Dynamic Neural Networks for Motion-Force Control of Redundant Manipulators: An Optimization Perspective

Abstract

Accurate position-force control is a core and challenging problem in robotics, especially for manipulators with redundant degrees of freedom (DOFs). For example, trajectory tracking-based control usually fails for grinding robots due to intolerable impact forces imposed onto the end effectors. The main difficulties lie in the coupling of motion and contact force, redundancy resolution, physical constraints, etc. In this article, we propose a novel motion-force control strategy in the framework of projection recurrent neural networks (RNN). Tracking error and contact force are described in orthogonal spaces, respectively, and by selecting minimizing joint torque as secondary task, the control problem is formulated as a quadratic-programming (QP) problem under multiple constraints. In order to obtain real-time optimization of joint toque, which is nonconvex relative to joint angles, the original QP is reconstructed in the velocity level, where the original objective function is replaced by its time derivative. Then, a dynamic neural network, which is convergence provable is established to solve the modified QP problem online. This work generalizes projection RNN-based position control of manipulators to that of position-force control, which opens a new avenue to shift position-force control of manipulators from pure control perspective to cross design with both convergence and optimality consideration. Numerical and experimental results show that the proposed scheme achieves accurate position-force control, and is capable of handling inequality constraints such as joint angular, velocity, and torque limitations, simultaneously, consumption of joint torque can be decreased effectively.