In this paper, we investigate the estimator-based
output feedback control problem of multi-delay systems. This
work is an extension of recently developed operator-value LMI
framework for infinite-dimensional time-delay systems. Based
on the optimal convex state feedback controller and generalized
Luenberger observer synthesis conditions we already have,
the estimator-based output feedback controller is designed to
contain the estimates of both the present state and history of
the state. An output feedback controller synthesis condition is
proposed using SOS method, which is expressed in a set of
LMI/SDP constraints. The simulation examples are displayed
to demonstrate the effectiveness and advantages of the proposed
results.
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Finite-Horizon Separation-Based Covariance Control for Discrete-Time Stochastic Linear Systems
In this paper, we address a finite-horizon stochastic optimal control problem with covariance assignment and input energy constraints for discrete-time stochastic linear systems with partial state information. In our approach, we consider separation-based control policies that correspond to sequences of control laws that are affine functions of either the complete history of the output estimation errors, that is, the differences between the actual output measurements and their corresponding estimated outputs produced by a discrete-time Kalman filter, or a truncation of the same history. This particular feedback parametrization allows us to associate the stochastic optimal control problem with a tractable semidefinite (convex) program. We argue that the proposed procedure for the reduction of the stochastic optimal control problem to a convex program has significant advantages in terms of improved scalability and tractability over the approaches proposed in the relevant literature.
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- Award ID(s):
- 1753687
- NSF-PAR ID:
- 10128476
- Date Published:
- Journal Name:
- 2018 IEEE Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 3299 to 3304
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC.2018.8619542
