Embedded Model Predictive Control With Certified Real-Time Optimization for Synchronous Motors

Model predictive control (MPC) is a very attractive candidate to replace standard field-oriented control algorithms for electrical motors. We demonstrate that it is possible to implement an MPC algorithm for continuous control set (CCS-MPC), with both inputs and states constraints, in which the associated quadratic programming (QP) problem is solved online, even on the computationally limited platforms used in control of electrical motors. We detail the implementation of an active-set algorithm to solve efficiently the associated QP problem. Moreover, by exploiting recent results on active-set solver certification, we are able to assess the computational complexity of the online optimization algorithm, providing the exact worst-case solution time. The controller is experimentally tested on an embedded control unit for the torque regulation of a permanent magnet synchronous motor and benchmarked against explicit MPC. Computational feasibility, low-memory occupancy, and worst-case certification are achieved, fulfilling all the requirements of embedded control.