Papers by Chidentree Treesatayapun
Reinforcement control with fuzzy-rules emulated network for robust-optimal drug-dosing of cancer dynamics
Neural Computing and Applications
Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI
En este artículo se desarrolla una ley de control para navegación autónoma en interiores basada e... more En este artículo se desarrolla una ley de control para navegación autónoma en interiores basada en la detección visual de marcadores de referencia. Técnicas de estimación de pose externas como la localización por GPS o sensores RGB-D en techos son complicados de implementar en entornos cerrados donde puede haber obstrucci´on de vista o señal, como en almacenes, por lo que la estimación de pose local con sensores a bordo presenta una solución más viable. La implementaci´on de cámaras web de alta definición supone una solución más económica que el uso de sensores de alta calidad como sensores láser tipo Lidar. En la ley de control diseñada se considera a un marcador visual ArUco en su campo de visión, como un marco referencial inercial local. Con base en los errores medidos por odometr´ıa es posible ejecutar la tarea de regulación hacia éste marcador.
Bulletin of Mathematical Biology
A dynamic model called SqEAIIR for the COVID-19 epidemic is investigated with the effects of vacc... more A dynamic model called SqEAIIR for the COVID-19 epidemic is investigated with the effects of vaccination, quarantine and precaution promotion when the traveling and immigrating individuals are considered as unknown disturbances. By utilizing only daily sampling data of isolated symptomatic individuals collected by Mexican government agents, an equivalent model is established by an adaptive fuzzy-rules network with the proposed learning law to guarantee the convergence of the model's error. Thereafter, the optimal controller is developed to determine the adequate intervention policy. The main theorem is conducted to demonstrate the setting of all designed parameters regarding the closed-loop performance. The numerical systems validate the efficiency of the proposed scheme to control the epidemic and prevent the overflow of requiring healthcare facilities. Moreover, the sufficient performance of the proposed scheme is achieved with the effect of traveling and immigrating individuals. Keywords COVID-19 • SqEAIIR model • Optimal control • Discrete-time systems • Impulsive disturbance • Fuzzy rules emulated networks B Chidentree Treesatayapun
Textural properties of Magnetic Xerogel monoliths and its Prediction of the Effect of pH on Arsenic (V) adsorption
2019 International Conference on Engineering, Science, and Industrial Applications (ICESI)
Magnetic xerogels (MCs) were prepared by the cross-linking polymerization of resorcinol and forma... more Magnetic xerogels (MCs) were prepared by the cross-linking polymerization of resorcinol and formaldehyde (RF) using the alkaline catalyst and magnetite (Fe3O4). The indirect sonication was applied to distribute magnetite particles in RF aqueous solution to obtain the magnetic adsorbent that could be used as an easier recovery of adsorbent for water treatment. As a result of the characterization by X-ray diffraction and energy dispersive X-ray spectroscopy was confirmed the presence of magnetite into the gel at 1.19% with low molar ratio of magnetite and resorcinol ratio at 0.01. The surface morphology and textural properties of RF gel characterized by field emission scanning electron microscope affect directly with the variation of molar ratio of resorcinol and catalyst (R/C), which were 50, 100 and 200. The behavior of arsenic (As(V)) adsorption by using MCs was studied in groundwater into the ranges of pH from 2.0 to 7.0. The maximum As(V) uptake was on MC prepared with low R/C molar ratio at 50 while R/C at 100 gave the best performance within the application range of pH. Furthermore, the prediction technique based on an adaptive fuzzy rules emulated network is utilized for evaluation the arsenic removal performance.
Soft Computing, 2016
An adaptive iterative learning controller (ILC) is designed for a class of nonlinear discrete-tim... more An adaptive iterative learning controller (ILC) is designed for a class of nonlinear discrete-time systems based on data driving control (DDC) scheme and adaptive networks called fuzzy rules emulated network (FREN). The proposed control law is derived by using DDC scheme with a compact form dynamic linearization for iterative systems. The pseudo-partial derivative of linearization model is estimated by the proposed tuning algorithm and FREN established by human knowledge of controlled plants within the format of IF-THEN rules related on input-output data set. An on-line learning algorithm is proposed to compensate unknown nonlinear terms of controlled plant, and the controller allows to change desired trajectories for other iterations. The performance of control scheme is verified by theoretical analysis under reasonable assumptions which can be held for a general class of practical controlled plants. The experimental system is constructed by a commercial DC motor current control to confirm the effectiveness and applicability. The comparison results are addressed with a general ILC scheme based on DDC. Keywords Iterative learning control • Data-driven control • Discrete-time systems • Adaptive control • DC motor • Neuro-fuzzy Communicated by V. Loia.
Data‐driven optimal fault‐tolerant‐control and detection for a class of unknown nonlinear discrete‐time systems
Optimal Control Applications and Methods, 2021
The design of optimal fault‐tolerant control with actuator and sensor faults is investigated. The... more The design of optimal fault‐tolerant control with actuator and sensor faults is investigated. The system dynamics and faults are considered as a class of unknown nonlinear discrete‐time systems when the data‐driven equivalent model is formulated by a multi‐input fuzzy rule emulated network (MiFREN). The multi‐gradient learning law is developed with the proposed fault‐detection algorithm to enchant the performance of MiFREN. By employing only pieces of information from the equivalent model, the proposed controller is designed and the closed‐loop performance is analyzed through the rigorous theorem. Simulation systems and comparison results are provided to validate the performance of the proposed scheme.
Dimmable LED current control with compact fuzzy rules network and embedded system
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018
An adaptive controller for regulating and dimming problems of LED current control is developed in... more An adaptive controller for regulating and dimming problems of LED current control is developed in this article when the LED driving system is considered as a class of unknown nonlinear discrete-time systems. The controller is designed by a Fuzzy-rules emulated network (FREN) with direct human knowledge as IF-THEN rules of the controlled plant. The online learning algorithm is established to tune all adjustable parameters of FREN with the convergence analysis. The prototyping system is constructed by using Raspberry Pi model B as a main processing unit. Experimental results validate the satisfied performance for both current regulation and dimmer applications with positive and negative slopes.
Prediction of Fecal Coliform Removal on Intermittent Media Infiltration by Varying Soil Content Based on FREN
International Journal of Environmental Research, 2013
Current global water shortage and water pollution problem are some of the crucial issues in thewo... more Current global water shortage and water pollution problem are some of the crucial issues in theworld, especially in the arid zones. The wastewater reuse was investigated the efficiency of fecal coliform(FC) removal using the intermittent media infiltration (IMI) with varying soil content and natural porousmedia (sand, zeolite, vermicompost and charcoal), and its prediction was introduced by applying fuzzy rulesemulated network (FREN). The physicochemical properties of the porous media were determined and themechanisms of FC removal were discussed as the effect of fine particle size and increasing of ion charges. The compositions of soil and porous media at a ratio of 75/25, respectively, gave the best performance of FC reduction. The network architecture was constructed by the knowledge regarding to the relation between soil content (25, 50 and 75) and FC removal, and was introduced IF-THEN rules for FREN construction as theirpredicted curves at 20 iterations. The learning rate was ...
An array of sensors for source localization has been used in various areas of engineering such as... more An array of sensors for source localization has been used in various areas of engineering such as seismology, oceanography and radar operations. However, there are limited studies for applications using guided waves. The aperture function which characterizes the sensors topology is fundamental for the performance of the array. In this work we study the array aperture function in the context of dispersive attenuated multimode Lamb waves. Time-frequency beamforming analysis was used to study the effect of the aperture function characteristics. The methodology was implemented on various symmetric and non-symmetric modes generated on an aluminum thin plate. The results show that it is possible to locate the wave source by optimizing the aperture of the array for the dispersive modes generated. CORE Metadata, citation and similar papers at core.ac.uk
IFAC-PapersOnLine, 2020
This paper proposes the control of a data driven model for an experimental robotic system. The co... more This paper proposes the control of a data driven model for an experimental robotic system. The components of the robotic system are a redundant robot and a motion capture system considered them as a Multi-Inputs and Mulit-Outputs system. The Pseudo Jacobian Matrix computes the equivalent model of the robotic system taking into account the input and output signals. Besides, we design the adaptive gains for a proportional controller using an artificial neuro-fuzzy network for the robot's endeffector control. The experimental results validate the proposed control scheme for a regulation control. We provided a Lyapunov analysis to guarantee convergence parameters of the controller.
Data-driven identification and control based on optic tracking feedback for robotic systems
The International Journal of Advanced Manufacturing Technology, 2021
This paper presents the control of a robotic system based on a data-driven model. The components ... more This paper presents the control of a robotic system based on a data-driven model. The components of the robotic system are a redundant robot and a motion capture system, both considered as a class of nonlinear discrete Multi-Input and Multi-Output system. The strong tracking Kalman filter algorithm approximates the Jacobian matrix of an equivalent model for the robotic system considering only the input and output on-line data. Moreover, a type of proportional controller based on the estimated Jacobian matrix for the robot’s end-effector is designed. The Lyapunov stability analysis guarantees the convergence of the equivalent model and the control law. The estimation and control approach are validated with thorough experimental results.
Journal of the Franklin Institute, 2020
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
IFAC-PapersOnLine, 2018
An adaptive controller based on neuro-fuzzy networks and sliding mode techniques is presented. Th... more An adaptive controller based on neuro-fuzzy networks and sliding mode techniques is presented. The algorithm is intended for nonlinear discrete-time plants under the assumption that their mathematical model is unknown. Two adaptive Fuzzy Rule Emulated Network (FREN) structures are implemented to estimate the only two control parameters to be adjusted by a single neural network. The first FREN structure uses the error measurement as input. While the second one uses the sliding surface parameter to provide higher robustness. The stability analysis and performance of the proposed FREN Sliding Mode Controller (FRENSMC) for position control are presented in this work. The experimental setting consists of the tracking of a desired trajectory by controlling a DC motor along the X axis of a cartesian robotic system. The proposed FRENSMC controller achieves excellent results and exhibits a robust performance.
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 2019
Purpose The purpose of this paper is to design an online-data driven adaptive control scheme base... more Purpose The purpose of this paper is to design an online-data driven adaptive control scheme based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems. Design/methodology/approach By using the input-output characteristic curve of controlled plant and the set of IF-THEN rules based on human knowledge inspiration, the adaptive controller is established by an adaptive FREN. The learning algorithm is established with convergence proof of the closed-loop system and controller’s parameters are directly designed by experimental data. Findings The convergence of tracking error is verified by the theoretical results and the experimental systems. The experimental systems and comparison results show that the proposed controller and its design procedure based on input-output data can achieve superior performance. Practical implications The theoretical aspect and experimental systems with the light-emitting diode (LED) current control and the robotic sys...
Applied System Innovation, 2019
Robotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-li... more Robotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-linear discrete-time systems, where the input is a command voltage of the driver unit and the output is the feedback signal obtained by the multiplexer unit. With only the input and output data available, an equivalent identification is formulated by a multi-input fuzzy rule emulated network. An online-learning algorithm is proposed to tune all adjustable parameters by using convergence analysis. Using the equivalent model, a controller is developed when the convergence of the tracking error and internal signals can be guaranteed. An experimental system validates the performance of the proposed scheme. Furthermore, the comparative results are also included, to demonstrate the advantage of the proposed controller.
Discrete-Time Sliding Mode Controller With Time-Varying Band for Unfixed Sampling-Time Systems
Journal of Dynamic Systems, Measurement, and Control, 2018
An adaptive discrete-time controller is developed for a class of practical plants when the mathem... more An adaptive discrete-time controller is developed for a class of practical plants when the mathematical model is unknown and the sampling time is nonconstant or unfixed. The data-driven model is established by the set of plant's input–output data under the pseudo-partial derivative (PPD) which represents the change of output with respect to the change of control effort. The multi-input fuzzy rule emulated network (MiFREN) is utilized to estimate PPD with an online-learning phase to tune all adjustable parameters of MiFREN with the convergence analysis. The proposed control law is developed by the discrete-time sliding mode control (DSMC), and the time-varying band is established according to the unfixed sampling time and unknown boundaries of disturbances and uncertainties. The prototype of direct current-motor current control with uncontrolled-sampling time is constructed to validate the performance of the proposed controller.
AIP Conference Proceedings, 2017
Continuous monitoring for damage detection on large fluid filled pipe-like structures is needed. ... more Continuous monitoring for damage detection on large fluid filled pipe-like structures is needed. To address this, we propose the use of the first order torsional mode and the detection of extra modes generated from an artificial discontinuity. The generation and detection of the propagated signals is carried out by a microfiber composite (MFC) sensor. Signals are post-processed with short time Fourier transform analysis. Numerical results were obtained and experimentally tested on a stainless steel pipe A-36 (43.6 and 48.2 mm in inner and outer diameter, respectively). It was found that it is possible to identify an artificial discontinuity by detecting the generated extra modes after its interaction with the propagated guided wave modes.
Nonlinear Systems Indentification Using Multi Input Fuzzy Rules Emulated Network
Itc Cscc 2006 Proceedings Volume 1, Jul 1, 2006
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
A direct adaptive control system for a class of unknown nonaffine discrete-time plants is introdu... more A direct adaptive control system for a class of unknown nonaffine discrete-time plants is introduced in this article. The proposed control law is constructed by the estimated system linearization with adjustable networks called Muti-input Fuzzy Rules Emulated Networks or MIFRENs. Only on-line learning phase, the bounded parameters inside MIFRENs and the boundary of control error are given by the proposed theorem. The validation of the main theorem is demonstrated by computer simulation system.
New Advances in Machine Learning, 2010
Recently, the linearization of a class of unknown discrete-time dynamic systems has achieved cons... more Recently, the linearization of a class of unknown discrete-time dynamic systems has achieved considerable topics for the controller design. The unknown functions after system linearization have been estimated by several methods including artificial intelligence techniques such as neural networks, fuzzy logic systems and neurofuzzy networks. In a number of published articles, the issues of system theoretic analysis have been introduced and addressed in the topics of stabilization, tracking performance and the bounded parameters. For all of these cases, the results are validated in the domain around the equilibrium point or state (9; 11). These methods of linearization including local linearization, Taylor series expansion and feedback linearization impose Lipschitz conditions (4; 6; 10; 14; 18). The closed-loop system stability and tracking error have been analyzed in the case of neural network adaptive control (5; 7) but during the learning phase the stability and convergence can not be ensured because of the special conditions. The system stability or bounded signals analysis has been verified (1; 13) and references therein. However, these nonlinear systems under control should be obtained in the format as y(k + 1) = f (k) + g(k)u(k) when y(k) and u(k) are the system output and the control input at time index k, respectively and f (k) and g(k) are unknown nonlinear functions. The small learning rate is often defined to solve the stability problem but the convergence is very slow. The discrete-time projection has been introduced for adaptive control systems in (16). The node number of multi-layer neural networks can take more effect of closed-loop stability and tracking performance. In (15), the unknown nonlinear part has been compensated by neural networks and the closed-loop system stability has been also guaranteed for a class on discrete-time systems. Nevertheless, this algorithm needs the renovation when the operating point is changed. In the case of robust system, the dead-zone function has been applied for feedback linearization systems (8) but this control algorithm are only limited for the system with slow trajectory tracking. In this chapter, we discuss about the controller for a class of nonlinear discrete-time systems with estimated unknown nonlinear functions by Muti-input Fuzzy Rules Emulated Networks (MIFRENs). These nonlinear functions are occurred when
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Papers by Chidentree Treesatayapun