374 research outputs found
Fault estimation and fault-tolerant control for discrete-time dynamic systems
In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques
A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters
In this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H8 sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.Peer ReviewedPostprint (author's final draft
Observer-based H∞ control of networked systems with stochastic communication protocol: The finite-horizon case
This paper is concerned with the H ∞ control problem for a class of linear time-varying networked control systems (NCSs) with stochastic communication protocol (SCP). The sensor-to-controller network (the controller-to-actuator network) is considered where only one sensor (one actuator) obtains access to the communication network at each transmission instant. The SCP is applied to determine which sensor (actuator) should be given the access to the network at a certain instant. The aim of the problem addressed is to design an observer-based controller such that the H ∞ performance of the closed-loop system is guaranteed over a given finite horizon. For the purpose of simplifying the NCS model, a new Markov chain is constructed to model the SCP scheduling of communication networks. Then, both the methods of stochastic analysis and completing squares are utilized to establish the sufficient conditions for the existence of the desired controller. The controller parameters are characterized by solving two coupled backward recursive Riccati difference equations subject to the scheduled SCP. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme
Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries
Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods
Fault detection and isolation in a networked multi-vehicle unmanned system
Recent years have witnessed a strong interest and intensive research activities in the area of networks of autonomous unmanned vehicles such as spacecraft formation flight, unmanned aerial vehicles, autonomous underwater vehicles, automated highway systems and multiple mobile robots. The envisaged networked architecture can provide surpassing performance capabilities and enhanced reliability; however, it requires extending the traditional theories of control, estimation and Fault Detection and Isolation (FDI). One of the many challenges for these systems is development of autonomous cooperative control which can maintain the group behavior and mission performance in the presence of undesirable events such as failures in the vehicles. In order to achieve this goal, the team should have the capability to detect and isolate vehicles faults and reconfigure the cooperative control algorithms to compensate for them. This dissertation deals with the design and development of fault detection and isolation algorithms for a network of unmanned vehicles. Addressing this problem is the main step towards the design of autonomous fault tolerant cooperative control of network of unmanned systems. We first formulate the FDI problem by considering ideal communication channels among the vehicles and solve this problem corresponding to three different architectures, namely centralized, decentralized, and semi-decentralized. The necessary and sufficient solvability conditions for each architecture are also derived based on geometric FDI approach. The effects of large environmental disturbances are subsequently taken into account in the design of FDI algorithms and robust hybrid FDI schemes for both linear and nonlinear systems are developed. Our proposed robust FDI algorithms are applied to a network of unmanned vehicles as well as Almost-Lighter-Than-Air-Vehicle (ALTAV). The effects of communication channels on fault detection and isolation performance are then investigated. A packet erasure channel model is considered for incorporating stochastic packet dropout of communication channels. Combining vehicle dynamics and communication links yields a discrete-time Markovian Jump System (MJS) mathematical model representation. This motivates development of a geometric FDI framework for both discrete-time and continuous-time Markovian jump systems. Our proposed FDI algorithm is then applied to a formation flight of satellites and a Vertical Take-Off and Landing (VTOL) helicopter problem. Finally, we investigate the problem of fault detection and isolation for time-delay systems as well as linear impulsive systems. The main motivation behind considering these two problems is that our developed geometric framework for Markovian jump systems can readily be applied to other class of systems. Broad classes of time-delay systems, namely, retarded, neutral, distributed and stochastic time-delay systems are investigated in this dissertation and a robust FDI algorithm is developed for each class of these systems. Moreover, it is shown that our proposed FDI algorithms for retarded and stochastic time-delay systems can potentially be applied in an integrated design of FDI/controller for a network of unmanned vehicles. Necessary and sufficient conditions for solvability of the fundamental problem of residual generation for linear impulsive systems are derived to conclude this dissertation
Advanced flight control system study
The architecture, requirements, and system elements of an ultrareliable, advanced flight control system are described. The basic criteria are functional reliability of 10 to the minus 10 power/hour of flight and only 6 month scheduled maintenance. A distributed system architecture is described, including a multiplexed communication system, reliable bus controller, the use of skewed sensor arrays, and actuator interfaces. Test bed and flight evaluation program are proposed
Transmissibility operators for state and output estimation in nonlinear systems
Transmissibility operators are mathematical objects that characterize the relationship between
two subsets of responses of an underlying system. The importance of transmissiblity operators
comes from the fact that these operators are independent on the system inputs. This work
develops the transmissibility theory for nonlinear systems for the first time. The system
nonlinearities are assumed to be unknown, which gives a wide range of possible engineering
applications in different disciplines. Four different methods are developed to deal with these
nonlinearities. The first method is by re-constructing the system nonlinearities as independent
excitations on the system. This method handles the inherent unmodeled nonlinearities within
the system. The second method is by designing a transmissibility-based sliding mode control.
This method rejects unwanted nonlinearities such as system faults. The third method is by
constructing the system as time-variant linear system, and use recursive least squares to solve
it. This method can handle nonlinear systems with time-variant dynamics. The fourth method
is by designing a new robust estimation technique called high-gain transmissibility (HGT)
that is inspired by high-gain observers. This estimator has the ability to robustly estimate the
system states in a high-gain form.
The majority of modern fault detection, control systems, and robots localization depend on
mathematically estimating the system states and outputs. Transmissibility-based estimation
is incorporated in this work with these three theoretical applications. For fault detection,
transmissibility operators are used along a set of outputs to estimate the measurements of
another set of outputs. Then faults are detected by comparing the estimated and measured
outputs with each other. Control approaches use the transmissibility-based estimation to
construct the control signal, in which is injected back to the original system. Robots
localization fuses the transmissibility-based estimation with real-time sensor measurements
to minimize the error in determining the robot displacements.
These three theoretical applications are applied on four different systems. The first
system is Connected Autonomous Vehicles (CAV) platoons. A CAV platoon is a network
of connected autonomous vehicles that communicate together to move in a specific path
with the desired velocity. Transmissibilities are proposed along with the measurements from
sensors available in CAV platoons to identify transmissibility operators. This will be then
developed to mixed autonomous and human-driven vehicle platoons. Besides the wide range
of physical and cyber faults in such systems, this is also motivated by the fact that on-road
human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators
are used here to handle both cyber-physical faults as well as the human-drivers’ behaviour.
The platoon faults are then proposed to be mitigated using a transmissibility-based sliding
mode controller. Moreover, transmissibilities are integrated with Kalman filter to localize
CAV platoons while operating under non-Gaussian environment as unknown nonlinearities.
The second system is a multi-actuator micro positioning system that is used in the
semi-conductors industry. Transmissibility operators are applied on this system for fault
detection and fault-tolerant control. Fault detection is represented in applying the proposed
developments to actuator fault detection. Some of the most common actuator faults such as
actuator loss of effectiveness and fatigue crack in the connection hinges will be considered.
Transmissibilities then will be used for fault detection without knowledge of the dynamics of
the system or the excitation that acts on the system. Next, a transmissibility-based sliding
mode control will be implemented to mitigate common actuator faults in multi-actuator
systems.
The third system is flexible structures subjected to unknown and random excitations.
Structures used in applications subjected to turbulent fluid flow such as aerospace and
underwater applications are subjected to random excitations distributed along the structure.
Transmissibility operators are used for the purpose of structural fault detection and
localization during the system operation. The fourth system is robotic manipulators with
bounded nonlinearities and time-variant parameters. Both parameter variation and system
nonlinearities are considered to be unknown. Transmissibility operators are integrated with
Recursive Least Squares (RLS) to overcome the unknown variant parameters. RLS identifies
transmissibilities used in the structure of noncausal FIR (Finite Impulse Response) models.
While parameter variation can be treated as system nonlinearities, the RLS algorithm is used
to optimize what time-variant dynamics to include in the transmissibility operator and what
dynamics to push to the system nonlinearities over time. The identified transmissibilities
are then used for the purpose of fault detection in an experimental robotic arm with variant
picked mass
Fault detection filter and fault accommodation controller design for uncertain systems
Model-based Fault Detection (FD) and Fault Accommodation (FA) approaches have been applied in a variety of cases. We propose several techniques to include uncertainties in the design process. First, we focus on the design of the Fault Detection Filter (FDF) and Fault Accommodation Controller (FAC) for Markovian Jump Linear Systems (MJLS). The MJLS framework allows us to include the network behavior (packet loss) during the design of the FDF and FAC.Second, we propose an FDF and FAC design for the MJLS, under the assumption that the Markov chain mode is not directly accessible. Since we are using the MJLS framework to model the network behavior, the assumption that the network state is not instantly accessible is useful because from a practical standpoint this is a truthful assumption. Third, from the results presented for the MJLS framework, we provided follow-up results using Lur'e Markov Jump System. This is compelling since on some occasions the non-linear behavior cannot be ignored. Therefore, applying the Lur'e MJS framework allows us to consider the same assumptions from MJLS, but now adds the non-linearities. Fourth, we propose the design Gain-Scheduled FDF and FAC for Linear Parameter Varying (LPV) systems, under the assumption that the schedule parameter is not directly acquired. We assume that the schedule parameter is subject to additive noise. This imprecision is included during the design, using change of variables and multi-simplex techniques. Finally, throughout the thesis, we provide some numerical examples to illustrate the viability of the proposed approaches
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