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Source command-line language translator tool powered by Google Translate, Yandex Translate, Apertium, and Bing Translator. It is available for most POSIX-compliant systems including Windows (via Cygwin, WSL, or MSYS2), GNU/Linux, macOS, and BSD.Translate Shell allows users to use it for simple translations or as an interactive shell. For simple translations, Translate Shell gives details of the translated text by default unless when made to do exclude the details using the keyword, brief.$ trans 'Saluton, Mondo!'Saluton, Mondo!Hello, World!Translations of Saluton, Mondo![ Esperanto -> English ]Saluton , Hello,Mondo ! World!$ trans -brief 'Saluton, Mondo!'Hello, World!When used as an interactive shell, it will translate the texts as you enter them line by line. For example,$ trans -shell -brief> Rien ne réussit comme le succès.Nothing succeeds like success.> Was mich nicht umbringt, macht mich stärker.What does not kill me makes me stronger.> Юмор есть остроумие глубокого чувства.Humor has a deep sense of wit.> 幸福になるためには、人から愛されるのが一番の近道。In order to be happy, the best way is to be loved by people.Install Translate Shell in LinuxMy recommended download method is for you to grab the self-contained executable file from here, place it in your path, and run the following commands:$ wget git.io/trans$ chmod +x ./transFor more details on installation and usage check its official GitHub page here.Do you know other awesome command line text translator apps for Linux? Add your suggestions in the comments section below. In DC Grid. IEEE Trans. Power Deliv. 2015, 30, 519–528. [Google Scholar] [CrossRef]Saad, H.; Dennetière, S.; Mahseredjian, J.; Delarue, P.; Guillaud, X.; Peralta, J.; Nguefeu, S. Modular Multilevel Converter Models for Electromagnetic Transients. IEEE Trans. Power Deliv. 2014, 29, 1481–1489. [Google Scholar] [CrossRef]Guo, C.; Yang, J.; Zhao, C. Investigation of Small-Signal Dynamics of Modular Multilevel Converter Under Unbalanced Grid Conditions. IEEE Trans. Ind. Electron. 2019, 66, 2269–2279. [Google Scholar] [CrossRef]Guo, C.; Liu, W.; Zhao, J.; Zhao, C. Impact of control system on small-signal stability of hybrid multi-infeed HVDC system. IET Gener. Transm. Distrib. 2018, 12, 4233–4239. [Google Scholar] [CrossRef]Peralta, J.; Saad, H.; Dennetiere, S.; Mahseredjian, J.; Nguefeu, S. Detailed and Averaged Models for a 401-Level MMC–HVDC System. IEEE Trans. Power Deliv. 2012, 27, 1501–1508. [Google Scholar] [CrossRef]Saad, H.; Peralta, J.; Dennetière, S.; Mahseredjian, J. Dynamic Averaged and Simplified Models for MMC-Based HVDC Transmission Systems. IEEE Trans. Power Deliv. 2013, 2013 28, 1723–1730. [Google Scholar] [CrossRef]Liu, S.; Xu, Z.; Hua, W.; Tang, G.; Xue, Y. Electromechanical Transient Modeling of Modular Multilevel Converter Based Multi-Terminal HVDC Systems. IEEE Trans. Power Syst. 2014, 29, 72–83. [Google Scholar] [CrossRef]Boyu, Q.; Wansong, L.; Ruowei, Z.; Tao, D.; Jialing, L. Small-signal stability analysis and optimal control parameters design of MMC-based MTDC transmission systems. IET Gener. Transm. Distrib. 2020. [Google Scholar] [CrossRef]Yu, S.; Zhang, S.; Wei, Y.; Zhu, Y.; Sun, Y. Efficient and accurate hybrid model of modular multilevel converters for large MTDC systems. IET Gener. Transm. Distrib. 2018, 12, 1565–1572. [Google Scholar] [CrossRef]Gnanarathna, U.N.; Gole, A.M.; Jayasinghe, R.P. Efficient Modeling of Modular Multilevel HVDC Converters (MMC) on Electromagnetic Transient Simulation Programs. IEEE Trans. Power Deliv. 2011, 26, 316–324. [Google Scholar] [CrossRef] [Green Version]Ajaei, F.B.; Iravani, R. Enhanced Equivalent Model of the Modular Multilevel Converter. IEEE Trans. Power Deliv. 2015, 30, 666–673. [Google Scholar] [CrossRef]Wang, S.; Alsokhiry, F.S.; Adam, G.P. Impact of Submodule Faults on the Performance of Modular Multilevel Converters. Energies 2020, 13, 4089. [Google Scholar] [CrossRef]Bucher, M.K.; Franck, C.M. Contribution of Fault Current Sources in Multiterminal HVDC Cable Networks. IEEE Trans. Power Deliv. 2013, 28, 1796–1803. [Google Scholar] [CrossRef] [Green Version]Xu, J.; Zhu, S.; Li,

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Be achieved as compared with that of using the linear activation function. Figure 13 shows the position errors of the end-effector and the convergence error of \(r_P\) with \(c_1=c_3=10\), and we could observe that, the error performance can be enhanced with larger parameters \(c_1\) and \(c_3\), and still, the error can be lowered by using the hyperbolic sine activation function.6 ConclusionIn this paper, for the first time, a RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on an industrial redundant manipulator model. ReferencesYang C, Peng G, Li Y, Cui R, Cheng L, Li Z (2019) Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE Trans Cybern 49(7):2568–2579Article Google Scholar Cao R, Cheng L, Yang C, Dong Z (2021) Iterative assist-as-needed control with interaction factor for rehabilitation robots. Sci China Technol Sci 64(4):836–846Article Google Scholar Sun T, Cheng L, Hou Z, Tan M (2021) Science China Information Sciences 64:172205Klein CA, Huang CH (1983) Review of pseudoinverse control for use with kinematically redundant manipulators. IEEE Trans Syst Man Cybern 13(2):245–250Article Google Scholar Maciejewski AA (1991) Kinetic limitations on the use of redundancy in robotic manipulators. IEEE Trans Robot Autom 7(2):205–210Article Google Scholar Li S, Zhang Y, Jin L (2017) Kinematic control of redundant manipulators using neural networks. IEEE Trans Neural Netw Learn Syst 28(10):2243–2254Article MathSciNet Google Scholar Li S, He J, Li Y, Rafique MU (2017) Distributed recurrent neural networks for cooperative control of manipulators: a game-theoretic perspective. IEEE Trans Neural Netw Learn Syst 28(2):415–426Article MathSciNet Google Scholar Li S, Shao Z, Guan Y (2019) A dynamic neural network approach for efficient control of manipulators. IEEE Trans Syst Man Cybern 49(5):932–941Article Google Scholar Li S, Zhou M, Luo X (2018) Modified primal-dual neural networks for motion control of redundant manipulators with dynamic rejection of harmonic noises. IEEE Trans Neural Netw Learn Syst 29(10):4791–4801Article MathSciNet Google Scholar Li Z, Xia Y, Wang D, Zhai D, Su C, Zhao X (2016) Neural network-based control of networked trilateral teleoperation with geometrically unknown constraints. IEEE Trans Cybern 46(5):1051–1064Article Google Scholar Zhou Q, Zhao S, Li H, Lu R, Wu C (2019) Adaptive neural network tracking control for robotic manipulators with dead zone. IEEE Trans Neural Netw Learn Syst 30(12):3611–3620Article MathSciNet Google Scholar Liao B, Zhang Y, Jin L (2016) Taylor \(o(h^{3})\) discretization of ZNN models for dynamic equality-constrained quadratic programming with application to manipulators. IEEE Trans Neural Netw Learn Syst 27(2):225–237Article MathSciNet Google Scholar Zhang Y, Li S, Zou J, Khan AH (2020) A passivity-based approach for kinematic control of manipulators with constraints. IEEE Trans Ind Inf 16(5):3029–3038Article Google Scholar Xiao L, Zhang Y (2013) Acceleration-level repetitive motion planning and its experimental

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