Adaptive robust tracking RBF neural networks control for industrial robot minipulators based on backstepping

This present study proposes a design and the analysis of novel adaptive robust neural networks (ARNNs) based on backstepping control method for industrial robot manipulators (IRMs). In this research, ARNNs controller has combined advantages Radial Basis Function network (RBFNN), term, technique without requirement prior knowledge. The RBFNN is used in order to approximate unknown function deal with external disturbances uncertain nonlinearities. addition, disturbance system compensated by Sliding Mode Control (SMC). All parameters are determined Lyapunov stability theorem, tuned online an training law. Therefore, stability, robustness, desired tracking performance IRMs guaranteed.