5,215 research outputs found
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure
Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
In this work, we explore finite-dimensional linear representations of
nonlinear dynamical systems by restricting the Koopman operator to an invariant
subspace. The Koopman operator is an infinite-dimensional linear operator that
evolves observable functions of the state-space of a dynamical system [Koopman
1931, PNAS]. Dominant terms in the Koopman expansion are typically computed
using dynamic mode decomposition (DMD). DMD uses linear measurements of the
state variables, and it has recently been shown that this may be too
restrictive for nonlinear systems [Williams et al. 2015, JNLS]. Choosing
nonlinear observable functions to form an invariant subspace where it is
possible to obtain linear models, especially those that are useful for control,
is an open challenge.
Here, we investigate the choice of observable functions for Koopman analysis
that enable the use of optimal linear control techniques on nonlinear problems.
First, to include a cost on the state of the system, as in linear quadratic
regulator (LQR) control, it is helpful to include these states in the
observable subspace, as in DMD. However, we find that this is only possible
when there is a single isolated fixed point, as systems with multiple fixed
points or more complicated attractors are not globally topologically conjugate
to a finite-dimensional linear system, and cannot be represented by a
finite-dimensional linear Koopman subspace that includes the state. We then
present a data-driven strategy to identify relevant observable functions for
Koopman analysis using a new algorithm to determine terms in a dynamical system
by sparse regression of the data in a nonlinear function space [Brunton et al.
2015, arxiv]; we show how this algorithm is related to DMD. Finally, we
demonstrate how to design optimal control laws for nonlinear systems using
techniques from linear optimal control on Koopman invariant subspaces.Comment: 20 pages, 5 figures, 2 code
Closed-Loop Control of a Piezo-Fluidic Amplifier
Fluidic valves based on the Coand\u{a} effect are increasingly being
considered for use in aerodynamic flow control applications. A limiting factor
is their variation in switching time, which often precludes their use. The
purpose of this paper is to demonstrate the closed-loop control of a recently
developed, novel piezo-fluidic valve that reduces response time uncertainty at
the expense of operating bandwidth. Use is made of the fact that a fluidic jet
responds to a piezo tone by deflecting away from its steady state position. A
control signal used to vary this deflection is amplitude modulated onto the
piezo tone. Using only a pressure measurement from one of the device output
channels, an output-based LQG regulator was designed to follow a desired
reference deflection, achieving control of a 90 m/s jet. Finally, the
controller's performance in terms of disturbance rejection and response time
predictability is demonstrated.Comment: 31 pages, 23 figures. Published in AIAA Journal, 4th May 202
The Caltech helicopter control experiment
This report describes the Caltech helicopter control experiment. The experiment consists of an electric model helicopter interfaced to and controlled by a PC. We describe the hardware and software. A state-space model for the angular position is identified from experimental data near hover, using the prediction error method. An LQR controller with integrators for set point tracking is designed for the system. We also undertake a separate identification and loop shaping control for the yaw dynamics
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