This paper develops a machine learning framework centered
around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and
unmodeled dynamics in general robotic systems. Our proposed method proceeds by
iteratively updating estimates of Lyapunov function derivatives and improving
controllers, ultimately yielding a stabilizing quadratic program model-based
controller. We validate our approach on a planar Segway simulation,
demonstrating substantial performance improvements by iteratively refining on a
base model-free controller
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