687 research outputs found
An Overview of Integral Quadratic Constraints for Delayed Nonlinear and Parameter-Varying Systems
A general framework is presented for analyzing the stability and performance
of nonlinear and linear parameter varying (LPV) time delayed systems. First,
the input/output behavior of the time delay operator is bounded in the
frequency domain by integral quadratic constraints (IQCs). A constant delay is
a linear, time-invariant system and this leads to a simple, intuitive
interpretation for these frequency domain constraints. This simple
interpretation is used to derive new IQCs for both constant and varying delays.
Second, the performance of nonlinear and LPV delayed systems is bounded using
dissipation inequalities that incorporate IQCs. This step makes use of recent
results that show, under mild technical conditions, that an IQC has an
equivalent representation as a finite-horizon time-domain constraint. Numerical
examples are provided to demonstrate the effectiveness of the method for both
class of systems
Stabilization of systems with asynchronous sensors and controllers
We study the stabilization of networked control systems with asynchronous
sensors and controllers. Offsets between the sensor and controller clocks are
unknown and modeled as parametric uncertainty. First we consider multi-input
linear systems and provide a sufficient condition for the existence of linear
time-invariant controllers that are capable of stabilizing the closed-loop
system for every clock offset in a given range of admissible values. For
first-order systems, we next obtain the maximum length of the offset range for
which the system can be stabilized by a single controller. Finally, this bound
is compared with the offset bounds that would be allowed if we restricted our
attention to static output feedback controllers.Comment: 32 pages, 6 figures. This paper was partially presented at the 2015
American Control Conference, July 1-3, 2015, the US
Sampling from a system-theoretic viewpoint
This paper studies a system-theoretic approach to the problem of reconstructing an analog signal from its samples. The idea, borrowed from earlier treatments in the control literature, is to address the problem as a hybrid model-matching problem in which performance is measured by system norms. \ud
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The paper is split into three parts. In Part I we present the paradigm and revise the lifting technique, which is our main technical tool. In Part II optimal samplers and holds are designed for various analog signal reconstruction problems. In some cases one component is fixed while the remaining are designed, in other cases all three components are designed simultaneously. No causality requirements are imposed in Part II, which allows to use frequency domain arguments, in particular the lifted frequency response as introduced in Part I. In Part III the main emphasis is placed on a systematic incorporation of causality constraints into the optimal design of reconstructors. We consider reconstruction problems, in which the sampling (acquisition) device is given and the performance is measured by the -norm of the reconstruction error. The problem is solved under the constraint that the optimal reconstructor is -causal for a given i.e., that its impulse response is zero in the time interval where is the sampling period. We derive a closed-form state-space solution of the problem, which is based on the spectral factorization of a rational transfer function
Input-output stabilization of linear systems on Z
A formal framework is set up for the discussion of generalized autoregressive with external input models of the form Ay__Bu, where A and B are linear operators, with the main emphasis being on signal spaces consisting of bounded sequences parametrized by the integers. Different notions of stability are explored, and topological notions such as the idea of a closed system are linked with questions of stabilizability in this very general context. Various problems inherent in using Z as the time axis are analyzed in this operatorial framework
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