Modular Multilevel Converters (MMCs) have established their position as the choice of next-generation high voltage converters. These converters, however, require a bulk number of sensors in each phase for Sub-Modules (SMs) voltage balancing. A limited number of studies are found in the literature where the number of sensors is reduced by incorporating observers to estimate the SM voltages. Nonetheless, most of these methods require accurate measurements of the arm voltage. Thus, the dependency on sensors is not completely eliminated. In this paper, a Kalman-filter-based estimation of SM voltages is presented, which does not require any voltage sensor to measure the arm voltage. Therefore, this method considerably simplifies the implementation of MMCs. Extensive simulations and experimental test cases are conducted to validate the performance of the proposed SM voltage estimation method. The performance assessments verify the accuracy and robustness of the proposed method for various test scenarios performed over a wide range of sampling frequencies.

Localization and mapping are very important for safe movement of robots. One possible way to assist with this functionality is to use laser scan matching. This paper describes a method to implement this functionality. It is based on well-known random sampling and consensus (RANSAC) and iterative closest point (ICP). The proposed algorithm belongs to the class of point to point scan matching approach with its matching criteria rule. The performance of the proposed algorithm is examined in real environment and found applicable in real-time application