Constraint control of robotic manipulator trajectory tracking based on improved Funnel control

Abstract

A trajectory tracking control method was proposed based on improved Funnel control and with RBF adaptive neural network to address the steady-state and transient performance constraints of a flexible joint robotic manipulator system with unknown input saturation. The non-differentiable in the control law was avoided by designing improved Funnel control variables. The construction of time-varying transient performance constraint function made it possible to qualitatively design the overshoot amount and convergence speed in the initial stage of the system output; The problem of excessive computational burden in the backstepping step method and the unknown function of the flexible-joint robotic manipulator system were approximated by the RBF adaptive neural network with minimum parameter learning method, which simplified the design of the controller; The Lyapunov stability theory justified that all variables in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB), and simulation experiments verified the effectiveness of the proposed method.

Keywords

• trajectory tracking control
• funnel control
• adaptive neural network
• input saturation
• output constraint