Nash-based robust distributed model predictive control for large-scale systems. (April 2020) - Shalmani, Reza Aliakbarpour; Rahmani, Mehdi; Bigdeli, Nooshin
Highlights: A new robust distributed model predictive control (RDMPC) is presented for large-scale systems with polytopic uncertainties. Interactions among subsystems are obtained by an extended distributed Kalman filter, and considered in the MPC design. Output feedback-interaction feedforward control is achieved by optimizing an infinite horizon objection function via LMIs. Quadratic boundedness is applied to guarantee the robust stability of the closed-loop system. An iterative Nash-based algorithm is presented to reach the overall optimal solution of the whole system in partially distributed fashion. Abstract: In this paper, a new robust distributed model predictive control (RDMPC) is proposed for large-scale systems with polytopic uncertainties. The time-varying system is first decomposed into several interconnected subsystems. Interactions between subsystems are obtained by a distributed Kalman filter, in which unknown parameters of the system are estimated using local measurements and measurements of neighboring subsystems that are available via a network. Quadratic boundedness is used to guarantee the stability of the closed-loop system. In the MPC algorithm, an output feedback-interaction feedforward control input is computed by an LMI-based optimization problem that minimizes an upper bound on the worst case value of an infinite-horizon objective function. Then, an iterative Nash-based algorithm is presented to achieve the overall optimal solution of the whole system in partially distributed fashion. Finally, the proposed distributed MPC approach is applied to a load frequency control (LFC) problem of a multi-area power network to study the efficiency and applicability of the algorithm in comparison with the centralized, distributed and decentralized MPC schemes.