16,014 research outputs found
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
Economic Games as Estimators
Discrete event games are discrete time dynamical systems whose state transitions are discrete events caused by actions taken by agents within the game. The agentsâ objectives and associated decision rules need not be known to the game designer in order to impose struc- ture on a gameâs reachable states. Mechanism design for discrete event games is accomplished by declaring desirable invariant properties and restricting the state transition functions to conserve these properties at every point in time for all admissible actions and for all agents, using techniques familiar from state-feedback control theory. Building upon these connections to control theory, a framework is developed to equip these games with estimation properties of signals which are private to the agents playing the game. Token bonding curves are presented as discrete event games and numerical experiments are used to investigate their signal processing properties with a focus on input-output response dynamics.Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc
Real-time flutter identification
The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability
Optimal Feedback Control Rules Sensitive to Controlled Endogenous Risk-Aversion
The objective of this paper is to correct and improve the results obtained by Van der Ploeg (1984a, 1984b) and utilized in the literature related to feedback stochastic optimal control sensitive to constant exogenous risk-aversion (Karp 1987; Whittle 1989, 1990; Chow 1993, amongst others). More realistic, the proposed approach deals with endoge- nous risks that are under the control of the decision-maker. It has strong implications on the policy decisions adopted by the decision-maker during the entire planning horizon.Controlled stochastic environment, rational decision-maker, adaptive control, optimal path, feedback optimal strategy, endogenous risk-aversion, dynamic active learning.Controlled stochastic environment, rational decision-maker, adaptive control, optimal path, feedback optimal strategy, endogenous risk-aversion, dynamic active learning.
Design of State-based Schedulers for a Network of Control Loops
For a closed-loop system, which has a contention-based multiple access
network on its sensor link, the Medium Access Controller (MAC) may discard some
packets when the traffic on the link is high. We use a local state-based
scheduler to select a few critical data packets to send to the MAC. In this
paper, we analyze the impact of such a scheduler on the closed-loop system in
the presence of traffic, and show that there is a dual effect with state-based
scheduling. In general, this makes the optimal scheduler and controller hard to
find. However, by removing past controls from the scheduling criterion, we find
that certainty equivalence holds. This condition is related to the classical
result of Bar-Shalom and Tse, and it leads to the design of a scheduler with a
certainty equivalent controller. This design, however, does not result in an
equivalent system to the original problem, in the sense of Witsenhausen.
Computing the estimate is difficult, but can be simplified by introducing a
symmetry constraint on the scheduler. Based on these findings, we propose a
dual predictor architecture for the closed-loop system, which ensures
separation between scheduler, observer and controller. We present an example of
this architecture, which illustrates a network-aware event-triggering
mechanism.Comment: 17 pages, technical repor
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Least Squares Estimation-Based Synchronous Generator Parameter Estimation Using PMU Data
In this paper, least square estimation (LSE)-based dynamic generator model
parameter identification is investigated. Electromechanical dynamics related
parameters such as inertia constant and primary frequency control droop for a
synchronous generator are estimated using Phasor Measurement Unit (PMU) data
obtained at the generator terminal bus. The key idea of applying LSE for
dynamic parameter estimation is to have a discrete
\underline{a}uto\underline{r}egression with e\underline{x}ogenous input (ARX)
model. With an ARX model, a linear estimation problem can be formulated and the
parameters of the ARX model can be found. This paper gives the detailed
derivation of converting a generator model with primary frequency control into
an ARX model. The generator parameters will be recovered from the estimated ARX
model parameters afterwards. Two types of conversion methods are presented:
zero-order hold (ZOH) method and Tustin method. Numerical results are presented
to illustrate the proposed LSE application in dynamic system parameter
identification using PMU data.Comment: 5 pages, 6 figures, accepted by IEEE PESGM 201
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