The hierarchical control structure was introduced to allow the integration of power electronics based distributed generation into the microgrid in a smart and flexible manner. The main aim of the primary controller in such a structure is to achieve accurate active and reactive power sharing whereas the secondary control aims to ensure voltage and frequency (V/f) stability. Generally, converter level secondary controllers utilize classical nested loop control that suffer from a slow dynamic response and cumbersome parameters tuning. The existing model based and estimation based secondary controllers are fast, but require complex design methodology, high communication bandwidth and consequently require higher data analysis and computational burden. This paper presents a simple predictive based secondary control for the AC microgrid that is fast, robust, has a low design complexity, low communication bandwidth and no parameter tuning requirement in the secondary control layer. The proposed predictive control optimally restores voltage and frequency in the microgrid by predicting their trajectory deviations and leveraging the droop characteristic curves. Experimental tests performed with three parallel connected grid forming inverters in an islanded operation validate that the controller can accurately maintain V/f stability, while ensuring active and reactive power sharing.

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.

The control of multibody drill-string systems is not easy and designing such systems is considered challenging because of the difficulty in the dynamic analysis of its nonlinear characteristics and parametric uncertainties. An optimal hybrid interval Type-2 fuzzy PID+I logic controller (OH-IT2FPID+I) using a practical swarm optimization algorithm for a multi-degree-of-freedom oil well drill-string system is proposed in this paper. The suggested control concept is aimed to overcome several bit sticking troubles and stick–slip vibration by regulating the rotary velocities of drill-string components, especially rotary table velocity and drill bit velocity, to a predefined value. The drill-string system considered here has four degrees of freedom in the down-hole parts and the model takes the nonlinear interactions of the drill bit and the rocks into account, including friction torque and the mud drilling effect. Extensive simulations in Matlab/Simulink and experimental validations through a real-time hardware in the loop (HIL) system were performed to demonstrate the effectiveness of the suggested controller in comparison with sliding mode, Type 1 fuzzy logic and PID controllers. The quantitative comparison performed using simulation results proves that the proposed OH-IT2FPID+I provides higher control performance in terms of settling time and peak overshoot under variation of the weight on bit, the desired drill bit rotary speed, and handling parametric uncertainties. On the other hand, HIL results confirm the control performance provided by the proposed control under all different testing scenarios.

This paper describes a novel single-phase thirteen-level packed E-cell inverter (PEC13) for grid-tied photovoltaic (PV) systems. PEC13 topology contains six power switches, two four-quadrant switches and four DC capacitors. The main advantage offered by the proposed PEC13 is the ability to produce different voltage levels without any modifications in power circuit when open circuit fault occurs on one or two four-quadrant switches. In faulty condition, PEC13 can continue its operation and behaves as PEC9 or PUC7 inverter configuration. Furthermore, a reconfigurable finite-control set model predictive control (R-FCS-MPC) is designed to control the proposed grid-connected PV systems based PEC13 inverter. The proposed R-FCS-MPC assures the transition between PEC13 to PEC9 or PUC7 configuration through the adjustment of DC-link capacitor voltages balancing. The effectiveness of the R-FCS-MPC with the proposed PEC13 topology is evaluated through experimental validations using real-time hardware in the loop (HIL) setup. Provided experimental results demonstrate that the R-FCS-MPC enables to achieve the normal operation, multi-functionality, besides the high performance of the novel PEC13 inverter topology under step change in solar irradiance and faulty operation of four-quadrant switches.