665 research outputs found
Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control
We study in this paper the problem of iterative feedback gains tuning for a
class of nonlinear systems. We consider Input-Output linearizable nonlinear
systems with additive uncertainties. We first design a nominal Input-Output
linearization-based controller that ensures global uniform boundedness of the
output tracking error dynamics. Then, we complement the robust controller with
a model-free multi-parametric extremum seeking (MES) control to iteratively
auto-tune the feedback gains. We analyze the stability of the whole controller,
i.e. robust nonlinear controller plus model-free learning algorithm. We use
numerical tests to demonstrate the performance of this method on a mechatronics
example.Comment: To appear at the IEEE CDC 201
Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time
10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN
Model-Based Control Using Koopman Operators
This paper explores the application of Koopman operator theory to the control
of robotic systems. The operator is introduced as a method to generate
data-driven models that have utility for model-based control methods. We then
motivate the use of the Koopman operator towards augmenting model-based
control. Specifically, we illustrate how the operator can be used to obtain a
linearizable data-driven model for an unknown dynamical process that is useful
for model-based control synthesis. Simulated results show that with increasing
complexity in the choice of the basis functions, a closed-loop controller is
able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore,
the specification of the basis function are shown to be of importance when
generating a Koopman operator for specific robotic systems. Experimental
results with the Sphero SPRK robot explore the utility of the Koopman operator
in a reduced state representation setting where increased complexity in the
basis function improve open- and closed-loop controller performance in various
terrains, including sand.Comment: 8 page
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