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An interior permanent magnet (IPM) motor is a prime electric motor used in electric vehicles (EVs), robots, and electric drones. In these applications, maximum torque per ampere (MTPA), flux-weakening, and maximum torque per volt (MTPV) techniques play a critical role in the efficient and reliable torque control of an IPM motor. Although several approaches have been proposed and developed for this purpose, each has its specific limitations. The objective of this paper is to develop a neural network (NN) method to determine MTPA, flux-weakening, and MTPV operating points for the most efficient torque control of the motor over its full speed range. The NN is trained offline by using the Levenberg-Marquardt backpropagation algorithm, which avoids the disadvantages associated with online NN training. A cloud computing system is proposed for routine offline NN training, which enables the lifetime adaptivity and learning capabilities of the offline-trained NN and overcomes the computational challenges related to online NN training. In addition, for the proposed NN mechanism, training data are collected and stored in a highly random manner, which makes it much more feasible and efficient to implement lifetime adaptivity than any other methods. The proposed method is evaluated via both simulation and hardware experiments, which shows the great performance of the NN-based MTPA, MTPV, and flux-weakening control for an IPM motor over its full speed range. Overall, the proposed method can achieve a fast and accurate current reference generation with a simple NN structure, for optimal torque control of an IPM motor. 

NASA's New Horizons mission unveiled a diverse landscape of Pluto's surface with massive regions being neutral in color, while others like Cthulhu Macula range from golden-yellow to reddish comprising up to half of Pluto's carbon budget. Here, we demonstrate in laboratory experiments merged with electronic structure calculations that the photolysis of solid acetylene – the most abundant precipitate on Pluto's surface – by low energy ultraviolet photons efficiently synthesizes benzene and polycyclic aromatic hydrocarbons via excited state photochemistry thus providing critical molecular building blocks for the colored surface material. Since low energy photons deliver doses to Pluto's surface exceeding those from cosmic rays by six orders of magnitude, these processes may significantly contribute to the coloration of Pluto's surface and of hydrocarbon-covered surfaces of Solar System bodies such as Triton in general. This discovery critically enhances our perception of the distribution of aromatic molecules and carbon throughout our Solar System.