146 research outputs found

    Design of nonlinear controllers through the virtual reference method and regularization

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    This work proposes a new extension for the nonlinear formulation of the data-driven control method known as the Nonlinear Virtual Reference Feedback Tuning. When the process to be controlled contains a significant quantity of noise, the standard Nonlinear VRFT approach – that uses the Least Squares method – yield estimates with poor statistical properties. These properties may lead the control system to undesirable closed loop performances and even instability. With the intention to improve these statistical properties and controller sparsity and hence, the system’s closed loop performance, this work proposes the use of ℓ1 regularization on the nonlinear formulation of the VRFT method. Regularization is a component that has been extensively employed and researched in the Machine Learning and System Identification communities lately. Furthermore, this technique is appropriate to reduce the variance in the estimates. A detailed analysis of the noise effect on the estimate is made for the Nonlinear VRFT method. Finally, three different regularization methods, the third one proposed in this work, are compared to the standard Nonlinear VRFT.Este trabalho propõe uma nova extensão para a formulação não linear do método de controle orientado por dados conhecido como Método da Referência Virtual Não Linear, ou Nonlinear Virtual Reference Feedback Tuning – denominado aqui somente como VRFT. Quando o processo a ser controlado contém uma quantidade significativa de ruído, a abordagem padrão do VRFT – que usa o método dos Mínimos Quadrados – fornece estimativas com propriedades estatísticas pobres. Essas propriedades podem levar o sistema de controle a desempenhos indesejáveis em malha fechada. Com a intenção de melhorar essas propriedades estatística, identificar um controlador simples em quantidade de parâmetros e melhorar o desempenho em malha fechada do sistema, este trabalho propõe o uso da regularização ℓ1 na formulação não linear do método VRFT. A regularização é uma técnica que tem sido amplamente empregada e pesquisada nas comunidades de Aprendizagem de Máquina e Identificação de Sistemas ultimamente. Além disso, esta técnica é apropriada para reduzir a variância das estimativas. Uma análise detalhada do efeito do ruído na estimativa é feita para o método VRFT não linear. Finalmente, três diferentes métodos de regularização, o terceiro proposto neste trabalho, são comparados com o VRFT

    Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

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    Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd

    Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

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    In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a ϵ\epsilon-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

    Networked Control System Design and Parameter Estimation

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    Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6). Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of H₂ and H∞ norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed H₂/H∞ control problem is solved under the framework of LMIs. To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The l₂-l∞ filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. Finally, several open problems are listed as the future research directions

    Advanced Control of Piezoelectric Actuators.

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    168 p.A lo largo de las últimas décadas, la ingeniería de precisión ha tenido un papel importante como tecnología puntera donde la tendencia a la reducción de tamaño de las herramientas industriales ha sido clave. Los procesos industriales comenzaron a demandar precisión en el rango de nanómetros a micrómetros. Pese a que los actuadores convencionales no pueden reducirse lo suficiente ni lograr tal exactitud, los actuadores piezoeléctricos son una tecnología innovadora en este campo y su rendimiento aún está en estudio en la comunidad científica. Los actuadores piezoeléctricos se usan comúnmente en micro y nanomecatrónica para aplicaciones de posicionamiento debido a su alta resolución y fuerza de actuación (pueden llegar a soportar fuerzas de hasta 100 Newtons) en comparación con su tamaño. Todas estas características también se pueden combinar con una actuación rápida y rigidez, según los requisitos de la aplicación. Por lo tanto, con estas características, los actuadores piezoeléctricos pueden ser utilizados en una amplia variedad de aplicaciones industriales. Los efectos negativos, como la fluencia, vibraciones y la histéresis, se estudian comúnmente para mejorar el rendimiento cuando se requiere una alta precisión. Uno de los efectos que más reduce el rendimiento de los PEA es la histéresis. Esto se produce especialmente cuando el actuador está en una aplicación de guiado, por lo que la histéresis puede inducir errores que pueden alcanzar un valor de hasta 22%. Este fenómeno no lineal se puede definir como un efecto generado por la combinación de acciones mecánicas y eléctricas que depende de estados previos. La histéresis se puede reducir principalmente mediante dos estrategias: rediseño de materiales o algoritmos de control tipo feedback. El rediseño de material comprende varias desventajas por lo que el motivo principal de esta tesis está enfocado al diseño de algoritmos de control para reducir la histéresis. El objetivo principal de esta tesis es el desarrollo de estrategias de control avanzadas que puedan mejorar la precisión de seguimiento de los actuadores piezoeléctricos comerciale

    Nonlinear identification and control of muscle relaxant dynamics.

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    The work reported in this thesis comprised two major parts which are: 1) Off-line nonlinear identification of muscle relaxant dynamics, 2) Simulation-based design of a variety of controllers (ranging from classical PID to nonlinear self-tuners) for the closed-loop control of muscle relaxation. Relaxant drugs namely, Vecuronium and Atracurium are considered throughout. Off-line identification studies, using two special nonlinear identification packages (Nonlinear Identification package and Nonlinear Orthogonal Identification package), were carried out to determine nonlinear difference equation models (NARMAX) that best fit (in the least squares sense) recorded data from trials on humans and dogs for each drug. After validation, these models were assumed to represent, in a nonlinear polynomial form, the muscle relaxant drugs pharmacology. Two different approaches were explored for determining the physiological structure of both relaxant drugs: a) The drug model to comprise a pharmacokinetics part to represent the drug distribution, and pharmacodynamics which are often modelled by using the well known Hill equation. b) A cross-correlation approach based on Volterra series. With the relaxant dynamics structure thus fixed, the work proceeded to the control phase. Simple three-term PID controllers were first designed with their parameters being optimised, off-line, using the Simplex method. The non-adaptive nature of this class of controllers makes their robustness open to question when the system parameters for which they have been optimised change. Hence adaptive controllers in the form of linear and nonlinear generalised minimum variance, self-tuners, generalised predictive and nonlinear k-step ahead predictive controllers were also considered. All these latter control approaches are shown to be satisfactory, in terms of transient and steady state performance

    Dual Bypass Gas Metal Arc Welding Process and Control

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    GMAW (Gas Metal Arc Welding) is one of the most important arc welding processes being adopted in modern manufacturing industry due to its advantages in productivity, energy efficiency and automation. By monitoring and improving some of the important properties of GMAW such as production rate, metal transfer and base metal heat input, researchers could bring the process efficiency and stability to a new level. In recent years, some innovative modifications of GMAW such as Twins, Tandem and laser-MIG hybrid welding have been adopted into many industrial applications for better productivity. In this dissertation, a novel GMAW called DB-GMAW (Dual Bypass Gas Metal Arc Welding) using two GTAW torches and one GMAW torch to construct a welding system, is proposed and developed. In DB-GMAW, two GTAW torches perform the bypass system which decouples the total welding current into base metal current and bypass current after the melt down of filler wire. Compared to conventional GMAW, DB-GMAW has many advantages in droplet formation, base metal heat input and penetration achievement due to its unique characteristics in welding arc and current flow. In the first place of the research, experimental system of DB-GMAW is constructed. Then, sufficient experiments under different parameters are performed to provide us a good understanding of the behaviors and characteristics of this novel GMAW process. Observation about metal transfer formation and base metal heat input is studied to verify its theoretical analysis. Full penetration of work piece via DB-GMAW is achieved based on a series of parameter testing experiments. Moreover, image processing techniques are applied to DB-GMAW to monitor the welding process and construct a feedback system for control. Considering the importance of maintaining stable full penetration during many welding applications, a nonlinear model of DB-GMAW full penetration is developed in this dissertation. To do that, we use machine vision techniques to monitor the welding profile of the work piece. A control algorithm based on the nonlinear model using adaptive control technique is also designed. The achievement of this dissertation provides a fundamental knowledge of a novel welding process: DB-GMAW, and a good guidance for further studies about DBGMAW
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