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

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.

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

“…Second, neural network controllers must contain enough tunable parameters to eliminate the uncertainty components of nonlinear systems. To deal with the uncertain components, recent studies on intelligent control have proposed directly incorporating human expertise into neural networks [28][29][30][31]. Fuzzy inference systems have been used as adaptive controllers for robots [32][33][34][35][36], showing one of the most successful applications of fuzzy logic systems [37][38][39][40].…”