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We present a geometric approach for fault detection and isolation (FDI) in robotic manipulators in presence of model uncertainty. A systematic procedure is introduced for representing robotic system model being affine with respect to faults and disturbances. The proposed residual generator has smooth dynamics with freely selectable functions and it does not require high gains or threshold adjustment for the FDI purpose. No assumption on amplitude of faults and their rate is used. The solvability conditions for the FDI problem lead to a quotient observable subspace unaffected by all unknown inputs except the faults. Simulation example demonstrates localization of faults in presence of... 

This paper presents a nonlinear geometric approach to fault detection and isolation (FDI) in grid-connected inverters. Faults are considered in inverter switches as well as in grid voltage and source current sensors. The FDI system is not sensitive to load variations or grid transients. No assumptions on balanced, zero-sum or sinusoidal form of voltages or currents are made. The resulting residual converges to zero in no-fault condition such that no threshold adjustment is needed. Simulations demonstrate performance of the proposed FDI system in presence of unknown disturbance. © 2018 IEEE  

Reliability of model-based failure detection and isolation (FDI) methods depends on the amount of uncertainty in a system model. Recently, it has been shown that the use of joint torque sensing results in a simplified manipulator model that excludes hardly identifiable link dynamics and other nonlinearities. We present a geometric approach to fault detection and isolation (FDI) for robotic manipulators using joint torque sensor in presence of model uncertainty. A systematic procedure is introduced for representing a robotic system model using joint torque sensor being affine with respect to faults and disturbances. The proposed FDI filter has smooth dynamics with freely selectable functions...