1,673 research outputs found
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
Observer-based fault-tolerant control for a class of networked control systems with transfer delays
Abstract not availableZehui Mao, Bin Jiang, Peng Sh
Gain-Scheduled Fault Detection Filter For Discrete-time LPV Systems
The present work investigates a fault detection problem using a gain-scheduled filter for discrete-time Linear Parameter Varying systems. We assume that we cannot directly measure the scheduling parameter but, instead, it is estimated. On the one hand, this assumption imposes the challenge that the fault detection filter should perform properly even when using an inexact parameter. On the other, it avoids the burden associated with designing a complex estimation process for this parameter. We propose three design approaches: the , , and mixed gain-scheduled Fault Detection Filters designed via Linear Matrix Inequalities. We also provide numerical simulations to illustrate the applicability and performance of the proposed novel methods
A study on fault diagnosis in nonlinear dynamic systems with uncertainties
In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The
objective of this work is to establish a framework, in which not only
model-based but also data-driven and machine learning based fault diagnosis
strategies can be uniformly handled. Instead of the well-established
input-output and the associated state space models, stable image and kernel
representations are adopted in our work as the basic process model forms. Based
on it, the nominal system dynamics can then be modelled as a lower-dimensional
manifold embedded in the process data space. To achieve a reliable fault
detection as a classification problem, projection technique is a capable tool.
For nonlinear dynamic systems, we propose to construct projection systems in
the well-established framework of Hamiltonian systems and by means of the
normalised image and kernel representations. For nonlinear dynamic systems,
process data form a non-Euclidean space. Consequently, the norm-based distance
defined in Hilbert space is not suitable to measure the distance from a data
vector to the manifold of the nominal dynamics. To deal with this issue, we
propose to use a Bregman divergence, a measure of difference between two points
in a space, as a solution. Moreover, for our purpose of achieving a
performance-oriented fault detection, the Bregman divergences adopted in our
work are defined by Hamiltonian functions. This scheme not only enables to
realise the performance-oriented fault detection, but also uncovers the
information geometric aspect of our work. The last part of our work is devoted
to the kernel representation based fault detection and uncertainty estimation
that can be equivalently used for fault estimation. It is demonstrated that the
projection onto the manifold of uncertainty data, together with the
correspondingly defined Bregman divergence, is also capable for fault
detection
Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects
In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults
Fault detection and isolation using viability theory and interval observers
This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the viability constraints characterisation of dynamic evolutions of complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behaviour by using simple sets that approximate the exact set of possible behaviour (in the parameter or state space). In this paper, FDI is based on checking for an inconsistency between the measured and predicted behaviours using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approachPeer ReviewedPostprint (author's final draft
Fault Tolerance for Spacecraft Attitude Management
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83657/1/AIAA-2010-8301-426.pd
NASA Space Engineering Research Center for VLSI System Design
This annual report outlines the activities of the past year at the NASA SERC on VLSI Design. Highlights for this year include the following: a significant breakthrough was achieved in utilizing commercial IC foundries for producing flight electronics; the first two flight qualified chips were designed, fabricated, and tested and are now being delivered into NASA flight systems; and a new technology transfer mechanism has been established to transfer VLSI advances into NASA and commercial systems
Adaptive Fault-Tolerant Tracking Control of Nonaffine Nonlinear Systems with Actuator Failure
This paper proposes an adaptive fault-tolerant control scheme for nonaffine nonlinear systems. A model approximation method which is a solution that bridges the gap between affine and nonaffine control systems is developed firstly. A joint estimation approach is based on unscented Kalman filter, in which both failure parameters and states are simultaneously estimated by means of the argument state vector composed of the unknown faults and states. Then, stability analysis is given for the closed-loop system. Finally, the proposed approach is verified using a three-degree-of-freedom simulation of a typical fighter aircraft and the significantly improved system response demonstrates the practical potential of the theoretic results obtained
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