COMPLEX ADAPTIVE SYSTEMS AND BIOROBOTICS

Detailed Course Content

• Complex Adaptive Systems

  The knowledge of the main system theory topics and automatic control techniques ais required. Moreover electronic circuit theory

  knowlwdge will suitably help the students.

  Complex theory paradigms. Non linear dynamical theory. Design of adaptive circuits based on non linear devices.

  Distributed architecture for information processing. Noise and uncertainty in complex systems.

• BIOROBOTICS

  ntroduction to Biorobotics and to its interdisciplinary aspects; detailed study on nonlinear dynamics in biological neural systems, biological neuron model and phase space analysis, models of synapses and of their modulation; computational models for biological neural networks; simulation examples referring to cases of study; biological neural paradigms for the generation and control of locomotion patterns: the Central Pattern Generator (CPG) and the decentralised control: study and comparison in relation to particular animals; implementation of the locomotion control paradigms through nonlinear circuits and systems (analog and digital implementation), examples of bio inspired robots controlled by models of biological neural networks: implementation of undulatory worm-like locomotion patterns, implementation of CPG networks and decentralised controllers on hexapod, quadruped and biped robots. The role of complex dynamics in modelling and control of perceptual systems for biorobotic applications. Toward an insect brain computational model.

Textbook Information

• Complex Adaptive Systems

  1) S. Strogatz, Non Linear Dynamics and Chaos, Pegaous Books 1994.
  2) L. Fortuna, M.Frasca, R.Caponetto,Advanced Topics on Cellular Self Organizing Nets and Chaotic non linear Dynamic to model and Control Complex Systems, World Scientific Series A, Vol. 63, 2008.

• BIOROBOTICS

  “Neuronal Control of Locomotion: From Mollusc to Man“, G. N. Orlovsky, T. G. Deliagina and S. Grillner;

  “Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots”, P. Arena ed.