Several key requirements would be met in an ideal fault-tolerant, adaptive spacecraft attitude controller, all centered around increasing tolerance to actuator non-idealities and other unknown quantities. This study seeks to better understand the application of lazy local learning to attitude control by characterizing the effect of bandwidth and the number of training points on the system’s performance. Using NASA’s 42 simulation framework, the experiment determined that in nominal operating scenarios, the actuator input/output relationship is linear. Once enough information is available to capture this linearity, additional training data and differing bandwidths did not significantly affect the system’s performance.
This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering.
Modern technological systems rely on sophisticated control functions to meet increased performance requirements. For such systems, Fault Tolerant Control Systems (FTCS) need to be developed. Active FTCS are dependent on a Fault Detection and Identification (FDI) process to monitor system performance and to detect and isolate faults in the systems. The main objective of this book is to study and to validate some important issues in real-time Active FTCS by means of theoretical analysis and simulation. Several models are presented to achieve this objective, taking into consideration practical aspects of the system to be controlled, performance deterioration in FDI algorithms, and limitations in reconfigurable control laws.
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
This book presents model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, test fault detectability and reveal redundancies that can be used to ensure fault tolerance. Case studies demonstrate the methods presented. The second edition includes new material on reconfigurable control, diagnosis of nonlinear systems, and remote diagnosis, plus new examples and updated bibliography.
High maintenance costs and unwanted disruptions in industrial processes are caused by machine fault problems. To overcome this problem, an appropriate fault tolerant control (FTC) technique has been presented; while to avoid deterioration of the quality of production or sudden stop, prognosis of parameters changes (PPC) techniques have been studied and developed. This PhD work has therefore been motivated by the need to develop fault tolerant control techniques and enhance prognosis of parameter changes techniques. To get interesting results from linear and nonlinear model-based control, the studies complied with different faults and noises such as additive fault, white noise and non-Gaussian noise. Moreover, a new nonlinear observer has been designed to diagnose the fault and eliminate the Coriolis and centrifugal torque effect of robotic systems. The FTC technique includes fault detection, diagnoses, states estimations and reconfiguration of controller techniques; whereas the PPC techniques consist of fault detection, diagnosis, parameter estimation, filters for parameter changes and analysis techniques of parameter deviation from desirable values. In this work, a design of two linear and nonlinear observers has been achieved to diagnose the fault and estimate the states of the plant. Furthermore, model-based controllers have been introduced as part of the FTC technique. Thus, a novel signal reference output regulation controller and controllers via performance have been introduced. Indeed, the reconfiguration of controllers is an interesting part of fault tolerant control, and is a major area of focus in this thesis. At a glance, this thesis introduces different reconfiguration techniques. One approach has been designed to update the control laws while a second approach has been implemented to update the adaptive controller. An intelligent algorithm without observer has been developed based on the outputs error and other errors like Cartesian errors of position of robot. However, the intelligent reconfiguration has been implemented without observers because some systems as robots may need more time in the fault diagnosis stage. Two proposed nonlinear observers and a prognosis algorithm have been designed based on filter type FSISF, which is based on a hybrid Sequential Important Sampling and Fuzzy system technique. They have been designed to be sensitive and present a novel approach of predication in the presence of faults and noise. The two new nonlinear observers; nonlinear observer based on filtered output NOFO and nonlinear observer based on filtered dynamic estimated states NOFS; have been based on FSISF, whereas the observers have been introduced via the filtered output and estimated dynamic states respectively. In this thesis, Chi distribution and Gaussian distribution for the SIS technique and their parameters (variance and mean) can be tuned and varied. Additionally, the Fuzzy part of FSISF uses a centre of gravity type of Fuzzy rule. The Fuzzy set of the Fuzzy part has been defined based on the type of filter. The Fuzzy set of filters for the observers implies the output of the Fuzzy at a previous time sample, the diagnosed fault and the residual of the observer and plant. Thus, the Fuzzy set of the prognosis filter consists of the diagnosed fault, a parameter change and a parameter change of the previous batch. However, the prognosis technique consists of steps: it first identifies the immeasurable parameters using the online recursive least square technique and then filters by a set of prognosis filter based on hybrid Fuzzy and Important Sampling algorithm PSFISF after an appropriate threshold. Moreover, to get an idea about the effect of data history, the mean and maximum values at same sample through size of batches processes. The data have been calculated and saved in a memory. Furthermore, two approaches have then been used to analyse the data: Kernel Density Function and Minimum Entropy of the parameter changes. Indeed, the highest changes have been clearly pointed at the highest fault using the proposed two approaches. Finally, all proposed algorithms have been tested and simulated where interesting results have been achieved.
In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. This paper illustrates analysis of a FTC system based on estimated fault parameter transient behavior which may include false fault detections during a short time interval. Using Lyapunov function analysis, the upper bound of an induced-L2 norm of the FTC system performance is calculated as a function of a fault detection time and the exponential decay rate of the Lyapunov function.
In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. This paper illustrates analysis of a FTC system based on estimated fault parameter transient behavior which may include false fault detections during a short time interval. Using Lyapunov function analysis, the upper bound of an induced-L2 norm of the FTC system performance is calculated as a function of a fault detection time and the exponential decay rate of the Lyapunov function. Belcastro, Christine (Technical Monitor) and Shin, Jong-Yeob Langley Research Center NASA/CR-2003-212682, NIA-2003-05
This thesis is concerned with fault estimation in Fault-Tolerant Control (FTC) and as such involves the joint problem of on-line estimation within an adaptive control system. The faults that are considered are significant uncertainties affecting the control variables of the process and their estimates are used in an adaptive control compensation mechanism. The approach taken involves the active FTC, as the faults can be considered as uncertainties affecting the control system. The engineering (application domain) challenges that are addressed are: (1) On-line model-based fault estimation and compensation as an FTC problem, for systems with large but bounded fault magnitudes and for which the faults can be considered as a special form of dynamic uncertainty. (2) Fault-tolerance in the distributed control of uncertain inter-connected systems The thesis also describes how challenge (1) can be used in the distributed control problem of challenge (2). The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control action and the second acting as an adaptive compensation for significant uncertainties and fault effects. The fault effects are a form of uncertainty which is considered too large for the application of passive FTC methods. The thesis considers several approaches to robust control and estimation: augmented state observer (ASO); sliding mode control (SMC); sliding mode fault estimation via Sliding Mode Observer (SMO); linear parameter-varying (LPV) control; two-level distributed control with learning coordination.
