Design New Robust Self Tuning Fuzzy Backstopping Methodology

This research is focused on proposed Proportional-Integral (PI) like fuzzy adaptive backstopping fuzzy algorithms based on Proportional-Derivative (PD) fuzzy rule base with the adaptation laws derived in the Lyapunov sense. Adaptive SISO PI like fuzzy adaptive backstopping fuzzy method has two main objectives; the first objective is design a SISO fuzzy system to compensate for the model uncertainties of the system, and the second objective is focused on the design PI like fuzzy controller based on PD method as an adaptive methodology. Classical backstopping control is robust to control model uncertainties and external disturbances and is a main controller in this research. The fuzzy controller is used in this method to system compensation. To increase the robust of this controller adaptive PI like fuzzy controller is introduced and applied to backstopping fuzzy controller. Classical backstopping control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a backstopping controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a backstopping fuzzy controller to online tune the coefficients in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.