Iterative Distributed Model Predictive Control of Nonlinear Systems: Handling Asynchronous, Delayed Measurements

Dept. of Chem. & Biomol. Eng., Univ. of California, Los Angeles, CA, USA
IEEE Transactions on Automatic Control (Impact Factor: 3.17). 03/2012; DOI: 10.1109/TAC.2011.2164729
Source: IEEE Xplore

ABSTRACT In this work, we focus on iterative distributed model predictive control (DMPC) of large-scale nonlinear systems subject to asynchronous, delayed state feedback. The motivation for studying this control problem is the presence of asynchronous, delayed measurement samplings in chemical processes and the potential use of networked sensors and actuators in industrial process control applications to improve closed-loop performance. Under the assumption that there exist upper bounds on the time interval between two successive state measurements and on the maximum measurement delay, we design an iterative DMPC scheme for nonlinear systems via Lyapunov-based control techniques. Sufficient conditions under which the proposed distributed MPC design guarantees that the state of the closed-loop system is ultimately bounded in a region that contains the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process example.


Available from: Jinfeng Liu, Jun 06, 2014
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    ABSTRACT: In this work, we focus on sequential and iterative distributed model predictive control (DMPC) of large scale nonlinear process systems subject to asynchronous measurements. Assuming that there is an upper bound on the maximum interval between two consecutive asynchronous measurements, we design DMPC schemes that take into account asynchronous feedback explicitly via Lyapunov techniques. Sufficient conditions under which the proposed distributed control designs guarantee that the states of the closed-loop system are ultimately bounded in regions that contain the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process example.