Robust Embedded MPC with Reduced-Precision Arithmetic for Cost-Optimized Implementations

The use of reduced-precision formats is a valuable strategy to improve performance and reduce costs in embedded computing. In case of embedded model predictive control (MPC), utilizing reduced-precision numbers to speed-up underlying optimization algorithms can help to extend the application scope of MPC. In this paper we show how the improved spectral properties of linear systems inside interior point-proximal method of multipliers (IP-PMM) combined with the application of online regularization and instability correction mechanisms, can prevent embedded MPC controllers from failure when reduced-precision arithmetic units are used. Thus, the proposed approach can also contribute to designing efficient domain-specific processors for embedded MPC using custom floating-point formats. To our knowledge this is the first time an IP-PMM algorithm is applied to solve quadratic programming (QP) problems in MPC.