Model Predictive Control Workshop

James B. Rawlings and Thomas A. Badgwell

ACC 2017, Seattle, WA
May 22-23, 2017

Model predictive control (MPC) has become the most popular advanced control
method in use today. Its main attractive features are (i) optimization of a
model forecast over the available actuators (ii) estimation of the state of
the system and disturbances from the process measurements, (iii) accounting
for the process and actuator constraints, and (iv) accounting for full
multivariable interactions. Initially developed in the process industries in
the 1970s, MPC has today become a pervasive control technology in many
fields, and is now being increasingly deployed for optimization of
high-level functions such as minimizing energy consumption and maximizing
product quality.  This two-day workshop is intended to introduce graduate
students and practitioners to the theory and design of MPC systems.
Simulation examples are implemented in a high-quality open source software
environment (python, octave, casADI).  Attendees are expected to bring their
own laptop computers and to download and install the workshop courseware
prior to the class.  Topics covered include regulation, state estimation,
disturbance models and offset-free control, nonlinear MPC, nonlinear moving
horizon estimation, economic MPC, suboptimal MPC, and MPC with discrete
actuators.

For more information on the conference, including information on workshop
registration, please visit the conference website: http://acc2017.a2c2.org/.
 Please note that the MPC Workshop class size is capped at 25, and typically
sells out well in advance of the conference.