Main Article Content
The parameters of the power systems change slowly with time, due to environmental effects or rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. Many kinds of control techniques with using Advanced Super-conducting Magnetic Energy Storage (ASMES) to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES actively reported. But it is sometimes difficult to specify the rule base for some plants, or the need could arise to tune the rule-base paÃ?Â¬rameters if the plant changes. In order to solve such problems, the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, where the focus is on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data to continually learn the fuzzy controller which will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus).