| Summary: | This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs) and fuzzy systems. The controller for each degree of freedom (DOF) consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1) it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2) the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.
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