Stabilization of Non-Linear System Using Single Input Neuro-Fuzzy Logic Controller Based on Radial Basis Function Network

This paper proposes a Single Input Neuro-fuzzy Logic Controller based on Radial Basis Function Network (SI-NFRBFN) for non-linear systems. To obtain single input from multi inputs a Distance Method is suggested. Using this method all the uncertain inputs are simplified into a single input known as distance. With the help of this variable the control unit matrix introduced in Hybrid Neuro-fuzzy Logic Controller based on Radial Basis Function Network (HNFRBFN) is reduced from its 2-dimensional state to a single dimension state, without much affecting the response of the system. By obtaining single dimensional control matrix it is seen that the computational analysis in the learning part of HNFRBFN controller reduces drastically. Due to this the tuning effort and execution time of the proposed SI-NFRBFN controller is minimized. The simulation results show that the SI-NFRBFN controller provides better performance and is more efficient in comparison to HNFRBFN controller.