70 research outputs found
An Extended Kalman Filter for Data-enabled Predictive Control
The literature dealing with data-driven analysis and control problems has
significantly grown in the recent years. Most of the recent literature deals
with linear time-invariant systems in which the uncertainty (if any) is assumed
to be deterministic and bounded; relatively little attention has been devoted
to stochastic linear time-invariant systems. As a first step in this direction,
we propose to equip the recently introduced Data-enabled Predictive Control
algorithm with a data-based Extended Kalman Filter to make use of additional
available input-output data for reducing the effect of noise, without
increasing the computational load of the optimization procedure
Experience Transfer for Robust Direct Data-Driven Control
Learning-based control uses data to design efficient controllers for specific
systems. When multiple systems are involved, experience transfer usually
focuses on data availability and controller performance yet neglects robustness
to variations between systems. In contrast, this letter explores experience
transfer from a robustness perspective. We leverage the transfer to design
controllers that are robust not only to the uncertainty regarding an individual
agent's model but also to the choice of agent in a fleet. Experience transfer
enables the design of safe and robust controllers that work out of the box for
all systems in a heterogeneous fleet. Our approach combines scenario
optimization and recent formulations for direct data-driven control without the
need to estimate a model of the system or determine uncertainty bounds for its
parameters. We demonstrate the benefits of our data-driven robustification
method through a numerical case study and obtain learned controllers that
generalize well from a small number of open-loop trajectories in a quadcopter
simulation
Data-driven Linear Quadratic Regulation via Semidefinite Programming
This paper studies the finite-horizon linear quadratic regulation problem
where the dynamics of the system are assumed to be unknown and the state is
accessible. Information on the system is given by a finite set of input-state
data, where the input injected in the system is persistently exciting of a
sufficiently high order. Using data, the optimal control law is then obtained
as the solution of a suitable semidefinite program. The effectiveness of the
approach is illustrated via numerical examples.Comment: Accepted for publication in the IFAC World Congress 202
Superstabilizing Control of Discrete-Time ARX Models under Error in Variables
This paper applies a polynomial optimization based framework towards the
superstabilizing control of an Autoregressive with Exogenous Input (ARX) model
given noisy data observations. The recorded input and output values are
corrupted with L-infinity bounded noise where the bounds are known. This is an
instance of Error in Variables (EIV) in which true internal state of the ARX
system remains unknown. The consistency set of ARX models compatible with noisy
data has a bilinearity between unknown plant parameters and unknown noise
terms. The requirement for a dynamic compensator to superstabilize all
consistent plants is expressed using polynomial nonnegativity constraints, and
solved using sum-of-squares (SOS) methods in a converging hierarchy of
semidefinite programs in increasing size. The computational complexity of this
method may be reduced by applying a Theorem of Alternatives to eliminate the
noise terms. Effectiveness of this method is demonstrated on control of example
ARX models.Comment: 12 pages, 0 figures, 5 table
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