14:00-18:00, Paper SuSAWS.1Workshop: Machine Learning and Model Predictive Control
Mesbah, AliUniversity of California, Berkeley
Paulson, Joel The Ohio State University
The workshop was live-streamed and not recorded.
Model predictive control (MPC) is an optimization-based control strategy that has become hugely popular due to its ability to handle systems with multivariable dynamics, nonlinearities, and constraints. Furthermore, due to its versatility in terms of its ability to provide robustness guarantees (by design) as well as consideration of economically oriented control objectives, MPC is increasingly being applied in emerging chemical process systems, energy systems, and biomedical systems, to name a few. Yet, many MPC applications face important challenges related to the difficulty of modeling complex systems and the need for MPC strategies to provide provably safe and robust performance with low online computational and memory requirements.
This pre-conference workshop will focus on how recent advances in machine learning can be leveraged to develop and implement improved MPC schemes. The workshop will focus on the following topics:
This half-day workshop will consist of two primary parts.
The first part will provide an overview of how machine learning approaches can be used for learning-based MPC of processes with significant plant-model mismatch, approximating MPC problems towards fast and embedded MPC applications, and automated tuning of MPC problems via constrained Bayesian optimization.
The second part of the workshop will entail demonstrating the application of several methods for learning- based MPC, approximate MPC, and automated tuning of MPC methods using various benchmark chemical engineering problems. The workshop will conclude by discussing the open research problems in this area.
All workshop notes and slides will be provided, and open-source codes for selected examples and applications will be made available on the workshop’s website.
The workshop is designed for researchers with a basic knowledge of model predictive control and machine learning who intend to gain an overview of how machine learning methods can be leveraged for learning-based and approximate predictive control of uncertain and nonlinear systems.Tentative Schedule (as of February 2021):
1.1) Opening remarks and introduction to machine learning for MPC (20 min)
1.2) Learning-based MPC (35 min)
The fundamental paradigm of learning-based MPC for dealing with problems with significant plant-model mismatch (e.g., due to unmolded and/or time-varying process dynamics) will be introduced. We will then discuss various learning-based MPC methods that use Gaussian process regression and Bayesian neural networks for online model adaptation.
1.3) Approximate MPC (35 min)
The notion of approximating (robust/stochastic) MPC laws with cheap-to-evaluate surrogates will be introduced. In particular, we will focus on deep learning approaches for approximating MPC laws, and discuss the theoretical and practical considerations for the application of approximate MPC strategies for safety-critical systems.
1.4) Automatic tuning of MPC controllers under uncertainty (35 min)
Recent developments in automated controller tuning will be discussed. We will then discuss Bayesian optimization methods for systematic and efficient tuning of MPC controllers under system constraints and uncertainties. We will specifically focus on performance-oriented model learning from data.
We will demonstrate the concepts and methods introduced in Part 1 for predictive control of various uncertain and nonlinear processes. The application examples range from biomanufacturing and materials processing applications to integrated design and control of energy systems. The open-source codes for the hands-on problems will be distributed.