Adaptive Neuro-fuzzy Control of AMRU-5, a six-legged walking robot

Abstract

Due to the complexity of walking robots which has in general a great number of degrees of freedom, cognitive modelling controller such as Fuzzy Logic, Neural Networks…seems to be reasonable in the design of adaptive control of such robot. Fuzzy Logic Controller is more used because it lets you describe desired system behaviour with simple “if-then ” relations. But it has a major limitation because in many applications, the designer has to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but we cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, we show an original method to design an adaptive Neuro-Fuzzy controller which consists in five steps that are: the initial design of an ANFIS controller, the identification of the dynamic model of the leg joints, the estimation of the parameters of the dynamic model, the calculation of an ideal torque and the updating of the parameters of the controller and finally the design of the supervisory control. 1