The quadrotor is an ideal platform for testing control strategies because of its non-linearity and under-actuated configuration, allowing researchers to evaluate and verify control strategies. Several control strategies are used, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), Backstepping, Feedback Linearization Control (FLC), Sliding Mode Control (SMC), and Model Predictive Control (MPC), Neural Network, H-infinity, Fuzzy Logic, and Adaptive Control. However, due to several drawbacks, such as high computation, a large amount of training data, approximation error, and the existence of uncertainty, the commercialization of those control technologies in various industrial applications is currently limited. This paper conducts a thorough analysis of the current literature on the effects of multiple controllers on quadrotors, focusing on two separate approaches: (i) controller hybridization and (ii) controller development. Besides, the limitations of the previous works are discussed, challenges and opportunities to work in this field are assessed, and potential research directions are suggested.
However, there is an insignificant number of NLP research invested in studying COVID-19. Most applications include the classification of chest X-rays and CT-scans to detect the presence of pneumonia in lungs [4], a consequence of the virus. Other research areas include studying the genome sequence of the virus [5] [6] [7] and replicating its structure to fight and find vaccines. Few NLP based research publications related to COVID-19 are sentiment classification of online tweets by Samuel et. al. [8] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodar et. al. [9].Our research dives deeper with NLP to extract crucial information from newspaper reports regarding the virus and pandemic. At this stage, we considered all genres of news that are related to the virus. This collection of newspaper reports, we termed as "NNK Dataset", is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus's awareness to the best of our knowledge. The contribution of this paper includes:
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