Design and Implementation of an Adjustable Speed Drive for Motion  Control Applications

Rodríguez‐Reséndiz, Juvenal; Gutierrez-Villalobos, J. M.; Duarte-Correa, D.; Mendiola-Santibáñez, Jorge D.; Santillán-Méndez, Israel Marcos

doi:10.22201/icat.16656423.2012.10.2.406

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…The stochastic gradient ( According to the model expressed in (1), where finite differences are described, its estimation shown in (3) is presented in Fig. 4.…”

Section: If the Noisesmentioning

confidence: 99%

Internal State Identification for Black Box Systems

Juárez¹,

Zagaceta²

2014

CyS

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In digital filter theory, the identification process describes internal dynamic states based on a reference system, commonly known as a black box. The identification process as a function of: a) transition function, b) identified delayed states, c) gain function which depends on convergence correlation error, and d) an innovation process based on the error described by the differences between the output reference system and the identification result. Unfortunately, in the black box concept, the exponential transition function considers the unknown internal parameters. This means that the identification process does not operate correctly because its transition function has no access to the internal dynamic gain. An approximation for solving this problem includes the estimation in the identification technique. This paper presents an estimation for a "single input single output" (SISO) system with stationary properties applied to internal state identification.

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“…The stochastic gradient ( According to the model expressed in (1), where finite differences are described, its estimation shown in (3) is presented in Fig. 4.…”

Section: If the Noisesmentioning

confidence: 99%

Internal State Identification for Black Box Systems

Juárez¹,

Zagaceta²

2014

CyS

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“…A synchronised reset is included in each block -this can help to avoid a meta-stable condition in which it is unclear which clock cycle the FPGA state machine is in; more to the point, it means that the process will always be aligned with the clock when the reset signal is de-asserted, avoiding certain meta-stability problems. 22,23 In a similar manner, Fig. 5(a) shows the block diagram of the hardware description entities used to control the DAC chip.…”

Section: Design Specifications For the Target Prototypementioning

confidence: 99%

An Approach to Motion Control Applications Based on Advanced Programmable Devices

Rodríguez‐Reséndiz

Mendoza-Mondragón

Gómez‐Loenzo

et al. 2012

International Journal of Electrical Engineering & Education

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In this article a methodology for constructing a simple servo loop for motion control applications which is suitable for educational applications is presented. The entire hardware implementation is demonstrated, focusing on a microcontroller-based (µC) servo amplifier and a field programmable gate array-digital signal processor (FPGA-DSP) motion controller. A novel hybrid architecture-based digital stage is featured providing a low-cost servo drive and a high performance controller, which can be used as a basis for an industrial application. Communication between the computer and the controller is exploited in this project in order to perform a simultaneous adaptive servo tuning. The USB protocol has been put into operation in the user front-end because a high speed sampling frequency is required for the PC to acquire position feedback signals. A software interface is developed using educational software, enabling features not only limited to a motion profile but also the supervisory control and data acquisition (SCADA) topology of the system. A classical proportionalintegral-derivative controller (PID) is programmed on a DSP in order to ensure a proper tracking of the reference at both low and high speeds in a d.c. motor. Furthermore, certain blocks are embedded on an FPGA. As a result, three of the most important technologies in signal processing are featured, permitting engineering students to understand several concepts covered in theoretical courses.

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“…In all positioning control applications, hysteresis and creep effects of electric actuators have been shown to significantly degrade system performance and even system stability, as seen in [ 23 , 24 ]. In general terms, an Artificial Neural Network performance depends strongly of the training stage and some studies present extremely long training periods.…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Control Using an Online-Trained Neural Network to Position a Linear Actuator

et al. 2022

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Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust is required under heavy loads, such as wheelchairs, medical beds, and lifting tables. Due to the remarkable ability to exert forces and good precision, they are used classic control systems and controls of high-order. Still, they present difficulties in changing their dynamics and are designed for a range of disturbances. Therefore, in this paper, we present the study of an electric linear actuator. We analyze the positioning in real-time and attack the sudden changes of loads and limitation range by the control. It uses a general-purpose control with self-tuning gains, which can deal with the essential uncertainties of the actuator and suppress disturbances, as they can change their weights to interact with changing systems. The neural network combined with PID control compensates the simplicity of this type of control with artificial intelligence, making it robust to drastic changes in its parameters. Unlike other similar works, this research proposes an online training network with an advantage over typical neural self-adjustment systems. All of this can also be dispensed with the engine model for its operation. The results obtained show a decrease of 42% in the root mean square error (RMSE) during trajectory tracking and saving in energy consumption by 25%. The results were obtained both in simulation and in real tests.

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Design and Implementation of an Adjustable Speed Drive for Motion Control Applications

Cited by 11 publications

References 26 publications

Internal State Identification for Black Box Systems

Internal State Identification for Black Box Systems

An Approach to Motion Control Applications Based on Advanced Programmable Devices

Self-Tuning Control Using an Online-Trained Neural Network to Position a Linear Actuator

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