Titlebook: Distributed Model Predictive Control Made Easy; José M. Maestre,Rudy R. Negenborn Book 2014 Springer Science+Business Media Dordrecht 2014

生氣的邊緣

embolus

Distributed Optimization for MPC of Linear Dynamic Networkse models consist of networks of subsystems with deterministic and uncertain dynamics subject to local and coupling constraints on the control and output signals. The distributed optimization algorithms are based on gradient-projection, subgradient, interior-point, and dual strategies that depend on

善辯

Integrate

貨物

空中

Genome

paragon

Distributed MPC of Interconnected Nonlinear Systems by Dynamic Dual Decomposition dynamics subject to both state and input constraints is proposed. The approach applies a dynamic dual decomposition method to reformulate the original centralized NMPC problem into a distributed quasi-NMPC problem by linearization of the nonlinear system dynamics and taking into account the couplin

壟斷

D-SIORHC, Distributed MPC with Stability Constraints Based on a Game Approachure, thereby reducing the computational load. This approach differs from other distributed MPC algorithms based on linear models in the inclusion of stability constraints in the local controllers that leads to a different control law. The controller usage is illustrated through its application to a

Malfunction

Adaptive Quasi-Decentralized MPC of Networked Process Systemsal process state within each local control system. Whenever the forecast shows signs of instability of the local process state, the measurements of the entire process state are transmitted to update the model within this particular control system to ensure stability; otherwise, the local control sys