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Fault estimation and accommodation for virtual sensor bias fault in image-based visual servoing using particle filter
This study develops a fault estimation and accommodation scheme for the image-based visual servoing (IBVS) system to eliminate the effects of the faults due to the image feature extraction task, which is named as bias virtual sensor fault. First, a bias virtual sensor fault in visual servoing is declared. Then, fault diagnosis (FD), which includes fault detection, isolation and estimation, is designed based on the means of particle filter (PF). Finally, a fault accommodation law is developed based on the information obtained from the fault estimation to compensate for the effects of the fault in the system. The proposed fault estimation and accommodation is verified through simulation and experimental studies, and the results show that the system can estimate and eliminate the unknown fault effects effectively
Robust fault-tolerant control for a class of second-order nonlinear systems using an adaptive third-order sliding mode control
Due to the robustness against the uncertainties, conventional sliding mode control (SMC) has been extensively developed for fault-tolerant control (FTC) system. However, the FTCs based on conventional SMC provide several disadvantages such as large transient state error, less robustness, and large chattering, that limit its application for real application. In order to enhance the performance, a novel adaptive third-order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed. Compared with other innovation approaches, the proposed controller has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence. The proposed method is then applied for an attitude control of a spacecraft and the results demonstrate the superior performance
Cooperative Control Reconfiguration in Networked Multi-Agent Systems
Development of a network of autonomous cooperating vehicles has attracted significant
attention during the past few years due to its broad range of applications in areas
such as autonomous underwater vehicles for exploring deep sea oceans, satellite formations
for space missions, and mobile robots in industrial sites where human involvement
is impossible or restricted, to name a few. Motivated by the stringent specifications
and requirements for depth, speed, position or attitude of the team and the possibility
of having unexpected actuators and sensors faults in missions for these vehicles have
led to the proposed research in this thesis on cooperative fault-tolerant control design of
autonomous networked vehicles.
First, a multi-agent system under a fixed and undirected network topology and subject
to actuator faults is studied. A reconfigurable control law is proposed and the so-called
distributed Hamilton-Jacobi-Bellman equations for the faulty agents are derived. Then,
the reconfigured controller gains are designed by solving these equations subject to the
faulty agent dynamics as well as the network structural constraints to ensure that the
agents can reach a consensus even in presence of a fault while simultaneously the team
performance index is minimized.
Next, a multi-agent network subject to simultaneous as well as subsequent actuator
faults and under directed fixed topology and subject to bounded energy disturbances is considered. An H∞ performance fault recovery control strategy is proposed that guarantees:
the state consensus errors remain bounded, the output of the faulty system behaves
exactly the same as that of the healthy system, and the specified H∞ performance bound
is guaranteed to be minimized. Towards this end, the reconfigured control law gains
are selected first by employing a geometric control approach where a set of controllers
guarantees that the output of the faulty agent imitates that of the healthy agent and the
consensus achievement objectives are satisfied. Then, the remaining degrees of freedom
in the selection of the control law gains are used to minimize the bound on a specified
H∞ performance index.
Then, control reconfiguration problem in a team subject to directed switching topology
networks as well as actuator faults and their severity estimation uncertainties is considered.
The consensus achievement of the faulty network is transformed into two stability
problems, in which one can be solved offline while the other should be solved online
and by utilizing information that each agent has received from the fault detection and
identification module. Using quadratic and convex hull Lyapunov functions the control
gains are designed and selected such that the team consensus achievement is guaranteed
while the upper bound of the team cost performance index is minimized.
Finally, a team of non-identical agents subject to actuator faults is considered. A
distributed output feedback control strategy is proposed which guarantees that agents
outputs’ follow the outputs of the exo-system and the agents states remains stable even
when agents are subject to different actuator faults