FAULT TOLERANT CONTROL BASED
ON DETECTION, DIAGNOSIS AND
PROGNOSIS
A thesis submitted to the University of
Manchester for the degree of
Doctor of Philosophy
In the Faculty of Engineering and Physical
Sciences
2012
By
AHMAD HUSSAIN TAQI AL-BAYATI
Abstract
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 nonGaussian 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 H∞ 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