Results 1  10
of
14
Determinant maximization with linear matrix inequality constraints
 SIAM Journal on Matrix Analysis and Applications
, 1998
"... constraints ..."
... Identification and Model Quality Evaluation
 IEEE Transactions on Automatic Control
, 1997
"... Set membership H1 identification is investigated using timedomain data and mixed parametric and nonparametric models as well as supposing power bounded measurement errors. The problem of optimally estimating the unknown parameters and evaluating the minimal worst case identification error, called r ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
Set membership H1 identification is investigated using timedomain data and mixed parametric and nonparametric models as well as supposing power bounded measurement errors. The problem of optimally estimating the unknown parameters and evaluating the minimal worst case identification error, called radius of information, is solved. For classes of models affine in the parameters, the radius of information is obtained as function of the H1 norm of the unmodeled dynamics. A method is given for estimating this norm from the available data and some general a priori information on the unmodeled dynamics, thus allowing the actual evaluation of the radius of information. The radius represents a measure of the "predictive ability" of the considered class of models, and it is then used for comparing the quality of different classes of models and for the order selection of their parametric part. The effectiveness of the proposed procedure is tested on some numerical examples and compared with stan...
Visual Data Fusion for Objects Localization by Active Vision
, 2002
"... Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowledge representation that makes integration and fusion of new, uncertain and partial sensor measures possible. It is based on a mixture of stochastic and set membership mod ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowledge representation that makes integration and fusion of new, uncertain and partial sensor measures possible. It is based on a mixture of stochastic and set membership models. We consider that, for a large class of applications, an approximated representation is sufficient to build a preliminary map of the scene. Our approximation mainly results in ellipsoidal calculus by means of a normal assumption for stochastic laws and ellipsoidal over or inner bounding for uniform laws. These approximations allow us to build an efficient estimation process integrating visual data on line. Based on this estimation scheme, optimal exploratory motions of the camera can be automatically determined. Real time experimental results validating our approach are finally given.
On Comparing Statistical and SetBased Methods in Sensor Data Fusion
 in Proc. IEEE Int. Conf. Robot. Automat
, 1993
"... We compare the theoretical and practical considerations of two common sensor data fusion methodologies: setbased and statistically based parameter estimation. We first examine their convergence behavior for a variety of simulated problems. We then describe robot localization systems implemented usi ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
We compare the theoretical and practical considerations of two common sensor data fusion methodologies: setbased and statistically based parameter estimation. We first examine their convergence behavior for a variety of simulated problems. We then describe robot localization systems implemented using both methods and compare their performance. Our conclusion is that setbased methods have performance that sometimes exceeds that of statistical methods, although this result is highly problem dependent. We then characterize these problem dependencies. 1 Introduction Recently, it has become common to express sensor data fusion problems in terms of parameter estimation or hypothesis testing, and to solve these problems using statistical estimation methods [6]. Practically without exception, solutions apply variations of classical meansquare estimation techniques [9]. However, the efficacy of these techniques depends greatly on the character and fidelity of mathematical sensor models [12,...
Computational Tools for the Verification of Hybrid Systems
"... The hybrid systems framework provides an appealing means for verifying the safety of dynamical systems. The authors address safety... ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
The hybrid systems framework provides an appealing means for verifying the safety of dynamical systems. The authors address safety...
A SetValued Approach to FDI and FTC: Theory and Implementation Issues
"... Macau. Abstract: A complete methodology to design robust Fault Detection and Isolation (FDI) filters and Fault Tolerant Control (FTC) schemes for Linear TimeVarying (LTV) systems is proposed. The paper takes advantage of the recent advances in model invalidation using SetValued Observers (SVOs) th ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Macau. Abstract: A complete methodology to design robust Fault Detection and Isolation (FDI) filters and Fault Tolerant Control (FTC) schemes for Linear TimeVarying (LTV) systems is proposed. The paper takes advantage of the recent advances in model invalidation using SetValued Observers (SVOs) that led to the development of FDI methods for uncertain linear timevarying systems, with promising results in terms of the time required to diagnose faults. An integration of such SVObased FDI methods with robust control synthesis is described, in order to deploy new FTC algorithms that are able to stabilize the plant under faulty environments. The FDI algorithm is assessed within a wind turbine benchmark model, using MonteCarlo simulation runs.
SetMembership Filtering for DiscreteTime Systems With Nonlinear Equality Constraints
"... have close zero frequency response. The model is truncated to 20 states by means of the described quasiconvex optimization technique (QCO method), Hankel model reduction. We implement QCO method on the frequency grid with 84 samples with tolerance in bisection procedure 10 06. The optimization toge ..."
Abstract
 Add to MetaCart
have close zero frequency response. The model is truncated to 20 states by means of the described quasiconvex optimization technique (QCO method), Hankel model reduction. We implement QCO method on the frequency grid with 84 samples with tolerance in bisection procedure 10 06. The optimization together with calculating frequency samples took 74 seconds and the resulting approximation error is 2:9 1 10 05. Hankel model reduction took around 20 minutes providing the error 7:98 1 10 05. Results, see in the Fig. 1. For the given frequency interval QCO provided a better model than Hankel reduction. However, in general we do not expect QCO approximations to be better than Hankel reduction approximations. This example shows, that for large/medium scale systems we win sufficiently in time and do not really lose in approximation quality. VII. CONCLUSION In this technical note we have discussed multiinputmultioutput extension
SetMembership Fuzzy Filtering for Nonlinear DiscreteTime Systems
"... Abstract—This paper is concerned with the setmembership filtering (SMF) problem for discretetime nonlinear systems. We employ the Takagi–Sugeno (TS) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state est ..."
Abstract
 Add to MetaCart
Abstract—This paper is concerned with the setmembership filtering (SMF) problem for discretetime nonlinear systems. We employ the Takagi–Sugeno (TS) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the setmembership framework. Based on the TS fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the Sprocedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknownbutbounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discretetime nonlinear systems via fuzzy switch. Index Terms—Convex optimization, linear setmembership filtering (SMF), nonlinear SMF, unknownbutbounded noise, Takagi–Sugeno (TS) fuzzy model. I.
SetMembership Filtering with State Constraints
"... In this paper, the problem of setmembership filtering is considered for discretetime systems with equality and inequality constraints between their state variables. We formulate the problem of setmembership filtering as finding the set of estimates that belong to an ellipsoid. A centre and a shap ..."
Abstract
 Add to MetaCart
In this paper, the problem of setmembership filtering is considered for discretetime systems with equality and inequality constraints between their state variables. We formulate the problem of setmembership filtering as finding the set of estimates that belong to an ellipsoid. A centre and a shape matrix of the ellipsoid are used to describe the set of estimates and the solution to the set of estimates is obtained in terms of matrix inequality. Unknown but bounded process and measurement noises are handled under the inequality constraints by using Sprocedure. We apply Finsler’s Lemma to project the set of estimates onto the constrained surface. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state under the state constraints, which is easily implemented by semidefinite programming via interiorpoint approach. A vehicle tracking example is provided to demonstrate the effectiveness of the proposed setmembership filtering with state equality constraints. I.
Ecole Doctorale “Sciences et Technologies de l’Information des Télécommunications et des Systèmes” LASERS INP SUR CIRCUITS SILICIUM POUR APPLICATIONS EN TELECOMMUNICATIONS
, 2013
"... 1 Introduction and state of the art of IIIV silicon lasers ..."