2,597 research outputs found
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems via hybrid impulsive control
This paper investigates the problem of robust normalization and guaranteed cost control for a class of uncertain singular Markovian jump systems. The uncertainties exhibit in both system matrices and transition rate matrix of the Markovian chain. A new impulsive and proportional-derivative control strategy is presented, where the derivative gain is to make the closed-loop system of the singular plant to be a normal one, and the impulsive control part is to make the value of the Lyapunov function does not increase at each time instant of the Markovian switching. A linearization approach via congruence transformations is proposed to solve the controller design problem. The cost function is minimized via solving an optimization problem under the designed control scheme. Finally, three examples (two numerical examples and an RC pulse divider circuit example) are provided to illustrate the effectiveness and applicability of the proposed methods
Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany
Impulse-Based Hybrid Motion Control
The impulse-based discrete feedback control has been proposed in previous
work for the second-order motion systems with damping uncertainties. The
sate-dependent discrete impulse action takes place at zero crossing of one of
both states, either relative position or velocity. In this paper, the proposed
control method is extended to a general hybrid motion control form. We are
using the paradigm of hybrid system modeling while explicitly specifying the
state trajectories each time the continuous system state hits the guards that
triggers impulsive control actions. The conditions for a stable convergence to
zero equilibrium are derived in relation to the control parameters, while
requiring only the upper bound of damping uncertainties to be known. Numerical
examples are shown for an underdamped closed-loop dynamics with oscillating
transients, an upper bounded time-varying positive system damping, and system
with an additional Coulomb friction damping.Comment: 6 pages, 4 figures, IEEE conferenc
A looped-functional approach for robust stability analysis of linear impulsive systems
A new functional-based approach is developed for the stability analysis of
linear impulsive systems. The new method, which introduces looped-functionals,
considers non-monotonic Lyapunov functions and leads to LMIs conditions devoid
of exponential terms. This allows one to easily formulate dwell-times results,
for both certain and uncertain systems. It is also shown that this approach may
be applied to a wider class of impulsive systems than existing methods. Some
examples, notably on sampled-data systems, illustrate the efficiency of the
approach.Comment: 13 pages, 2 figures, Accepted at Systems & Control Letter
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