3,900 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
Multi-innovation stochastic gradient algorithms for dual-rate sampled systems with preload nonlinearity
AbstractSince the stochastic gradient algorithm has a slower convergence rate, this letter presents a multi-innovation stochastic gradient algorithm for a class of dual-rate sampled systems with preload nonlinearity. The basic idea is to transform the dual-rate system model into an identification model which can use dual-rate data by using the polynomial transformation technique. A simulation example is provided to verify the effectiveness of the proposed method
Economic scheduling in electric power systems: a mathematical model for the U.A.E
Economic scheduling in electric power systems: a mathematical model for the U.A.
Parameter and State Estimator for State Space Models
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective
Parameter Estimation from Time-Series Data with Correlated Errors: A Wavelet-Based Method and its Application to Transit Light Curves
We consider the problem of fitting a parametric model to time-series data
that are afflicted by correlated noise. The noise is represented by a sum of
two stationary Gaussian processes: one that is uncorrelated in time, and
another that has a power spectral density varying as . We present
an accurate and fast [O(N)] algorithm for parameter estimation based on
computing the likelihood in a wavelet basis. The method is illustrated and
tested using simulated time-series photometry of exoplanetary transits, with
particular attention to estimating the midtransit time. We compare our method
to two other methods that have been used in the literature, the time-averaging
method and the residual-permutation method. For noise processes that obey our
assumptions, the algorithm presented here gives more accurate results for
midtransit times and truer estimates of their uncertainties.Comment: Accepted in ApJ. Illustrative code may be found at
http://www.mit.edu/~carterja/code/ . 17 page
Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems
This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One
method is the missing output estimation based stochastic gradient identification algorithm and the other
method is the auxiliary model based stochastic gradient identification algorithm. Different from the
polynomial transformation based identification methods, the two methods in this paper can estimate
the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the
proposed methods
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
The performance of a feedforward controller is primarily determined by the
extent to which it can capture the relevant dynamics of a system. The aim of
this paper is to develop an input-output linear parameter-varying (LPV)
feedforward parameterization and a corresponding data-driven estimation method
in which the dependency of the coefficients on the scheduling signal are
learned by a neural network. The use of a neural network enables the
parameterization to compensate a wide class of constant relative degree LPV
systems. Efficient optimization of the neural-network-based controller is
achieved through a Levenberg-Marquardt approach with analytic gradients and a
pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The
performance of the developed feedforward learning method is validated in a
simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE
Conference on Decision and Control, Singapore, 202
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