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Implementation of soft-constrained MPC for Tracking using its semi-banded problem structure
Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function.
Efficient implementation of MPC for tracking using ADMM by decoupling its semi-banded structure
Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced.
Efficient online update of model predictive control in embedded systems using first-order methods
Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems.
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