Model predictive control (MPC) is a model‐based control strategy that introduces many advantages but also some drawbacks. The biggest challenge that faces MPC controller is its sensitivity towards parametric variation. This study aims to robustify MPCs by adding a least mean square (LMS) identification algorithm within their structure. The feasibility of this method is tested on a simple example which is the MPC current control of a permanent magnet synchronous motor driven by a two‐level power converter. Many tests are firstly performed without including inside the MPC algorithms the LMS online tuning procedure. These tests are later compared with the ones obtained with the same control technique but where the identification algorithm is turned on. Results clearly show all the benefits of using this approach on the controller performances.

Model Predictive Control is an optimization based control technique. It has the benefits of being flexible and achieving a quick time response. However this controller suffers from its computational burden and its 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 such as 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 firstly 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 but weak against measurement noises. Therefore, an ultimate improvement is added in a third algorithm in order to achieve a robust MPC controller. Moreover, 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 PI controller. Finally conclusions are drawn.

Modulation techniques for Modular Multilevel Converters (MMCs) encounter a limit in the tradeoff between reducing the total harmonic distortion (THD) of the load current and minimizing the number of commutations, primarily due to traditional sorting algorithms. This paper presents a novel sorting algorithm to address this challenge. This study compares six modulation methods for MMCs: Nearest Level Control (NLC), Integral Modulation (IM), and Pulse Width Modulation (PWM) using a traditional sorting algorithm, alongside their modified versions featuring the novel sorting algorithm. The effectiveness of each modulation technique discussed in this paper is verified through experimental tests. The results show that the proposed modulation techniques utilizing the suggested sorting algorithm, effectively decrease the switching number while maintaining the THD of the load currents. However, there is a slight increase in the disparity of the capacitor voltages, which remains controllable. Additionally, the study fills a gap in the literature by providing experimental evidence and a theoretical framework demonstrating the superior efficiency of IM over NLC through frequency analysis and experimental results. These findings highlight the potential of the proposed techniques to optimize the MMC performance.