Embedded ADMM-based QP solver for MPC with polytopic constraints
We propose an algorithm for solving quadratic programming (QP) problems with inequality and equality constraints arising from linear MPC. The proposed algorithm is based on the ‘alternating direction method of multipliers’ (ADMM), with the introduction of slack variables. In comparison with algorithms available in the literature, our proposed algorithm can handle the so-called sparse MPC formulation with general inequality constraints. Moreover, our proposed algorithm is suitable for implementation on embedded platforms where computational resources are limited. In some cases, our algorithm is division-free when certain fixed matrices are computed offline. This enables our algorithm to be implemented in fixed-point arithmetic on a FPGA. In this paper, we also propose heuristic rules to select the step size of ADMM for a good convergence rate.