94,571 research outputs found
Scenario-based Economic Dispatch with Uncertain Demand Response
This paper introduces a new computational framework to account for
uncertainties in day-ahead electricity market clearing process in the presence
of demand response providers. A central challenge when dealing with many demand
response providers is the uncertainty of its realization. In this paper, a new
economic dispatch framework that is based on the recent theoretical development
of the scenario approach is introduced. By removing samples from a finite
uncertainty set, this approach improves dispatch performance while guaranteeing
a quantifiable risk level with respect to the probability of violating the
constraints. The theoretical bound on the level of risk is shown to be a
function of the number of scenarios removed. This is appealing to the system
operator for the following reasons: (1) the improvement of performance comes at
the cost of a quantifiable level of violation probability in the constraints;
(2) the violation upper bound does not depend on the probability distribution
assumption of the uncertainty in demand response. Numerical simulations on (1)
3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this
approach could be a promising alternative in future electricity markets with
multiple demand response providers
Approximated Computation of Belief Functions for Robust Design Optimization
This paper presents some ideas to reduce the computational cost of
evidence-based robust design optimization. Evidence Theory crystallizes both
the aleatory and epistemic uncertainties in the design parameters, providing
two quantitative measures, Belief and Plausibility, of the credibility of the
computed value of the design budgets. The paper proposes some techniques to
compute an approximation of Belief and Plausibility at a cost that is a
fraction of the one required for an accurate calculation of the two values.
Some simple test cases will show how the proposed techniques scale with the
dimension of the problem. Finally a simple example of spacecraft system design
is presented.Comment: AIAA-2012-1932 14th AIAA Non-Deterministic Approaches Conference.
23-26 April 2012 Sheraton Waikiki, Honolulu, Hawai
Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance
This paper presents a tutorial-style review on the recent results about the
disturbance observer (DOB) in view of robust stabilization and recovery of the
nominal performance. The analysis is based on the case when the bandwidth of
Q-filter is large, and it is explained in a pedagogical manner that, even in
the presence of plant uncertainties and disturbances, the behavior of real
uncertain plant can be made almost similar to that of disturbance-free nominal
system both in the transient and in the steady-state. The conventional DOB is
interpreted in a new perspective, and its restrictions and extensions are
discussed
Unified control/structure design and modeling research
To demonstrate the applicability of the control theory for distributed systems to large flexible space structures, research was focused on a model of a space antenna which consists of a rigid hub, flexible ribs, and a mesh reflecting surface. The space antenna model used is discussed along with the finite element approximation of the distributed model. The basic control problem is to design an optimal or near-optimal compensator to suppress the linear vibrations and rigid-body displacements of the structure. The application of an infinite dimensional Linear Quadratic Gaussian (LQG) control theory to flexible structure is discussed. Two basic approaches for robustness enhancement were investigated: loop transfer recovery and sensitivity optimization. A third approach synthesized from elements of these two basic approaches is currently under development. The control driven finite element approximation of flexible structures is discussed. Three sets of finite element basic vectors for computing functional control gains are compared. The possibility of constructing a finite element scheme to approximate the infinite dimensional Hamiltonian system directly, instead of indirectly is discussed
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed
properties, such as stability or safety, and have been successfully applied to
the domain of robotics. However, model uncertainty remains a persistent
challenge, weakening theoretical guarantees and causing implementation failures
on physical systems. This paper develops a machine learning framework centered
around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and
unmodeled dynamics in general robotic systems. Our proposed method proceeds by
iteratively updating estimates of Lyapunov function derivatives and improving
controllers, ultimately yielding a stabilizing quadratic program model-based
controller. We validate our approach on a planar Segway simulation,
demonstrating substantial performance improvements by iteratively refining on a
base model-free controller
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