In the last decade, model predictive control (MPC) has been widely studied in power converters, such as voltage source inverters (VSIs). Unfortunately, MPC often presents a high computational burden that limits their applicability, especially when driving multilevel inverters (MLIs) because of their higher number of switching combinations than two-level inverters. As a result, some strategies have been developed to reduce the computational complexity of MPC. One of the most relevant is the use of artificial neural networks (ANNs) to approximate the behavior of an MPC. However, ANNs require to be evaluated at bounded inputs. Otherwise, their response cannot be guaranteed to be a good approximation of the controller they learned from. Furthermore, when driving an LC-filtered VSI, the inductor current can present high peaks due to the cross-coupling effect between the inductor and the capacitor. These current peaks can cause physical damages and loss of performance of an ANN-emulated MPC. This paper presents a new constrained modulated MPC (M ² PC), better suited for ANN emulation, to overcome these issues. The proposed strategy evaluates the cost function once per switching region, allowing easy and intuitive constraint inclusion. Additionally, an overmodulation stage is used to handle negative duty cycles and enhance disturbance rejection. Finally, the proposal is validated through simulations, using MATLAB-Simulink, taking into account different load conditions. Simulations show that the constrained M ² PC keeps the inductor current below its desired limit while having a good performance (low harmonic distortions and fast dynamics) even when the inverter operates near its boundaries.

In recent years, the study of model predictive control (MPC) in power electronics has gained significant attention due to its ability to optimize system performance and improve the dynamic control of complex power converters. There are two types of MPC: finite control set (FCS) and continuous control set (CCS). The FCS–MPC has been studied more in regard to these two types of control due to its easy and intuitive implementation. However, FCS–MPC has some drawbacks, such as the exponential growth of the computational burden as the prediction horizon increases and, in some cases, a variable frequency. In contrast, generalized predictive control (GPC), part of CCS–MPC, offers significant advantages. It enables the use of a longer prediction horizon without increasing the computational burden in regard to its implementation, which has practical implications for the efficiency and performance of power converters. This paper presents the design of GPC applied to single-phase multilevel voltage source inverters, highlighting its advantages over FCS–MPC. The controller is optimized offline, significantly reducing the computational cost of implementation. Moreover, the controller is tested in regard to R, RL, and nonlinear loads. Finally, the validation results using a medium-performance controller and a Hardware-in-the-Loop device highlight the improved behavior of the proposed GPC, maintaining a harmonic distortion of less than 1.2% for R and RL loads.

Expanding university campuses face a dual challenge: meeting higher internal travel demand while reducing congestion and emissions. Using stated-preference data (412 respondents; 9 choice tasks per person), this study quantifies the expected adoption of two low-emission micromobility options—bicycle and electric scooter—relative to walking and car for intra-campus trips at an Ecuadorian university. Under Random Utility Theory, we estimate a multinomial logit (MNL) and a panel mixed logit (MIXL) model, treating MIXL as the preferred specification. Simulated maximum likelihood with 12,000 draws shows MIXL substantially improves fit and reveals marked heterogeneity in time sensitivity. In out-of-sample prediction (TEST), the two prospective modes achieve comparable average choice probabilities (bicycle = 0.289; electric scooter = 0.286), with a joint acceptance of 0.575, supporting the feasibility of a micromobility program under the evaluated scenarios. Environmental impact (gCO₂/km) exhibits limited aggregate influence compared with operational factors (time and cost). A complementary WTP-space MIXL provides VOT estimates and confirms that 10–20% time reductions increase expected acceptance. Overall, results indicate that adoption is most likely when sustainability objectives are translated into tangible service improvements in effective speed, reliability, and affordability.