Adaptive neuro-fuzzy control of a flexible manipulator

Mechatronics Journal., Volume 15, Issue 10, Pages 1159-1320 (December 2005)
Adaptive neuro-fuzzy control of a flexible manipulator
By: Lianfang Tian, Curtis Collins
Abstract
This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator
with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC)
in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic
recurrent identification network (RIN) is used to identify the output of the manipulator system, and a
dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It
is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the
merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The
fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control
strategy into control action and offering a high level of computation. On the other hand, the ability of a
dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to
approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning
algorithm. Simulations for determining the number of modes to describe the dynamics of the system and
investigating the robustness of the control system are carried out. Results demonstrate the good
performance of the proposed control system.

Keywords: Flexible manipulator; Fuzzy logic; Dynamic recurrent neural network; Neuro-fuzzy
controller