This paper presents a model predictive control (MPC) method for single-phase three-level grid-connected F-type inverters. The main control objective in grid-connected inverters is to regulate the grid current with low total harmonic distortion. Since the F-type inverter has emerged recently, there is no specific control method developed for this inverter topology in the literature yet. In this paper, the mathematical model of the F-type inverter and the design of model predictive control is presented. Since the dc capacitor voltage balancing is essential for F-type inverters, both current control and dc capacitor voltage controllers are combined in a multi-objective cost function. Thus, the control of the dc and ac sides of the F-type inverter is achieved successfully. The theoretical considerations were verified through simulation studies. The effectiveness of the proposed MPC method was investigated in the steady state as well as dynamic transients under variations in grid current, input dc voltage, and grid voltage. The simulation results show that the grid current and dc capacitor voltages are successfully controlled in all operating conditions.

Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.

The correlation relations of batch process variables are quite complex. For local abnormalities, there is a problem that the variant features are overwhelmed. In addition, batch process variables have obvious non-Gaussian distributions. In response to the above two problems, a new multiple subspace monitoring method called principal component analysis - multiple subspace support vector data description (PCA-MSSVDD) is proposed, which combines the subspace design of latent variables with the SVDD modeling method. Firstly, PCA is introduced to obtain latent variables for removing redundant information. Secondly, the subspace design result is obtained through K-means clustering. Finally, SVDD is introduced to build the monitoring model. Numerical simulation and penicillin fermentation process prove that the proposed PCA-MSSVDD method has better monitoring performance than traditional methods.

This paper presents a Model Predictive Control (MPC) algorithm for Nonlinear systems represented through quasi-Linear Parameter Varying (qLPV) embeddings. Input-to-state stability is ensured through parameter-dependent terminal ingredients, computed offline via Linear Matrix Inequalities. The online operation comprises three consecutive Quadratic Programs (QPs) and, thus, is computationally efficient and able to run in real-time for a variety of applications. These QPs stand for the control optimization (MPC) and a Moving-Horizon Estimation (MHE) scheme that predicts the behaviour of the scheduling parameters along the future horizon. The method is practical and simple to implement. Its effectiveness is assessed through a benchmark example (a CSTR system).