Research: Robotics

Fault Detection and Identification for Robot Manipulators


Several factors must be considered for robotic task execution in th presence of a fault, including: detection, identification, and accomodation for the fault. In this research work, a prediction error based dead-zone residual function and a nonlinear observer are used to detect and identify a class of actuator faults. Advantages of the proposed fault detection and identification methods are that they are based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault. Simulation results are provided to illustrate the performance of the detection and identification methods.

Simulation Results

A numerical simulation was performed to demonstrate the performance of the proposed fault detection and identification system.

Block diagram for the Fault Detection and Identification

Experimental Setup

For this experiment we use the Barrett WAM. Five joints of the robot were locked at a fixed angle during the experiment and the remaining two joints of the manipulator are controlled as a planar 2 DOF robot manipulator. The control algorithm is written in C++ and are hosted on an AMD Athlon 1.2GHz PC running the QNX 6.2.1 real time OS. Data logging and online parameter tuning is performed with Qmotor 3.0 at 1KHz.
We have set up a framework where we can inject either free-swinging or ramp actuator faults on either of the joints. We can control the time when the fault will be injected and for how long the fault lasts. Also it is possible to inject different faults on the two joints.
To detect when a fault occurs we use the residual threshold based fault detection algorithm presented in [W. E. Dixon, et. al, "Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach," (2000)]. Once a fault is detected, we start the integration routine for the fault observer.
Standard P.D. control with feedforward desired acceleration is used to track the desired trajectory,

qd(1) = 0.8sin(t) [rad],
qd(2) = 0.8sin(1.3t) [rad]

Experimental Results


In conclusion, a fault detection and identification method is proposed for robot manipulators. The proposed fault identification method is independent from the controller. The fault identification scheme can be applied to a generic class of actuator faults that are second order differentiable. The effectiveness of the proposed fault detection/identification methods are illustrated through a numerical simulation.


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