1,721,272 research outputs found
Long-run tolerance of metabolic networks to transient random faults
No full textKatholieke Universiteit Leuven, Belgiu
Data-driven input-to-state stabilization with respect to measurement errors
Full text linkWe consider noisy input/state data collected from an experiment on a
polynomial input-affine nonlinear system. Motivated by event-triggered control,
we provide data-based conditions for input-to-state stability with respect to
measurement errors. Such conditions, which take into account all dynamics
consistent with data, lead to the design of a feedback controller, an ISS
Lyapunov function, and comparison functions ensuring ISS with respect to
measurement errors. When solved alternately for two subsets of the decision
variables, these conditions become a convex sum-of-squares program. Feasibility
of the program is illustrated with a numerical example.Comment: Submitted for peer review on 31 March 2023. To appear in the
Proceedings of the 62nd IEEE Conference on Decision and Control, 13-15
December 2023, Singapor
Stabilizability by state feedback implies stabilizability by encoded state feedback
No full textEncoded state feedback is a term which refers to the situation in which the state feedback signal is sampled every T units of time and converted (encoded) into a binary representation. In this note stabilization of nonlinear systems by encoded state feedback is studied. It is shown that any nonlinear control system which can be globally asymptotically stabilized by “standard” (i.e. with no encoding) state feedback can also be globally asymptotically stabilized by encoded state feedback, provided that the number of bits used to encode the samples is not less than an explicitly determined lower bound. By means of this bound, we are able to establish a direct relationship between the size of the expected region of attraction and the data rate, under the stabilizability assumption only, a result which—to the best of our knowledge—does not have any precedent in the literature.
n-Bit Stabilization of n-Dimensional Nonlinear Systems in Feedforward Form
No full textA methodology is presented which allows to design encoder, decoder and controller for stabilizing a nonlinear system in feedforward form using saturated encoded state feedback basically under standard assumption, namely local Lipschitz property of the vector field defining the system. n (respectively, n+1) bits are used to encode the state information needed to the purpose of semiglobally (globally) stabilizing an n-dimensional system. Minimality of the data rate is discussed.
On the passivity approach to quantized coordination problems
Full text linkWe investigate a passivity approach to collective coordination problems in the presence of quantized measurements and show that coordination tasks can be achieved in a practical sense for a large class of passive systems. Both static and time-varying graphs are considered. The results are then specialized to some particular coordination problems and compared with existing results.
Setpoint control of bilinear systems from noisy data
Full text linkWe consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it. The controller should in principle achieve regulation to a given state setpoint and provide a guaranteed basin of attraction. Determining the equilibrium input to achieve that setpoint is not trivial in a data-based setting and we propose the design of a controller in two scenarios. The design takes the form of linear matrix inequalities and is validated numerically for a Ćuk converter
On Self-Triggered Synchronization of Linear Systems
No full textSynchronization is one of the basic problems in coordinated control. The standard underlying assumption is that the systems that aim at synchronizing exchange information continuously. However, the real-time implementation of coordinating algorithms poses severe constraints on the amount and the schedule of exchanged information. In this paper we propose distributed hybrid controllers that guarantee synchronization of linear systems while collecting information sporadically. Each controller designs online the times at which information from neighbors is collected. At these times, it resets its internal state and then evolves continuously until new information is collected. We introduce hybrid controllers that achieve synchronization practically and asymptotically for networks of identical and heterogeneous linear systems. Copyright © 2013 IFAC
Trade-offs in learning controllers from noisy data
Full text linkIn data-driven control, a central question is how to handle noisy data. In this work, we consider the problem of designing a stabilizing controller for an unknown linear system using only a finite set of noisy data collected from the system. For this problem, many recent works have considered a disturbance model based on energy-type bounds. Here, we consider an alternative more natural model where the disturbance obeys instantaneous bounds. In this case, the existing approaches, which would convert instantaneous bounds into energy-type bounds, can be overly conservative. In contrast, without any conversion step, simple arguments based on the S-procedure lead to a very effective controller design through a convex program. Specifically, the feasible set of the latter design problem is always larger, and the set of system matrices consistent with data is always smaller and decreases significantly with the number of data points. These findings and some computational aspects are examined in a number of numerical examples
Learning controllers for performance through LMI regions
Full text linkIn an open-loop experiment, an input sequence is applied to an unknown linear
time-invariant system (in continuous or discrete time) affected also by an
unknown-but-bounded disturbance sequence (with an energy or instantaneous
bound); the corresponding state sequence is measured. The goal is to design
directly from the input and state sequences a controller that enforces a
certain performance specification on the transient behaviour of the unknown
system. The performance specification is expressed through a subset of the
complex plane where closed-loop eigenvalues need to belong, a so called LMI
region. For this control design problem, we provide here convex programs to
enforce the performance specification from data in the form of linear matrix
inequalities (LMI). For generic LMI regions, these are sufficient conditions to
assign the eigenvalues within the LMI region for all possible dynamics
consistent with data, and become necessary and sufficient conditions for
special LMI regions. In this way, we extend classical model-based conditions
from a seminal work in the literature to the setting of data-driven control
from noisy data. Through two numerical examples, we investigate how these
data-based conditions compare with each other
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