Fuzzy rules extraction by GA for controlling 2-DOF underactuated planar manipulators: switching computed torque approach

We propose a method of fuzzy rule extraction for switching partly stable controllers (PSCs) for online operations of underactuated manipulators. The fuzzy rule base of switching the PSCs is optimized using GA and the optimization is performed off-line. Design parameters of the fuzzy rules are encoded into chromosomes and shapes of the Gaussian functions are evolved to minimize the angular position errors. The angular position errors are used as the inputs to the Gaussian membership functions in the antecedent part and the one index of the PSCs is assigned in the consequent part of the fuzzy reasoning. Then, this trained rule base can be brought into the online operation of the underactuated manipulator. The effectiveness of the concept is illustrated by taking a 2-DOF RR planar manipulator and results show the effectiveness of the proposed control methodology.