39 research outputs found
Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations
A control design approach is developed for a general class of uncertain
strict-feedback-like nonlinear systems with dynamic uncertain input
nonlinearities with time delays. The system structure considered in this paper
includes a nominal uncertain strict-feedback-like subsystem, the input signal
to which is generated by an uncertain nonlinear input unmodeled dynamics that
is driven by the entire system state (including unmeasured state variables) and
is also allowed to depend on time delayed versions of the system state variable
and control input signals. The system also includes additive uncertain
nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
input nonlinearities with time-varying uncertain time delays. The proposed
control design approach provides a globally stabilizing delay-independent
robust adaptive output-feedback dynamic controller based on a dual dynamic
high-gain scaling based structure.Comment: 2017 IEEE International Carpathian Control Conference (ICCC
High-Dimensional Controller Tuning through Latent Representations
In this paper, we propose a method to automatically and efficiently tune
high-dimensional vectors of controller parameters. The proposed method first
learns a mapping from the high-dimensional controller parameter space to a
lower dimensional space using a machine learning-based algorithm. This mapping
is then utilized in an actor-critic framework using Bayesian optimization (BO).
The proposed approach is applicable to complex systems (such as quadruped
robots). In addition, the proposed approach also enables efficient
generalization to different control tasks while also reducing the number of
evaluations required while tuning the controller parameters. We evaluate our
method on a legged locomotion application. We show the efficacy of the
algorithm in tuning the high-dimensional controller parameters and also
reducing the number of evaluations required for the tuning. Moreover, it is
shown that the method is successful in generalizing to new tasks and is also
transferable to other robot dynamics
Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection
This paper proposes a data-efficient detection method for deep neural
networks against backdoor attacks under a black-box scenario. The proposed
approach is motivated by the intuition that features corresponding to triggers
have a higher influence in determining the backdoored network output than any
other benign features. To quantitatively measure the effects of triggers and
benign features on determining the backdoored network output, we introduce five
metrics. To calculate the five-metric values for a given input, we first
generate several synthetic samples by injecting the input's partial contents
into clean validation samples. Then, the five metrics are computed by using the
output labels of the corresponding synthetic samples. One contribution of this
work is the use of a tiny clean validation dataset. Having the computed five
metrics, five novelty detectors are trained from the validation dataset. A meta
novelty detector fuses the output of the five trained novelty detectors to
generate a meta confidence score. During online testing, our method determines
if online samples are poisoned or not via assessing their meta confidence
scores output by the meta novelty detector. We show the efficacy of our
methodology through a broad range of backdoor attacks, including ablation
studies and comparison to existing approaches. Our methodology is promising
since the proposed five metrics quantify the inherent differences between clean
and poisoned samples. Additionally, our detection method can be incrementally
improved by appending more metrics that may be proposed to address future
advanced attacks.Comment: Published in the IEEE Transactions on Information Forensics and
Securit
Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance
Control barrier functions (CBFs) provide a simple yet effective way for safe
control synthesis. Recently, work has been done using differentiable
optimization (diffOpt) based methods to systematically construct CBFs for
static obstacle avoidance tasks between geometric shapes. In this work, we
extend the application of diffOpt CBFs to perform dynamic obstacle avoidance
tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can
perform obstacle avoidance for dynamic geometric obstacles. Additionally, we
show how to extend the TVCBF constraint to consider measurement noise and
actuation limits. To demonstrate the efficacy of our proposed approach, we
first compare its performance with a model predictive control based method and
a circular CBF based method on a simulated dynamic obstacle avoidance task.
Then, we demonstrate the performance of our proposed approach in experimental
studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator
Aerial Manipulator Force Control Using Control Barrier Functions
This article studies the problem of applying normal forces on a surface,
using an underactuated aerial vehicle equipped with a dexterous robotic arm. A
force-motion high-level controller is designed based on a Lyapunov function
encompassing alignment and exerted force errors. This controller is coupled
with a Control Barrier Function constraint under an optimization scheme using
Quadratic Programming. This aims to enforce a prescribed relationship between
the approaching motion for the end-effector and its alignment with the surface,
thus ensuring safe operation. An adaptive low-level controller is devised for
the aerial vehicle, capable of tracking velocity commands generated by the
high-level controller. Simulations are presented to demonstrate the force
exertion stability and safety of the controller in cases of large disturbances
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs
We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal
portfolio mean-variance preferences in the setting of multivariate generalized
autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on
trading. A numerical solution is obtained using a neural network (NN)
architecture within a recursive RL loop. A fixed-point theorem proves that NN
approximation error has a big-oh bound that we can reduce by increasing the
number of NN parameters. The functional form of the trading penalty has a
parameter that controls the magnitude of transaction costs. When
is small, we can implement an NN algorithm based on the expansion of
the solution in powers of . This expansion has a base term equal to a
myopic solution with an explicit form, and a first-order correction term that
we compute in the RL loop. Our expansion-based algorithm is stable, allows for
fast computation, and outputs a solution that shows positive testing
performance
The Impact of Visual Impairment on Quality of Life
Our goal was to identify and describe factors relating to quality of life (QOL) in subjects with low vision and blindness in Iran's Sistan and Baluchestan Province. This cross-sectional study was carried out in randomly selected subjects with vision disability who were covered by the Zahedan Welfare Organization in Zahedan, Iran. The following factors related to visual impairment were evaluated: visual field (VF), visual acuity (VA), and stereopsis. Data were collected using a demographic questionnaire and the Influence of Vision Impairment (IVI) questionnaire. One-hundred and twenty-one patients were enrolled for participation in the study. T-test analyses indicated that the mean QOL score for women was significantly lower than that for men (P < 0.001). Mann-Whitney U tests indicated that mean social (P = 0.003) and leisure (P = 0.009) QOL scores were significantly lower in participants without stereopsis. In addition, participants with tunnel vision scored lower on the mobility and self-care categories (P < 0.001) than others. The results of this study indicate that providing education, providing employment, improving, and expanding social programs for the blind and individuals with low vision people, especially women, are necessary.脗