Implementation of FLC for speed control of a DC servomotor using real-type tabu search

In this paper, real-type tabu search (RTS) is proposed, which adaptively changes the range of the neighbor solution to be searched each iteration according to an objective function. We design-the adaptive fuzzy logic controller (AFLC) for speed control of DC servomotor using RTS and neural network (NN). In this paper, the operation of the AFLC for speed control of a DC servomotor is composed of two steps. The first step is for optimizing the weighting value of NN and the initial value of input/output gain needed in real-time adjusting input/output gain of a fuzzy logic controller using a NN using the improved RTS. The second step is for real-time changing of the input/output gain of FLC using NN. We apply the proposed AFLC to the speed control of an actual DC servomotor system. Experimental results show that AFLC has the better control performance than a PI controller in terms of settling time, rise time and overshoot