Automation Engineering and Robotics

CFU: 6

Prerequisites

Basic knowledge about closed loop control systems.

Preliminary Courses

None. 

Learning Goals 

The course aims at providing students with

• a set of tools for the analysis and control of networks of dynamical systems, with a special emphasis on their optimization and safety, and on their possible use for the design and management in diverse engineering applications
• a set of tools to model discrete event systems in the context of industrial automation and to design supervisory control systems. The course will focus on the finite state automata and language theory, as well as on Petri nets.

Expected Learning Outcomes 

Knowledge and understanding

The students need to acquire the main methodological tools to model, analyze and control complex systems, with special emphasis on those that can be described as networks of interconnected dynamical systems. The lectures will guide the students toward a) the comprehension of the links between the topological properties of the graph and the individual dynamics of the nodes, and b) the identification of the causal mechanisms determining the spontaneous emergence of collective behaviors, such as consensus and synchronization. The analytical and numerical tools acquired by the students will be then used to understand the specificities of control design for network systems.

Applying knowledge and understanding

The students need to be capable of applying the acquired methodology to model and analyze real systems that can be described by complex network models, as for instance wireless sensor networks, population dynamics, or formation of autonomous vehicles. Furthermore, the students will need to showcase the ability apply the control techniques that they learned to design controllers for complex systems in the presence of constraints on the number of input signals and observable nodes.

Course Content - Syllabus 

Part 1 Introduction and background

• Introduction
  • Definition of a complex system
  • Complex networks of dynamical systems
  • Examples: wireless sensor networks and compartmental models
• Elements of matrix theory
  • Convergent and semi-convergent matrices; eigenvalue classification
  • Spectral properties of stochastic matrices
  • Geršgorin disks theorem
  • Perron-Frobenius theorem
  • Examples

Parte 2 Graph theory

• Elements of graph theory
  • Directed and undirected graphs
  • Main definitions
  • Paths, connectivity, and periodicity
  • Condensation graphs
  • Weighted graphs
  • Adjacency matrix
• Linking graphs and matrices
  • Properties of the adjacency matrix
  • Some elementary equivalences
  • Paths in the graph and powers of the adjacency matrix
  • Graphs and irreducible matrices
  • Graphs and primitive matrices

Parte 3 Analysis and control of networks of linear dynamical systems: consensus problem

• Discrete-time consensus problem
  • Networks of discrete-time integrators
  • Definition of consensus: min-max consensus, average consensus
  • Condition on the graph topology for consensus in time-invariant networks
  • Example: Leslie’s population model
• Continuous-time consensus problem
  • Laplacian matrix of a graph: definition and properties
  • Example: modeling collective dynamics in animal groups
  • Network of continuous-time integrators
  • Rank of the Laplacian matrix and equilibria in the network system
  • Globally reachable nodes and consensus emergence
  • Condition on the graph topology for consensus in time-invariant networks
• Convergence rates
  • One-step convergence factor
  • Asymptotic convergence factor
  • Linking convergence rates and graph topology
• Consensus problems on time-varying graphs
  • Examples of network systems on time-varying graphs
  • Convergence over time-varying graphs connected at all times
  • Convergence over time-varying graphs connected over time

Parte 4 Networks of nonlinear dynamical systems: synchronization

• Networks of nonlinear dynamical systems
  • Modeling and fundamental assumptions
  • Standard model of a network dynamical systems
  • Example
• Synchronization
  • Definition
  • Example: Kuramoto oscillators
  • Lyapunov-based stability analysis
  • Sufficient conditions for synchronization
  • Assumption on the node vector field and graph topology

Parte 5 Control of networks of nonlinear dynamical systems

• Decentralized control of network of nonlinear systems
  • Centralized vs decentralized control
  • Controllability of network systems
  • Pinning control
  • Partial control of networks
• Emerging problems and advanced network control techniques
  • Elements on adaptive control of complex networks
  • Control of networks with state-dependent topology
  • Coevolution of graph topology and node states
  • Emerging applications

Readings/Bibliography

- F. Bullo, Lectures on Network Systems, Edizione 1.3, 2019.

- M. E. J. Newman, A. L. Barabasi, and D. J. Watts, The structure and dynamics of networks, Princeton University Press, 2006.

- Additional references and lecture notes available in the tab file of the Teams class of the module

Further reading and material:

- Siljak, D. D. Decentralized control of complex systems. Courier Corporation, 2011.

- A. Barrat, M. Barthelemy, A. Vespignani, Dynamical Processes on Complex Networks, Cambridge University Press, 2008.

- Uri Alon lab dataset. Available at http://www.weizmann.ac.il

- Pajek’s dataset Available at: http://vlado.fmf.uni-lj.si/pub/networks/data

Teaching Method

The teaching activities will be organized as follows: a) lectures for about 70% of the total hours, b) practical exercise in the classroom based on software tools (Matlab-simulink) for about 30% of the total hours.

Examination/Evaluation criteria

Exam type

Only oral. The oral exam focused on the discussion of a homework assigned to student by the instructor. The oral examination will also aim at assessing the knowledge of all the concepts and contents given during the lectures.

Evaluation pattern 

The final mark is weighted with respect to the CFU of each module as follows:

• Module Discrete event systems and supervisory control, 6 CFU, 50%
• Module Control of complex systems and networks, 6 CFU, 50%

CFU: 6

Prerequisites

Knowledge on the foundations of robotics.

Preliminary Courses

Foundations of Robotics

Nonlinear Dynamics and Control

Learning Goals 

The course aims to provide students with:

• Skills for controlling the interaction between robots and poorly structured environments, through force control, visual control, manipulation and cooperation.
• The tools for modeling, planning and control of self-driving mobile robots (with wheels, drones, legged, underwater).

Expected Learning Outcomes 

Knowledge and understanding

The course path aims to provide students with the essential methodological tools for modeling, planning and control of autonomous mobile robot systems. The fundamental problems concerning robots with locomotion mechanisms in open spaces, structured and not, are dealt with. The analytical methods acquired by the students are then used to understand the peculiarities in the design of planning techniques and control laws for such robots.

Applying knowledge and understanding

The student must demonstrate that (s)he is able to apply the methodologies acquired to model, plan and control autonomous-drive robots with different locomotion mechanisms, such as land rovers, drones (in particular quadcopters), underwater robots, quadrupedal and bipedal robots.

Course Content - Syllabus 

• Field and service robotics
• Wheeled robots
• Kinematics and dynamics
• Planning
• Motion control
• Odometric localization
• Motion planning
• Probabilistic planning
• Planning through the method of artificial potentials
• Aerial robotics
• Drone kinematics
• Dynamics of a quadcopter
• Hierarchical control and geometric control
• Passive control with external disturbance estimator
• Underwater robotics
• Kinematics and dynamics
• Mixed controller
• Legged robots
• Kinematics of the floating base
• Dynamics and centroidal dynamics
• Stability and criteria
• Whole-body control
• Planner
• Momentum-based estimator

Readings/Bibliography

• B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics – Modeling, Planning and Control, Springer, London, 2009, ISBN 978-1-84628-641-4
• A. Ollero, B. Siciliano (Eds.), Aerial Robotic Manipulation, Springer, Berlin, 2019, ISBN 978-3-030-12945-3
• G. Antonelli, Underwater Robots, 3rd Ed., Springer, Berlin, ISBN 978-3-319-02877-4
• Lecture notes available at https://prisma.dieti.unina.it/index.php/education/education-courses/18-education/education-courses/722-field-and-service-robotics

Teaching Method

The teacher will use: a) lectures for about 70% of the total hours, b) seminars for about 20% of the total hours; c) classroom examples through the use of analysis and simulation tools in Matlab / Simulink® for about 10% of the total hours

Examination/Evaluation criteria

Exam type

Only oral exam. The interview consists in ascertaining the acquisition of the concepts and contents introduced during the lessons. The oral interview of the Field and Service Robotics module also includes the discussion of a design project assigned by the teacher during the first month of the course.

Evaluation pattern 

The final grade will be weighted on the credits of each module and therefore composed as follows:

• Module: Robot Interaction Control, 6 CFU, 50%
• Module: Field and Service Robotics, 6 CFU, 50%

CFU: 6

Prerequisites

Knowledge on the foundations of robotics.

Preliminary Courses

Foundations of Robotics

Nonlinear Dynamics and Control

Learning Goals 

The course aims to provide students with:

• Skills for controlling the interaction between robots and poorly structured environments, through force control, visual control, manipulation and cooperation.
• The tools for modeling, planning and control of self-driving mobile robots (with wheels, drones, legged, underwater).

Expected Learning Outcomes 

Knowledge and understanding

The course path aims to provide students with methodological tools for controlling robots in interacting with poorly structured environments. to modeling, planning and control of robots. Force control and visual control techniques for rigid manipulators and control for manipulators with elastic joints are introduced, as well as techniques for controlling manipulation and cooperation of robotic systems. The students must demonstrate that they have learned the solutions to the interaction control problem based on the techniques studied in the course.

Applying knowledge and understanding

The student must demonstrate that (s)he is able to apply the methodologies acquired to model and control robotic systems interacting with the environment.

Course Content - Syllabus 

• Interaction of manipulator with the environment
• Compliance control
• Impedance control
• Force control
• Parallel force/motion control
• Constrained motion
• Natural and artificial constraints
• Hybrid force/motion control
• Vision for control
• Image processing
• Pose estimate
• Stereo vision and camera calibration
• Task-space visual control
• Image-space visual control
• Hybrid visual control
• Modeling of manipulators with elastic joints
• Control of manipulators with elastic joints
• Robotic manipulation
• Contact models
• Models of friction
• Definition of grasps
• Internal and external forces
• Kinematic and dynamic models of a system consisting of cooperating robots and manipulated object
• Control and planning of a manipulation task

Readings/Bibliography

• B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics – Modeling, Planning and Control, Springer, London, 2009, ISBN 978-1-84628-641-4
• B. Siciliano, O. Khatib (Eds.), Springer Handbook of Robotics, 2^nd Edition, Springer, Berlin, 2016 ISBN 978-3-319-32552-1
• B. Siciliano, O. Khatib, T. Kröger, Multimedia Extension to Springer Handbook of Robotics, 2016.
• Lecture notes available at https://prisma.dieti.unina.it/index.php/education/education-courses/723-robot-interaction-control

Teaching Method

The teacher will use: a) lectures for about 70% of the total hours, b) classroom exercises for about 20% of the total hours, c) seminars for about 10% of the total hours.

Examination/Evaluation criteria

Exam type

Only oral exam. The interview consists in ascertaining the acquisition of the concepts and contents introduced during the lessons. 

Evaluation pattern 

The final grade will be weighted on the credits of each module and therefore composed as follows:

• Module: Robot Interaction Control, 6 CFU, 50%
• Module: Field and Service Robotics, 6 CFU, 50%

CFU: 6

Prerequisites

Basic knowledge about closed loop control systems.

Preliminary Courses

None.

Learning Goals 

The course aims at providing students with advanced competences on the analysis and design of Networked Control Systems (NCSs)/Cyber-Physical Systems (CPSs) used for the monitoring and control of distributed processes. Furthermore, it provides students with advanced methodologies for the synthesis of resilient, fault-tolerant and distributed algorithms for estimation, control and optimization over networks, with application to Industrial, civil and social domains (i.e. Smart Factory, Internet of Thing, Industry 4.0, Smart City, communication infrastructures and networks, distributed computing). The methodologies will be illustrated by the software/hardware design of representative Cyber-Physical Systems.

Expected Learning Outcomes 

Knowledge and understanding

The course provides students the methodology for the analysis and software/hardware design of modern Networked Control Systems (NCSs)/Cyber-Physical Systems (CPSs). The student needs to show that she/he learned the typical requirements that are peculiar for both hardware and software components of Networked Control Systems (NCSs)/Cyber-Physical Systems (CPSs) used for monitoring/control of industrial/civil processes. The student needs to show ability to know the main design phases of Cyber-Physical Systems and, specifically, of distributed algorithms for estimation, control and optimization over networks. Finally, the student needs to show understanding for the validation and performance evaluation test of distributed algorithms and Cyber-Physical Systems, including the relevance of co-simulation tools to support the design phases.

Applying knowledge and understanding

The student needs to show that she/he is able to formalize the main requirements for a Networked Control System (NCSs)/Cyber-Physical System (CPS) in terms of control system and network performance, including the energy autonomy. Moreover, by starting from such formalized requirements, the student needs to show the ability to design simple algorithms for estimation, control and optimization over networks; and to select the main  hardware components. Eventually, the student needs to show the ability to design the validation and performance evaluation test for the developed distributed algorithms and designed Cyber-Physical System; the possibility to exploit simulation tools for such tests should be envisaged.

Course Content - Syllabus 

1. Introduction to Networked Control Systems and Cyber-Physical Systems
   • Complex and large-scale distributed systems
   • Remote control Systems
   • Centralized, decentralized and distributed systems
   • Distributed algorithms
   • Requirements of Cyber-Physical Systems and distributed algorithms
   • Examples of applications
2. Multi-Layer model of Networked Cyber-Physical systems
   • “Application” layer
   • “Network” layer
   • “Physical” layer
   • Requirements of Application, Network and Physical layer
3. Distributed algorithms and design of a Cyber-Physical System
   • Multi-agent systems and consensus algorithms
   • Design of control systems at network layer
   • Design of distributed algorithms for load balancing, flow and congestion control
   • Design of control systems at application layer
   • Design of cooperative algorithms for distributed estimation, control and optimization over networks
   • Energy efficiency and energy harvesting in Cyber-Physical Systems. Design of distributed algorithms for energy management
   • Stability analysis, convergence and computational complexity of distributed algorithms
4. Resilience and robustness of Cyber-Physical Systems and Distributed Algorithms
   • Effects of communication delay, packet loss, channel and measurement noise and uncertainty parameters on Cyber-Physical system performance
   • Robust, resilient and fault-tolerant distribute algorithms
5. Distributed algorithms for Cyber-Physical Systems composed of: wireless sensor networks/embedded systems, computer networks and elaboration systems, swarm of drones and vehicles
6. Illustrative applications of software/hardware design methodologies to representative Cyber-Physical Systems for Smart Cities e Smart Factories (Industry 4.0)

Readings/Bibliography

[1] Supplementary materials

[2] S. Manfredi, “Multilayer Control of Networked Cyber-Physical Systems. Application to Monitoring, Autonomous and Robot Systems”. Advances in Industrial Control, Springer, 2017

[3] A. Bemporad, M. Heemels, M. Vejdemo-Johansson, “Networked Control Systems”, Lecture Notes in Control and Information Sciences, Springer, 2010

Teaching Method

The teaching activities will be organized as follows: a) lectures for about 50% of the total hours, b) practical exercise in the classroom based on the simulation tools and/or lab activities for about 50% of the total hours.

Examination/Evaluation criteria

Exam type

Only oral exam and project discussion.