Fuzzy-neural-network inherited backstepping control for robot manipulator

This study presents the fuzzy-neural-network inherited backstepping control (FNNIBSC) for an n-link robot manipulator including actuator dynamics. First, a conventional backstepping control (BSC) scheme is developed for the joint position tracking of the robot manipulator. Then, a FNNIBSC scheme is proposed to relax the requirement of detailed system information, to improve the robustness of BSC and to deal with serious chattering caused by the discontinuous function. In the FNNIBSC strategy, the FNN framework is designed to mimic the BSC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNIBSC system, and the superiority of the proposed FNNIBSC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.