Objectives of Programme's Learning Outcomes

• Imagine, design, carry out and operate mechatronic systems and automated mechanical units to provide a solution to a complex problem by integrating needs, constraints, context and technical, economic, societal, ethical and environmental issues.
• Implement a chosen solution in the form of a drawing, a schema, a diagram or a plan that complies with standards, a model, a prototype, software and/or digital model
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out mechanical engineering missions, with a focus on mechatronics, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques related to the mechanics of solids and fluids, energy exchanges, dynamic and vibratory behaviour of systems, manufacturing and mechanical production, the management of mechatronic systems: dynamic modelling, control, instrumentation, and hardware and software operation and implementation.
• Study a mechatronic system or an automated mechanical unit by critically selecting theories, models and methodological approaches, and taking into account multidisciplinary aspects.

Learning Outcomes of UE

describe the dynamic behaviour of multiple-input multiple-output (MIMO) processes using deterministic or stochastic state equations; linearize nonlinear state space equations; compute equilibrium points and analyze the local stability; compute the analytic solution of linear state space models; transform state space representation into canonical forms; compute matrices of transfert functions;  analyze controllability and observability of linear systems; design state feedback controllers; design Luenberger observers and Kalman filters; analyze input-output coupling and pairing using the relative gain array; compute decentralized control structures using PID controllers; apply these approaches either to continuous or discrete time systems.

Content of UE

deterministic and stochastic state equations of continuous-time and discrete-time systems; stability analysis; commandability; observability; state feedback control; state estimation; Luenberger observer; Kalman filter; decentralized control;

Prior Experience

differential equations, transfer functions, PID controller

Type of Assessment for UE in Q2

• Oral Examination

Q2 UE Assessment Comments

Oral exam with a written preparation (joint exam for Advanced Control and State Observers and Estimators)

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Oral exam with a written preparation (joint exam for Advanced Control and State Observers and Estimators)