2,213 research outputs found
A non-autonomous stochastic discrete time system with uniform disturbances
The main objective of this article is to present Bayesian optimal control
over a class of non-autonomous linear stochastic discrete time systems with
disturbances belonging to a family of the one parameter uniform distributions.
It is proved that the Bayes control for the Pareto priors is the solution of a
linear system of algebraic equations. For the case that this linear system is
singular, we apply optimization techniques to gain the Bayesian optimal
control. These results are extended to generalized linear stochastic systems of
difference equations and provide the Bayesian optimal control for the case
where the coefficients of these type of systems are non-square matrices. The
paper extends the results of the authors developed for system with disturbances
belonging to the exponential family
Quantum Multiobservable Control
We present deterministic algorithms for the simultaneous control of an
arbitrary number of quantum observables. Unlike optimal control approaches
based on cost function optimization, quantum multiobservable tracking control
(MOTC) is capable of tracking predetermined homotopic trajectories to target
expectation values in the space of multiobservables. The convergence of these
algorithms is facilitated by the favorable critical topology of quantum control
landscapes. Fundamental properties of quantum multiobservable control
landscapes that underlie the efficiency of MOTC, including the multiobservable
controllability Gramian, are introduced. The effects of multiple control
objectives on the structure and complexity of optimal fields are examined. With
minor modifications, the techniques described herein can be applied to general
quantum multiobjective control problems.Comment: To appear in Physical Review
Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion.
A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV
Escaping expectation traps: how much commitment is required?
We study the degree of precommitment that is required to eliminate multiplicity of policy equilibria, which arise if the policy maker acts under pure discretion. We apply a framework developed by Schaumburg and Tambalotti (2007) and Debertoli and Nunes (2010) to a standard New Keynesian model with government debt. We demonstrate the existence of expectation traps under limited commitment and identify the minimum degree of commitment which is needed to escape from these traps. We find that the degree of precommitment which is sufficient to generate uniqueness of the Pareto-preferred equilibrium requires the policy maker to stay in office for a period of two to five years. This is consistent with monetary policy arrangements in many developed countries
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