A New Neural Network PI-Funnel Distributed Control for Cooperative Manipulator With Global Prescribed Performance

This article addresses the distributed global prescribed-performance control problem for uncertain Lagrangian dynamics, with a particular emphasis on minimizing steady-state error oscillations. A novel global distributed prescribed-performance control framework is proposed based on a dynamic funnel function and neural network design. Specifically, by integrating funnel barrier properties and derivative information, a new neural network learning law is developed. Furthermore, a projection operator is incorporated into the learning law to guarantee the boundedness of the weight estimates in the stability proof, ultimately avoiding potential constraint incompatibility problems caused by neural network integration. The established control framework ensures that the trajectory consensus error of robotic manipulators under distributed control satisfies global arbitrary convergence rates and steady-state error bounds while leveraging neural network approximation to mitigate the inherent uncertainties of controllers that do not require precise mathematical model, thereby effectively suppressing steady-state error oscillations. Unlike existing literature, this work pioneers the incorporation of neural networks into distributed funnel control, achieving global prescribed performance while significantly reducing steady-state error oscillations. Finally, simulation results validate the effectiveness of the proposed method.