13,826 research outputs found
A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization
The frequency-domain data of a multivariable system in different operating
points is used to design a robust controller with respect to the measurement
noise and multimodel uncertainty. The controller is fully parametrized in terms
of matrix polynomial functions and can be formulated as a centralized,
decentralized or distributed controller. All standard performance
specifications like , and loop shaping are considered in a
unified framework for continuous- and discrete-time systems. The control
problem is formulated as a convex-concave optimization problem and then
convexified by linearization of the concave part around an initial controller.
The performance criterion converges monotonically to a local optimal solution
in an iterative algorithm. The effectiveness of the method is compared with
fixed-structure controllers using non-smooth optimization and with full-order
optimal controllers via simulation examples. Finally, the experimental data of
a gyroscope is used to design a data-driven controller that is successfully
applied on the real system
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
3 sampled-data control of nonlinear systems
This chapter provides some of the main ideas resulting from recent developments in sampled-data control of nonlinear systems. We have tried to bring the basic parts of the new developments within the comfortable grasp of graduate students. Instead of presenting the more general results that are available in the literature, we opted to present their less general versions that are easier to understand and whose proofs are easier to follow. We note that some of the proofs we present have not appeared in the literature in this simplified form. Hence, we believe that this chapter will serve as an important reference for students and researchers that are willing to learn about this area of research
ℋ∞ optimization with spatial constraints
A generalized ℋ∞ synthesis problem where non-euclidian spatial norms on the disturbances and output error are used is posed and solved. The solution takes the form of a linear matrix inequality. Some problems which fall into this class are presented. In particular, solutions are presented to two problems: a variant of ℋ∞ synthesis where norm constraints on each component of the disturbance can be imposed, and synthesis for a certain class of robust performance problems
Delay-Based Controller Design for Continuous-Time and Hybrid Applications
Motivated by the availability of different types of delays in embedded systems and biological circuits, the objective of this work is to study the benefits that delay can provide in simplifying the implementation of controllers for continuous-time systems. Given a continuous-time linear time-invariant (LTI) controller, we propose three methods to approximate this controller arbitrarily precisely by a simple controller composed of delay blocks, a few integrators and possibly a unity feedback. Different problems associated with the approximation procedures, such as finding the optimal number of delay blocks or studying the robustness of the designed controller with respect to delay values, are then investigated. We also study the design of an LTI continuous-time controller satisfying given control objectives whose delay-based implementation needs the least number of delay blocks. A direct application of this work is in the sampled-data control of a real-time embedded system, where the sampling frequency is relatively high and/or the output of the system is sampled irregularly. Based on our results on delay-based controller design, we propose a digital-control scheme that can implement every continuous-time stabilizing (LTI)
controller. Unlike a typical sampled-data controller, the hybrid controller introduced here -— consisting of an ideal sampler, a digital controller, a number of modified second-order holds and possibly a unity feedback -— is robust to sampling jitter and can operate at arbitrarily high sampling frequencies without requiring expensive, high-precision computation
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
Robustness and performance trade-offs in control design for flexible structures
Linear control design models for flexible structures are only an approximation to the “real” structural system. There are always modeling errors or uncertainty present. Descriptions of these uncertainties determine the trade-off between achievable performance and robustness of the control design. In this paper it is shown that a controller synthesized for a plant model which is not described accurately by the nominal and uncertainty models may be unstable or exhibit poor performance when implemented on the actual system. In contrast, accurate structured uncertainty descriptions lead to controllers which achieve high performance when implemented on the experimental facility. It is also shown that similar performance, theoretically and experimentally, is obtained for a surprisingly wide range of uncertain levels in the design model. This suggests that while it is important to have reasonable structured uncertainty models, it may not always be necessary to pin down precise levels (i.e., weights) of uncertainty. Experimental results are presented which substantiate these conclusions
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