The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is performed using an artificial neural network (ANN) based on the measured injected grid current. Simulation and experimental results are presented to show the high performance of the proposed strategy in handling multi-objective control problems.

This article presents safe ship control optimization design for navigator advisory system. Optimal safe ship control is presented as multistage decision-making in a fuzzy environment and as multistep decision-making in a game environment. The navigator’s subjective and the maneuvering parameters are taken under consideration in the model process. A computer simulation of fuzzy neural anticollision (FNAC) and matrix game anticollision (MGAC) algorithms was carried out on MATLAB software on an example of the real navigational situation of passing three encountered ships in the Skagerrak Strait, in good and restricted visibility at sea. The developed solution can be applied in decision-support systems on board a ship.

Ship collisions cause major losses in terms of property, equipment, and human lives. Therefore, more investigations should be focused on this problem, which mainly results from human error during ship control. Indeed, to reduce human error and considerably improve the safe traffic of ships, an intelligent tool based on fuzzy set theory is proposed in this paper that helps navigators make fast and competent decisions in eventual collision situations. Moreover, as a result of selecting the shortest collision avoidance trajectory, our tool minimizes energy consumption. The main aim of this paper was the development of a decision-support system based on an artificial intelligence technique for safe ship trajectory determination in collision situations. The ship’s trajectory optimization is ensured by multistage decision making in collision situations in a fuzzy environment. Furthermore, the navigator’s subjective evaluation in decision making is taken into account in the process model and is included in the modified membership function of constraints. A comparative analysis of two methods, i.e., a method based on neural networks and a method based on the evolutionary algorithm, is presented. The proposed technique is a promising solution for use in real time in onboard decision-support systems. It demonstrated a high accuracy in finding the optimal collision avoidance trajectory, thus ensuring the safety of the crew, property, and equipment, while minimizing energy consumption.

The article presents the synthesis of a multi-layer group control system for a marine autonomous surface vessel with the use of modern control theory methods. First, an evolutionary programming algorithm for determining the optimal route path was presented. Then the algorithms—dynamic programming with neural state constraints, ant colony, and neuro-phase safe control algorithms—were presented. LMI and predictive line-of-sight methods were used for optimal control. The direct control layer is implemented in multi-operations on the principle of switching. The results of the computer simulation of the algorithms were used to assess the quality control.

This paper focuses on the application of the direct torque control based on space vector modulation (DTC-SVM), combined with the input–output feedback linearization (IOFL) technique on a three-phase dual open-end windings induction motor (DOEWIM) fed by two dual indirect matrix converters. The main aim of integrating the non-linear technique is to overcome the main drawbacks met within the application of the conventional DTC-SVM on dual-stator induction motor (DSIM), such as the torque and flux ripples reduction, the stator harmonics current minimization, and the elimination of the common-mode voltage (CMV). Furthermore, it is proved in this paper that the proposed control on DOEWIN can ensure more flexibility versus speed reverse and variation, load torque changes, and motor parameters variation. The obtained results prove the validity of the proposed control on the studied induction motor topology in ensuring the main aforementioned advantages compared to the conventional DTC-SVM control on DSIM, which presents a promising solution, especially in industrial applications in which high-power motors are required.

The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the development of new voltage regulation systems. This requires voltage regulation systems capable of responding quickly to sudden voltage changes. In substations with control transformers, it is possible thanks to the use of semiconductor tap changers. Moreover, voltage regulation and reactive power compensation systems should be built as one system. This is due to the close dependence of voltage and reactive power in the network node. Therefore, it was proposed to use artificial intelligence methods to build a new voltage regulation and reactive power compensation system using all measurement voltages of network nodes. In the first stage of the research, active and reactive powers, as well as the voltage of the reference node, were selected for 6420 periods of the mains voltage. The simulation results were compared for the classic voltage regulation system with semiconductor tap changers and the evolution algorithm based on voltage measurements from the entire MV network. A significant improvement in the quality of voltage regulation with the use of an evolutionary algorithm was demonstrated. Then, a second set of input data with increased values of reactive power was generated. The results of the evolutionary algorithm after the application of the classic, independent reactive power compensation system and two-criteria optimization were compared. It has been shown that only the two-criteria optimization algorithm keeps both |tgφ| within the acceptable range and the quality of voltage regulation is the best. The article compares different working algorithms for semiconductor tap changers.