  This paper proposes a hierarchical decentralized reference governor for
constrained cascade systems. The reference governor (RG) approach is
reformulated in terms of receding horizon strategy such that a locally receding
horizon optimization is obtained for each subsystem with a pre-established
prediction horizon. The algorithm guarantees that not only the nominal overall
closed-loop system without any constraint is recoverable but also the state and
control constraints are satisfied in transient conditions. Also, considering
unfeasible reference signals, the output of any subsystem goes locally to the
nearest feasible value. The proposed dynamic constraint tightening strategy
uses a receding horizon to reduce the conservatism of conventional robust RGs.
Moreover, a decentralized implementation of the algorithms used to compute
tightened constraints and output admissible sets is introduced that allow to
deal with large scale systems. Furthermore, a set of dynamic constraints are
presented to preserve recursive feasibility of distributed optimization
problem. Feasibility, stability, convergence, and robust constraint
satisfaction of the proposed algorithm are also demonstrated. The proposed
approach is verified by simulating a system composed of three cascade jacketed
continuous stirred tank reactors.
