Intelligent robust control of redun-dant smart robotic arm Pt II: Quantum computing KB optimizer
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DOI: https://doi.org/10.30564/aia.v2i2.1395
Abstract: In the first part of the article, two ways of fuzzy controller’s implementation showed. First way applied one controller for all links of the manipulator and showed the best performance. However, such an implementation is not possible in complex control objects, such as a planar redundant manipulator with seven degrees of freedom (DoF). The second way use of separated control when an independent fuzzy controller controls each link. The decomposition control due to a slight decrease in the quality of management has greatly simplified the processes of creating and placing knowledge bases. In this paper (Part II), the advantages and limitations of intelligent control systems based on soft computing technology described. To eliminate the mismatch of the work of separate independent fuzzy controllers, methods for self-organizing coordination control based on quantum computing technologies to create and design robust intelligent control systems for robotic manipulators with 3DOF and 7DOF described. Quantum fuzzy inference as quantum self-organization algorithm of imperfect KBs introduced. Quantum computational intelligence smart toolkit QCOptKBTMbased on quantum fuzzy inference applied. QCOptKBTM toolkit include quantum deep machine learning in on line. Successful engineering application of end-to-end quantum computing information technologies (as quantum sophisticated algorithms and quantum programming) in searching of solutions of algorithmic unsolved problems in classical dynamic intelligent control systems, artificial intelligence (AI) and intelligent cognitive robotics discussed. Quantum computing supremacy in efficient solution of intractable classical tasks as global robustness of redundant robotic manipulator in unpredicted control situations demonstrated. As result, the new synergetic self-organization information effect of robust KB design from responses of imperfect KBs (partial KB robustness cretead on toolkit SCOptKBTM in Pat I) fined. References:[1] S. V. Ulyanov, System and method for control using quantum soft computing [P]. US Patent No 7,383,235 B1, 2003; EP PCT 1 083 520 A2, 2001; Efficient simulation system of quantum algo-rithm gates on classical computer based on fast algorithm [P]. US Patent No 2006/0224547 A1, 2006. [2] S. V. Ulyanov, Quantum fast algorithm computational intelligence PT I: SW / HW smart toolkit [J]. Artificial Intelligence Advances. 2019. (1): 18-43. DOI: https://doi.org/10.30564/aia.v1i1.619. [3] L. K. Grover, A fast quantum mechanical algorithm for database search [P]. US Patent US 6,317,766 B1, 2001. [4] L. V. Litvintseva, S.V. Ulyanov, Quantum fuzzy inference for knowledge base design in ro-bust intelligent controllers [J] J. Computer and Systems Sci. Intern. 2007. 46 (6): 946–984. DOI: 10.1134/S1064230707060081. [5] L. V. Litvintseva, S.V. Ulyanov, Intelligent control systems. 1. Quantum computing and self-organization algorithm [J] J. Computer and Systems Sci. Intern. 2009. 48(6): 908–96. DOI: 10.1134/S1064230709060112. [6] L. V. Litvintseva, S.V. Ulyanov, Intelligent control systems. II. Design of self-organized ro-bust knowledge bases in contingency control situations [J] J. Computer and Systems Sci. Intern. 2011. 50(2): 250–292. DOI: 10.1134/S1064230710061036. [7] M. A. Nielsen, I. L Chuang, Quantum computation and quantum information [M] UK, Cam-bridge, University Press, 2000: 700 p. [8] D. C. Marinescu, G. M. Marinescu, Approaching quantum computing [M] New Jersey: Pear-son Prentice Hall, 2005: 400 p. [9] A. V. Nikolaeva, S. V. Ulyanov, Design of an intelligent robot control system by a manipula-tor. Part 3: Modeling and physical experiment based on quantum computing technologies [J] System analysis in science and education: a network scientific publication. 2013. (1): 1-25 [in Russian]. [10] S. V. Ulyanov, L. V. Litvintseva, S. V. Sorokin, Certificate of state registration of computer programs No. 2011619257. Optimizer of robust knowledge bases for the design of intelligent con-trol systems on soft computing: [P] Application No. 2011617532 dated 11.10.2011 RF Registered in the Register of computer programs on December 1, 2011 [in Russian]. [11] V. S. Mikhailov, Control Theory [M] K: Higher School, 1988: 312 p [in Russian]. [12] A. V. Nikolaeva, S. V. Ulyanov, A. V. Nozdrachev, Intelligent control system for redun-dant robotic arm with seven degrees of freedom based on soft computing [J] System analysis in sci-ence and education: a network scientific publication. 2014. (2): 48-55 [in Russian]. [13] S. V. Ulyanov, Self-Organized Intelligent Robust Control Based on Quantum Fuzzy Infer-ence [J] Recent Advances in Robust Control - Novel Approaches and Design Methods, ISBN: 978-953-307-339-2, InTech, 2011. Available from: http://www.intechopen.com/books/recent-advances-in-robust-controlnovel-approaches-and-design-methods/self-organized-intelligent-robust-control-based-on-quantum-fuzzy-inference. [14] S.V. Ulyanov, Self-organizing quantum robust control methods and systems for situations with uncertainty and risk [P]. Patent US 8788450 B2, 2014. [15] S.V. Ulyanov Self-organization of robust intelligent controller using quantum fuzzy infer-ence // Proc. IEEE 2nd Intern. Conf. ISKE’2008. Xiamen, 2008. 1: 726 – 732. [16] L.V. Litvintseva, S.V. Ulyanov, Design technology of robust KB for integrated fuzzy intel-ligent control based on quantum fuzzy inference: Inverted pendulum as Benchmark of quantum fuzzy control in unpredicted control situations. Proc. 4th Intern. Conf. ICSCCW’2007. Antalya, Turkey, 2007: 219 – 237. [17] S.V. Ulyanov, L.V. Litvintseva., T. Hagiwara et al. Design of self-organized intelligent con-trol systems based on quantum fuzzy inference: Intelligent system of systems engineering approach // Proc. IEEE Intern. Conf. SMC'2005. Hawaii. 2005. 4: P. 3835 – 3840.