Observer-based adaptive neural control of robotic systems with prescribed performance

Abstract

In this paper, an adaptive neural output feedback control (ANOFC) scheme is proposed for controlling an electrically driven robotic manipulator (EDRM) system with prescribed errors constraint by using a neural network-based adaptive observer (NNAO) and a backstepping design technique. First, the NNAO is designed to observe the unknown and unmeasured joint velocities of EDRM. Second, the prescribed performance bounds of output tracking are used to achieve the prescribed transient and steady-state performance based on barrier Lyapunov function. Then, the ANOFC scheme is derived by using the backstepping design methodology, where neural networks with adaptive update laws are utilized to approximate the unknown nonlinear functions. By using the Lyapunov stability analysis method, the observer and closed-loop control system can be proven to be stable such that all the uniformly bounded variables in the system are guaranteed and the output tracking errors remain within the specified prescribed bounds. Finally, the simulation results demonstrate that the proposed NNAO and ANOFC schemes can achieve the satisfied estimation capability and tracking effectiveness.