Fault Detection and Identification for Robot Manipulators
Abstract
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
- Free Swinging Actuator Fault (Joint 1 40 sec, Joint 2 50 sec)
- Ramp Actuator Fault (Joint 1 40 sec, Joint 2 50 sec)
- Saturated Actuator Fault (Joint 1 40 sec, Joint 2 50 sec)
Conclusion
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.
Publications
For more details on this research, please refer the following:
- M. McIntyre, W. E. Dixon, D. M. Dawson, and I. D. Walker, “Fault Detection and Identification for Robot Manipulators,” IEEE Transactions on Robotics, Vol. 21, No. 5, pp. 1028-1034, (2005).
- M. L. McIntyre, W. E. Dixon, D. M. Dawson, and I. D. Walker, “Fault Detection and Identification for Robot Manipulators,” Proceedings of the IEEE International Conference Robotics and Automation, New Orleans, Louisiana, April 2004, pp. 4981-4986.