13,470 research outputs found
Co-design of aperiodic sampled-data min-jumping rules for linear impulsive, switched impulsive and sampled-data systems
Co-design conditions for the design of a jumping-rule and a sampled-data
control law for impulsive and impulsive switched systems subject to aperiodic
sampled-data measurements are provided. Semi-infinite discrete-time
Lyapunov-Metzler conditions are first obtained. As these conditions are
difficult to check and generalize to more complex systems, an equivalent
formulation is provided in terms of clock-dependent (infinite-dimensional)
matrix inequalities. These conditions are then, in turn, approximated by a
finite-dimensional optimization problem using a sum of squares based
relaxation. It is proven that the sum of squares relaxation is non conservative
provided that the degree of the polynomials is sufficiently large. It is
emphasized that acceptable results are obtained for low polynomial degrees in
the considered examples.Comment: 27 pages; 5 figure
Cooperative Global Robust Stabilization for a Class of Nonlinear Multi-Agent Systems and its Application
This paper studies the cooperative global robust stabilization problem for a
class of nonlinear multi-agent systems. The problem is motivated from the study
of the cooperative global robust output regulation problem for the class of
nonlinear multi-agent systems in normal form with unity relative degree which
was studied recently under the conditions that the switching network is
undirected and some nonlinear functions satisfy certain growth condition. We
first solve the stabilization problem by using the multiple Lyapunov functions
approach and the average dwell time method. Then, we apply this result to the
cooperative global robust output regulation problem for the class of nonlinear
systems in normal form with unity relative degree under directed switching
network, and have removed the conditions that the switching network is
undirected and some nonlinear functions satisfy certain growth condition.Comment: 9 pages, 1 figure. This paper was submitted to the journal
"Automatica
Stabilization Based Networked Predictive Controller Design for Switched Plants
Stabilizing state feedback controller has been designed in this paper for a
switched DC motor plant, controlled over communication network. The switched
system formulation for the networked control system (NCS) with additional
switching in a plant parameter along with the switching due to random packet
losses, have been formulated as few set of non-strict Linear Matrix
Inequalities (LMIs). In order to solve non-strict LMIs using standard LMI
solver and to design the stabilizing state feedback controller, the Cone
Complementary Linearization (CCL) technique has been adopted. Simulation
studies have been carried out for a DC motor plant, operating at two different
sampling times with random switching in the moment of inertia, representing
sudden jerks.Comment: 6 pages, 4 figure
Convex lifted conditions for robust stability analysis and stabilization of linear discrete-time switched systems
Stability analysis of discrete-time switched systems under minimum dwell-time
is studied using a new type of LMI conditions. These conditions are convex in
the matrices of the system and shown to be equivalent to the nonconvex
conditions proposed by Geromel and Colaneri. The convexification of the
conditions is performed by a lifting process which introduces a moderate number
of additional decision variables. The convexity of the conditions can be
exploited to extend the results to uncertain systems, control design and
-gain computation without introducing additional conservatism. Several
examples are presented to show the effectiveness of the approach.Comment: 9 pages, 3 figure
A para-model agent for dynamical systems
Consider a dynamical system where
is a nonlinear (convex or nonconvex) function, or a combination of
nonlinear functions that can eventually switch. We present, in this preliminary
work, a generalization of the standard model-free control, that can either
control the dynamical system, given an output reference trajectory, or optimize
the dynamical system as a derivative-free optimization based "extremum-seeking"
procedure. Multiple applications are presented and the robustness of the
proposed method is studied in simulation.Comment: 41 pages, 38 figures, partially presented at the French Symposium of
Electrical Engineering in Grenoble, Jun. 2016 and at the Sparse days in St
Girons III, Jul. 201
Stability analysis of positive semi-Markovian jump linear systems with state resets
This paper studies the mean stability of positive semi-Markovian jump linear
systems. We show that their mean stability is characterized by the spectral
radius of a matrix that is easy to compute. In deriving the condition we use a
certain discretization of a semi-Markovian jump linear system that preserves
stability. Also we show a characterization for the exponential mean stability
of continuous-time positive Markovian jump linear systems. Numerical examples
are given to illustrate the results
Input-Output Finite-Time Stability
This paper introduces the extension of Finite-Time Stability (FTS) to the
input-output case, namely the Input-Output FTS (IO-FTS). The main differences
between classic IO stability and IO-FTS are that the latter involves signals
defined over a finite time interval, does not necessarily require the inputs
and outputs to belong to the same class of signals, and that quantitative
bounds on both inputs and outputs must be specified. This paper revises some
recent results on IO-FTS, both in the context of linear systems and in the
context of switching systems. In the final example the proposed methodology is
used to minimize the maximum displacement and velocity of a building subject to
an earthquake of given magnitude.Comment: 14 pages, 9 figures, 2 tables. This paper has been accepted for
presentation at AUTOMATICA.IT, Convegno Annuale dei Docenti e Ricercatori
Italiani in Automatica, Pisa, Italy, September 201
Model Predictive Control for Regular Linear Systems
The present work extends known finite-dimensional constrained optimal control
realizations to the realm of well-posed regular linear infinite-dimensional
systems modelled by partial differential equations. The structure-preserving
Cayley-Tustin transformation is utilized to approximate the continuous-time
system by a discrete-time model representation without using any spatial
discretization or model reduction. The discrete-time model is utilized in the
design of model predictive controller accounting for optimality, stabilization,
and input and output/state constraints in an explicit way. The proposed model
predictive controller is dual-mode in the sense that predictive controller
steers the state to a set where exponentially stabilizing unconstrained
feedback can be utilized without violating the constraints. The construction of
the model predictive controller leads to a finite-dimensional constrained
quadratic optimization problem easily solvable by standard numerical methods.
Two representative examples of partial differential equations are considered.Comment: 19 pages, 4 figure
Robust stability and stabilization of uncertain linear positive systems via Integral Linear Constraints: L1- and Linfinity-gains characterization
Copositive linear Lyapunov functions are used along with dissipativity theory
for stability analysis and control of uncertain linear positive systems. Unlike
usual results on linear systems, linear supply-rates are employed here for
robustness and performance analysis using L1- and Linfinity-gains. Robust
stability analysis is performed using Integral Linear Constraints (ILCs) for
which several classes of uncertainties are discussed. The approach is then
extended to robust stabilization and performance optimization. The obtained
results are expressed in terms of robust linear programming problems that are
equivalently turned into finite dimensional ones using Handelman's Theorem.
Several examples are provided for illustration.Comment: Accepted in the International Journal of Robust and Nonlinear Contro
Fractional-order Generalized Principle of Self-Support (FOG PSS) in Control Systems Design
This paper reviews research that studies the principle of self-support (PSS)
in some control systems and proposes a fractional-order generalized PSS
framework for the first time. The existing PSS approach focuses on practical
tracking problem of integer-order systems including robotic dynamics, high
precision linear motor system, multi-axis high precision positioning system
with unmeasurable variables, imprecise sensor information, uncertain parameters
and external disturbances. More generally, by formulating the fractional PSS
concept as a new generalized framework, we will focus in the possible fields on
the fractional-order control problems such as practical tracking,
-tracking, etc. of robot systems, multiple mobile agents, discrete
dynamical systems, time delay systems and other uncertain nonlinear systems.
Finally, the practical tracking of a first-order uncertain model of automobile
is considered as a simple example to demonstrate the efficiency of the
fractional-order generalized principle of self-support (FOGPSS) control
strategy
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