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Real-time Optimization for Distributed Model Predictive Control

Author(s):

M. Morari
Conference/Journal:

2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys'10), Annecy, France, Centre de Congrès de L'Impérial Palace, 13 - 14 September
Abstract:

Distributed Model Predictive Control aims to control massively complex networks of coupled subsystems in a scalable fashion. The paradigm functions by solving many small MPC optimization problems in parallel at each of the subsystems before exchanging information and repeating the process. The ability to reliably solve these small-scale problems in real-time is a critical piece of the distributed MPC puzzle, and is the focus of this talk. I will discuss some of our recent work that brings the benefits of model predictive control to small-scale, high-speed systems and that can dramatically reduce online MPC loop execution to microsecond timescales. We show how our new real-time MPC schemes provide constraint satisfaction and stability, while optimizing for performance, at speeds several orders of magnitude faster than state-of-the-art. No single real-time optimization method will be able to tackle the broad range of possible problem classes, and so we present three techniques suitable for fast-sampled systems in the range of nano-, micro- and milli-seconds respectively. The controller synthesis methodologies are unique in that they take the available online computational resources as input and produce a controller that will execute within these bounds while satisfying constraints and stabilizing the system. I will motivate our work on real-time MPC with several applications, including wave power systems, camera networks, racing cars and smart power grids. Industrial examples will be presented throughout, which serve to highlight theoretical advancements and the extensive software tools that help to bring the developed theory to bear in practical scenarios.

Year:

2010
Type of Publication:

(05)Plenary/Invited/Honorary Lecture
Supervisor:



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