Abstract
In this paper, a distributed output feedback model predictive control (MPC) framework with guaranteed nominal stability and performance properties is described. Distributed state estimation strategies are developed for supporting distributed output feedback MPC of large-scale systems, such as power systems. It is shown that under certain (easily verifiable) conditions, local measurements are sufficient for observer stability. More generally, stable observers can be designed by exchanging measurements between adjacent subsystems. Both estimation strategies are suboptimal, but the estimates generated converge exponentially to the optimal estimates. A disturbance modeling framework for achieving zero-offset control in the presence of nonzero mean disturbances and modeling errors is presented. Automatic generation control (AGC) provides a practical example for contrasting the performance of centralized and distributed controllers
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