Intelligent active force control for mobile manipulator

Abstract

This thesis presents a resolved acceleration control (RAC) and intelligent schemes of active force control (AFC) as approaches for the robust motion control of a mobile manipulator (MM) comprising a differentially driven wheeled mobile platform with a two-link planar arm mounted on top of the platform. The study emphasizes on the integrated kinematic and dynamic control strategy in which the RAC is used to manipulate the kinematic component while the intelligent schemes are implemented to compensate the dynamic effects including the bounded known/unknown disturbances and uncertainties. The proposed intelligent schemes are based on iterative learning control (ILC) and knowledge-based fuzzy (KBF) strategies. The effectiveness and robustness of the proposed schemes are investigated through a rigorous simulation study and later complemented with experimental results obtained through a number of experiments performed on a fully developed working prototype in a laboratory environment. A number of disturbances in the form of applied constant, vibratory and impact forces are deliberately introduced into the system to evaluate the system performances. The investigation clearly demonstrates the extreme robustness feature of the proposed control schemes compared to other systems considered in the study