Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification

Agand, Pedram; Shoorehdeli, Mahdi Aliyari; Khaki‐Sedigh, Ali

doi:10.1016/j.engappai.2017.07.009

Cited by 35 publications

(19 citation statements)

References 41 publications

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“…Wu & Jahanshahi (2019) utiliza una red neuronal convolucional para estimar el movimiento de tres sistemas dinámicos. Por su parte, Agand et al (2017), utilizan una red neuronal recurrente para compensar el torque de un controlador PID en un helicóptero colocado en una base firme. Sus resultados de más de 100 segundos muestran que el helicóptero se estabiliza en la posición deseada.…”

Section: Trabajos Relacionadosunclassified

Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiento

León

Vergara-Limon

Calleros

et al. 2021

Rev. colomb. comput.

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Las redes neuronales artificiales son útiles para procesar datos con información incompleta para obtener una salida deseada. En los sistemas de medición de robots manipuladores, solo se toman muestras cuantificadas de la posición y, por lo tanto, no se puede ejecutar en un tiempo razonable algoritmos deterministas para extraer los parámetros del robot. En el estado del arte, se abordan algoritmos de extracción de parámetros basados en la suposición de que no existe la cuantificación de las señales del movimiento del robot y que la primera y segunda derivada de la posición son muestreadas y no estimadas. En este trabajo, se propone un algoritmo basado en una red neuronal entrenada para extraer los parámetros de un determinado robot para reducir el tiempo de caracterización del robot, además, con la metodología propuesta se pueden extraer parámetros dinámicos del mismo tipo de robot con el que se ha entrenado la red neuronal.

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Section: Trabajos Relacionadosunclassified

Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiento

León

Vergara-Limon

Calleros

et al. 2021

Rev. colomb. comput.

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“…To achieve the efficiency in the controlling mechanism of the system, the neural network should be trained in such a way that the given input of the controller should produce a proper gain signals. The descriptions of RNN along with other details are included (Agand et al, 2017). RNN has associations among the concealed neurons and it structures a coordinated cycle.…”

Section: Process For Recurrent Neural Network Trainingmentioning

confidence: 99%

Brushless direct current motor with MV-PID controller management system based on arm microcontroller

Prathibanandhi

Ramadoss

Yaashuwanth³

et al. 2021

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Purpose The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed. Design/methodology/approach The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. Findings The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system. Originality/value The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.

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“…The simulated results showed the efficiency of the proposed methodology in improving the desired tracking and disturbance decoupling performance. Agand et al [68] demonstrated, experimentally the efficiency of using an adaptive neural network based inverse dynamic control (IDC) for a helicopter system. In this work, an enhancement in the steady-state performance of two to three times compared to the conventional PID was exhibited.…”

Section: Twin-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

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Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification

Cited by 35 publications

References 41 publications

Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiento

Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiento

Brushless direct current motor with MV-PID controller management system based on arm microcontroller

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

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