This thesis presents an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the definition of a set of fault indicators, which are appropriate for monitoring purposes, the availability of real-time process measurements, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. The incorporation of particle-filtering (PF) techniques in the proposed scheme not only allows for the implementation of real time algorithms, but also provides a solid theoretical framework to handle the problem of fault detection and isolation (FDI), fault identification, and failure prognosis. Founded on the concept of sequential importance sampling (SIS) and Bayesian theory, PF approximates the conditional state probability distribution by a swarm of points called particles and a set of weights representing discrete probability masses. Particles can be easily generated and recursively updated in real time, given a nonlinear process dynamic model and a measurement model that relates the states of the system with the observed fault indicators. Two autonomous modules have been considered in this research. On one hand, the fault diagnosis module uses a hybrid state-space model of the plant and a particle-filtering algorithm to (1) calculate the probability of any given fault condition in real time, (2) estimate the probability density function (pdf) of the continuous-valued states in the monitored system, and (3) provide information about type I and type II detection errors, as well as other critical statistics. Among the advantages offered by this diagnosis approach is the fact that the pdf state estimate may be used as the initial condition in prognostic modules after a particular fault mode is isolated, hence allowing swift transitions between FDI and prognostic routines. The failure prognosis module, on the other hand, computes (in real time) the pdf of the remaining useful life (RUL) of the faulty subsystem using a particle-filtering-based algorithm. This algorithm consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters) and predicts the evolution in time of the fault indicator pdf. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, 95% confidence intervals, and other hypothesis tests for the failure condition under study. Finally, inner and outer correction loops (learning schemes) are used to periodically improve the parameters that characterize the performance of FDI and/or prognosis algorithms. Illustrative theoretical examples and data from a seeded fault test for a UH-60 planetary carrier plate are used to validate all proposed approaches. Contributions of this research include: (1) the establishment of a general methodology for real time FDI and failure prognosis in nonlinear processes with unknown model parameters, (2) the definition of appropriate procedures to generate dependable statistics about fault conditions, and (3) a description of specific ways to utilize information from real time measurements to improve the precision and accuracy of the predictions for the state probability density function (pdf).

The dissertation addresses direct adaptive control frameworks for Lyapunov stabilization of the MIMO nonlinear uncertain systems for both uncertain discrete-time and continuous-time systems. For system theory, the development of continuous-time theory always comes along with its discrete-time counterpart. However, for direct adaptive control frameworks we find relative few Lyapunov-based results published, which is mainly due to difficulty to find feasible Lyapunov candidates and to prove negative definiteness of the Lyapunov difference. Furthermore, digital computer is widely used in all fields. Most of time, we have to deal with the direct source of discrete-time signals, even the discrete-time signals arise from continuous-time settings as results of measurement or data collection process. These motivate our study in this field. For discrete-time systems, we have investigated the results with trajectory dependent hypothesis, where the Lyapunov candidate function V combines the information from the current state k and one step ahead k-1 along the track x(k), for k≥0. The proposed frameworks guarantee partial stability of the closed-loop systems, such that the feedback gains stabilize the closed-loop system without the knowledge of the system parameters. In addition, our results show that the adaptive feedback laws can be characterized by Kronecker calculus. Later, we release this trajectory dependent hypothesis for normal discrete-time nonlinear systems. At the same time, the continuous-time cases are also studied when system with matched disturbances, where the disturbances can be characterized by known continuous function matrix and unknown parameters. Here, the trajectory dependent Lyapunov candidates (tdLC), so long as the time step |t(k)-t(k-1) | ≤ £_ and the corresponding track |x(k)-x(k-1)| ≤ £` are sufficiently small, only exist in discrete-time case. In addition, we have extended the above control designs to systems with exogenous disturbances and £d2 disturbances. Finally, we develop a robust direct adaptive control framework for linear uncertain MIMO systems under the variance of unknow system matrix from given stable solution is bounded, that is |A-Ac| ¡Ý |B Kg| ≤ |£GA|. In general, through Lyapunov-based design we can obtain the global solutions and direct adaptive control design can simultaneously achieve parameter estimation and closed-loop stability.

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Thesis (M.S.)--University of North Carolina at Wilmington, 2003. / Includes bibliographical references (leaf : [37]).

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The author received the Ph.D. degree in Automatic Control from the Saint-Petersburg State Electrical Engineering University (Russia) in 2001, and the Dr.Sc. degree in Automatic control in 2006 from Institute for Problems of Mechanical Engineering RAS (Saint-Petersburg, Russia). From 2000 to 2009 he was research fellow of the Institute for Problems of Mechanical Engineering RAS, Control of Complex Systems Laboratory. From 2006 to 2011 he was working with the LSS (Supelec, France), the Montefiore Institute (University of Liege, Belgium) and the Automatic control group at IMS lab (University of Bordeaux I, France). Since 2011 he joined the Non-A team at INRIA-LNE centre in Lille, France. He is a member of the IFAC TCs on Adaptive and Learning Systems and Control for Society. He is Senior member of IEEE. His main research interests include nonlinear oscillation analysis, observation and control, switched and nonlinear system stability. During his scientific carrier he published over 140 papers, books and technical reports. The subject of the dissertation deals with the main research direction of the author: analysis, estimation and control in nonlinear oscillating systems. It is an emerging area of research touching many applicative domains. This field strives for new estimation and control algorithms since frequently, due to peculiarities of this type of systems, the conventional approaches do not provide solutions with a satisfactory performance. Some control and estimation solutions for oscillating systems proposed by the author are given in the thesis. The hybrid or/and supervisory systems method is selected as the basement of design of new tools for analysis, observation and control of nonlinear oscillations. The first chapter of the thesis deals with presentation of scientific background and experience of the author. In the second chapter an approach to analysis of existence of oscillations is presented, an adaptive control algorithm for bifurcation control is briefly described, and an approach for entrainment of periodical systems based on PRC is introduced. In the third chapter the main planned future directions of research of the author are given. Appendices contain the full list of publications and the texts of four selected papers.

Recent advances in sensor and microcomputer technology and in control and aeroydynamics theories has made small unmanned aerial vehicles a reality. The small size, low cost and manoueverbility of these systems has positioned them to be potential solutions in a large class of applications. However, the small size of these vehicles pose significant challenges. The small sensors used on these systems are much noisier than their larger counterparts.The compact structure of these vehicles also makes them more vulnerable to environmental effects. This work develops several different control strategies for two sUAV platforms and provides the rationale for judging each of the controllers based on a derivation of the dynamics, simulation studies and experimental results where possible. First, the coaxial helicopter platform is considered. This sUAV’s dual rotor system (along with its stabilizer bar technology) provides the ideal platform for safe, stable flight in a compact form factor. However, the inherent stability of the vehicle is achieved at the cost of weaker control authority and therefore an inability to achieve aggressive trajectories especially when faced with heavy wind disturbances. Three different linear control strategies are derived for this platform. PID, LQR and H∞ methods are tested in simulation studies. While the PID method is simple and intuitive, the LQR method is better at handling the decoupling required in the system. However the frequency domain design of the H∞ control method is better at suppressing disturbances and tracking more aggressive trajectories. The dynamics of the quadrotor are much faster than those of the coaxial helicopter. In the quadrotor, four independent fixed pitch rotors provide the required thrust. Differences between each of the rotors creates moments in the roll, pitch and yaw directions. This system greatly simplifies the mechanical complexity of the UAV, making quadrotors cheaper to maintain and more accessible. The quadrotor dynamics are derived in this work. Due to the lack of any mechanical stabilization system, these quadrotor dynamics are not inherently damped around hover. As such, the focus of the controller development is on using nonlinear techniques. Linear quadratic regulation methods are derived and shown to be inadequate when used in zones moderately outside hover. Within nonlinear methods, feedback linearization techniques are developed for the quadrotor using an inner/outer loop decoupling structure that avoids more complex variants of the feedback linearization methodology. Most nonlinear control methods (including feedback linearization) assume perfect knowledge of vehicle parameters. In this regard, simulation studies show that when this assumption is violated the results of the flight significantly deteriorate for quadrotors flying using the feedback linearization method. With this in mind, an adaptation law is devised around the nonlinear control method that actively modifies the plant parameters in an effort to drive tracking errors to zero. In simple cases with sufficiently rich trajectory requirements the parameters are able to adapt to the correct values (as verified by simulation studies). It can also adapt to changing parameters in flight to ensure that vehicle stability and controller performance is not compromised. However, the direct adaptive control method devised in this work has the added benefit of being able to modify plant parameters to suppress the effects of external disturbances as well. This is clearly shown when wind disturbances are applied to the quadrotor simulations. Finally, the nonlinear quadrotor controllers devised above are tested on a custom built quadrotor and autopilot platform. While the custom quadrotor is able to fly using the standard control methods, the specific controllers devised here are tested on a test bench that constrains the movement of the vehicle. The results of the tests show that the controller is able to sufficiently change the necessary parameter to ensure effective tracking in the presence of unmodelled disturbances and measurement error.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 83-85).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 131-143). Also available in electronic version.