The goal of the course is to enable students to master the engineering methods, processes and tools that are necessary to design advanced control systems with industrial strength quality. The course covers the modeling, control system design and validation processes and their organization. The course aims at exposing the students to real-world examples of the control system design and implementation process.

Knowledge and understanding

Students will learn:

1) how the Systems Engineering method is applied to control system design.

2) he strength and weakness of different modeling approaches.

3) the basic tools to desing and implement control systems

Applying knowledge and understanding

Given specific project cases, students will be able to

1) implement analyze and simulate a nonlinear dynamical model

2) quantitatively describe the uncertainty affecting the model

3) translate qualitative requirements into performance and robustness requirements for Multiple Input Multiple Output systems. design a MIMO controller.

4) implement the designed control system on a given architecture.

5) validate the proposed design

Making judgements

Given a relatively complex problem, students will be able to:

1) Analyze and understand the goals, assumptions and requirements associated with that problem and model them

2) choose the most suitable modeling approach

3) find a proper way of quantify the uncertainty

4) choose the most suitable control architecture

Communication

Students will learn to:

1) Present their work in an oral format

2) Defend their design choices.

Lifelong learning skills

Students will learn how to develop a realistic project and manage the typical trade-offs of design projects.

1. The life cycle of a control system: from requirement specification to verification and validation.
• Introduction to system engineering and general description of the life cycle.
• Analogies with software life cycle.
• A case study: the life cycle for the control system of a space vehicle.
2. The role of mathematical modelling in control systems engineering.
• Models for preliminary design.
• Models, languages and tools for simulation: causal and acausal models.
• Control-oriented models: order reduction, representation of uncertainty, parameter estimation.
• Models for verification and validation.
3. Controller synthesis: from requirements to control laws.
• Stability and performance analysis for linear control systems.
• Requirement analysis and formulation of the synthesis problem.
• Advanced methods for control system synthesis.
4. Implementation of control systems.
• Development of control software: modular functional specification according to the IEC 61131 - IEC 61499 specifications; typical I/O structure (both for modulating and logic control) for a function control block; time management and synchronous/asynchronous communication among function block; optimisation of computational performance; parameters’ management and run time modification; issues related to finite numerical precision; an example: function block for a LTI SISO regulator.
• Role of the architecture on control system performance: sizing of quantizing devices; effect of time delays for typical digital buses; effect of exception report transmission and "modulating equivalent" actuators; effect of non uniform sampling; example: assessment of the above-described effects on a single loop control system.
5. Functional verification of control systems.
• Robustness analysis for control system stability and performance.
• Monte Carlo methods for control system analysis.
• Design of verification experiments.
• Assessment and statistical interpretation of the results.
6. A case study: a hybrid electric vehicle. The case study aims at presenting the design problem for the control system of a hybrid electric vehicle (with specific reference to an electrically-assisted bycicle). More precisely, the following sub-systems will be analysed:

• Control of a brushless electric motor.
• Battery management system.
• Control laws for vehicle dynamics and energy management.
• HMI and vehicle-network integration via a wireless connection with a smartphone.

   Besides the relevant estimation and control algorithm, the mechanical and electronic components, as well as the software architecture, will be discussed.

In order to successfully understand the subject matter, the student needs a working knowledge of  basic concepts of Automatic Control: dynamic systems representation (both frequency and time domain analysis), stability, and control systems design for Single Input Single Output Systems. Furthermore, the course assumes a graduate level knowledge of linear algebra, calculus and an undergraduate level understanding of physics and mechanics.

The exam consists in an oral examination. In the examination, the students will need to show:

• A clear understanding and knowledge of the subject matter.
• The ability to apply the techniques studied in the course to real world engineering problems
• The ability to clearly and concisely express themselves in a professional setting.

The students may decide to carry out a simulation and analysis project to show their ability to apply the techniques to real world problems. The project can be freely chosen as long as it uses any technique studied in the course.