14,418 research outputs found
Optimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks
We study the system-level effects of the introduction of large populations of
Electric Vehicles on the power and transportation networks. We assume that each
EV owner solves a decision problem to pick a cost-minimizing charge and travel
plan. This individual decision takes into account traffic congestion in the
transportation network, affecting travel times, as well as as congestion in the
power grid, resulting in spatial variations in electricity prices for battery
charging. We show that this decision problem is equivalent to finding the
shortest path on an "extended" transportation graph, with virtual arcs that
represent charging options. Using this extended graph, we study the collective
effects of a large number of EV owners individually solving this path planning
problem. We propose a scheme in which independent power and transportation
system operators can collaborate to manage each network towards a socially
optimum operating point while keeping the operational data of each system
private. We further study the optimal reserve capacity requirements for pricing
in the absence of such collaboration. We showcase numerically that a lack of
attention to interdependencies between the two infrastructures can have adverse
operational effects.Comment: Submitted to IEEE Transactions on Control of Network Systems on June
1st 201
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
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
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