Papers by Edgar Adan Jara Sanchez
Computación y Sistemas, 2010
Resumen en: This paper deals with the discrete-time nonlinear system identification via Recurrent... more Resumen en: This paper deals with the discrete-time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter ...
Journal of Applied Research and Technology, 2003
Fuzzy techniques have been successfully used in control in several fields, and engineers and rese... more Fuzzy techniques have been successfully used in control in several fields, and engineers and researchers are today considering fuzzy logic algorithms in order to implement intelligent functions in embedded systems. We have started to develop a set of teaching tools to support our courses on intelligent control. Low cost implementations of didactic systems are particularly important in developing countries. In this paper we present the implementation of a minimal PD fuzzy four-rule algorithm in a lowcost 8-bit microcontroller, using a fuzzy logic software development system. On this ground we constructed a stand-alone fuzzy controller for a didactic liquid level system. We describe the methodology we followed, and present simulation and real time results of this controller.
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009
This paper presents a discrete-time decentralized control scheme for identification and trajector... more This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discretetime control law is derived, which combines discrete-time block control and sliding modes techniques. The neural network learning is performed online by Kalman filtering. A controller is designed for each joint, using only local angular position and velocity measurements. These simple local joint controllers allow trajectory tracking with reduced computations. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.
This paper presents a discrete-time neural observer for nonlinear systems, whose mathematical mod... more This paper presents a discrete-time neural observer for nonlinear systems, whose mathematical model is assumed to be unknown. The observer is based on a recurrent high order neural network (RHONN), which is trained on-line with an extended Kalman filter (EKF)-based algorithm. The respective stability analysis based on the Lyapunov approach is included. The neural observer is tested by application to an immunological interaction model for HIV. The observer estimates the non-measured number of infected CD4+T cells in the blood torrent, the measured number of non-infected CD4+T cells and the measured concentration of viral load. The observer performance is illustrated via simulations.
Revista Iberoamericana de Automática e Informática Industrial RIAI, 2008
Resumen: En este art´ culo, los autores presentan un nuevo enfoque de s´ ntesis para entrenar mem... more Resumen: En este art´ culo, los autores presentan un nuevo enfoque de s´ ntesis para entrenar memorias asociativas implementadas con redes neuronales recurrentes. Los pesos de la red recurrente se determinan como la soluciónóptima de la combinación lineal de vectores soporte. El algoritmo de entrenamiento propuesto maximiza el margen entre los patrones de entrenamiento y la super cie de decisión. El problema de diseño considera: 1) la obtención de los pesos por medio del algoritmo de hiperplanoóptimo utilizado para máquinas de vector soporte y 2) la obtención de las condiciones para reducir el número total de memorias espurias. El nuevo algoritmo desarrollado se utiliza para diseñar una memoria asociativa que diagnostique fallas en centrales termoeléctricas.
Proceedings of the International Joint Conference on Neural Networks, 2008
This paper presents a recurrent neural observer to estimate substrate and biomass concentrations ... more This paper presents a recurrent neural observer to estimate substrate and biomass concentrations in an activated sludge wastewater treatment. The observer is based on a discretetime high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. This observer is then associated with a hybrid intelligent system to control the substrate/biomass concentration ratio. The neural observer performance is illustrated via simulations.
First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)
The problem of the optimal state estimation is solved for the system described by the continuous,... more The problem of the optimal state estimation is solved for the system described by the continuous, linear, n-dimensional ordinary differential equation with multiplicative and additive Wiener noises. The obtained solution essentially relies on the recently developed optimal filtering theory for Itô-Volterra systems.
Revista Iberoamericana de Automática e Informática Industrial RIAI, 2014
En este trabajo se presenta un análisis dinámico de un reactor anaeróbico a escala laboratorio, e... more En este trabajo se presenta un análisis dinámico de un reactor anaeróbico a escala laboratorio, el cual es empleado para la obtención de biogás a partir del tratamiento de aguas residuales. El reactor utiliza un volumen de reacción de 5 L y es operado en modo continuo con un flujo de entrada de 0.5 L h-1. Utilizando análisis de polos y ceros, respuesta al escalón y retratos de fase, se estudian dos comportamientos hidrodinámicos de las poblaciones bacterianas: razón de dilución y filtro de biomasa; este análisis es realizado vía simulación. El objetivo principal es determinar el efecto de la inmovilización de bacterias en soportes sólidos, así como de variaciones de las condiciones de operación, sobre las propiedades del proceso: estabilidad, degradación de sustratos, producción de biogás y límites de las condiciones de operación. Con los resultados obtenidos se pretende establecer estrategias de control que permitan mejorar el desempeño de este tipo de procesos.
International journal of neural systems, 2010
This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems,... more This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. This work includes the stability proof of the estimation error on the basis of the Lyapunov approach; to illustrate the applicability, simulation results for a nonlinear oscillator are included.
International journal of neural systems, 2010
In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed... more In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.
Studies in Computational Intelligence, 2008
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
IEEE Conference on Decision and Control and European Control Conference, 2011
In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positi... more In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positive systems is proposed. The scheme is developed for MIMO (multi-input, multi-output) ane systems. This approach is adapted for glycemic control of type 1 diabetes mellitus (T1DM) patients. The control law calculates the insulin delivery rate in order to prevent hyperglycemia levels. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an ane form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results illustrate the aplicability of the control law in biological processes.
Lecture Notes in Control and Information Sciences, 2004
This chapter presents an application of neural networks to chaos synchronization. The two main me... more This chapter presents an application of neural networks to chaos synchronization. The two main methodologies, on which the approach is based, are recurrent neural networks and inverse optimal control for nonlinear systems. On the basis of the last technique, chaos is first produced by a stable recurrent neural network; an adaptive recurrent neural controller is then developed for chaos synchronization.
Proceedings of the 2011 American Control Conference, 2011
This paper designs the central finite-dimensional H ∞ filter for linear stochastic systems with i... more This paper designs the central finite-dimensional H ∞ filter for linear stochastic systems with integralquadratically bounded deterministic disturbances, that is suboptimal for a given threshold γ with respect to a modified Bolza-Meyer quadratic criterion including the attenuation control term with the opposite sign. The original H ∞ filtering problem for a linear stochastic system is reduced to the corresponding mean-square H 2 filtering problem, using the technique proposed in [1]. In the example, the designed filter is applied to estimation of the pitch and yaw angles of a two degrees of freedom (2DOF) helicopter.
Journal of the Franklin Institute, 2010
This article appeared in a journal published by Elsevier. The attached copy is furnished to the a... more This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright
Intelligent Automation & Soft Computing, 2011
Reliable corner detection is an important task in determining the shape of different regions with... more Reliable corner detection is an important task in determining the shape of different regions within an image. Real-life image data are always imprecise due to inherent uncertainties that may arise from the imaging process such as defocusing, illumination changes, noise, etc. Therefore, the localization and detection of corners has become a difficult task to accomplish under such imperfect situations. On the other hand, Fuzzy systems are well known for their efficient handling of impreciseness and incompleteness, which make them inherently suitable for modelling corner properties by means of a rule-based fuzzy system. The paper presents a corner detection algorithm which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional corner detectors and its performance is also tested over a number of benchmark images to illustrate the efficiency of the algorithm under uncertainty.
Intelligent Automation & Soft Computing, 2009
Color-segmentation is quite sensitive to changes in light intensity. Many algorithms do not toler... more Color-segmentation is quite sensitive to changes in light intensity. Many algorithms do not tolerate variations in color hue which correspond, in fact, to the same object. In this work an image segmentator algorithm based on Learning Vector Quantization (LVQ) networks is proposed and tested on a tracking application. In LVQ networks, neighboring neurons learn to recognize neighboring sections of the input space. Neighboring neurons would correspond to object regions illuminated in a different manner. The segmentator involves a LVQ network that operate directly on the image pixels and a decision function. This algorithm has been applied to spotting and tracking human faces, and have shown more robustness on illumination changes than other standard algorithms.
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2003
As a continuation of their previous published results, in this paper the authors propose a new me... more As a continuation of their previous published results, in this paper the authors propose a new methodology, for input-to-state stabilization of a dynamic neural network. This approach is developed on the basis of the recent introduced inverse optimal control technique for nonlinear control. An example illustrates the applicability of the proposed approach.
IEEE Transactions on Neural Networks, 1999
In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dyn... more In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation.
IEEE Transactions on Circuits and Systems I: Regular Papers, 2004
This paper derives some sufficient conditions for asymptotic stability of neural networks with co... more This paper derives some sufficient conditions for asymptotic stability of neural networks with constant or time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed to investigate the problem. It shows how some well-known results can be refined and generalized in a straightforward manner. For the case of constant time delays, the stability criteria are delay-independent; for the case of time-varying delays, the stability criteria are delay-dependent. The results obtained in this paper are less conservative than the ones reported so far in the literature and provides one more set of criteria for determining the stability of delayed neural networks.
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Papers by Edgar Adan Jara Sanchez