218 research outputs found
Nonlinear Compressive Particle Filtering
Many systems for which compressive sensing is used today are dynamical. The
common approach is to neglect the dynamics and see the problem as a sequence of
independent problems. This approach has two disadvantages. Firstly, the
temporal dependency in the state could be used to improve the accuracy of the
state estimates. Secondly, having an estimate for the state and its support
could be used to reduce the computational load of the subsequent step. In the
linear Gaussian setting, compressive sensing was recently combined with the
Kalman filter to mitigate above disadvantages. In the nonlinear dynamical case,
compressive sensing can not be used and, if the state dimension is high, the
particle filter would perform poorly. In this paper we combine one of the most
novel developments in compressive sensing, nonlinear compressive sensing, with
the particle filter. We show that the marriage of the two is essential and that
neither the particle filter or nonlinear compressive sensing alone gives a
satisfying solution.Comment: Accepted to CDC 201
Identification of Structured LTI MIMO State-Space Models
The identification of structured state-space model has been intensively
studied for a long time but still has not been adequately addressed. The main
challenge is that the involved estimation problem is a non-convex (or bilinear)
optimization problem. This paper is devoted to developing an identification
method which aims to find the global optimal solution under mild computational
burden. Key to the developed identification algorithm is to transform a
bilinear estimation to a rank constrained optimization problem and further a
difference of convex programming (DCP) problem. The initial condition for the
DCP problem is obtained by solving its convex part of the optimization problem
which happens to be a nuclear norm regularized optimization problem. Since the
nuclear norm regularized optimization is the closest convex form of the
low-rank constrained estimation problem, the obtained initial condition is
always of high quality which provides the DCP problem a good starting point.
The DCP problem is then solved by the sequential convex programming method.
Finally, numerical examples are included to show the effectiveness of the
developed identification algorithm.Comment: Accepted to IEEE Conference on Decision and Control (CDC) 201
Robust inversion based fault estimation for discrete-time LPV systems
The article presents a state-space based Fault Diagnosis (FD) method for discrete-time, affine Linear Parameter Varying (LPV) systems. The goal of the technical note is to develop a robust and dynamic inversion based technique for systems with parameter varying representations when an additive, exogenous disturbance signal perturbs the system. After applying geometric concepts for explicit fault inversion, a robust strategy is proposed to attenuate the effect of the unknown disturbance input signal on the fault estimation error. The proposed robust observer is derived as a solution of off-line Linear Matrix Inequality (LMI) conditions. The technical note demonstrates the viability of the novel methodology through a numerical example
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