The Experts below are selected from a list of 636291 Experts worldwide ranked by ideXlab platform
Pengyung Woo - One of the best experts on this subject based on the ideXlab platform.
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fuzzy supervisory sliding mode and neural Network Control for robotic manipulators
IEEE Transactions on Industrial Electronics, 2006Co-Authors: Hui Hu, Pengyung WooAbstract:Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from structured and unstructured uncertainties. Sliding-mode Control (SMC) is effective in overcoming uncertainties and has a fast transient response, while the Control effort is discontinuous and creates chattering. The neural Network has an inherent ability to learn and approximate a nonlinear function to arbitrary accuracy, which is used in the Controllers to model complex processes and compensate for unstructured uncertainties. However, the unavoidable learning procedure degrades its transient performance in the presence of disturbance. A novel approach is presented to overcome their demerits and take advantage of their attractive features of robust and intelligent Control. The proposed Control scheme combines the SMC and the neural-Network Control (NNC) with different weights, which are determined by a fuzzy supervisory Controller. This novel scheme is named fuzzy supervisory sliding-mode and neural-Network Control (FSSNC). The convergence and stability of the proposed Control system are proved by using Lyapunov's direct method. Simulations for different situations demonstrate its robustness with satisfactory performance.
Chunfei Hsu - One of the best experts on this subject based on the ideXlab platform.
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supervisory recurrent fuzzy neural Network Control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems, 2004Co-Authors: Chihmin Lin, Chunfei HsuAbstract:Wing rock is a highly nonlinear phenomenon in which an aircraft undergoes limit cycle roll oscillations at high angles of attack. In this paper, a supervisory recurrent fuzzy neural Network Control (SRFNNC) system is developed to Control the wing rock system. This SRFNNC system is comprised of a recurrent fuzzy neural Network (RFNN) Controller and a supervisory Controller. The RFNN Controller is investigated to mimic an ideal Controller and the supervisory Controller is designed to compensate for the approximation error between the RFNN Controller and the ideal Controller. The RFNN is inherently a recurrent multilayered neural Network for realizing fuzzy inference using dynamic fuzzy rules. Moreover, an on-line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. Finally, a comparison between the sliding-mode Control, the fuzzy sliding Control and the proposed SRFNNC of a wing rock system is presented to illustrate the effectiveness of the SRFNNC system. Simulation results demonstrate that the proposed design method can achieve favorable Control performance for the wing rock system without the knowledge of system dynamic functions.
Ryuta Ozawa - One of the best experts on this subject based on the ideXlab platform.
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adaptive neural Network Control of tendon driven mechanisms with elastic tendons
Automatica, 2003Co-Authors: Hiroaki Kobayashi, Ryuta OzawaAbstract:We propose an adaptive Control and an adaptive neural Network Control (composed of two RBF neural components and one adaptive component) for tendon-driven robotic mechanisms with elastic tendons. These Controllers can be applied to serial or parallel tendon-driven manipulators having linear or non-linear elastic tendons. We begin by proving the stability of the adaptive Control system for our mechanism, and then we prove the stability of the adaptive neural Network system and report on the results of numerical simulations and experimental results performed using a 2-DOF tendon-driven mechanism having six elastic tendons.
Rong-jong Wai - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Design of adaptive fuzzy-neural-Network Control for DC-DC boost converter
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Co-Authors: Rong-jong Wai, You-wei Lin, Li-chung ShihAbstract:In this study, an adaptive fuzzy-neural-Network Control (AFNNC) scheme is designed for the voltage tracking Control of a conventional dc-dc boost converter. First, a total sliding-mode Control (TSMC) strategy without the reaching pahse in the conventional SMC is developed for enhancing the system robustness during the transient response of the voltage Control. In order to alleviate chattering phenomena caused by the sign function in TSMC design and reduce the dependence on detailed system dynamics, it further designs an AFNNC scheme to imitate the TSMC law for the boost converter. In the AFNNC scheme, on-line learning algorithms are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the stability of the Controlled system without the requirement of auxiliary compensated Controllers despite the existence of uncertainties. The output of the AFNNC scheme can be easily supplied to the duty cycle of the power switch in the boost converter without strict constraints on Control parameters selection in conventional Control strategies. In addition, the effectiveness of the proposed AFNNC scheme is verified by numerical simulations, and its advantages are indicated in comparison with the TSMC strategy.
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Direct adaptive fuzzy-neural-Network Control for robot manipulator by using only position measurements
2010 5th IEEE Conference on Industrial Electronics and Applications, 2010Co-Authors: Rong-jong Wai, Zhi-wei Yang, C.-y. ShihAbstract:This study focuses on the development of a direct adaptive fuzzy-neural-Network Control (DAFNNC) for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this Control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, a DAFNNC strategy is investigated without the requirement of prior system information. In this model-free Control topology, a FNN Controller is directly designed to imitate a predetermined model-based stabilizing Control law, and then the stable Control performance can be achieved by only using joint position information. The DAFNNC law and the adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable Control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed methodology. In addition, the superiority of the proposed Control scheme is indicated in comparison with proportional-differential Control (PDC), fuzzy-model-based Control (FMBC), T-S type fuzzy-neural-Network Control (T-FNNC), and robust-neural-fuzzy-Network Control (RNFNC) systems.
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FUZZ-IEEE - Adaptive fuzzy-neural-Network Control of robot manipulator using T-S Fuzzy model design
2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008Co-Authors: Rong-jong Wai, Zhi-wei YangAbstract:This study focuses on the development of an adaptive fuzzy-neural-Network Control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this Control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNC system is investigated without the requirement of prior system information. In this model-free Control scheme, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with on-line learning ability is constructed for representing the system dynamics of an n-link robot manipulator. Then, a four-layer fuzzy-neural-Network (FNN) is utilized for estimating nonlinear dynamic functions in this fuzzy model. Moreover, the AFNNC law and adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the Network convergence as well as stable Control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed AFNNC methodology. In addition, the superiority of the proposed Control scheme is indicated in comparison with proportional-differential Control (PDC), Takagi-Sugeno-Kang (TSK) type fuzzy-neural-Network Control (T-FNNC), robust-neural-fuzzy-Network Control (RNFNC), and fuzzy-model-based Control (FMBC) systems.
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adaptive fuzzy neural Network Control for maglev transportation system
IEEE Transactions on Neural Networks, 2008Co-Authors: Rong-jong Wai, Jengdao LeeAbstract:A magnetic-levitation (maglev) transportation system including levitation and propulsion Control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode Control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode Control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-Network Control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the Controlled system without the requirement of auxiliary compensated Controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated Control transformations for relaxing strict constrains in conventional model-based Control methodologies. The effectiveness of the proposed Control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.
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robust neural fuzzy Network Control for robot manipulator including actuator dynamics
IEEE Transactions on Industrial Electronics, 2006Co-Authors: Rong-jong Wai, Pochen ChenAbstract:This paper addresses the design and analysis of an intelligent Control system for an n-link robot manipulator to achieve the high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link robot manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based Control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to deal with the mentioned difficulties, a robust neural-fuzzy-Network Control (RNFNC) system is investigated to the joint position Control of an n-link robot manipulator for periodic motion. In this Control scheme, a four-layer neural fuzzy Network (NFN) is utilized for the major Control role, and the adaptive tuning laws of Network parameters are derived in the sense of a projection algorithm and the Lyapunov stability theorem to ensure Network convergence as well as stable Control performance. The merits of this model-free Control scheme are that not only can the stable position tracking performance be guaranteed but also no prior system information and auxiliary Control design are required in the Control process. In addition, numerical simulations and experimental results of a two-link robot manipulator actuated by dc servo motors are provided to verify the effectiveness and robustness of the proposed RNFNC methodology
Hui Hu - One of the best experts on this subject based on the ideXlab platform.
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fuzzy supervisory sliding mode and neural Network Control for robotic manipulators
IEEE Transactions on Industrial Electronics, 2006Co-Authors: Hui Hu, Pengyung WooAbstract:Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from structured and unstructured uncertainties. Sliding-mode Control (SMC) is effective in overcoming uncertainties and has a fast transient response, while the Control effort is discontinuous and creates chattering. The neural Network has an inherent ability to learn and approximate a nonlinear function to arbitrary accuracy, which is used in the Controllers to model complex processes and compensate for unstructured uncertainties. However, the unavoidable learning procedure degrades its transient performance in the presence of disturbance. A novel approach is presented to overcome their demerits and take advantage of their attractive features of robust and intelligent Control. The proposed Control scheme combines the SMC and the neural-Network Control (NNC) with different weights, which are determined by a fuzzy supervisory Controller. This novel scheme is named fuzzy supervisory sliding-mode and neural-Network Control (FSSNC). The convergence and stability of the proposed Control system are proved by using Lyapunov's direct method. Simulations for different situations demonstrate its robustness with satisfactory performance.
