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Owing to its ability to deal with hard constraints, model predictive control (MPC) can meet the good control performance requirements of modern complex industrial control systems. But the large computational load limits its application to more fields such as fast dynamic systems. In order to apply MPC to millisecond timescale systems, this paper studies the fast MPC based on multiscale system theory...
Model predictive control (MPC), also called moving horizon control or receding horizon control, is one of the most successful and the most popular advanced control methods. The basis of MPC is the online solution of a constrained optimization problem updated by the actual state. The obtained control is injected into the system until the next sampling time, while the procedure is repeated whenever...
MPAQM algorithm is able to adapt to the varying network environment and improve the robustness by moving horizon optimization, and handling network constraints in the process of obtaining the drop probability. Based on MPAQM scheme proposed previously, the predictive model is improved to reduce the order of optimization problem in this paper. Considering the causality of time-delay system, the predicted...
Based on the assumption that the parameter can be measured in real time, we propose a model predictive control (MPC) method for linear-parameter varying (LPV) systems subject to possibly asymmetric constraints which adopts the analogous framework of terminal control law, terminal set and terminal penalty of nonlinear model predictive control. The optimization problem is formulated as a convex optimization...
This paper proposes a robust model predictive control scheme for nonlinear systems with state and input constraints and unknown but bounded disturbances. A standard nominal model predictive control problem with tightened constraints is solved online, and its solution defines the nominal trajectory. An ancillary control law is determined off-line which keeps the trajectories of the error system in...
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