Numerous methods have been developed to gain reliable real-time remote control over pilotless flying aircraft and to perform teleoperation. Recently, state-of-the-art brain-computer interface (BCI) research has provided an avant-garde approach to reach this goal. Due to its broad range of application, BCI has been the center of attention as a promising candidate for deciphering brain signals into corresponding control commands for various systems. This paper surveys the application of BCI in designing control modules for unmanned aerial vehicles (UAVs). We first describe the basic configuration of UAVs, as well as identify the principle components of their control systems. We proceeded to describe different classes of BCI with emphasis on their applicability in controlling UAVs, and highlight the potential benefits and challenges in implementing each BCI paradigm. Details will be given on how essential strategies and key techniques regarding feature extraction, and the classification of data, as well as hybrid-modality, could be applied in this field to develop robust systems demonstrating optimal fidelity and performance. Moreover, by reviewing the primary trends in previous studies, we attempt to address the missing steps in current research and shed light on the road map for future innovation.

In this paper, a novel energy management system with two operating horizons is proposed for a residential micro-grid application. The micro-grid utilises the energies of a photovoltaic (PV), a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed micro-grid operates in a large number of grid-connected and off-grid operation modes. The energy management system includes a long-term data prediction unit based on a 2D dynamic programming and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and fuel cell state of hydrogen. The efficiency performance of the micro-grid components, predicted energy generation and demand, energy cost and the system constraints are taken into account. The resultant data then is sent to the short-term fuzzy controller which determines the operation mode of the micro-grid based on the real-time condition of the micro-grid elements. A prototype of the proposed micro-grid including the energy management system is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.

In this paper a grid-tied residential smart micro-grid topology is proposed which integrates energies of a PV, a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. In contrast to multiple-converter based micro-grids with a common electric bus, using a multi-port converter with a common magnetic bus can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid and isolates the conversion ports. The resultant topology utilizes a centralized system level control which leads to the faster and more flexible energy management. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes. A fuzzy controlled energy management unit is designed to select the appropriate operation mode considering both real-time and long-term-predicted data of the system. A mode transition process is designed to smooth the mode variation by using a state transition diagram and bridging modes. To improve the micro-grid operation performance, appropriate control techniques such as synchronized bus-voltage balance are used. A prototype of the proposed micro-grid is developed and experimentally tested for three different energy management scenarios. Energy distribution and energy cost analysis are performed to validate the proposed control method.

Formation control of multiple agents has attracted many robotic and control researchers recently because of its potential applications in various fields. This paper presents a novel approach to the formation control and obstacle avoidance of multiple rectangular agents with limited communication ranges. The distributed control algorithm is designed by utilizing an artificial potential function. The convergence and stability analysis of the proposed control algorithm is given. The proposed control algorithm can guarantee fast formation performance and no collision among agents. Also, by proposing a potential repulsive function and utilizing it as an obstacle avoidance function, rectangular agents can perfectly avoid obstacles with different shapes and sizes. As a result, the rectangular agents can move together and quickly form a pre-defined shape of formation, such as a straight line, circle, and lattice, etc., while avoiding the obstacles. Simulation results are conducted to demonstrate the effectiveness of the proposed algorithm.

It is undeniable that marine vessel systems play an important role to transfer huge loads and weapons with low cost. However, ship power systems produce a lot of greenhouse gases, which in turn lead to serious environmental pollution. Hence, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESSs) in marine vessel power systems have been attracting a lot of attention in recent years. In this paper, it is assumed that a marine vessel power system with photovoltaic (PV), WT, SWE, and ESS can be regarded as a mobile-islanded MG. Then, a novel topology for hybrid shipboard microgrids (MGs) is presented. Next, in order to make a balance between consumption and power generation in shipboard MGs, an optimal modified model-free nonlinear sliding mode controller is introduced for the secondary load frequency control. Since the quality of the control actions of the proposed model-free approach depends on its parameters, a hybrid version of the sine-cosine algorithm (SCA) and wavelet-mutation (WM), called SCAWM, is employed to find the best value of these coefficients. Comparisons are conducted with other existing methodologies, such as model predictive control, interval type-2 fuzzy logic controller, and conventional PI (PI) to establish the supremacy of the newly suggested control strategy. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested controller, from a systemic perspective, for the load frequency control problem in the shipboard MG.