(→Further Informations:) |
(→Further Information) |
||

(11 versioni intermedie di 2 utenti non mostrate) | |||

Riga 9: | Riga 9: | ||

== Examination method == | == Examination method == | ||

+ | A final project consists of: | ||

+ | # studying some papers, notes or documentation on a specific subject | ||

+ | # performing simulations or numerical tests on a software platform | ||

+ | # writing a report | ||

+ | # giving a presentation | ||

+ | |||

+ | Students are encouraged to contact directly the lab assistants to decide the project to be carried out. Further information regarding the proposed projects is given in the following pages. More detailed information, references, and tools will be given once the project has been assigned. | ||

==Contents== | ==Contents== | ||

− | ''' | + | '''The MPC framework''' |

+ | Prediction horizon, control horizon. Mathematical formulation of the MPC problem as an optimization problem | ||

+ | |||

+ | '''Case studies''' | ||

+ | Autonomous guided vehicles Drones Active suspension | ||

+ | |||

+ | '''MPC with constraints''' | ||

+ | Mathematical formulation of MPC with constraints. Examples. | ||

+ | |||

+ | '''MPC with Matlab''' | ||

+ | Instructions: mpc Examples: tuning Q and R and constraints | ||

+ | |||

+ | '''MPC design''' | ||

+ | Extended example: the paper machine | ||

+ | |||

+ | '''MPC with disturbance''' | ||

+ | MPC formulation with disturbance Observer Disturbance model with augmented state Example: the Cesna | ||

+ | aircraft model | ||

+ | |||

+ | '''Solving MPC problems''' | ||

+ | Solving MPC problems. The unconstrained problem as a least square problem Constrained problems. | ||

+ | Lagrange multiplier. Solving QP problems: - Active set method - Interior points method. | ||

+ | |||

+ | '''paper machine case''' | ||

+ | Extended analysis using matlab MPC | ||

+ | |||

+ | '''The case of Cesna Aircraft''' | ||

+ | Extended analysis using MPC in matlab | ||

+ | |||

+ | '''case studies''' | ||

+ | Inverted pendulum using MPC+VREP | ||

− | + | '''Stabiliy analysis''' | |

+ | The stability analysis of the MPC controller Example by changing Hp The Lyapunov function | ||

− | + | '''Stability of MPC''' | |

+ | Exercise 6.2 Hp=2 Hu=2 Hp=2 Hu=1 | ||

+ | |||

+ | '''Projects''' | ||

+ | Illustration of possible projects | ||

+ | |||

+ | '''Stabilty''' | ||

+ | Stability proof using infinite horizon Terminal constraints | ||

+ | |||

+ | '''Inverted pendulum''' | ||

+ | nonlinear model of an inverted pendulum position and angle control Swing up with MPC | ||

+ | |||

+ | '''MPC tuning''' | ||

+ | Tuning: Hp, Hu, Q, R | ||

+ | |||

+ | '''Sensitivity for MIMO systems''' | ||

+ | Sensitivity function for MIMO systems SVD decomposition | ||

+ | |||

+ | '''Projects''' | ||

+ | the case of a mobile robot. Path planning, odometry | ||

+ | |||

+ | '''MPC for photovoltaic panel''' | ||

+ | MPC for the photovoltaic panel. identification of the linearized model and mpc design | ||

+ | |||

+ | '''Robust MPC''' | ||

+ | Robust MPC Small gain theorem SVD ||k(z)S(z)D||<1 | ||

+ | |||

+ | '''simulink''' | ||

+ | MPC for a photovoltaic panel | ||

+ | The case of a ship model variables: rudder angle and propeller speed MPC: edit scenario | ||

+ | |||

+ | '''Multiple MPC design''' | ||

+ | |||

+ | |||

+ | '''Libro di testo''': Predictive Control: With Constraints by Jan Maciejowski | ||

== Further Information == | == Further Information == | ||

Riga 22: | Riga 94: | ||

Email: <tt>mascolo at poliba dot it</tt> <br> | Email: <tt>mascolo at poliba dot it</tt> <br> | ||

<br> | <br> | ||

+ | Lab. Assistant: <br> | ||

'''Dott. Vito Andrea Racanelli''' <br> | '''Dott. Vito Andrea Racanelli''' <br> | ||

+ | |||

Email: <absHtml><img src="https://services.nexodyne.com/email/customicon/BA3u4uFtGbdt.swNKiZToCQ0voOtSIqT/1uG%2BJXA%3D/000000/ffffff/000000/0/image.png"/></absHtml><br> | Email: <absHtml><img src="https://services.nexodyne.com/email/customicon/BA3u4uFtGbdt.swNKiZToCQ0voOtSIqT/1uG%2BJXA%3D/000000/ffffff/000000/0/image.png"/></absHtml><br> |

Saverio Mascolo | |

Professore Ordinario (Full Professor)
IEEE Fellow |

The course describes the main properties of Model Predictive Control (MPC), the most widely used and successful control method in the process industry and nowadays also applied in distribution networks, coordination of autonomous systems, automotive, and in many other fields of application.

Knowledge of Automatic Control, Dynamical Systems Theory and Estimation and Control of Dynamical Systems.

A final project consists of:

- studying some papers, notes or documentation on a specific subject
- performing simulations or numerical tests on a software platform
- writing a report
- giving a presentation

Students are encouraged to contact directly the lab assistants to decide the project to be carried out. Further information regarding the proposed projects is given in the following pages. More detailed information, references, and tools will be given once the project has been assigned.

**The MPC framework**
Prediction horizon, control horizon. Mathematical formulation of the MPC problem as an optimization problem

**Case studies**
Autonomous guided vehicles Drones Active suspension

**MPC with constraints**
Mathematical formulation of MPC with constraints. Examples.

**MPC with Matlab**
Instructions: mpc Examples: tuning Q and R and constraints

**MPC design**
Extended example: the paper machine

**MPC with disturbance**
MPC formulation with disturbance Observer Disturbance model with augmented state Example: the Cesna
aircraft model

**Solving MPC problems**
Solving MPC problems. The unconstrained problem as a least square problem Constrained problems.
Lagrange multiplier. Solving QP problems: - Active set method - Interior points method.

**paper machine case**
Extended analysis using matlab MPC

**The case of Cesna Aircraft**
Extended analysis using MPC in matlab

**case studies**
Inverted pendulum using MPC+VREP

**Stabiliy analysis**
The stability analysis of the MPC controller Example by changing Hp The Lyapunov function

**Stability of MPC**
Exercise 6.2 Hp=2 Hu=2 Hp=2 Hu=1

**Projects**
Illustration of possible projects

**Stabilty**
Stability proof using infinite horizon Terminal constraints

**Inverted pendulum**
nonlinear model of an inverted pendulum position and angle control Swing up with MPC

**MPC tuning**
Tuning: Hp, Hu, Q, R

**Sensitivity for MIMO systems**
Sensitivity function for MIMO systems SVD decomposition

**Projects**
the case of a mobile robot. Path planning, odometry

**MPC for photovoltaic panel**
MPC for the photovoltaic panel. identification of the linearized model and mpc design

**Robust MPC**
Robust MPC Small gain theorem SVD ||k(z)S(z)D||<1

**simulink**
MPC for a photovoltaic panel
The case of a ship model variables: rudder angle and propeller speed MPC: edit scenario

**Multiple MPC design**

**Libro di testo**: Predictive Control: With Constraints by Jan Maciejowski

C3Lab, Department of Electrical and Information Engineering, Polytechnic of Bari

**Prof. Saverio Mascolo**

Email: `mascolo at poliba dot it`

Lab. Assistant:

**Dott. Vito Andrea Racanelli**

Email:

Saverio Mascolo | |

Professore Ordinario (Full Professor)
IEEE Fellow |

The course describes the main properties of Model Predictive Control (MPC), the most widely used and successful control method in the process industry and nowadays also applied in distribution networks, coordination of autonomous systems, automotive, and in many other fields of application.

Knowledge of Automatic Control, Dynamical Systems Theory and Estimation and Control of Dynamical Systems.

**Argument 1**

State-space formulation and the MPC control problem: open loop solutions; closed loop solutions; treatment of constraints on state and control variables (8 HOURS - 1 ECTS)

Examples and exercises (4 hours - 0.25 CFU)

C3Lab, Department of Electrical and Information Engineering, Polytechnic of Bari

**Prof. Saverio Mascolo**

Email: `mascolo at poliba dot it`

**Dott. Vito Andrea Racanelli**

Email: