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Architectural Issues in the Design of Model Predictive Control: From Centralized to Distributed

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This talk aims to address some architectural issues in the design of MPC . Starting from the centralized case, we will present a general tuning framework so that an MPC controller (without constraints) can either replace a well-designed linear controller (i.e., PID ) or gradually improve the control performance (without and with constraints) based on the latter. The second part of the talk considers the design of cooperative distributed MPC . We propose to apply a state transformation to the problem setup considered in Chapter 6 of the MPC book by Rawlings and Mayne so that the couplings in the original question can be handled more effectively. In contrast with the belief established in the former reference that the computation of any local input in cooperative distributed MPC requires information about the plant-wide state and inputs of other local systems, the proposed framework allows one to compute the local inputs in parallel and independently, requiring partial plant-wide state information. Implications of this finding in communication, computation and stability analysis will be thoroughly discussed.

About the speaker:

He KONG received Bachelor, Master, and PhD degrees all in Electrical Engineering, from China University of Mining and Technology, Harbin Institute of Technology (China), and University of Newcastle (Australia), in 2008, 2010, and 2014, respectively. In early 2015, he joined Cranfield University, as a Research Fellow. His research interests include model predictive control, distributed control, networked control systems, and their applications in automotive engineering.

This talk is part of the CUED Control Group Seminars series.

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