A Robust Scheme for Tuning of Fuzzy PI Type Controller

In this paper, a simple and effective scheme for tuning of fuzzy PI (proportional-integral) controller based on fuzzy logic is proposed. Here the input scaling factors are tuned online by gain updating factors whose values are determined by rule base with the error and change in error as inputs according to the required controlled process. The performance comparison of conventional fuzzy logic controller with auto tuned fuzzy PI type controllers has been done in terms of several performance measures such as peak overshoot, settling time and rise time and integral square error (ISE). In addition to the responses due to step set-point change, a random noise is also added in some systems. Simulation results show the effectiveness and robustness of the proposed tuning mechanism. Furthermore, a clustering method is used to reduce the fuzzy inference rules of the three fuzzy reasoning blocks which reduces the computational time and memory. The clustering based fuzzy logic controllers is compared with those of conventional fuzzy logic controllers in both cases (with and without tuning). A simulation analysis of a wide range of linear and nonlinear processes is carried out and comparison of results shows computational time and memory is reduced to a great extent