67,472 research outputs found
Dual adaptive control: Design principles and applications
The design of an actively adaptive dual controller based on an approximation of the stochastic dynamic programming equation for a multi-step horizon is presented. A dual controller that can enhance identification of the system while controlling it at the same time is derived for multi-dimensional problems. This dual controller uses sensitivity functions of the expected future cost with respect to the parameter uncertainties. A passively adaptive cautious controller and the actively adaptive dual controller are examined. In many instances, the cautious controller is seen to turn off while the latter avoids the turn-off of the control and the slow convergence of the parameter estimates, characteristic of the cautious controller. The algorithms have been applied to a multi-variable static model which represents a simplified linear version of the relationship between the vibration output and the higher harmonic control input for a helicopter. Monte Carlo comparisons based on parametric and nonparametric statistical analysis indicate the superiority of the dual controller over the baseline controller
Optimal byzantine resilient convergence in oblivious robot networks
Given a set of robots with arbitrary initial location and no agreement on a
global coordinate system, convergence requires that all robots asymptotically
approach the exact same, but unknown beforehand, location. Robots are
oblivious-- they do not recall the past computations -- and are allowed to move
in a one-dimensional space. Additionally, robots cannot communicate directly,
instead they obtain system related information only via visual sensors. We draw
a connection between the convergence problem in robot networks, and the
distributed \emph{approximate agreement} problem (that requires correct
processes to decide, for some constant , values distance
apart and within the range of initial proposed values). Surprisingly, even
though specifications are similar, the convergence implementation in robot
networks requires specific assumptions about synchrony and Byzantine
resilience. In more details, we prove necessary and sufficient conditions for
the convergence of mobile robots despite a subset of them being Byzantine (i.e.
they can exhibit arbitrary behavior). Additionally, we propose a deterministic
convergence algorithm for robot networks and analyze its correctness and
complexity in various synchrony settings. The proposed algorithm tolerates f
Byzantine robots for (2f+1)-sized robot networks in fully synchronous networks,
(3f+1)-sized in semi-synchronous networks. These bounds are optimal for the
class of cautious algorithms, which guarantee that correct robots always move
inside the range of positions of the correct robots
About the Power to Enforce and Prevent Consensus by Manipulating Communication Rules
We explore the possibilities of enforcing and preventing consensus in
continuous opinion dynamics that result from modifications in the communication
rules. We refer to the model of Weisbuch and Deffuant, where agents adjust
their continuous opinions as a result of random pairwise encounters whenever
their opinions differ not more than a given bound of confidence \eps. A high
\eps leads to consensus, while a lower \eps leads to a fragmentation into
several opinion clusters. We drop the random encounter assumption and ask: How
small may \eps be such that consensus is still possible with a certain
communication plan for the entire group? Mathematical analysis shows that
\eps may be significantly smaller than in the random pairwise case. On the
other hand we ask: How large may \eps be such that preventing consensus is
still possible? In answering this question we prove Fortunato's simulation
result that consensus cannot be prevented for \eps>0.5 for large groups. %
Next we consider opinion dynamics under different individual strategies and
examine their power to increase the chances of consensus. One result is that
balancing agents increase chances of consensus, especially if the agents are
cautious in adapting their opinions. However, curious agents increase chances
of consensus only if those agents are not cautious in adapting their opinions.Comment: 21 pages, 6 figure
Consensus in the Presence of Multiple Opinion Leaders: Effect of Bounded Confidence
The problem of analyzing the performance of networked agents exchanging
evidence in a dynamic network has recently grown in importance. This problem
has relevance in signal and data fusion network applications and in studying
opinion and consensus dynamics in social networks. Due to its capability of
handling a wider variety of uncertainties and ambiguities associated with
evidence, we use the framework of Dempster-Shafer (DS) theory to capture the
opinion of an agent. We then examine the consensus among agents in dynamic
networks in which an agent can utilize either a cautious or receptive updating
strategy. In particular, we examine the case of bounded confidence updating
where an agent exchanges its opinion only with neighboring nodes possessing
'similar' evidence. In a fusion network, this captures the case in which nodes
only update their state based on evidence consistent with the node's own
evidence. In opinion dynamics, this captures the notions of Social Judgment
Theory (SJT) in which agents update their opinions only with other agents
possessing opinions closer to their own. Focusing on the two special DS
theoretic cases where an agent state is modeled as a Dirichlet body of evidence
and a probability mass function (p.m.f.), we utilize results from matrix
theory, graph theory, and networks to prove the existence of consensus agent
states in several time-varying network cases of interest. For example, we show
the existence of a consensus in which a subset of network nodes achieves a
consensus that is adopted by follower network nodes. Of particular interest is
the case of multiple opinion leaders, where we show that the agents do not
reach a consensus in general, but rather converge to 'opinion clusters'.
Simulation results are provided to illustrate the main results.Comment: IEEE Transactions on Signal and Information Processing Over Networks,
to appea
Budgetary Forecasts in Europe – The Track Record of Stability and Convergence Programmes
We analyse the performance of budgetary and growth forecasts of all stability and convergence programmes submitted by EU member states over the last decade. Differences emerge for the bias in budgetary projections across countries. As a second step we explore whether economic, political and institutional factors can explain this pattern. Our analysis indicates that the cyclical position and the form of fiscal governance are major determinants of forecast biases. Projected changes in the budgetary position are mainly affected by the cycle, the need of convergence before EMU and by electoral cycles.Fiscal forecasting; forecast evaluation; budget processes; Stability and Growth Pact
Evaluation of the effect of vibration nonlinearity on convergence behavior of adaptive higher harmonic controllers
Effect of nonlinearity on convergence of the local linear and global linear adaptive controllers is evaluated. A nonlinear helicopter vibration model is selected for the evaluation which has sufficient nonlinearity, including multiple minimum, to assess the vibration reduction capability of the adaptive controllers. The adaptive control algorithms are based upon a linear transfer matrix assumption and the presence of nonlinearity has a significant effect on algorithm behavior. Simulation results are presented which demonstrate the importance of the caution property in the global linear controller. Caution is represented by a time varying rate weighting term in the local linear controller and this improves the algorithm convergence. Nonlinearity in some cases causes Kalman filter divergence. Two forms of the Kalman filter covariance equation are investigated
Caution or activism? Monetary policy strategies in an open economy
We examine optimal policy in an open-economy model with uncertainty and learning, where monetary policy actions affect the economy through the real exchange rate channel. Our results show that the degree of caution or activism in optimal policy depends on whether central banks are in coordinated or uncoordinated equilibrium. If central banks coordinate their policy actions then activism is optimal. In contrast, if there is no coordination, caution prevails. In the latter case caution is optimal because it helps central banks to avoid exposing themselves to manipulative actions by other central banks
Application of an adaptive blade control algorithm to a gust alleviation system
The feasibility of an adaptive control system designed to alleviate helicopter gust induced vibration was analytically investigated for an articulated rotor system. This control system is based on discrete optimal control theory, and is composed of a set of measurements (oscillatory hub forces and moments), an identification system using a Kalman filter, a control system based on the minimization of the quadratic performance function, and a simulation system of the helicopter rotor. The gust models are step and sinusoidal vertical gusts. Control inputs are selected at the gust frequency, subharmonic frequency, and superharmonic frequency, and are superimposed on the basic collective and cyclic control inputs. The response to be reduced is selected to be that at the gust frequency because this is the dominant response compared with sub- and superharmonics. Numerical calculations show that the adaptive blade pitch control algorithm satisfactorily alleviates the hub gust response. Almost 100% reduction of the perturbation thrust response to a step gust and more than 50% reduction to a sinusoidal gust are achieved in the numerical simulations
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