Bridging the Model-Reality Gap with Lipschitz Network Adaptation
- URL: http://arxiv.org/abs/2112.03756v1
- Date: Tue, 7 Dec 2021 15:12:49 GMT
- Title: Bridging the Model-Reality Gap with Lipschitz Network Adaptation
- Authors: Siqi Zhou, Karime Pereida, Wenda Zhao and Angela P. Schoellig
- Abstract summary: As robots venture into the real world, they are subject to unmodeled dynamics and disturbances.
Traditional model-based control approaches have been proven successful in relatively static and known operating environments.
We propose a method that bridges the model-reality gap and enables the application of model-based approaches even if dynamic uncertainties are present.
- Score: 22.499090318313662
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As robots venture into the real world, they are subject to unmodeled dynamics
and disturbances. Traditional model-based control approaches have been proven
successful in relatively static and known operating environments. However, when
an accurate model of the robot is not available, model-based design can lead to
suboptimal and even unsafe behaviour. In this work, we propose a method that
bridges the model-reality gap and enables the application of model-based
approaches even if dynamic uncertainties are present. In particular, we present
a learning-based model reference adaptation approach that makes a robot system,
with possibly uncertain dynamics, behave as a predefined reference model. In
turn, the reference model can be used for model-based controller design. In
contrast to typical model reference adaptation control approaches, we leverage
the representative power of neural networks to capture highly nonlinear
dynamics uncertainties and guarantee stability by encoding a certifying
Lipschitz condition in the architectural design of a special type of neural
network called the Lipschitz network. Our approach applies to a general class
of nonlinear control-affine systems even when our prior knowledge about the
true robot system is limited. We demonstrate our approach in flying inverted
pendulum experiments, where an off-the-shelf quadrotor is challenged to balance
an inverted pendulum while hovering or tracking circular trajectories.
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