Neuromorphic control of embodied central pattern generators
|Neuromorphic control of embodied central pattern generators
|Translated title :
|[fr] Contrôle neuromorphique de générateurs de rythmes centraux incarnés
|Fernandez Lorden, Christian
|Date of defense :
|Committee's member(s) :
|Number of pages :
[en] Embodied Intelligence
|Engineering, computing & technology > Multidisciplinary, general & others
|Target public :
Professionals of domain
|Université de Liège, Liège, Belgique
|Master : ingénieur civil électricien, à finalité spécialisée en "signal processing and intelligent robotics"
|Master thesis of the Faculté des Sciences appliquées
[en] The control of robotic locomotion poses important challenges. In particular, we
are still very far from achieving robotic locomotion control with the same degree of
robustness and adaptability to unexpected environmental perturbations exhibited
by moving biological systems.
This master’s thesis aims to create a robust and efficient controller for regulating
a simple mechanical system. Biological neuron models are used to create artificial
central pattern generators (CPGs) that form the core of the controller. Similar to
Yu et al., the inspiration of this thesis is the known electrophysiology, sensory
response, and modulation of biological CPGs.
This study explores the control of a simple resonant mechanical system (a pendulum) to achieve high-amplitude periodic motion without fine-tuning the neuron
parameters and with sensory feedback and weak actuation. The design follows multiple steps. It starts with the design and tuning of the controller using a single
neuron. This uncovers that only the motor neurons exhibiting a robust type of
bursting are able to robustly and easily adapt their excitable behavior to the
unknown mechanical system’s properties (damping, resonant frequency, mass, etc.).
This is followed by the natural addition of another motor neuron to form a CPG
and make the controller symmetric. This increases the achievable amplitude and
improves the resilience to perturbations in the controller parameters. Then, neuromodulation is added to allow the dynamic change of the controller properties to
control the amplitude of the oscillations. This leads to a trade-off between the speed
of convergence to the desired amplitude and the stability of the controller. Finally,
multiple controller-pendulum systems are interconnected at the controller level to
achieve the desired spatiotemporal pattern between the pendulums.
The results indicate that the neuromorphic approach is well-suited for the design
of robust controllers. The proposed controller demonstrates the ability to easily
adapt to the mechanical system properties to achieve the amplitude goal, as well
as the ability to interconnect in a network of controllers. Extensions of the model
could be used to control locomotion in robotics or other domains.
Cite this master thesis
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