11,046 research outputs found
Control vector parameterization with sensitivity based refinement applied to baking optimization
In bakery production, product quality attributes as crispness, brownness, crumb and water content are developed
by the transformations that occur during baking and which are initiated by heating. A quality driven procedure
requires process optimization to improve bakery production and to find operational procedures for new products.
Control vector parameterization (CVP) is an effective method for the optimization procedure. However, for accurate
optimization with a large number of parameters CVP optimization takes a long computation time. In this work, an
improved method for direct dynamic optimization using CVP is presented. The method uses a sensitivity based step
size refinement for the selection of control input parameters. The optimization starts with a coarse discretization
level for the control input in time. In successive iterations the step size was refined for the parameters for which the
performance index has a sensitivity value above a threshold value.With this selection, optimization is continued for
a selected group of input parameters while the other nonsensitive parameters (below threshold) are kept constant.
Increasing the threshold value lowers the computation time, however the obtained performance index becomes less.
A threshold value in the range of 10–20% of the mean sensitivity satisfies well. The method gives a better solution for
a lower computation effort than single run optimization with a large number of parameters or refinement procedures
without selection
Discrete Adaptive Second Order Sliding Mode Controller Design with Application to Automotive Control Systems with Model Uncertainties
Sliding mode control (SMC) is a robust and computationally efficient solution
for tracking control problems of highly nonlinear systems with a great deal of
uncertainty. High frequency oscillations due to chattering phenomena and
sensitivity to data sampling imprecisions limit the digital implementation of
conventional first order continuous-time SMC. Higher order discrete SMC is an
effective solution to reduce the chattering during the controller software
implementation, and also overcome imprecisions due to data sampling. In this
paper, a new adaptive second order discrete sliding mode control (DSMC)
formulation is presented to mitigate data sampling imprecisions and
uncertainties within the modeled plant's dynamics. The adaptation mechanism is
derived based on a Lyapunov stability argument which guarantees asymptotic
stability of the closed-loop system. The proposed controller is designed and
tested on a highly nonlinear combustion engine tracking control problem. The
simulation test results show that the second order DSMC can improve the
tracking performance up to 80% compared to a first order DSMC under sampling
and model uncertainties.Comment: 6 pages, 6 figures, 2017 American Control Conferenc
Simultaneous Variable and Covariance Selection with the Multivariate Spike-and-Slab Lasso
We propose a Bayesian procedure for simultaneous variable and covariance
selection using continuous spike-and-slab priors in multivariate linear
regression models where q possibly correlated responses are regressed onto p
predictors. Rather than relying on a stochastic search through the
high-dimensional model space, we develop an ECM algorithm similar to the EMVS
procedure of Rockova & George (2014) targeting modal estimates of the matrix of
regression coefficients and residual precision matrix. Varying the scale of the
continuous spike densities facilitates dynamic posterior exploration and allows
us to filter out negligible regression coefficients and partial covariances
gradually. Our method is seen to substantially outperform regularization
competitors on simulated data. We demonstrate our method with a re-examination
of data from a recent observational study of the effect of playing high school
football on several later-life cognition, psychological, and socio-economic
outcomes
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