Disturbance observer-based full-state constrained control for robotic systems with dead zone

This paper studies the trajectory tracking control problem of uncertain manipulators with full-state constraints and dead-zone inputs. By utilizing the backstepping method and barrier Lyapunov functions, a novel disturbance observer-based full-state constrained control approach is proposed. It is noted that the dead-zone inputs can be expressed in terms of a nonlinear disturbance term and a nominal part. The unknown dynamic uncertainty of robotic system is approximated by the adaptive neural network, and the disturbance observer is integrated into the control design to compensate neural network approximation errors, nonlinear disturbances caused by dead-zone inputs, and external disturbance. A barrier Lyapunov function (BLF) is introduced to illustrate the boundedness of signals within the robotic control system. Finally, the effectiveness of the proposed control scheme is illustrated through simulation results of a two-link manipulator.