328 research outputs found
Markov Parameter Identification via Chebyshev Approximation
This paper proposes an identification algorithm for Single Input Single
Output (SISO) Linear Time-Invariant (LTI) systems. In the noise-free setting,
where the first Markov parameters can be precisely estimated, all Markov
parameters can be inferred by the linear combination of the known Markov
parameters, of which the coefficients are obtained by solving the uniform
polynomial approximation problem, and the upper bound of the asymptotic
identification bias is provided. For the finite-time identification scenario,
we cast the system identification problem with noisy Markov parameters into a
regularized uniform approximation problem. Numerical results demonstrate that
the proposed algorithm outperforms the conventional Ho-Kalman Algorithm for the
finite-time identification scenario while the asymptotic bias remains
negligible.Comment: Accepted by IFAC World Congress (IFAC WC 2023) Conferenc
Finite Time Performance Analysis of MIMO Systems Identification
This paper is concerned with the finite time identification performance of an
n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear
Time-Invariant system, with p inputs and m outputs. We prove that the
widely-used Ho-Kalman algorithm and Multivariable Output Error State Space
(MOESP) algorithm are ill-conditioned for MIMO system when n/m or n/p is large.
Moreover, by analyzing the Cramer-Rao bound, we derive a fundamental limit for
identifying the real and stable (or marginally stable) poles of MIMO system and
prove that the sample complexity for any unbiased pole estimation algorithm to
reach a certain level of accuracy explodes superpolynomially with respect to
n/(pm). Numerical results are provided to illustrate the ill-conditionedness of
Ho-Kalman algorithm and MOESP algorithm as well as the fundamental limit on
identification.Comment: 9 pages, 4 figure
Linear Model Predictive Control under Continuous Path Constraints via Parallelized Primal-Dual Hybrid Gradient Algorithm
In this paper, we consider a Model Predictive Control(MPC) problem of a
continuous time linear time-invariant system under continuous time path
constraints on the states and the inputs. By leveraging the concept of
differential flatness, we can replace the differential equations governing the
system with linear mapping between the states, inputs and the flat outputs (and
their derivatives). The flat output is then parameterized by piecewise
polynomials and the model predictive control problem can be equivalently
transformed into an Semi-Definite Programming (SDP) problem via Sum-of-Squares
with guaranteed constraint satisfaction at every continuous time instant. We
further observe that the SDP problem contains a large number of small-size
semi-definite matrices as optimization variables, and thus a Primal-Dual Hybrid
Gradient (PDHF) algorithm, which can be efficiently parallelized, is developed
to accelerate the optimization procedure. Simulation on a quadruple-tank
process illustrates that our formulation can guarantee strict constraint
satisfaction, while the standard MPC controller based on discretized system may
violate the constraint in between a sampling period. On the other hand, we
should that the our parallelized PDHG algorithm can outperform commercial
solvers for problems with long planning horizon
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
Graph Representation Learning (GRL) is an influential methodology, enabling a
more profound understanding of graph-structured data and aiding graph
clustering, a critical task across various domains. The recent incursion of
attention mechanisms, originally an artifact of Natural Language Processing
(NLP), into the realm of graph learning has spearheaded a notable shift in
research trends. Consequently, Graph Attention Networks (GATs) and Graph
Attention Auto-Encoders have emerged as preferred tools for graph clustering
tasks. Yet, these methods primarily employ a local attention mechanism, thereby
curbing their capacity to apprehend the intricate global dependencies between
nodes within graphs. Addressing these impediments, this study introduces an
innovative method known as the Graph Transformer Auto-Encoder for Graph
Clustering (GTAGC). By melding the Graph Auto-Encoder with the Graph
Transformer, GTAGC is adept at capturing global dependencies between nodes.
This integration amplifies the graph representation and surmounts the
constraints posed by the local attention mechanism. The architecture of GTAGC
encompasses graph embedding, integration of the Graph Transformer within the
autoencoder structure, and a clustering component. It strategically alternates
between graph embedding and clustering, thereby tailoring the Graph Transformer
for clustering tasks, whilst preserving the graph's global structural
information. Through extensive experimentation on diverse benchmark datasets,
GTAGC has exhibited superior performance against existing state-of-the-art
graph clustering methodologies
Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control
Inertia drift is an aggressive transitional driving maneuver, which is
challenging due to the high nonlinearity of the system and the stringent
requirement on control and planning performance. This paper presents a solution
for the consecutive inertia drift of an autonomous RC car based on
primitive-based planning and data-driven control. The planner generates complex
paths via the concatenation of path segments called primitives, and the
controller eases the burden on feedback by interpolating between multiple real
trajectories with different initial conditions into one near-feasible reference
trajectory. The proposed strategy is capable of drifting through various paths
containing consecutive turns, which is validated in both simulation and
reality.Comment: 9 pages, 10 figures, to appear to IROS 202
Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks
The vulnerability of deep neural networks (DNNs) to adversarial examples is
well documented. Under the strong white-box threat model, where attackers have
full access to DNN internals, recent work has produced continual advancements
in defenses, often followed by more powerful attacks that break them.
Meanwhile, research on the more realistic black-box threat model has focused
almost entirely on reducing the query-cost of attacks, making them increasingly
practical for ML models already deployed today.
This paper proposes and evaluates Blacklight, a new defense against black-box
adversarial attacks. Blacklight targets a key property of black-box attacks: to
compute adversarial examples, they produce sequences of highly similar images
while trying to minimize the distance from some initial benign input. To detect
an attack, Blacklight computes for each query image a compact set of one-way
hash values that form a probabilistic fingerprint. Variants of an image produce
nearly identical fingerprints, and fingerprint generation is robust against
manipulation. We evaluate Blacklight on 5 state-of-the-art black-box attacks,
across a variety of models and classification tasks. While the most efficient
attacks take thousands or tens of thousands of queries to complete, Blacklight
identifies them all, often after only a handful of queries. Blacklight is also
robust against several powerful countermeasures, including an optimal black-box
attack that approximates white-box attacks in efficiency. Finally, Blacklight
significantly outperforms the only known alternative in both detection coverage
of attack queries and resistance against persistent attackers
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