In thispaper, a new robustfault detection techniquefor robotic manipulators is developed. The new approach, called robust nonlinear analytic redundancy (RIVLAR) technique, detects both sensor and actuatorfaults in a robotic manipulator The proposed RNLAR technique can compensate for the effects ofmodel-plan-mismatch (MPM) and process disturbance. A nonlinear primary residual vectors (PRV) design method to detect faults is proposed where the PRVs are highly sensitive tofaults and less sensitive to MPM andprocess disturbance. Experimental results on a PUMA 560 are presented to justify the effectiveness ofthe RNL4R scheme.
A robust nonlinear analytical redundancy (RNLAR) technique is presented to detect and isolate actuator and sensor faults in a mobile robot. Both model-plant-mismatch (MPM) and process disturbance are considered during fault detection. The RNLAR is used to design primary residual vectors (PRV), which are highly sensitive to the faults and less sensitive to MPM and process disturbance, for sensor and actuator fault detection. The PRVs are then transformed into a set of structured residual vectors (SRV) for fault isolation. Experimental results on a Pioneer 3-DX mobile robot are presented to justify the effectiveness of the RNLAR scheme.
A new approach to sensor and actuator fault detection in the presence of model uncertainty and disturbances, and its application to a wheeled mobile robot (WMR) are presented in this paper. Robust fault detection is important because of the universal existence of model uncertainties and process disturbances in most systems. This paper proposes a new approach, called robust nonlinear analytic redundancy (RNLAR) technique, to sensor and actuator fault detection for input-affine nonlinear multivariable dynamic systems in the presence of model-plant-mismatch and process disturbance. The proposed RNLAR can be used to design primary residual vectors (PRV) for nonlinear systems to detect sensor fault that are completely insensitive to both the model-plant-mismatch and process disturbance. It is shown that the PRV for actuator fault cannot be made completely insensitive to these factors. In order to overcome this problem, a nonlinear PRV design method to detect actuator faults is proposed where the PRVs are highly sensitive to the actuator faults and less sensitive to model-plant-mismatch and process disturbance. The proposed robust fault detection methodology is applied to a WMR and the simulation results are presented to demonstrate the effectiveness of this new approach.
The goal of our research is to develop a high-level controller to provide reference trajectories automatically to the low-level controller of a rehabilitation robotic device. The high-level controller, which is the supervisory controller, is an event driven, asynchronous discrete event system (DES), described by a finite state automaton. Extensive simulations are performed using the supervisory controller for a rehabilitation task. The results demonstrate the feasibility of the proposed supervisory controller.
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