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The measurement analysis and diagnosis problem is gaining increasing consideration worldwide in both theory and application. This is due to the growing demand for higher reliability in control systems, and hence the importance of having a monitoring system that detects any existing measurement errors, and indicates their location and significance in the control loop. The observer-based approach is one of the most popular techniques used for fault diagnosis in general and for the measurement error diagnosis problem in particular. Many standard observer-based techniques exist in the literature providing different solutions to both the theoretical and practical aspects of the problem for the Linear Time-Invariant (LTI) case (see (Frank, 1990; Willsky, 1976) for good surveys on this subject). The main idea behind the observerbased approach is to estimate the outputs of the system from the measurements by using either static gain observers in a deterministic framework (Zhong et al., 2003) or Kalman filters in a stochastic framework (Chen et al., 2003). The output estimation error is then used as the residual signal, which can be analyzed further to obtain an accurate estimation of the measurement errors which affect the control system. Unlike the LTI case, however, the nonlinear problem lacks a universal approach and is currently an active area of research (see (Adjallah et al., 1994; Garcia & Frank, 1997; Hammouri et al., 1999; Kabore & Wang, 2001; Vemuri, 2001; Wang et al., 1997; Yu & Shields, 1996) for important results in this direction). The main obstacle in the solution of the observer-based nonlinear fault detection problem is the lack of a universal approach for nonlinear observer synthesis. Robot manipulators, characterized by largely nonlinear dynamics, are no exception to this dilemma and therefore need a unified framework for measurement error detection and diagnosis. The well known Euler-Lagrange model of a robot manipulator is as follows (Sciavicco & Sicliano, 1989):

Measurement Analysis and Diagnosis for Robot Manipulators Using Advanced Nonlinear Control Techniques