This paper develops a generalized adaptive fuzzy control scheme for MIMO nonlinear second order systems. Here, the example robotic manipulators is used to illustrate the control algorithm. The controller for each degree of freedom (DOF) consists of a feedback fuzzy PD systems used to keep the closed-loop stable. | Journal of Automation and Control Engineering Vol. 4, No. 3, June 2016 An Efficient Adaptive Fuzzy Control Scheme for Industrial Manipulators Abdel Badie Sharkawy, Douglas A. Plaza, and Daniel E. Ochoa Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador. Email: {asharkaw, dochoa, douplaza}@ Abstract—This paper develops a generalized adaptive fuzzy control scheme for MIMO nonlinear second order systems. Here, the example robotic manipulators is used to illustrate the control algorithm. The controller for each degree of freedom (DOF) consists of a feedback fuzzy PD systems 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 timesaving because of the simple structure of the fuzzy systems; and 2) the controller is robust against various 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. Fuzzy controllers have demonstrated excellent robustness in both simulations and real-life applications. However, it has been proved that standard fuzzy logic controllers are not suitable for loop controllers, [3]. This fact is referred to that there are many tuning parameters in membership functions and control rules. Furthermore, standard fuzzy logic controller has a long computation time since it performs fuzzification, inference, and defuzzification processes in determining control inputs. Thus, it is difficult for control inputs of standard fuzzy logic control to be computed within the sampling time of a loop controller. For this reason, complexity reduction of fuzzy feedback controllers was .
