Repetitive Motion Control for Redundant Manipulator under False Data Injection Attacks *

Abstract

Repetitive motion control (RMC) for redundant manipulators has been extensively studied from the kinematic perspective, whereas security concerns under malicious adversaries have received limited attention. In network-controlled manipulators, when control commands sent from the control center to the remote manipulator are subject to false data injection attacks (FDIAs), serious incidents and potential harm to individuals can occur. This paper proposes a novel resilient controller such that the manipulator can successfully complete motion tracking tasks and address the non-repetitive motion problem, even in the presence of FDIAs. The problem is first reformulated as a convex optimization problem with an unknown parameter relative to FDIAs, where the RMC criteria serves as the objective function and physical limitations are incorporated as inequality constraints. A recurrent neural network (RNN) is then introduced to solve the problem, improving computational efficiency. Additionally, a detection mechanism is integrated to estimate the unknown attack parameter, allowing the RNN to find the optimal control command. Simulations and experiments are conducted on an RM65-B manipulator to validate the efficacy of the proposed method, and comparisons with existing approaches highlight its superior performance.