| Summary: | Systemwide control problem is a problem that contains several subsystems
and involving interaction each other. Model Predictive Control (MPC) is an
optimal control technique that calculate the optimal control action by makes the
state and input prediction along horizon, then apply the first element of the
optimal control sequence that calculated using optimization technique. The classic
MPC (only for single system) can be applied for systemwide using decentralized
or centralized approach, but the decentralized approach ignoring the interaction,
so it can made the bad performance, whereas the centralized approach is not
flexible and involving the large scale computation. To handle these problems,
MPC can be applied for each subsystem using distributed approach. Distributed
MPC is based on cooperative dynamic game theory, where the subsystems is
reputed as players, then this problem solved by finding the Pareto solution
(Feasible-Cooperation-MPC), that continued by finding the Nash-Bargaining
solution (Nash-Bargaining-MPC), namely the Pareto solution that determined
according to the disagreement point of players. The Pareto and Nash-Bargaining
solution was determined using weighting of objective functions. We can prove
that distributed MPC gives equilibrium point 0 is asymptotical stable. Distributed
MPC was applied to control an irrigation canal that has 5 subsystems and involves
a branch. This study case was simulated using MATLAB to illustrate the
performance of this control technique. From the results, the Nash-Bargaining-
MPC with the proportional weighting to the control load gives the fewest total
cost.
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