ME-425: Model predictive controlProvide an introduction to the theory and practice of Model Predictive Control (MPC). Main benefits of MPC: flexible specification of time-domain objectives, performance optimization of highly complex
ME-422: Multivariable controlThis course covers methods for the analysis and control of systems with multiple inputs and outputs, which are ubiquitous in modern technology and industry. Special emphasis will be placed on discrete
ME-321: Control systems + TPProvides the students with basic notions and tools for the analysis and control of dynamic systems. Shows them how to design controllers and analyze the performance of controlled systems.
ME-326: Control systems and discrete-time controlCe cours inclut la modélisation et l'analyse de systèmes dynamiques, l'introduction des principes de base et l'analyse de systèmes en rétroaction, la synthèse de régulateurs dans le domain fréquentiel
EE-472: Smart grids technologiesLearn the technologies and methodologies used in the context of the operation of future power grids and be able to deploy/implement/test them.
ENG-639: Dynamic programming and optimal controlThis course provides an introduction to stochastic optimal control and dynamic programming (DP), with a variety of engineering
applications. The course focuses on the DP principle of optimality, and i
MICRO-507: Legged robotsThe course presents the design, control, and applications of legged robots. It gives a review of different types of legged robots (including two-, four- and multi-legged robots), and an analysis of di
EE-465: Industrial electronics IThe course deals with the control of grid connected power electronic converters for renewable applications, covering: converter topologies, pulse width modulation, modelling, control algorithms and co
ME-421: System identificationIdentification of discrete-time linear models using experimental data is studied. The correlation method and spectral analysis are used to identify nonparametric models and the subspace and prediction
MATH-414: Stochastic simulationThe student who follows this course will get acquainted with computational tools used to analyze systems with uncertainty arising in engineering, physics, chemistry, and economics. Focus will be on s
EE-715: Optimal controlThis doctoral course provides an introduction to optimal control covering fundamental theory, numerical implementation and problem formulation for applications.
EE-726: Sparse stochastic processesWe cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a
EE-803: Optimal networked controlThis summer school will focus on large-scale optimization and its applications in the control and operation of the infrastructure networks, in particular it will build comprehensive framework from the
EE-717: Learning to controlThis course offers an overview of direct data-driven approaches to control design. In such methods, learning tools are used to compute optimal control laws from data without relying on a model of the
