Treffer: Static output feedback based distributed robust model predictive control for parallel systems in process networks.

The parallel system is a fundamental system architecture frequently encountered in process networks. A distributed robust model predictive control strategy is formulated for parallel systems with unmeasured states, utilizing a static output feedback framework. Parallel system architectures typically incorporate coupling as well as constraints and these distinguishing features are directly addressed by the controller design approach. A non-convex control problem results which is first solved by linear matrix inequalities (LMIs). Then, the input to state stability (ISS) of the resulting system is demonstrated. The effectiveness of the proposed approach is verified by both numerical simulation and experimentation. • In this paper, a SOF-DRMPC algorithm is first presented for a parallel system. • To handle the competitive couplings, an iterative algorithm is designed and the demand on the state is changed to a demand on the initial conditions. • To predict the initial values for the algorithm and couplings, previous information is used and the predicted error is proved to be bounded to ensure the system stability. Then, a control algorithm is proposed and the ISS is analyzed. [ABSTRACT FROM AUTHOR]