Abstract
The concept of learning and training machines, and some early methodologies were introduced about two decades ago. Robotic systems, undoubtedly, can be developed to a more advanced and intelligent stage. The realization of the learning capability, analogous to the human learning and thinking process, is a desired primary function. A betterment process has been investigated in the literature for applications of learning control to robotic systems. The existing schemes are a type of one-step learning process. A neighboring (2m+1)-step learning control scheme for robotic systems is presented in this paper. For each process, a betterment algorithm which chooses a generalized momentum as an output function, is executed. Also, it is associated with a conceptual learning process by adding a self-teaching knowledge base for speeding up the convergence, so that the learning capability of the resulting robotic systems can be enhanced.
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Gu, YL., Loh, N.K. Learning control in robotic systems. J Intell Robot Syst 2, 297–305 (1989). https://doi.org/10.1007/BF00238694
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DOI: https://doi.org/10.1007/BF00238694