3 research outputs found
Sparse Sensing and Optimal Precision: Robust Optimal Observer Design with Model Uncertainty
We present a framework which incorporates three aspects of the estimation
problem, namely, sparse sensor configuration, optimal precision, and robustness
in the presence of model uncertainty. The problem is formulated in the
optimal observer design framework. We consider two types
of uncertainties in the system, i.e. structured affine and unstructured
uncertainties. The objective is to design an observer with a given
performance index with minimal number of sensors and
minimal precision values, while guaranteeing the performance for all admissible
uncertainties. The problem is posed as a convex optimization problem subject to
linear matrix inequalities. Numerical simulations demonstrate the application
of the theoretical results presented in this work
Sparse Sensing and Optimal Precision: An Integrated Framework for Optimal Observer Design
In this paper, we simultaneously determine the optimal sensor precision and
the observer gain, which achieves the specified accuracy in the state
estimates. Along with the unknown observer gain, the formulation parameterizes
the scaling of the exogenous inputs that correspond to the sensor noise.
Reciprocal of this scaling is defined as the sensor precision, and sparseness
is achieved by minimizing the norm of the precision vector. The
optimization is performed with constraints guaranteeing specified accuracy in
state estimates, which are defined in terms of or
norms of the error dynamics. The results presented in
this paper are applied to the linearized longitudinal model of an F-16
aircraft
Sensor Selection and Optimal Precision in Estimation Framework: Theory and Algorithms
We consider the problem of sensor selection for designing observer and filter
for continuous linear time invariant systems such that the sensor precisions
are minimized, and the estimation errors are bounded by the prescribed
performance criteria. The proposed
integrated framework formulates the precision minimization as a convex
optimization problem subject to linear matrix inequalities, and it is solved
using an algorithm based on the alternating direction method of multipliers
(ADMM). We also present a greedy approach for sensor selection and demonstrate
the performance of the proposed algorithms using numerical simulations