A memory-efficient representation of explicit MPC solutions

Amount of memory needed to describe explicit model predictive control (MPC) solutions is an often neglected, but a very important factor which decides whether it will be possible to implement such a control strategy on a selected control platform. We show how to exploit geometric properties of explicit MPC controllers to obtain their memory-efficient representation. The three-layer procedure first identifies similarities between polytopic regions in form of an affine transformation. If such amapping exists, certain regions can be represented using less data. The second layer then applies data de-duplication to identify and remove repeating sequences of data. Regions are then described by integer pointers to such a unique set. Finally, Huffman encoding is applied to compress such integer pointers using prefix-free variable-length bit encoding. Reduction in memory is traded for an increase in evaluation time, which is quantified for each layer. Main advantage of the overall procedure is that it can be applied on top of most existing complexity reduction schemes available in the literature.