Parsimonious cooperative distributed MPC algorithms for offset-free tracking. (December 2017) - Razzanelli, Matteo; Pannocchia, Gabriele
Highlights: Available distributed MPC algorithms for setpoint tracking use a centralized dynamic model and centralized target model. The proposed algorithms instead use more parsimonious models, in which only the necessary subset of the overall state vector is deployed. Single step and two step algorithm variants are proposed, along with an offset-free extension which uses distributed estimators of local state and disturbance. Simulation studies of both nominal case and disturbed case are presented to show the favorable features of the proposed algorithms. Abstract: We propose in this paper novel cooperative distributed MPC algorithms for tracking of piecewise constant setpoints in linear discrete-time systems. The available literature for cooperative tracking requires that each local controller uses the centralized state dynamics while optimizing over its local input sequence. Furthermore, each local controller must consider a centralized target model. The proposed algorithms instead use a suitably augmented local system, which in general has lower dimension compared to the centralized system. The same parsimonious parameterization is exploited to define a target model in which only a subset of the overall steady-state input is the decision variable. Consequently the optimization problems to be solved by each local controller are made simpler. We also present a distributed offset-free MPC algorithm for tracking in the presence of modeling errors and disturbances, and we illustrate the main features and advantages of the proposed methods by means of a multiple evaporator process case study.