Prediction-driven coordination of distributed MPC controllers for linear unconstrained dynamic systems

Abstract

In this paper, a coordinated-distributed model predictive control (CDMPC) scheme is proposed for discrete-time, linear, unconstrained dynamic systems. The proposed control scheme incorporates a coordinator that communicates with local CDMPC controllers. With the assistance of the coordinator, the local CDMPC controllers adjust their calculated control actions iteratively to achieve the optimal plant-wide operation. A 'prediction-driven' algorithm is used to coordinate the local CDMPC controllers. Convergence of the prediction-driven algorithm is shown along with a stability analysis of the closed-loop system under coordinated-distributed control. A simulation example is used to illustrate the effectiveness of the proposed coordinated-distributed control scheme.

Hierarchical distributed model predictive control based on fuzzy negotiation

“…Additionally, this centralized supervisor is performed with lower computation time than a centralized MPC, which needs to solve an overall optimization problem subject to constraints. See (Marcos et al, 2014;Saad et al, 2018;Velarde et al, 2019) as examples of distributed MPC approaches where a supervisor communicates with local controllers.…”

Global Coordinated Distributed Predictive Control Algorithm Based on System Dynamic Decomposition Index

“…The time sequence between input data and output data is matched according to the lag time obtained by this method [21] and the matching input and output data is used to decompose the system. In our experiment, these three output variables are divided into three subsystems.…”

“…This approach allows the coordination system to generate a feedback control structure but lacks the ability to update information. Later, Marcos [21] proposed a prediction-driven coordinated-distributed model predictive control scheme where the price vector is updated according to the predicted value of state variables and the transfer function information of subsystems. To deal with the large deviations of predicted values a coordination method that the coordinator directly processes and optimizes the objective function of each subsystem controller is proposed in [22].…”

Takagi–Sugeno fuzzy generalised predictive control of a time‐delay non‐linear hydro‐turbine governing system

“…Predictive control has the advantages of prediction, online optimisation, and processing delays [25][26][27]. Currently, theories and applications for linear predictive control are relatively mature [28][29][30][31][32]. However, research on non-linear predictive control methods is still under exploration [33][34][35].…”