Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

Cooperative control consensus is one of the most actively studied topics within the realm of multi-agent systems. It generally aims to drive multi-agent systems to achieve a common group objective. The core aim of this paper is to promote research in cooperative control community by presenting the latest trends in this field. A summary of theoretical results regarding consensus for agreement analysis for complex dynamic systems and time-invariant information exchange topologies is briefly described in a unified way. The application under both non-formation and formation cooperative control consensus for multi-agent system also investigated. In addition, future recommendations and some open problems are also proposed.

Sign language recognition is challenging due to the lack of communication between normal and affected people. Many social and physiological impacts are created due to speaking or hearing disability. A lot of different dimensional techniques have been proposed previously to overcome this gap. A sensor-based smart glove for sign language recognition (SLR) proved helpful to generate data based on various hand movements related to specific signs. A detailed comparative review of all types of available techniques and sensors used for sign language recognition was presented in this article. The focus of this paper was to explore emerging trends and strategies for sign language recognition and to point out deficiencies in existing systems. This paper will act as a guide for other researchers to understand all materials and techniques like flex resistive sensor-based, vision sensor-based, or hybrid system-based technologies used for sign language until now.

In this paper, a comprehensive review of essential components of the PV (Photovoltaic) system is elaborated, and their comparative unique features are discussed. The paper describes hardware design (power converters topologies specifically) employed in PV based energy generation systems to harvest maximum power from the available energy source. In this study, thirty different Maximum Power Point Tracking (MPPT) techniques have been critically analyzed and their response with respect to partial shading condition has been discussed. It is very difficult to say which technique is best as one must consider various factors and parameters while selecting a technique such as application, convergence speed, accuracy, efficiency, system reliability, and cost and performance of available hardware. Aiming at the complexity, hardware implementation, tracking speed, steady-state accuracy, or global maximum detection of the algorithm, an MPPT algorithm based on a rule table is proposed. In addition, the MPPT of a PV system based on bio inspired techniques is considered. The bio inspired algorithms and its application in PV system are compared for the authenticity of the review, and six different MPPT techniques are implemented on PV systems. A comparative analysis is made based on the results of four different cases of irradiance.

The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.