Low‐Computational‐Complexity Zeroing Neural Network for Resisting Multiple‐Type Noise With Solution of Constrained Quadratic Programming

ABSTRACT Harmonic noise interference is commonly encountered during the operation of robotic manipulators and is often accompanied by various types of non‐harmonic noise (e.g., polynomial, constant, and random noise). To tackle the challenges presented by these mixed‐harmonic noise environments, this paper proposes a multi‐noise‐resistance low‐computational‐complexity zeroing neural network (MNR‐LCCZNN) model. The proposed framework integrates an adaptive compensation mechanism to effectively suppress harmonic noise and employs a low‐computational‐complexity zeroing neural network (LCCZNN) structure that eliminates the need for matrix inversion, thereby enabling efficient handling of multi‐task constraints. Furthermore, the incorporation of an advanced activation function significantly improves the model's convergence speed and robustness under noise‐mixture conditions. Theoretical analysis rigorously establishes the stability and noise resistance of the model. To validate its effectiveness, the MNR‐LCCZNN is applied to numerical simulations involving multiple constraints, as well as trajectory control experiments on the UR3e robotic arm. These experiments are conducted under both non‐harmonic and harmonic noise interference. The results demonstrate that the proposed model delivers superior accuracy, robustness, and practical applicability across a range of representative noise scenarios.