Hybrid Motion-Force Control for Redundant Manipulators With Robust Finite-Time Gradient Neural Network

Hybrid motion-force control (HMFC) of redundant manipulators is a fundamental yet challenging problem due to structural redundancy and multiple constraints. Traditional approaches exhibit low convergence and sensitivity to time-varying disturbances. To address these limitations, a HMFC approach for redundant manipulators with a robust finite-time gradient neural network (RFTGNN) is proposed. It reformulates the motion-force control problem into a constrained quadratic program (QP) and derives the corresponding Karush-Kuhn-Tucker (KKT) conditions. RFTGNN, implemented with an adaptive scaling mechanism, is established to address the HMFC problem of redundant manipulators. The proposed method enables adaptive adjustments of its convergence rate in response to real-time task errors, ensuring stable finite-time tracking while preserving stability in the presence of noise. Theoretical analyses confirm the finite-time tracking of redundant manipulators under HMFC. The effectiveness of the proposed approach is demonstrated through simulations and physical experiments conducted on a Franka Emika Panda manipulator.