Novel Predefined Performance Control of Robotic Manipulators with FDI Attacks and Actuator Faults

Abstract

Based on a fixed-time extended state observer (FESO), this paper proposes a novel predefined performance control (PPC) method for robotic manipulators with false data injection (FDI) attacks and actuator faults. First, a mathematical model of robotic manipulators with parameter uncertainties and external disturbances is constructed. Then, to compensate for the FDI attacks and actuator faults, an extended state is introduced such that the FESO is designed. Moreover, to avoid the transformation from nonlinear constraints to unconstrained variables in PPC, the barrier Lyapunov function (BLF) is introduced. By adopting the novel PPC, tracking errors of the robotic manipulators are driven into a predefined region. Finally, simulations on a two-degree-of-freedom manipulator demonstrate that, compared with FTESO-based sliding mode control, the proposed method has shorter settling times and better tracking accuracy.