Master's Degree in Automation Engineering and Control of Complex Systems
Academic Year 2018/2019 - 2° Year
COMPLEX ADAPTIVE SYSTEMS AND BIOROBOTICS
Teaching Staff
MATTIA FRASCA - Module Complex adaptive systems - ING-INF/04 - 6 CFUPAOLO PIETRO ARENA - Module Biorobotics - ING-INF/04 - 6 CFU
Learning Objectives
- Complex adaptive systems
To learn the main analysis methods for a complex adaptive system
To learn the main control methods for a complex adaptive system
To learn how to design simple nonlinear adaptive systems
- Biorobotics
The course offers the main guidelines for understanding, designing and realising nonlinear bioinspired circuits and systems wirh adaptive capabilities. The course includes an experimental software/hardware laboratory phase.The course gives also the guidelines for the desiign and realization of neurocontrol models for bio-inspired robots. The learning objectives are integrated within the Automation Engineering degree, both in terms of acquisition of specific skills in the analysis and design of nonòinear dynamical neural systems, and through specific emergent competences in the control of complex dynamics focalised to motion control; relevant aspects are related also to the capability of designijng and realising adaptive and learning systems for new machines woth perceptual capabilities inspired to the brain of some specific living beings selected as model organisms.
Course Structure
- Complex adaptive systems
Fundamentals of nonlinear dynamical systems. Design of adaptive circuits based on nonlinear devices.
1) Fundamentals of nonlinear dynamical systems: continuous-time systems
2) Theory of elementary bifurcations for continuous-time systems
3) Discrete-time dynamical systems: logistic map and bifurcations
4) Equilibrium points, limit cycles, strange attractors
5) Oscillations in second-order nonlinear circuits
6) Chaotic systems
7) Distributed systems, Cellular Neural Networks and reaction-diffusion systems
8) Design of nonlinear systems
9) Complex networks: analysis and control
- Biorobotics
Frontal lessons; exercitations and demonstrfations with multimedial material; laboratory
Detailed Course Content
- Complex adaptive systems
Fundamentals of nonlinear dynamical systems. Design of adaptive circuits based on nonlinear devices.
1) Fundamentals of nonlinear dynamical systems: continuous-time systems
2) Theory of elementary bifurcations for continuous-time systems
3) Discrete-time dynamical systems: logistic map and bifurcations
4) Equilibrium points, limit cycles, strange attractors
5) Oscillations in second-order nonlinear circuits
6) Chaotic systems
7) Distributed systems, Cellular Neural Networks and reaction-diffusion systems
8) Design of nonlinear systems
9) Complex networks: analysis and control
- Biorobotics
Introduction 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. H. Strogatz, Nonlinear dynamics and chaos, Westview Press; 2nd edition (July 29, 2014)
2. A. Buscarino, L. Fortuna, M. Frasca, Essentials of Nonlinear Circuit Dynamics with MATLAB® and Laboratory Experiments, CRC Press, 2017
3. V. Latora, V. Nicosia, G. Russo, Complex Networks: Principles, Methods and Applications, Cambridge University Press, 2017
- 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.
Open in PDF format Versione in italiano