Realization issues, tuning, and testing of a distributed predictive control algorithm

• We discuss the main application issues of distributed predictive control (DPC).
• We propose new simplified methods for required set-theoretical computations.
• We propose new distributed methods for reference trajectory computation.
• We apply the DPC scheme to three process control examples, including nonlinear ones.

A non-iterative, non-cooperative distributed state-feedback control algorithm based on neighbor-to-neighbor communication, named distributed predictive control (DPC), has been recently proposed in the literature for constrained linear discrete-time systems, see  [15], [14], [2] and [4]. The theoretical properties of DPC, such as convergence and stability, its extensions to the output feedback and tracking problems, and applications to simulated plants have been investigated in these papers. However, for a practical use of DPC some realization issues are still open, such as the automatic selection of some tuning parameters, the initialization of the algorithm, or its response to unexpected disturbances which could lead to the lack of the recursive feasibility, a fundamental property for any model predictive control (MPC) technique.This paper presents novel solutions to all these issues, with the goal to make DPC attractive for industrial and practical applications. Three realistic simulation examples are also discussed to evaluate the proposed numerical algorithms and to compare the performances of DPC to those of a standard centralized MPC algorithm.