Enhanced adaptive grey-prediction self-organizing fuzzy sliding-mode controller for robotic systems

A grey-prediction self-organizing fuzzy controller (GPSOFC) has been proposed to control robotic systems. It solves the problems caused by the inappropriate selection of parameters in a self-organizing fuzzy controller (SOFC) and eliminates the dynamic coupling effects between degrees of freedom (DOFs) in robotic systems. However, its stability is difficult to demonstrate. To overcome the stability issue, this study developed an enhanced adaptive grey-prediction self-organizing fuzzy sliding-mode controller (EAGSFSC) for robotic systems. The EAGSFSC not only solves the problem of a GPSOFC implementation by determining the stability of the system but also applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller for manipulating a robotic system to improve its control performance. The stability of the EAGSFSC was proven using the Lyapunov stability theorem. To confirm the suitability of the proposed method, this study applied the EAGSFSC to manipulate a 6-DOF robot to determine its control performance. Experimental results showed that the EAGSFSC achieved better control performance than the GPSOFC as well as the SOFC for robotic motion control.