12,145 research outputs found
Contingency Model Predictive Control for Automated Vehicles
We present Contingency Model Predictive Control (CMPC), a novel and
implementable control framework which tracks a desired path while
simultaneously maintaining a contingency plan -- an alternate trajectory to
avert an identified potential emergency. In this way, CMPC anticipates events
that might take place, instead of reacting when emergencies occur. We
accomplish this by adding an additional prediction horizon in parallel to the
classical receding MPC horizon. The contingency horizon is constrained to
maintain a feasible avoidance solution; as such, CMPC is selectively robust to
this emergency while tracking the desired path as closely as possible. After
defining the framework mathematically, we demonstrate its effectiveness
experimentally by comparing its performance to a state-of-the-art deterministic
MPC. The controllers drive an automated research platform through a left-hand
turn which may be covered by ice. Contingency MPC prepares for the potential
loss of friction by purposefully and intuitively deviating from the prescribed
path to approach the turn more conservatively; this deviation significantly
mitigates the consequence of encountering ice.Comment: American Control Conference, July 2019; 6 page
Bayesian Approximate Kernel Regression with Variable Selection
Nonlinear kernel regression models are often used in statistics and machine
learning because they are more accurate than linear models. Variable selection
for kernel regression models is a challenge partly because, unlike the linear
regression setting, there is no clear concept of an effect size for regression
coefficients. In this paper, we propose a novel framework that provides an
effect size analog of each explanatory variable for Bayesian kernel regression
models when the kernel is shift-invariant --- for example, the Gaussian kernel.
We use function analytic properties of shift-invariant reproducing kernel
Hilbert spaces (RKHS) to define a linear vector space that: (i) captures
nonlinear structure, and (ii) can be projected onto the original explanatory
variables. The projection onto the original explanatory variables serves as an
analog of effect sizes. The specific function analytic property we use is that
shift-invariant kernel functions can be approximated via random Fourier bases.
Based on the random Fourier expansion we propose a computationally efficient
class of Bayesian approximate kernel regression (BAKR) models for both
nonlinear regression and binary classification for which one can compute an
analog of effect sizes. We illustrate the utility of BAKR by examining two
important problems in statistical genetics: genomic selection (i.e. phenotypic
prediction) and association mapping (i.e. inference of significant variants or
loci). State-of-the-art methods for genomic selection and association mapping
are based on kernel regression and linear models, respectively. BAKR is the
first method that is competitive in both settings.Comment: 22 pages, 3 figures, 3 tables; theory added; new simulations
presented; references adde
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in modern
applications and dimension reduction is often used to extract relevant
information from data. A variety of popular dimension reduction approaches can
be framed as symmetric generalized eigendecomposition problems. In this paper
we outline how taking into account the low rank structure assumption implicit
in these dimension reduction approaches provides both computational and
statistical advantages. We adapt recent randomized low-rank approximation
algorithms to provide efficient solutions to three dimension reduction methods:
Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and
Localized Sliced Inverse Regression (LSIR). A key observation in this paper is
that randomization serves a dual role, improving both computational and
statistical performance. This point is highlighted in our experiments on real
and simulated data.Comment: 31 pages, 6 figures, Key Words:dimension reduction, generalized
eigendecompositon, low-rank, supervised, inverse regression, random
projections, randomized algorithms, Krylov subspace method
Integrated design and control of chemical processes : part I : revision and clasification
[EN] This work presents a comprehensive classification of the different methods and procedures for integrated synthesis, design and control of chemical processes, based on a wide revision of recent literature. This classification fundamentally differentiates between “projecting methods”, where controllability is monitored during the process design to predict the trade-offs between design and control, and the “integrated-optimization methods” which solve the process design and the control-systems design at once within an optimization framework. The latter are revised categorizing them according to the methods to evaluate controllability and other related properties, the scope of the design problem, the treatment of uncertainties and perturbations, and finally, the type the optimization problem formulation and the methods for its resolution.[ES] Este trabajo presenta una clasificación integral de los diferentes métodos y procedimientos para la síntesis integrada, diseño y control de procesos químicos. Esta clasificación distingue fundamentalmente entre los "métodos de proyección", donde se controla la controlabilidad durante el diseño del proceso para predecir los compromisos entre diseño y control, y los "métodos de optimización integrada" que resuelven el diseño del proceso y el diseño de los sistemas de control a la vez dentro de un marco de optimización. Estos últimos se revisan clasificándolos según los métodos para evaluar la controlabilidad y otras propiedades relacionadas, el alcance del problema de diseño, el tratamiento de las incertidumbres y las perturbaciones y, finalmente, el tipo de la formulación del problema de optimización y los métodos para su resolución
- …