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Cooperative Distributed Predictive Control 123

The simulation duration is 600 min. And in the first 300 min, the set-point of system
outputs is (−0.3, − 0.1, − 0.2), and in the last 300 minutes, the set-point of system outputs
is (−0.3, − 0.2, − 0.2). The simulation results are shown in Figure 6.4. Each subsystem can
track the set-point. Figure 6.5 is the comparison between the global cost optimization-based
DMPC and the Nash optimization-based DMPC.

6.5 Conclusions

In this chapter, the DMPC methods based on global cost optimization and Pareto optimality
are developed for large-scale linear systems when the global information is accessible by each
subsystem. The main advantage of this scheme is that the online optimization of a large-scale
system can be converted to that of several small-scale systems, and thus can significantly
reduce the computational complexity while keeping satisfactory performance.
The first part of the chapter gives the idea of global cost optimization-based DMPC, the
closed-loop solution, and the stability conditions. This method provides acceptable regions
of tuning parameters for which the stability is guaranteed and the performances are satis-
factory. Usually, the stable regions are associated with big prediction horizon P and small
weight R. The second part of this chapter provides the Pareto optimization-based DMPC and
investigates the performance of the distributed control scheme. The nominal stability and the
convergence of the DMPC algorithm is analyzed. These will provide users a better under-
standing to the developed algorithm and sensible guidance in applications. In addition, some
simulation examples are presented to verify the efficiency and practicality of the distributed
MPC algorithms.