44,159 research outputs found
Robustness of the Thirty Meter Telescope Primary Mirror Control System
The primary mirror control system for the Thirty Meter Telescope (TMT) maintains the alignment of the 492 segments in the presence of both quasi-static (gravity and thermal) and dynamic disturbances due to unsteady wind loads. The latter results in a desired control bandwidth of 1Hz at high spatial frequencies. The achievable bandwidth is limited by robustness to (i) uncertain telescope structural dynamics (control-structure interaction) and (ii) small perturbations in the ill-conditioned influence matrix that relates segment edge sensor response to actuator commands. Both of these effects are considered herein using models of TMT. The former is explored through multivariable sensitivity analysis on a reduced-order Zernike-basis representation of the structural dynamics. The interaction matrix ("A-matrix") uncertainty has been analyzed theoretically elsewhere, and is examined here for realistic amplitude perturbations due to segment and sensor installation errors, and gravity and thermal induced segment motion. The primary influence of A-matrix uncertainty is on the control of "focusmode"; this is the least observable mode, measurable only through the edge-sensor (gap-dependent) sensitivity to the dihedral angle between segments. Accurately estimating focus-mode will require updating the A-matrix as a function of the measured gap. A-matrix uncertainty also results in a higher gain-margin requirement for focus-mode, and hence the A-matrix and CSI robustness need to be understood simultaneously. Based on the robustness analysis, the desired 1 Hz bandwidth is achievable in the presence of uncertainty for all except the lowest spatial-frequency response patterns of the primary mirror
Sliding Mode Control of Two-Level Quantum Systems
This paper proposes a robust control method based on sliding mode design for
two-level quantum systems with bounded uncertainties. An eigenstate of the
two-level quantum system is identified as a sliding mode. The objective is to
design a control law to steer the system's state into the sliding mode domain
and then maintain it in that domain when bounded uncertainties exist in the
system Hamiltonian. We propose a controller design method using the Lyapunov
methodology and periodic projective measurements. In particular, we give
conditions for designing such a control law, which can guarantee the desired
robustness in the presence of the uncertainties. The sliding mode control
method has potential applications to quantum information processing with
uncertainties.Comment: 29 pages, 4 figures, accepted by Automatic
Sampled-data design for robust control of a single qubit
This paper presents a sampled-data approach for the robust control of a
single qubit (quantum bit). The required robustness is defined using a sliding
mode domain and the control law is designed offline and then utilized online
with a single qubit having bounded uncertainties. Two classes of uncertainties
are considered involving the system Hamiltonian and the coupling strength of
the system-environment interaction. Four cases are analyzed in detail including
without decoherence, with amplitude damping decoherence, phase damping
decoherence and depolarizing decoherence. Sampling periods are specifically
designed for these cases to guarantee the required robustness. Two sufficient
conditions are presented for guiding the design of unitary control for the
cases without decoherence and with amplitude damping decoherence. The proposed
approach has potential applications in quantum error-correction and in
constructing robust quantum gates.Comment: 33 pages, 5 figures, minor correction
Stability and Performance Analysis of Systems Under Constraints
All real world control systems must deal with actuator and state constraints. Standard conic sector bounded nonlinearity stability theory provides methods for analyzing the stability and performance of systems under constraints, but it is well-known that these conditions can be very conservative. A method is developed to reduce conservatism in the analysis of constraints by representing them as nonlinear real parametric uncertainty
Ellipsoidal Prediction Regions for Multivariate Uncertainty Characterization
While substantial advances are observed in probabilistic forecasting for
power system operation and electricity market applications, most approaches are
still developed in a univariate framework. This prevents from informing about
the interdependence structure among locations, lead times and variables of
interest. Such dependencies are key in a large share of operational problems
involving renewable power generation, load and electricity prices for instance.
The few methods that account for dependencies translate to sampling scenarios
based on given marginals and dependence structures. However, for classes of
decision-making problems based on robust, interval chance-constrained
optimization, necessary inputs take the form of polyhedra or ellipsoids.
Consequently, we propose a systematic framework to readily generate and
evaluate ellipsoidal prediction regions, with predefined probability and
minimum volume. A skill score is proposed for quantitative assessment of the
quality of prediction ellipsoids. A set of experiments is used to illustrate
the discrimination ability of the proposed scoring rule for misspecification of
ellipsoidal prediction regions. Application results based on three datasets
with wind, PV power and electricity prices, allow us to assess the skill of the
resulting ellipsoidal prediction regions, in terms of calibration, sharpness
and overall skill.Comment: 8 pages, 7 Figures, Submitted to IEEE Transactions on Power System
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been proposed
to solve particular classes of Markov Decision Problems. Mathematically, such
approaches are governed by a variational free energy principle and allow
solving MDP planning problems with information-processing constraints expressed
in terms of a Kullback-Leibler divergence with respect to a reference
distribution. Here we consider a generalization of such MDP planners by taking
model uncertainty into account. As model uncertainty can also be formalized as
an information-processing constraint, we can derive a unified solution from a
single generalized variational principle. We provide a generalized value
iteration scheme together with a convergence proof. As limit cases, this
generalized scheme includes standard value iteration with a known model,
Bayesian MDP planning, and robust planning. We demonstrate the benefits of this
approach in a grid world simulation.Comment: 16 pages, 3 figure
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