Robust Unknown Input Observer-Based Fast Adaptive Fault Estimation: Application to Mobile Robot

Robust Unknown Input Observer-Based Fast Adaptive Fault Estimation: Application to Mobile Robot

Olfa Hrizi, Boumedyen Boussaid, Ahmed Zouinkhi, M. Naceur Abdelkrim
DOI: 10.4018/978-1-4666-7248-2.ch016
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This chapter studies the problem of fault estimation using a fast adaptive fault diagnosis observer. Note that the advance of observer-based fault diagnosis is outlined and the idea of fault class estimation is introduced and studied. A new form of the estimator bloc considered for this purpose is an Unknown Input Observer (UIO). This observer is designed for an unknown input and fault free system, which is obtained by coordinate transformations of original systems with unknown inputs (disturbance) and faults. Stability of the adaptive estimation is provided by a Lyapunov function ending with solving the Linear Matrix Inequalities (LMI). Due to technological advances in the field of electronic devices, the family of robots is of particular interest. To overcome the drawback of robots' model responses when including a fault, a robust observer is adopted for a Pioneer robot to improve the fault estimation and thereafter to repair its trajectory.
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In the last several years, considerable attention has been focused on the emerging field of robotics. Therefore, many successful robotic manipulator designs have been introduced thanks to their good terrain adaptability. Many researchers are proposed different ways of control robot design, classified types and ensure performance of robots (Shigeo & Akio, 1990).

A large effort has been devoted by the scientific community especially to the field of mobile robot systems. In particular wheeled robots will be expected to provide many convenient and user friendly transport solutions for both people and objects. The importance of the wheeled mobile robots has long been recognized by the robotics research community, as shown by the numerous robotic competitions and research projects run worldwide in the last decades in the RoboCup federation site at 2008.

The class of unicycle type (mobile) robots, i.e. robots having some forward speed but zero instantaneous lateral motion, is frequently selected for designing and modeling robots. For example many of the robotic competition teams of the last decade selected those robots due to their simplicity and good maneuverability, allowing for example to follow complex trajectories (Pedro et al, Luis et al, 1998). At the same time research was conducted on controllability, feedback linearization and -stabilization (Canudas de Wit, 1997).

Key Terms in this Chapter

Actuator Fault: A failure is defined as a deviation of system parameters ( Blanke et al., 2003 ). An actuator fault is a type of failure affecting the system inputs. Due to abnormal operation or material aging, actuator faults may occur in the system. An actuator can be represented by additive and/or multiplicative faults. Actuator failures may drastically change the system behavior, resulting in degradation or even instability.

Fault Tolerance: Means to avoid service failures in the presence of faults. It is noteworthy that repair and fault tolerance are related concepts; ( Avizienis et al. 2004 ); the distinction between fault tolerance and maintenance is that maintenance involves the participation of an external agent, e.g., a repairman, test equipment, remote reloading of software. Furthermore, repair is part of fault removal (during the use phase), and fault forecasting usually considers repair situations. In fact, repair can be seen as a fault tolerance activity within a larger system that includes the system being repaired and the people and other systems that perform such repairs.

Unknown Input Observer (UIO): This observer is subsequently used for a robust fault detection scheme and also as an adaptive detection scheme for a certain class of actuator faults wherein the time instance and characteristics of an incipient actuator fault are detected.

Cooperative Diagnosis: Based on the communication between a numbers of interconnected systems.

Linear Matrix Inequalities (LMI): A formulation which gives an effective way to calculate the design parameters.

Fast Fault Estimation: A good strategy only using the measurable input and output vector. It is proposed to enhance the rapidity of fault estimation and ameliorate the system’s performances.

Fast Adaptive Fault Estimation (FAFE): Proposed to provide much better performances for the process. It is based on adaptive observer (or estimator) composed of a proportional term and an integral one to guarantee both satisfactory dynamical and steady state performances.

Stability: The ability of properties to converge to existence conditions. It is provided by a parameter-dependent Lyapunov function ( Rouche et al, 1977 ).

Rapidity: In this work, it means the rapidity of convergence to the detection threshold.

Disturbance: An unknown input included in the system’s model. It can be incertitude, noise or a nonlinearity term. It has a considerable affect in process’s performances. It is an event that occurs when the delivered service deviates from correct service. A service fails either because it does not comply with the functional specification, or because this specification did not adequately describe the system function.

Robustness: I.e., dependability with respect to external faults, which characterizes a system reaction to a specific class of faults. It is ability of a system to avoid service failures that are more frequent or more severe than is acceptable.

Mobile Robots: There are a set of processes working in the same cooperative network. They will be providing a better life not only to common people but especially to elderly and impaired.

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