Reducing memory footprints in explicit model predictive control using universal numbers
Abstract Explicit Model Predictive Control (MPC) is an effective alternative to reduce the on-
line computational demand of traditional MPC. The idea of explicit MPC is to pre-compute
the optimal MPC feedback law off-line and store it in a form of look-up table which is to be
used in on-line phase. One of the main bottlenecks in an implementation of explicit MPC is
memory required to store optimal solutions. This limit its applicability to systems with few
states, small number of constraints, and short prediction horizons. In this paper, we present a …
line computational demand of traditional MPC. The idea of explicit MPC is to pre-compute
the optimal MPC feedback law off-line and store it in a form of look-up table which is to be
used in on-line phase. One of the main bottlenecks in an implementation of explicit MPC is
memory required to store optimal solutions. This limit its applicability to systems with few
states, small number of constraints, and short prediction horizons. In this paper, we present a …
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