Learning model predictive control for iterative tasks. a data-driven control framework
U Rosolia, F Borrelli - IEEE Transactions on Automatic Control, 2017 - ieeexplore.ieee.org
IEEE Transactions on Automatic Control, 2017•ieeexplore.ieee.org
A learning model predictive controller for iterative tasks is presented. The controller is
reference-free and is able to improve its performance by learning from previous iterations. A
safe set and a terminal cost function are used in order to guarantee recursive feasibility and
nondecreasing performance at each iteration. This paper presents the control design
approach, and shows how to recursively construct terminal set and terminal cost from state
and input trajectories of previous iterations. Simulation results show the effectiveness of the …
reference-free and is able to improve its performance by learning from previous iterations. A
safe set and a terminal cost function are used in order to guarantee recursive feasibility and
nondecreasing performance at each iteration. This paper presents the control design
approach, and shows how to recursively construct terminal set and terminal cost from state
and input trajectories of previous iterations. Simulation results show the effectiveness of the …
A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
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