Model-Based FDI Schemes For Robot Manipulators Using Soft Computing Techniques

While modern control methods were becoming widespread, in addition to demanded repeatability and accuracy specifications, reliability and detection and isolation of probable faults have become an obligation for automatic control systems. In the early 70’s, first studies were appeared on this subject. While the first studies on fault detection and isolation (FDI) were implemented for supervisory of chemical processes, following studies were extended to systems like air and spacecrafts, automobiles, nuclear reactors, turbines and HVACs with high reliability mandatories after especially aircraft accidents with high mortality. In 1991, with extending and increasing studies, IFAC SAFEPROCESS comittee was founded and in 1993, this comittee issued some definitions about fault types, fault detection and isolation, fault diagnosis and fault tolerant control (FTC) (Isermann & Ballé, 1997). Robots are accepted as an assistant subsystem or an individual part of a complex system in most applications. In addition to applications like serial product lines in which they can work harder, faster and with higher accuracy than humans, they are assigned to missions like waste treatment in nuclear reactors, data and sample collection, maintenance in space and underwater tasks which can be very risky for humans. As a consequence, a fault in one product line may cause a pause in all connected lines even in flexible automation systems or a developing and undetected fault may cause abortion of a whole space or underwater mission with big money costs, it may even cause harm to humans. With the increase of these events in real-life applications and with 90’s, studies on robot reliability and fault detection and diagnosis in robotics have become common. In addition to these studies, NASA and US Army issued some standards on robots and on the reliability and fault possibilities of robotparts (Cavallaro & Walker, 1994). This study is focused on model-based FDI schemes, how they can be applied to robot manipulators, how soft computing techniques can be used in these schemes and three different FDI schemes are proposed. Soft computing techniques which can overcome the difficulties of schemes using analytical methods for nonlinear systems are used as modelling, fault isolator and fault function approximator tools in the proposed schemes. In the following section, a literature overview on FDI for nonlinear systems and robot 7