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W. Z. Gao and R. R. Selmic, “Neural Network Control of a Class of Nonlinear Systems with Actuator Saturation,” American Control Conference, Boston, 2006, pp. 147-156.

has been cited by the following article:

• TITLE: Self-Structured Organizing Single-Input CMAC Control for De-icing Robot Manipulator

  AUTHORS: Thanhquyen Ngo, Yaonan Wang, Youhui Chen, Zan Xiao

  KEYWORDS: Cerebellar Model Articulation Controller (CMAC), De-Icing Robot Manipulator, Gradient-Descent Method, Self-Organizing, Signed Distance

  JOURNAL NAME: Intelligent Control and Automation, Vol.2 No.3, August 8, 2011

  ABSTRACT: This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.