Fuzzy self-organizing controller and its application for dynamic processes
Fuzzy Sets and Systems - Fuzzy Control
Introduction to Grey system theory
The Journal of Grey System
EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots
Information Sciences: an International Journal
Information Sciences: an International Journal
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Technical communique: Sliding mode control of singular stochastic hybrid systems
Automatica (Journal of IFAC)
MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators
Information Sciences: an International Journal
Single-step ahead prediction based on the principle of concatenation using grey predictors
Expert Systems with Applications: An International Journal
A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets
Expert Systems with Applications: An International Journal
Grey-prediction self-organizing fuzzy controller for robotic motion control
Information Sciences: an International Journal
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
Adaptive fuzzy controller with sliding surface for vehicle suspension control
IEEE Transactions on Fuzzy Systems
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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.