Papers by Carlos Cárdenas castañeda
Energies, 2021
The wind power systems of variable velocity using a doubly-fed induction generator dominate large... more The wind power systems of variable velocity using a doubly-fed induction generator dominate large-scale electrical generation within renewable energy sources. The usual control goal of the wind systems consists of maximizing the wind energy capture and streamlining the energy conversion process. In addition, these systems are an intermittent energy source due to the variation of the wind velocity. Consequently, the control system designed to establish a reliable operation of the wind system represents the main challenge. Therefore, emulating the operation of the wind turbine by means of an electric motor is a common strategy so that the controller design is focused on the induction generator and its connection to the utility grid. Thus, we propose to emulate the dynamical operation of a wind turbine through a separately excited DC motor driving by a sensor-less velocity controller. This controller is synthesized based on the state-feedback linearization technique combined with the s...
2020 International Joint Conference on Neural Networks (IJCNN), 2020
In this paper, a Morlet wavelet and super-twisting control algorithm are designed and implemented... more In this paper, a Morlet wavelet and super-twisting control algorithm are designed and implemented to a three-phase induction motor. The mathematical model of the squirrel-cage induction motor to be controlled is approximated by the Morlet wavelet artificial neural network, which is trained on-line with the error filtered algorithm in order to reproduce the dynamics of the plant to be controlled. The structure of the artificial neural network is proposed in series-parallel configuration and block control form to design the sliding variety, where the super-twisting control algorithm is applied indirectly. For the non-measurable state variables of the plant, state observers of the super-twisting type are proposed to feed the inputs of the artificial neural network. The simulation of the complete system in closed loop is performed where the variables to be controlled are the angular velocity and the square modulus of flux linkages. The results obtained in Matlab/Simulink validate the efficiency of the proposed neural network for the identification of states and the application of the controller.
Applied Sciences, 2021
In this paper, a real-time implementation of a sliding-mode control (SMC) in a hardware-in-loop a... more In this paper, a real-time implementation of a sliding-mode control (SMC) in a hardware-in-loop architecture is presented for a robot with two degrees of freedom (2DOF). It is based on a discrete-time recurrent neural identification method, as well as the high performance obtained from the advantages of this architecture. The identification process uses a discrete-time recurrent high-order neural network (RHONN) trained with a modified extended Kalman filter (EKF) algorithm. This is a method for calculating the covariance matrices in the EKF algorithm, using a dynamic model with the associated and measurement noises, and it increases the performance of the proposed methodology. On the other hand, the decentralized discrete-time SMC technique is used to minimize the motion error. A Virtex 7 field programmable gate array (FPGA) is configured based on a hardware-in-loop real-time implementation to validate the proposed controller. A series of several experiments demonstrates the robust...
In this paper, it is considered the design of a telescope in an altazimuth configuration. Its pri... more In this paper, it is considered the design of a telescope in an altazimuth configuration. Its primary objective is a rotating liquid mirror made of mercury (any rotating liquid naturally adopts a perfect paraboloidal shape). This liquid mirror cannot be oriented. Hence, a mechanical and optical system is needed to conduct the light of a celestial body to it. The latter system is composed of two plane mirrors which, rotate around a horizontal and a vertical axis, two motors are employed to fulfill this purpose. The non-linear-block-control method is used to control these motors. A third motor keeps-up rotating a container filled with mercury to form the liquid mirror, the focal length of the rotating mirror depends on the angular velocity of this last motor. Hence, its rotation rate also needs to be controlled. The Methodology’s part A describes the design of a 2-links mechanical and optical system. The Methodology’s part B introduces the gravitational potential and the kinetic energ...
Energies, 2021
In this article, we propose a mathematical model using the port-Hamiltonian formalism for a satel... more In this article, we propose a mathematical model using the port-Hamiltonian formalism for a satellite’s three-axis attitude system comprising fluid rings. Fluid rings are an alternative to reaction wheels used for the same purpose, since, for the same mass, they can exert a greater torque than a reaction wheel as the fluid can circulate the periphery of the satellite. The port-Hamiltonian representation lays the foundation for a posterior controller that is feasible, stable, and robust based on the interconnection of the system to energy shaping and/or damping injection components, and by adding energy routing controllers. The torques exerted by the fluid rings are modeled using linear regression analysis on the experimental data got from a prototype of a fluid ring. Since the dynamics of turbulent flows is complex, the torques obtained by the prototype lead to a simpler first approach, leaving its uncertainties to a controller. Thus, the attitude system model could be tested in a f...
Energies, 2021
In this work, a neural super-twisting algorithm is applied to the design of a controller for a fl... more In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant’s states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RW...
Energies, 2020
A DC motor velocity control in feedback systems usually requires a velocity sensor, which increas... more A DC motor velocity control in feedback systems usually requires a velocity sensor, which increases the controller cost. Additionally, the velocity sensor used in industrial applications presents several disadvantages such as maintenance requirements and signal conditioning. In this work, we propose a robust velocity control scheme applied to a DC motor based on estimation strategies using a sliding-mode observer. This means that measurements with mechanical sensors are not required in the controller design. The proposed observer estimates the rotational velocity and load torque of the motor. The controller design applies the exact-linearization technique combined with the super-twisting algorithm to achieve robust performance in the closed-loop system. The controller validation was carried out by experimental tests using a workbench, which is composed of a control and data acquisition Digital Signal Proccessor board, a DC-DC electronic converter, an interface board for signals cond...
Experimental Techniques, 2020
In this article, the dynamics of a double-acting pneumatic actuator was experimentally studied. T... more In this article, the dynamics of a double-acting pneumatic actuator was experimentally studied. To understand the details of dynamics and provide experimental data for further modeling and numerical simulation of actuators, a special test bench was designed. The bench includes a high-resolution linear encoder as well as a dSPACE 1104 data acquisition board, that permits high-frequency sampling to identify important features in the measured data at different source pressures; it also incorporates two pressure sensors, a solenoid valve, a push button, and a measuring interface to automate the acquisition of data. High precision measurements of the time of displacement and position of the moving elements, piston rod or cylinder body, allowed us to calculate velocity and acceleration. We found that the velocity of the moving element is near constant in a major part of the stroke. The recording of the dynamics of upstream and downstream pressures in the pneumatic cylinder chambers at dif...
Neural Processing Letters, 2018
This paper presents an online neural identification and control scheme in continuous-time for tra... more This paper presents an online neural identification and control scheme in continuous-time for trajectory tracking of a robotic arm evolving in the vertical plane. A recurrent high-order neural network (RHONN) structure in a block strict-feedback form is proposed to identify online in a series-parallel configuration, using the filtered error learning law, the dynamics of the plant. Based on the RHONN identifier structure, a stabilizing controller is derived via integrator backstepping procedure. The performance of the neural control scheme proposed is tested on a two degrees of freedom robotic arm, of our own design and unknown parameters, powered by industrial servomotors.
IEEE Access, 2018
On this paper, a robust velocity controller applied to a three-phase squirrel-cage induction moto... more On this paper, a robust velocity controller applied to a three-phase squirrel-cage induction motor under variable load conditions is designed. The induction motor drives an induction generator representing the load which freely delivers the generated power to the utility grid. The closed-loop scheme is designed at αβ coordinate frame and is based on the linearization block control technique in combination with the super-twisting algorithm. The controlled output variables are the angular mechanical velocity and the square modulus of rotor flux linkages. The motor reference velocity is set up by a pulse train above the synchronous velocity and, consequently, the impelled induction machine operates in generator mode; meanwhile, the reference of rotor flux square modulus varies according to load condition of the induction motor. In order to estimate the non-measurable variables, both a rotor flux linkages observer and a load torque observer are designed. For the first one, a non-linear state observer using first order sliding modes is applied at αβ coordinate frame and, for the second one, a Luenberger reduced asymptotic observer is used. The validation of the robustness for the proposed velocity controller is performed in a real-time experiment using a work-bench. INDEX TERMS Block control, induction motor, sliding mode observer, super-twisting.
Neural Processing Letters, 2018
Wavelets are designed to have compact support in both time and frequency, giving them the ability... more Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time-frequency plane. The Gaussian, the Mexican hat, and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using continuous wavelet transform. Continuous wavelets are favoured when a high temporal resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called recurrent wavelet first-order neural network, is proposed in order to achieve a better identification of the behavior of dynamic systems. The effectiveness of our proposal is explored through the design of a centralized neural integrator backstepping control scheme for a two degree-of-freedom robot manipulator evolving in the vertical plane. The performance of the overall neural identification and control scheme is verified through numerical simulation using the mathematical model for a benchmark prototype. Moreover, real-time results validate the effectiveness of our proposal when using a robotic arm, of our own design, powered by industrial servomotors.
Energies, 2017
In this paper, a real-time robust closed-loop control scheme for controlling the velocity of a Di... more In this paper, a real-time robust closed-loop control scheme for controlling the velocity of a Direct Current (DC) motor in a compound connection is proposed. This scheme is based on the state-feedback linearization technique combined with a second-order sliding mode algorithm, named super-twisting, for stabilizing the system and achieving control goals. The control law is designed to track a periodic square reference signal, being one of the most severe tests applied to closed-loop systems. The DC motor drives a squirrel-cage induction generator which represents the load; this generator must work above the synchronous velocity to deliver the generated power towards the grid. A classical proportional-integral (PI) controller is designed for comparison purposes of the time-domain responses with the proposed second-order sliding mode (SOSM) super-twisting controller. This robust controller uses only a velocity sensor, as is the case of the PI controller, as the time derivative of the velocity tracking variable is estimated via a robust differentiator. Therefore, the measurements of field current and stator current, the signal from a load torque observer, and machine parameters are not necessary for the controller design. The validation and robustness test of the proposed controller is carried out experimentally in a laboratory, where the closed-loop system is subject to an external disturbance and a time-varying tracking signal. This test is performed in real time using a workbench consisting of a DC motor-Alternating Current (AC) generator group, a DC/AC electronic drive, and a dSPACE 1103 controller board.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
This paper describes an identification process for a class of discrete-time nonlinear systems, wh... more This paper describes an identification process for a class of discrete-time nonlinear systems, which includes the Xilinx system generator software and the process is implemented in a Virtex 7 (V7) field programmable gate array (FPGA). This procedure consists of programming a discrete-time nonlinear plant where the dynamics of this plant is reproduced by a discrete-time recurrent high order neural network (RHONN). The neural network is trained on-line with the extended Kalman filter algorithm where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. Additionally, a sliding window-based method for dynamical modeling of nonstationary systems is presented in order to improve the neural identification process. This identification process is implemented on a Virtex 7 (V7) FPGA using Xilinx system generator software where are programed in this FPGA: the discrete-time dynamics of the two degrees of freedom (2DOF) robot manipulator, the RHONN, the extended Kalman filter (EKF) training algorithm and the sliding windowbased method. The obtained results from the FPGA are compared with the results obtained from Matlab/SImulink in order to validate the identification process for the present proposal.
The European Physical Journal Special Topics, 2016
Abstract This paper presents a novel electronic locking key based on discrete-time chaos synchron... more Abstract This paper presents a novel electronic locking key based on discrete-time chaos synchronization. Two Chen chaos generators are synchronized using the Model-Matching Approach, from non-linear control theory, in order to perform the encryption/decryption of the signal to be transmitted. A model/transmitter system is designed, generating a key of chaotic pulses in discrete-time. A plant/receiver system uses the above mentioned key to unlock the mechanism. Two alternative schemes to transmit the private chaotic key are proposed. The first one utilizes two transmission channels. One channel is used to encrypt the chaotic key and the other is used to achieve output synchronization. The second alternative uses only one transmission channel for obtaining synchronization and encryption of the chaotic key. In both cases, the private chaotic key is encrypted again with chaos to solve secure communication-related problems. The results obtained via simulations contribute to enhance the electronic locking devices.
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Papers by Carlos Cárdenas castañeda