Failure detection and isolation in robotic manipulators using joint torque sensors

Reliability of any model-based failure detection and isolation (FDI) method 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 such as friction, backlash, and flexibilities. In this paper, we show that the application of the simplified model in a fault detection algorithm increases reliability of fault monitoring system against modeling uncertainty. The proposed FDI filter is based on a smooth velocity observer of degree 2n where n stands for the number of manipulator joints. No velocity measurement and assumptions on smoothness of faults are used in the fault detection process. The paper focuses on actuator faults and investigates the effect of torque sensor noise on threshold selection. The FDI filter is further improved to become robust against an unknown bias in torque sensor reading. The effect of position sensor noise together with position sensor faults are also investigated. Simulation example on a 6-degrees of freedom manipulator is carried out to illustrate the performance of the proposed FDI method.