31,795 research outputs found
System Level Synthesis
This article surveys the System Level Synthesis framework, which presents a
novel perspective on constrained robust and optimal controller synthesis for
linear systems. We show how SLS shifts the controller synthesis task from the
design of a controller to the design of the entire closed loop system, and
highlight the benefits of this approach in terms of scalability and
transparency. We emphasize two particular applications of SLS, namely
large-scale distributed optimal control and robust control. In the case of
distributed control, we show how SLS allows for localized controllers to be
computed, extending robust and optimal control methods to large-scale systems
under practical and realistic assumptions. In the case of robust control, we
show how SLS allows for novel design methodologies that, for the first time,
quantify the degradation in performance of a robust controller due to model
uncertainty -- such transparency is key in allowing robust control methods to
interact, in a principled way, with modern techniques from machine learning and
statistical inference. Throughout, we emphasize practical and efficient
computational solutions, and demonstrate our methods on easy to understand case
studies.Comment: To appear in Annual Reviews in Contro
Parameterization of Stabilizing Linear Coherent Quantum Controllers
This paper is concerned with application of the classical Youla-Ku\v{c}era
parameterization to finding a set of linear coherent quantum controllers that
stabilize a linear quantum plant. The plant and controller are assumed to
represent open quantum harmonic oscillators modelled by linear quantum
stochastic differential equations. The interconnections between the plant and
the controller are assumed to be established through quantum bosonic fields. In
this framework, conditions for the stabilization of a given linear quantum
plant via linear coherent quantum feedback are addressed using a stable
factorization approach. The class of stabilizing quantum controllers is
parameterized in the frequency domain. Also, this approach is used in order to
formulate coherent quantum weighted and control problems for
linear quantum systems in the frequency domain. Finally, a projected gradient
descent scheme is proposed to solve the coherent quantum weighted control
problem.Comment: 11 pages, 4 figures, a version of this paper is to appear in the
Proceedings of the 10th Asian Control Conference, Kota Kinabalu, Malaysia, 31
May - 3 June, 201
H_2-Optimal Decentralized Control over Posets: A State-Space Solution for State-Feedback
We develop a complete state-space solution to H_2-optimal decentralized
control of poset-causal systems with state-feedback. Our solution is based on
the exploitation of a key separability property of the problem, that enables an
efficient computation of the optimal controller by solving a small number of
uncoupled standard Riccati equations. Our approach gives important insight into
the structure of optimal controllers, such as controller degree bounds that
depend on the structure of the poset. A novel element in our state-space
characterization of the controller is a remarkable pair of transfer functions,
that belong to the incidence algebra of the poset, are inverses of each other,
and are intimately related to prediction of the state along the different paths
on the poset. The results are illustrated by a numerical example.Comment: 39 pages, 2 figures, submitted to IEEE Transactions on Automatic
Contro
Localized LQR Optimal Control
This paper introduces a receding horizon like control scheme for localizable
distributed systems, in which the effect of each local disturbance is limited
spatially and temporally. We characterize such systems by a set of linear
equality constraints, and show that the resulting feasibility test can be
solved in a localized and distributed way. We also show that the solution of
the local feasibility tests can be used to synthesize a receding horizon like
controller that achieves the desired closed loop response in a localized manner
as well. Finally, we formulate the Localized LQR (LLQR) optimal control problem
and derive an analytic solution for the optimal controller. Through a numerical
example, we show that the LLQR optimal controller, with its constraints on
locality, settling time, and communication delay, can achieve similar
performance as an unconstrained H2 optimal controller, but can be designed and
implemented in a localized and distributed way.Comment: Extended version for 2014 CDC submissio
Optimal Control of Two-Player Systems with Output Feedback
In this article, we consider a fundamental decentralized optimal control
problem, which we call the two-player problem. Two subsystems are
interconnected in a nested information pattern, and output feedback controllers
must be designed for each subsystem. Several special cases of this architecture
have previously been solved, such as the state-feedback case or the case where
the dynamics of both systems are decoupled. In this paper, we present a
detailed solution to the general case. The structure of the optimal
decentralized controller is reminiscent of that of the optimal centralized
controller; each player must estimate the state of the system given their
available information and apply static control policies to these estimates to
compute the optimal controller. The previously solved cases benefit from a
separation between estimation and control which allows one to compute the
control and estimation gains separately. This feature is not present in
general, and some of the gains must be solved for simultaneously. We show that
computing the required coupled estimation and control gains amounts to solving
a small system of linear equations
Minimax frequency domain performance and robustness optimization of linear feedback systems
It is shown that feedback system design objectives, such as disturbance attenuation and rejection, power and bandwidth limitation, and robustness, may be expressed in terms of required bounds of the sensitivity function and its complement on the imaginary axis. This leads to a minimax frequency domain optimization problem, whose solution is reduced to the solution of a polynomial equation
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