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Page 68 Harib et al. Intell Robot 2022;2(1):37-71 https://dx.doi.org/10.20517/ir.2021.19

Syst, Man, Cybern 1983;SMC-13:834-46. DOI
84. Michie D, Chambers RA. Boxes: an experiment in adaptive control. Edinburgh, UK: Oliver and Boyd; 1968. p. 137-52.
85. Michie D, Chambers RA. Boxes’ as a model of pattern-formation. 1st ed. Edinburgh: Edinburgh univ. press; 1968. p. 206-15. DOI
86. Anderson CW. Strategy Learning with multilayer connectionist representations. proceedings of the fourth international workshop on
machine learning. Elsevier; 1987. p. 103-14. DOI
87. Anderson C. Learning to control an inverted pendulum using neural networks. IEEE Control Syst Mag 1989;9:31-7. DOI
88. Lin CS, Kim H. CMAC-based adaptive critic self-learning control. IEEE Trans Neural Netw 1991;2:530-3. DOI PubMed
89. A l b u s J S . T h e o r e t i c a l a n d e x p e r i m e n t a l a s p e c t s o f a C e r e b e l l a r M o d e l . A v a i l a b l e f r o m :
https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=820153 [Last accessed on 8 Mar 2022].
90. Albus JS. A new approach to manipulator control: the cerebellar model articulation controller (CMAC). J Dyn Syst Meas Control
1975;97:220-7. DOI
91. Albus JS. Mechanisms of planning and problem solving in the brain. Math Biosci 1979;45:247-93. DOI
92. Albus JS. Brains, behavior, and robotics. 1st ed. Peterborough: BYTE Books; 1981.
93. Huang, Chien-lo Huang. Control of an inverted pendulum using grey prediction model. IEEE Trans on Ind Applicat 2000;36:452-8.
DOI
94. Pathak K, Franch J, Agrawal S. Velocity and position control of a wheeled inverted pendulum by partial feedback linearization. IEEE
Trans Robot 2005;21:505-13. DOI
95. Li, Jun Luo. Adaptive Robust dynamic balance and motion controls of mobile wheeled inverted pendulums. IEEE Trans Contr Syst
Technol 2009;17:233-41. DOI
96. Chaoui H, Gueaieb W, Yagoub MCE. ANN-based adaptive motion and posture control of an inverted pendulum with unknown
dynamics. 2009 3rd International Conference on Signals, Circuits and Systems (SCS); 2009 Nov 6-8; Medenine, Tunisia. IEEE;
2009. p. 1-6. DOI
97. Guez A, Ahmad Z. Solution to the inverse kinematics problem in robotics by neural networks. IEEE 1988 International Conference
on Neural Networks; 1988 Jul 24-27; San Diego, CA, USA. IEEE; 1988. p. 617-24. DOI
98. . Elsley. A learning architecture for control based on back-propagation neural networks. IEEE 1988 International Conference on
Neural Networks; 1988 Jul 24-27; San Diego, CA, USA. IEEE; 1988. p. 587-94. DOI
99. Jamshidi M, Horne B, Vadiee N. A neural network-based controller for a two-link robot. 29th IEEE Conference on Decision and
Control; 1990 Dec 5-7; Honolulu, HI, USA. IEEE; 1990. p. 3256-7. DOI
100. Karakasoglu A, Sundareshan MK. Decentralized variable structure control of robotic manipulators: neural computational algorithms.
29th IEEE Conference on Decision and Control; 1990 Dec 5-7; Honolulu, HI, USA. IEEE; 1990. p. 3258-9. DOI
101. Xu G, Scherrer H, Schweitzer G. Application of neural networks on robot grippers. 1990 IJCNN International Joint Conference on
Neural Networks; 1990 Jun 17-21; San Diego, CA, USA. IEEE; 1990. p. 337-42. DOI
102. Wilhelmsen K, Cotter N. Neural network based controllers for a single-degree-of-freedom robotic arm. 1990 IJCNN International
Joint Conference on Neural Networks; 1990 Jun 17-21; San Diego, CA, USA. IEEE; 1990. p. 407-13. DOI
103. Miller WT, Glanz FH, Kraft LG. Application of a general learning algorithm to the control of robotic manipulators. Int J Rob Res
1987;6:84-98. DOI
104. Miller W. Sensor-based control of robotic manipulators using a general learning algorithm. IEEE J Robot Automat 1987;3:157-65.
DOI
105. Miller WT. Real time learned sensor processing and motor control for a robot with vision. Neural Networks 1988;1:347. DOI
106. Miller WT, Hewes RP. Real time experiments in neural network based learning control during high speed nonrepetitive robotic
operations. Proceedings IEEE International Symposium on Intelligent Control 1988; 1988 Aug 24-26; Arlington, VA, USA. IEEE;
1988. p. 513-8. DOI
107. Miller W. Real-time application of neural networks for sensor-based control of robots with vision. IEEE Trans Syst, Man, Cybern
1989;19:825-31. DOI
108. Miller W, Glanz F, Kraft L. CMAC: an associative neural network alternative to backpropagation. Proc IEEE 1990;78:1561-7. DOI
109. Huan L, Iberall, Bekey. Building a generic architecture for robot hand control. IEEE 1988 International Conference on Neural
Networks; 1988 Jul 24-27; San Diego, CA, USA. IEEE; 1988. p. 567-74. DOI
110. Wang SD, Yeh HMS. Self-adaptive neural architectures for control applications. 1990 IJCNN International Joint Conference on
Neural Networks; 1990 Jun 17-21; San Diego, CA, USA. IEEE; 1990. p. 309-14. DOI
111. Seidl D, Lam SL, Putman J, Lorenz R. Neural network compensation of gear backlash hysteresis in position-controlled mechanisms.
IEEE Trans on Ind Applicat 1995;31:1475-83. DOI
112. Olsson H, Åström K, Canudas de Wit C, Gäfvert M, Lischinsky P. Friction models and friction compensation. European Journal of
Control 1998;4:176-95. DOI
113. Katsura S, Suzuki J, Ohnishi K. Pushing operation by flexible manipulator taking environmental information into account. IEEE
Trans Ind Electron 2006;53:1688-97. DOI
114. Katsura S, Ohnishi K. Force servoing by flexible manipulator based on resonance ratio control. IEEE Trans Ind Electron
2007;54:539-47. DOI
115. Ghorbel F, Hung J, Spong M. Adaptive control of flexible-joint manipulators. IEEE Control Syst Mag 1989;9:9-13. DOI
116. Chien M, Huang A. Adaptive control for flexible-Joint electrically driven robot with time-varying uncertainties. IEEE Trans Ind