Abstract
In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
Highlights
Performance of many tracking control systems is limited by variation of parameters and disturbances
An adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems
In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control
Summary
Performance of many tracking control systems is limited by variation of parameters and disturbances This specially applies for direct drive robots with highly nonlinear dynamics and model uncertainties. Control chattering, an inherent problem associated with SMC, can evoke un-modeled and undesired high frequency dynamics, Ho et al [17] have proposed an adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems. An adaptive fuzzy sliding mode control (AFSMC) scheme is proposed for robotic systems. SMC employs a discontinuous control effort to derive the system trajectories toward a sliding surface, and switching on that surface It will gradually approach the control objective, the origin of the phase plane. The controller results in large implementation cost and leads to chattering efforts
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