Improved Interval Type2 Fuzzy Neural Networks Control of Manipulator

In this paper, an adaptive backstepping control with improved interval type2 fuzzy neural networks approximator is proposed for doublelink manipulator system. Compared with type1 fuzzy system, interval type2 fuzzy system can obtain better performance for highly complex nonlinear systems because of its uncertainties in the antecedent and membership functions. However, KM algorithm in the process of finding the cross points between upper output and lower output in type2 fuzzy system leads to heavy computational burden and time consumption which makes it difficult to apply in practical engineering. In this paper, instead of KM algorithm, an adaptive factor is used to build an adaptive ponderation between upper output and lower output. The improved interval type2 fuzzy neural networks approximator is applied to handle the uncertain parameters in doublelink manipulator system. The ultimately boundedness of all signals and the stability of the closedloop system can be mathematically proved by the Lyapunov stability analysis. The simulation results demonstrate the proposed adaptive backstepping control with improved interval type2 fuzzy neural networks approximator obtains transient response, short stabilizing time and high approximation accuracy.