Robustness Study of a Cascaded Dual Model-Predictive Control Applied to Synchronous Motors

Abstract

Model-predictive control (MPC) is an optimization-based control technique. It has the benefits of being flexible and achieving a quick time response. However, this controller suffers from computational burden and sensitivity toward measurement noises, parametric variations, and model inaccuracies. Nowadays, the computation of MPC is no longer a problem, thanks to the new digital devices that exist on the market. However, robustness is still a major problem that complicates the usage of MPCs in applications, where the system model and parameters are not accurately known. A good example is the control of outer loops in cascaded control schemes. This paper presents multiple attempts to robustify the speed outer loop of a synchronous motor driven by a two-level power converter. An intuitive MPC speed outer-loop algorithm is first introduced. This first algorithm is weak against parametric variation, model inaccuracies, and measurement noises. Thus, a second more robust algorithm based on a torque balance expression is presented. This algorithm is robust toward parametric variations and model inaccuracies yet weak against measurement noises. Therefore, a subsequent improvement is added in a third algorithm in order to achieve a robust MPC controller. Furthermore, a least mean square identification algorithm is added to all control laws in order to identify the dynamical model parameters. These MPC controllers were experimentally tested and compared with a classic proportional-integral controller. Finally conclusions are drawn.