Fault Detection, Isolation, and Accommodation Control in Robotic Systems

In this paper, fault diagnosis and accommodation control are developed for robotic systems. First, a nonlinear observer in the proposed method is designed based on the available model. The fault detection is carried out by comparing the observer states with their signatures. Secondly, state observers are constructed based on possible fault function sets. Thirdly, the accommodation control design is developed using a normal controller plus a neural network compensator to capture the nonlinear characteristics of faults. Finally, if the fault isolation is completed successfully, the second fault accommodation controller is presented based on the fault information obtained by the isolation scheme.

[1]  Xiaoli Ma,et al.  Adaptive state feedback control of systems with actuator failures , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[2]  Paul M. Frank,et al.  Dynamic Model Based Incipient Fault Detection Concept for Robots , 1990 .

[3]  B.D. Appleby,et al.  Using learning techniques to accommodate unanticipated faults , 1993, IEEE Control Systems.

[4]  Hong Wang,et al.  Actuator fault diagnosis: an adaptive observer-based technique , 1996, IEEE Trans. Autom. Control..

[5]  Ron J. Patton,et al.  Fault-Tolerant Control: The 1997 Situation , 1997 .

[6]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[7]  Kok Kiong Tan,et al.  An improvement on stable adaptive control for a class of nonlinear systems , 2004, IEEE Trans. Autom. Control..

[8]  Paul M. Frank,et al.  Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.

[9]  Joseph R. Cavallaro,et al.  Expert system framework for fault detection and fault tolerance in robotics , 1994 .

[10]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[11]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[12]  R. Tinos,et al.  Fault detection and isolation in cooperative manipulators via artificial neural networks , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[13]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[14]  Kok Kiong Tan,et al.  Adaptive friction compensation using neural network approximations , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Carlos Canudas de Wit,et al.  A new model for control of systems with friction , 1995, IEEE Trans. Autom. Control..

[16]  Marios M. Polycarpou,et al.  Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach , 2004, IEEE Transactions on Automatic Control.

[17]  Marios M. Polycarpou,et al.  Neural-network-based robust fault diagnosis in robotic systems , 1997, IEEE Trans. Neural Networks.

[18]  B. Armstrong-Hélouvry.  Stick slip and control in low-speed motion , 1993, IEEE Trans. Autom. Control..

[19]  Marios M. Polycarpou.  Fault accommodation of a class of multivariable nonlinear dynamical systems using a learning approach , 2001, IEEE Trans. Autom. Control..

[20]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .