COMPLEX ADAPTIVE SYSTEMS AND BIOROBOTICS

Learning Objectives

• Complex adaptive systems

  Knowledge and understanding

  Learn the main analysis and control methods for a complex adaptive system

   

  Applying knowledge and understanding

  Know how to integrate a nonlinear system (either isolated or coupled), know how to calculate the bifurcation diagram, know how to analyse simple circuits and nonlinear systems with adaptive capabilities

   

  Making judgements

  Be able to decide which analysis techniques to apply to a complex dynamical system

   

  Communication skills

  Know and be able to use the technical terms related to nonlinear systems and to complex systems. Be able to discuss the main problems related to these systems in research or professional contexts

   

  Learning skills

  Be able to apply the basic notions on complex systems to study more advanced topics related to them, but not explicitly discussed in the course

• 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 nonlinear 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

   

  Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

• 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 interdisciplinary aspects; in-depth study of non-linear dynamics in biological neural systems; biological neuron modeling and phase plane study; models of synapses and their modulation; computationally efficient models of artificial and biological neural networks; simulation examples with reference to case studies. Biological neural paradigms for the generation and control of locomotion systems; the Central Pattern Generator (CPG) and decentralized control: in-depth study and comparisons in relation to animals taken as reference; implementation of control locomotion paradigms through non-linear systems and circuits (analog and digital implementation); study of real examples of biologically inspired robots, controlled by models of biological neural networks (implementation of "worm-like" wave dynamics, implementation of CPG networks and decentralized controllers on hexapods and multilegged robots). The role of complex dynamic systems in modeling and in the control of perceptive dynamics, with application to biorobotics: study of complex dynamics for the control of perceptive systems for biorobotics. Towards the computational model of the brain of an insectoid robot.

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

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

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