650 research outputs found
Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs
Deep neural networks that approximate nonlinear function-to-function
mappings, i.e., operators, which are called DeepONet, have been demonstrated in
recent articles to be capable of encoding entire PDE control methodologies,
such as backstepping, so that, for each new functional coefficient of a PDE
plant, the backstepping gains are obtained through a simple function
evaluation. These initial results have been limited to single PDEs from a given
class, approximating the solutions of only single-PDE operators for the gain
kernels. In this paper we expand this framework to the approximation of
multiple (cascaded) nonlinear operators. Multiple operators arise in the
control of PDE systems from distinct PDE classes, such as the system in this
paper: a reaction-diffusion plant, which is a parabolic PDE, with input delay,
which is a hyperbolic PDE. The DeepONet-approximated nonlinear operator is a
cascade/composition of the operators defined by one hyperbolic PDE of the
Goursat form and one parabolic PDE on a rectangle, both of which are bilinear
in their input functions and not explicitly solvable. For the delay-compensated
PDE backstepping controller, which employs the learned control operator,
namely, the approximated gain kernel, we guarantee exponential stability in the
norm of the plant state and the norm of the input delay state.
Simulations illustrate the contributed theory
Model based fault diagnosis and prognosis of nonlinear systems
Rapid technological advances have led to more and more complex industrial systems with significantly higher risk of failures. Therefore, in this dissertation, a model-based fault diagnosis and prognosis framework has been developed for fast and reliable detection of faults and prediction of failures in nonlinear systems. In the first paper, a unified model-based fault diagnosis scheme capable of detecting both additive system faults and multiplicative actuator faults, as well as approximating the fault dynamics, performing fault type determination and time-to-failure determination, is designed. Stability of the observer and online approximator is guaranteed via an adaptive update law. Since outliers can degrade the performance of fault diagnostics, the second paper introduces an online neural network (NN) based outlier identification and removal scheme which is then combined with a fault detection scheme to enhance its performance. Outliers are detected based on the estimation error and a novel tuning law prevents the NN weights from being affected by outliers. In the third paper, in contrast to papers I and II, fault diagnosis of large-scale interconnected systems is investigated. A decentralized fault prognosis scheme is developed for such systems by using a network of local fault detectors (LFD) where each LFD only requires the local measurements. The online approximators in each LFD learn the unknown interconnection functions and the fault dynamics. Derivation of robust detection thresholds and detectability conditions are also included. The fourth paper extends the decentralized fault detection from paper III and develops an accommodation scheme for nonlinear continuous-time systems. By using both detection and accommodation online approximators, the control inputs are adjusted in order to minimize the fault effects. Finally in the fifth paper, the model-based fault diagnosis of distributed parameter systems (DPS) with parabolic PDE representation in continuous-time is discussed where a PDE-based observer is designed to perform fault detection as well as estimating the unavailable system states. An adaptive online approximator is incorporated in the observer to identify unknown fault parameters. Adaptive update law guarantees the convergence of estimations and allows determination of remaining useful life --Abstract, page iv
Neural Operators for Delay-Compensating Control of Hyperbolic PIDEs
The recently introduced DeepONet operator-learning framework for PDE control
is extended from the results for basic hyperbolic and parabolic PDEs to an
advanced hyperbolic class that involves delays on both the state and the system
output or input. The PDE backstepping design produces gain functions that are
outputs of a nonlinear operator, mapping functions on a spatial domain into
functions on a spatial domain, and where this gain-generating operator's inputs
are the PDE's coefficients. The operator is approximated with a DeepONet neural
network to a degree of accuracy that is provably arbitrarily tight. Once we
produce this approximation-theoretic result in infinite dimension, with it we
establish stability in closed loop under feedback that employs approximate
gains. In addition to supplying such results under full-state feedback, we also
develop DeepONet-approximated observers and output-feedback laws and prove
their own stabilizing properties under neural operator approximations. With
numerical simulations we illustrate the theoretical results and quantify the
numerical effort savings, which are of two orders of magnitude, thanks to
replacing the numerical PDE solving with the DeepONet
Logical Control Theory Applied to Mechanical Arms
Submitted to the Department of Electrical Engineering and Computer Science on January 19, 1979 in partial fulfillment of the requirements for the Degrees of Master of Science and Electrical Engineer.
This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.
Thesis supervisor:
Berthold K. P. Horn,
Associate Professor of Electrical Engineering and Computer ScienceA new control algorithm based upon Logical Control Theory is developed for mechanical manipulators. The controller uses discrete tesselations of state space and a finite set of fixed torques to regulate non-rehearsed movements in real time. Varying effective inertia, coupling between degrees of freedom, and fictional, gravitational and Coriolis forces are readily handled. A logical controller was implemented on a mini-computer for the MIT Scheinman Vicarm. The controller's performance compares favorably with that of controllers designed according to existing methodologies as used, for example, in the control of present day industrial manipulators.MIT Artificial Intelligence Laboratory
Department of Defense Advanced Research Projects Agenc
Numerical study of fluidic oscillators with compressible flow
Se estudiará el fllujo en el interior de osciladores fluídicos mediante el uso de un código abierto de Mecánica de Fluidos Computacional, prestando especial atención al comportamiento con flujo compresible.1. Documentación y estudio del estado del arte. 2. Aprendizaje de los conceptos básicos de la Mecánica de Fluidos Computacional. 3. Aprendizaje del software OpenFOAM. 4. Mallado del oscilador fluídico de referencia. 5. Lanzamiento de las simulaciones. 7. Extracción y análisis de resultados, comparándolos con los resultados obtenidos en simulaciones con flujo incompresible. 8. Conclusiones
Robust control strategies for unstable systems with input/output delays
Los sistemas con retardo temporal aparecen con frecuencia en el ámbito de la ingeniería, por ejemplo en transmisiones hidráulicas o mecánicas, procesos metalúrgicos o sistemas de control en red. Los retardos temporales han despertado el interés de los investigadores en el ámbito del control desde finales de los años 50. Se ha desarrollado una amplia gama de herramientas para el análisis de su estabilidad y prestaciones, especialmente durante las dos últimas décadas.
Esta tesis se centra en la estabilización de sistemas afectados por retardos temporales en la actuación y/o la medida. Concretamente, las contribuciones que aquí se incluyen tienen por objetivo mejorar las prestaciones de los controladores existentes en presencia de perturbaciones. Los retardos temporales degradan, inevitablemente, el desempeño de un bucle de control. No es de extrañar que el rechazo de perturbaciones haya sido motivo de estudio desde que emergieron los primeros controladores predictivos para sistemas con retardo. Las estrategias presentadas en esta tesis se basan en la combinación de controladores predictivos y observadores de perturbaciones. Estos últimos han sido aplicados con éxito para mejorar el rechazo de perturbaciones de controladores convencionales. Sin embargo, la aplicación de esta metodología a sistemas con retardo es poco frecuente en la literatura, la cual se investiga exhaustivamente en esta tesis.
Otro inconveniente de los controladores predictivos está relacionado con su implementación, que puede llevar a la inestabilidad si no se realiza cuidadosamente. Este fenómeno está relacionado con el hecho de que las leyes de control predictivas se expresan mediante una ecuación integral. En esta tesis se presenta una estructura de control alternativa que evita este problema, la cual utiliza un observador de dimensión infinita, gobernado por una ecuación en derivadas parciales de tipo hiperbólico.Time-delay systems are ubiquitous in many engineering applications, such as mechanical or fluid transmissions, metallurgical processes or networked control systems. Time-delay systems have attracted the interest of control researchers since the late 50's. A wide variety of tools for stability and performance analysis has been developed, specially over the past two decades.
This thesis is focused on the problem of stabilizing systems that are affected by delays on the actuator and/or sensing paths. More specifically, the contributions herein reported aim at improving the performance of existing controllers in the presence of external disturbances. Time delays unavoidably degrade the control loop performance. Disturbance rejection has been a matter of concern since the first predictive controllers for time-delay systems emerged. The key idea of the strategies presented in this thesis is the combination of predictive controllers and disturbance observers. The latter have been successfully applied to improve the disturbance rejection capabilities of conventional controllers. However, the application of this methodology to time-delay systems is rarely found in the literature. This combination is extensively investigated in this thesis.
Another handicap of predictive controllers has to do with their implementation, which can induce instability if not done carefully. This issue is related to the fact that predictive control laws take the form of integral equations. An alternative control structure that avoids this problem is also reported in this thesis, which employs an infinite-dimensional observer, governed by a hyperbolic partial differential equation.Sanz Díaz, R. (2018). Robust control strategies for unstable systems with input/output delays [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/111830TESI
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