Dynamic neural networks based adaptive optimal impedance control for redundant manipulators under physical constraints

This paper presents a dynamic neural network based adaptive impedance control method for redundant robots under multiple physical constraints. In order to provide optimal contact performance without an accurate environment model, an adaptive impedance learning method is proposed to establish the optimal interaction between robot and environment. In the inner loop, a theoretical framework of constraint optimization is constructed, and then a dynamic neural network is established to compensate the nonlinear dynamics, and compliance to physical limitations is also satisfied. These limitations include joint angle restriction, angular velocity restriction, angular acceleration restriction, and torque restriction. Theoretical analysis proves the stability of the closed loop system. Numerical results show the effectiveness of the proposed control scheme. © 2021 Elsevier B.V.