Stable computed-torque control of robot manipulators via fuzzy self-tuning

Llama, Miguel A.; Kelly, Rafael; Santibáñez, Víctor

doi:10.1109/3477.826954

Cited by 73 publications

(35 citation statements)

References 13 publications

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“…Many efforts have been made in developing control schemes to achieve the precise tracking control of robot manipulators. Among available options, neural networks and fuzzy systems (Er & Chin 2000;Llama et al 2000;Wang & Lin 2000;Huang & Lian 1997) are used more and more frequently in recent years. In the simulation experiments of this chapter, the proposed RFNNBAC is applied to control the trajectory of the two-link robotic manipulator described in chapter 2.4 to prove its effectiveness.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Adaptive Control Based On Neural Network

Sun¹,

Lujin²,

Zou³

et al. 2009

Adaptive Control

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IntroductionNeural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent fuzzy neural network based adaptive control (RFNNBAC), and another is neural network based adaptive robust control (NNBARC). In RFNNBAC, a kind of recurrent fuzzy neural network (RFNN) is constructed by using recurrent neural network to realize fuzzy inference, In which, temporal relations are embedded in the network by adding feedback connections on the first layer of the network. Two RFNNs are used to identify and control plant respectively. Base on the Lyapunov stability approach, the convergence of the proposed RFNN is analyzed. In NNBARC, A robust controller and a neural network are combined into an adaptive robust robotic tracking control scheme. Neural network is used to approximate the modeling uncertainties in robotic system. Then the disadvantageous effects on tracking performance, due to the approximating error of the neural network and non-measurable external disturbances in robotic system, are attenuated to a prescribed level by robust controller. The robust controller and the adaptation law of neural network are designed based on Hamilton-JacobiIssacs (HJI) inequality theorem. The weights of NN are easily tuned on-line by a simple adaptation law, with no need of a tedious and lengthy off-line training phase. This chapter is organized in the following manner. In the first section a robust robotic tracking controller based on neural network is designed and its effectiveness is proved by applying it to control the trajectories of a two-link robot. Secondly, a recurrent fuzzy neural network based adaptive control is proposed and simulation experiments are made by applying it on robotic tracing control problem to confirm its effectiveness. Finally, some conclusions are drawn. A robust robotic tracking controller based on neural networkIn the past decades, there has been much research on the applications of nonlinear control theory to control robots, and many useful properties of robot dynamics such as the skewsymmetry property were discovered. There are basically two strategies to control such uncertain nonlinear systems: the robust control strategy and the adaptive control strategy. A www.intechopen.com Adaptive Control 182 convenient point of robust control strategy is that it can attenuate disadvantageous effects of various uncertainties (e.g., structured parametric uncertainties and unstructured disturbances) to a required level, provided that the upper bound of uncertainties is well known (Abdallah et al. 1991). However, since this strategy use max-min method to design the controller, it can not yield good transient performance. On the other hand, regressor matrixes are always used in the design ...

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Section: Simulation Experimentsmentioning

confidence: 99%

Adaptive Control Based On Neural Network

Sun¹,

Lujin²,

Zou³

et al. 2009

Adaptive Control

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“…Los controladores auto sintonizables (self tuning controllers) existen desde hace tiempo con diferentes concepciones Åström and Wittenmark (1973), reglas de sintonía en función del error de posición con lógica difusa y redes neuronales para las ganancias proporcional K p y derivativa K v se encuentran en Llama et al (2000Llama et al ( , 2001; Santibáñez et al (2002Santibáñez et al ( , 2004; Meza et al (2009) ;Llama et al (2010); Meza et al (2012); Armendariz et al (2012). El concepto de ganancias variables se discute en Salas and Llama (2010); Salas et al (2012bSalas et al ( , 2013.…”

Section: Introductionunclassified

Regulación Saturada con Ganancia Variable Derivativa de Robots Manipuladores

Limón-Díaz

Reyes-Cortés

González-Galván

2017

Revista Iberoamericana de Automática e Informática Industrial R

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ResumenEn este trabajo se presenta una familia grande de reguladores saturados tipo hiperbólicos para robots manipuladores. La propuesta considera a la ganancia proporcional constante y a la ganancia derivativa variable con sintonía automática definida en función del error de posición, velocidad de movimiento y un factor de inyección de amortiguamiento para modificar la velocidad de respuesta del robot. La acción de control derivativa con ganancia variable permite reducir sobreimpulsos, oscilaciones y rizo, tal que alcance el estado estacionario en forma suave. Asimismo, se presenta la propuesta de una función estricta de Lyapunov que permite demostrar la estabilidad asintótica global de la ecuación en lazo cerrado. Para mostrar el desempeño y funcionalidad de la familia propuesta de esquemas de control, un análisis comparativo experimental fue desarrollado entre siete estructuras de control, cinco reguladores pertenecen a la familia propuesta, y dos algoritmos de control bien conocidos como son el proporcional derivativo (PD) y tangente hiperbólico (Tanh). Los resultados experimentales fueron obtenidos con un robot manipulador de transmisión directa de tres grados de libertad. Palabras Clave:Regulador, Función de saturación, Ganancia Variable, Manipulador robótico, Algoritmos de control. IntroducciónActualmente el control de robots manipuladores representa un tema clave y estratégico para México y en general para Iberoamérica. Los robots manipuladores se han convertido en herramientas indispensables para un amplio sector de la sociedad, como han sido los casos de la industria, aplicaciones especiales y médicas: cirugías robotizadas, fisioterapia, rehabilitación, asistencia robotizada a personas con capacidades diferenciadas, entre otras más.Para que un robot manipulador realice correctamente una tarea encomendada, se requiere de un algoritmo de control de alto desempeño. Por lo tanto, el diseño de algoritmos de control se ha convertido en una actividad científica permanente y sistemática con la finalidad de proponer nuevos esquemas con alto desempeño y prestaciones adecuadas para una correcta ejecución en la tarea programada.La técnica de moldeo de energía (energy shaping) propuesta por Takegaki and Arimoto (1981) ha sido utilizada por diversos * Autor en correspondencia Correos electrónicos: miangellim@hotmail.com (Miguel A. Limón-Díaz ), fernando.reyes@correo.buap.mx (Fernando Reyes-Cortés), egonzale@uaslp.mx (Emilio J. González-Galván) investigadores; esta metodología utiliza el teorema de invarianza de LaSalle para demostrar estabilidad asintótica. Más tarde Tomei (1991) perfecciona el método de Takegaki-Arimoto sin usar LaSalle, proponiendo una función estricta de Lyapunov para estudiar la estabilidad asintótica del control PD con compensación de gravedad. En esta dirección, es importante resaltar particularmente el trabajo de Santibáñez and Kelly (1997) quienes generalizaron la función estricta propuesta en Whitcomb et al. (1993) para el caso de regulación, así como su extensión al problema de control ...

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“…Also the works above are based only on the steering kinematics and assume that there exists perfect velocity tracking, that is, the control signals affect the vehicle velocities instantaneously and this is not true. Some works developed adaptive fuzzy controller with Lyapunov stability analysis for manipulators control [18,19]. About applications to nonholonomic vehicles of fuzzy control with Lyapunov stability, relatively few results have been obtained [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Theoretical and Experimental Approach of Fuzzy Adaptive Motion Control for Wheeled Autonomous Nonholonomic Vehicles

Melluso

2013

ISRN Robotics

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A new fuzzy adaptive control is applied to solve a problem of motion control of nonholonomic vehicles with two independent wheels actuated by a differential drive. e major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov stability can be ensured. In particular the parameters of the kinematical control law are obtained using a fuzzy mechanism, where the properties of the fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e., fuzzy rules and value of the rule), the parameters above are not constant, but, time aer time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating ensures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, dynamical and kinematical adaptive controllers have been added. e proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. erefore, system robustness and stability performance are veri�ed through simulations and experimental studies.

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Stable computed-torque control of robot manipulators via fuzzy self-tuning

Cited by 73 publications

References 13 publications

Adaptive Control Based On Neural Network

Adaptive Control Based On Neural Network

Regulación Saturada con Ganancia Variable Derivativa de Robots Manipuladores

A Theoretical and Experimental Approach of Fuzzy Adaptive Motion Control for Wheeled Autonomous Nonholonomic Vehicles

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