Smooth residual generation for robust isolation of faults in manipulators using joint torque sensors

Abstract

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 and it does not require high gains or threshold adjustment for the FDI purpose. The paper focus on actuator and torque sensor faults which are more common in practical cases. No information on manipulator model or on amplitude of faults and their rate are used. Simulation examples on a 3-degrees of freedom manipulator is carried out to illustrate performance of the proposed FDI method.