Conventional finite control set model-predictive control (FCS-MPC) presents a high computational burden, especially in three-level neutral-point-clamped (NPC) converters. This article proposes a low-complexity optimal switching time-modulated model-predictive control (OST-M2PC) method for a three-level NPC converter. In the proposed OST-M2PC method, the optimal switching time is calculated using a cost function. Compared with the conventional FCS-MPC, the proposed OST-M2PC method has a fixed switching frequency as well as better power quality. The proposed OST-M2PC can operate at a 20-kHz sampling frequency, reducing the computational burden of the processor. Simulation and experimental results validate the operation of the proposed method.

In order to enhance the dynamic performance and anti-disturbance ability of speed control for a permanent magnet synchronous motor (PMSM), a sliding mode control method based on a PID sliding surface and an improved terminal sliding mode reaching law (ITSMRL) is proposed. Firstly, an ITSMRL is proposed to increase the reaching speed and reduce chattering; moreover, it has been verified that the reaching law (RL) can achieve a sliding mode surface in finite time. Then, based on the dynamic model of PMSMs with uncertainties, an extended state observer (ESO) is used to estimate the lumped disturbance, and it is proven that the estimated error is bounded. Finally, on the basis of the observed feedforward disturbance, to enhance the disturbance rejection ability of PMSMs, a controller that combines the PID sliding mode surface and the ITSMRL is proposed. Moreover, the stability of the closed-loop system is proven. The composite method has the characteristics of a fast reaching speed, small chattering and strong robustness, and is verified by experiments.

To ensure overcurrent protection, fast dynamic performance and good robustness, a novel speed regulation controller is proposed. The interconnection and damping assignment passivity-based control (IDA-PBC) of Port-controlled Hamiltonian (PCH) systems has the advantages of simple structure and explicit physical meaning. On account of fast dynamic performance and q-axis current-constraint are contradictory, this paper presents a current-constraint speed regulation method for the permanent magnet synchronous motor (PMSM) based on port-controlled Hamiltonian (PCH) realization and deep deterministic policy gradient (DDPG) to balance them. First, a modified IDA-PBC controller with integral action(IA) is constructed, which can regulate the speed and the current of the PMSM simultaneously, and be more suitable for practical applications due to the addition of IA. For both current-constraint and fast dynamic performance, the reinforcement learning of DDPG is utilized to find the optimal parameters of the controller. Finally, experiments are carried out to verify the effectiveness and robustness of the method.

Modulated model predictive control (M2PC) has recently emerged as a possible solution for control in starter generator systems in the more electric aircraft (MEA), due to its advantages of fixed switching frequency, fast response and good performance. However, conventional M2PC requires the prediction of each possible output voltage vector, which involves a heavy computational burden for the processor, especially for multilevel converters. This is an obstacle for practical industrial applications. To solve this problem this paper introduces a new, low-complexity modulated model predictive control (LC-M2PC) for a starter generator control system with a neutral point clamped (NPC) converter. The proposed LC-M2PC only needs prediction action once in each control interval, which can reduce the computational burden of processor. Fixed switching frequency is maintained and it can achieve a lower total harmonic distortion (THD) current than conventional M2PC, using space vector modulation (SVM). This proposed LC-M2PC method is validated on a prototype electrical starter generator (ESG) system test rig with three-level NPC converter. Experimental results verify the effectiveness of the proposed method.