Inspired by works in soft computing this research applies the constituents of soft
computing to act as the "brain" that controls the positioning process of a robot
manipulator's tool. This work combines three methods in artificial intelligence: fuzzy
rules, neural networks, and genetic algorithm to form the soft computing plant
uniquely planned for a six degree-of-freedom serial manipulator. The forward
kinematics of the manipulator is made as the feedforward control plant while the soft
computing plant replaces the inverse kinematics in the feedback loop. Fine
manipulator positioning is first achieved from the learning stage, and later execution
through forward kinematics after the soft computing plant proposes inputs and the
iterations. It is shown experimentally that the technique proposed is capable of
producing results with very low errors. Experiment A for example resulted the
position errors onpx: 0.004%;py: 0.006%; andpz: 0.002%
