388 research outputs found
Towards Efficient Maximum Likelihood Estimation of LPV-SS Models
How to efficiently identify multiple-input multiple-output (MIMO) linear
parameter-varying (LPV) discrete-time state-space (SS) models with affine
dependence on the scheduling variable still remains an open question, as
identification methods proposed in the literature suffer heavily from the curse
of dimensionality and/or depend on over-restrictive approximations of the
measured signal behaviors. However, obtaining an SS model of the targeted
system is crucial for many LPV control synthesis methods, as these synthesis
tools are almost exclusively formulated for the aforementioned representation
of the system dynamics. Therefore, in this paper, we tackle the problem by
combining state-of-the-art LPV input-output (IO) identification methods with an
LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step.
The resulting modular LPV-SS identification approach achieves statical
efficiency with a relatively low computational load. The method contains the
following three steps: 1) estimation of the Markov coefficient sequence of the
underlying system using correlation analysis or Bayesian impulse response
estimation, then 2) LPV-SS realization of the estimated coefficients by using a
basis reduced Ho-Kalman method, and 3) refinement of the LPV-SS model estimate
from a maximum-likelihood point of view by a gradient-based or an
expectation-maximization optimization methodology. The effectiveness of the
full identification scheme is demonstrated by a Monte Carlo study where our
proposed method is compared to existing schemes for identifying a MIMO LPV
system
Retrospective Cost Adaptive Control for Systems with Unknown Nonminimum-Phase Zeros
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90721/1/AIAA-2011-6203-626.pd
Adaptive Controller Placement for Wireless Sensor-Actuator Networks with Erasure Channels
Wireless sensor-actuator networks offer flexibility for control design. One
novel element which may arise in networks with multiple nodes is that the role
of some nodes does not need to be fixed. In particular, there is no need to
pre-allocate which nodes assume controller functions and which ones merely
relay data. We present a flexible architecture for networked control using
multiple nodes connected in series over analog erasure channels without
acknowledgments. The control architecture proposed adapts to changes in network
conditions, by allowing the role played by individual nodes to depend upon
transmission outcomes. We adopt stochastic models for transmission outcomes and
characterize the distribution of controller location and the covariance of
system states. Simulation results illustrate that the proposed architecture has
the potential to give better performance than limiting control calculations to
be carried out at a fixed node.Comment: 10 pages, 8 figures, to be published in Automatic
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