4,158 research outputs found
Optimal control of discrete-time switched linear systems via continuous parameterization
The paper presents a novel method for designing an optimal controller for
discrete-time switched linear systems. The problem is formulated as one of
computing the discrete mode sequence and the continuous input sequence that
jointly minimize a quadratic performance index. State-of-art methods for
solving such a control problem suffer in general from a high computational
requirement due to the fact that an exponential number of switching sequences
must be explored. The method of this paper addresses the challenge of the
switching law design by introducing auxiliary continuous input variables and
then solving a non-smooth block-sparsity inducing optimization problem.Comment: 6 pages, 2 figures, 2 tables; To appear in the Proceedings of IFAC
World Congress, 201
Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors
This paper presents a detection algorithm for sensor attacks and a resilient
state estimation scheme for a class of uniformly observable nonlinear systems.
An adversary is supposed to corrupt a subset of sensors with the possibly
unbounded signals, while the system has sensor redundancy. We design an
individual high-gain observer for each measurement output so that only the
observable portion of the system state is obtained. Then, a nonlinear error
correcting problem is solved by collecting all the information from those
partial observers and exploiting redundancy. A computationally efficient,
on-line monitoring scheme is presented for attack detection. Based on the
attack detection scheme, an algorithm for resilient state estimation is
provided. The simulation results demonstrate the effectiveness of the proposed
algorithm
Analysis of A Nonsmooth Optimization Approach to Robust Estimation
In this paper, we consider the problem of identifying a linear map from
measurements which are subject to intermittent and arbitarily large errors.
This is a fundamental problem in many estimation-related applications such as
fault detection, state estimation in lossy networks, hybrid system
identification, robust estimation, etc. The problem is hard because it exhibits
some intrinsic combinatorial features. Therefore, obtaining an effective
solution necessitates relaxations that are both solvable at a reasonable cost
and effective in the sense that they can return the true parameter vector. The
current paper discusses a nonsmooth convex optimization approach and provides a
new analysis of its behavior. In particular, it is shown that under appropriate
conditions on the data, an exact estimate can be recovered from data corrupted
by a large (even infinite) number of gross errors.Comment: 17 pages, 9 figure
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