Keynote Abstracts

Speaker

Affiliation

Tentative Title

Alberto Bemporad  IMT School for Advanced Studies Lucca (Italy) Model Predictive Control: Dreams, Possibilities, and Reality
Jonas Buchli Swiss Federal Institute of Technology (Switzerland) Efficient optimal and learning control for real robots
Timm Faulwasser Karlsruhe Institute of Technology (Germany)  
Paul Goulart  University of Oxford (UK) Distributionally Robust Optimization for Chance-Constrained Systems.
Ali Mesbah  University of California, Berkley (US) Moment-Matching Scenario Tree Generation for Robust Nonlinear Model Predictive Control under Arbitrary Probabilistic Uncertainty
Joe Qin  University of South California (US)  
Angela Schoellig University of Toronto (Canada)  
Melanie Zeilinger Swiss Federal Institute of Technology (Switzerland) MPC for Learning-based Control with Constraints 

 

 

Alberto Bemporad

Model Predictive Control: Dreams, Possibilities, and Reality

Model Predictive Control (MPC) has become one of the most popular techniques adopted in industry to control multivariable systems in an optimized way under constraints on input and output variables. MPC hinges upon the availability of good dynamical models for prediction and good numerical solvers for real-time computations. For MPC to be applicable in industrial production one would like to reduce the time/difficulty involved in developing prediction models and also to have solvers that require limited resources (CPU time, memory), are numerically very robust, and are certifiable for worst-case execution time. In my talk I will present recent developments in data-driven design of MPC controllers and in embedded quadratic optimization, giving a concrete example of designs of multivariable MPC systems that are scheduled for mass production in the automotive industry in 2018.

 

 

 

    

 

Jonas Buchli

Efficient optimal and learning control for real robots

   

 

Timm Faulwasser

 

 

 

 

 

 

Paul Goulart 

Distributionally Robust Optimization for Chance-Constrained Systems

   

 

Ali Mesbah

Moment-Matching Scenario Tree Generation for Robust Nonlinear Model Predictive Control under Arbitrary Probabilistic Uncertainty

We present an efficient framework for generating a “minimal” scenario tree for multi-stage NMPC, which is based on a recourse formulation that accounts for future measurements along different branches of the tree. The proposed framework enables providing closed-loop guarantees for constraint satisfaction and control performance when the uncertainty is modeled as a random vector with an arbitrary joint distribution (e.g., multi-modal and correlated) defined over an arbitrary set. Optimization-based moment matching is used to approximate probability integrals with a cubature rule that yields a drastically smaller number of scenarios than alternative multivariate integration methods such as quasi-Monte Carlo, sparse grids, and tensor products. We then demonstrate how the probability distribution of closed-loop states can be accurately and efficiently approximated using polynomial chaos theory to enable verification of chance constraint satisfaction using a limited number of closed-loop simulations.

mesbah_abstract 

 

 

Joe Qin

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Angela Schoellig

 

 

 

 

 

 

 

Melanie Zeilinger

MPC for Learning-based Control with Constraints 

   

 

 

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