Proceedings ArticleDOI
Online adaptive control of robot manipulators using dynamic fuzzy neural networks
Yang Gao,Meng Joo Er,William Leithead,Douglas J. Leith +3 more
- Vol. 6, pp 4828-4833
TLDR
This paper presents a robust adaptive fuzzy neural controller suitable for motion control of a multi-link robot manipulator that has the dynamic fuzzy neural networks structure, i.e. fuzzy control rules, can be generated or deleted automatically.Abstract:
This paper presents a robust adaptive fuzzy neural controller suitable for motion control of a multi-link robot manipulator. The proposed controller has the following salient features: (1) the dynamic fuzzy neural networks structure, i.e. fuzzy control rules, can be generated or deleted automatically; (2) adaptive learning; (3) online learning of the robot dynamics; (4) fast learning speed; and (5) fast convergence of tracking error. The global stability of the system is established using the Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that an excellent tracking performance can be achieved under external disturbances.read more
Citations
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Journal ArticleDOI
Robust adaptive control of robot manipulators using generalized fuzzy neural networks
Meng Joo Er,Yang Gao +1 more
TL;DR: Experimental evaluation conducted on an industrial selectively compliant assembly robot arm demonstrates that excellent tracking performance can be achieved under time-varying conditions.
Journal ArticleDOI
A dynamic recurrent neural network-based controller for a rigid–flexible manipulator system
Lianfang Tian,Curtis Collins +1 more
TL;DR: This study proposes a fuzzy logic controller in the feedback configuration and an efficient dynamic recurrent neural network in the feedforward configuration, which can successfully identify the inverse dynamics of the flexible manipulator system and perform accurate tracking for a given trajectory.
Proceedings ArticleDOI
Robust adaptive fuzzy neural control of robot manipulators
Yang Gao,Meng Joo Er +1 more
TL;DR: This paper presents a robust adaptive fuzzy neural controller suitable for trajectory control of robot manipulators and asymptotic stability of the control system is established using Lyapunov theorem.
Journal ArticleDOI
Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators
TL;DR: This paper proposes to use neuro-fuzzy networks Sequential Adaptive Fuzzy Inference System (SAFIS) to estimate the parameters of the controlled robot manipulator.
Proceedings ArticleDOI
Robust Neural networks Compensating Motion Control of Reconfigurable Manipulator in Geometric Form
Ying Li,Yuanchun Li +1 more
TL;DR: To enhance computed torque control (CTC) based method, robust neural networks (RNN) compensating control scheme is developed to compensate structured and unstructured uncertainties.
References
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Book
Applied Nonlinear Control
TL;DR: Covers in a progressive fashion a number of analysis tools and design techniques directly applicable to nonlinear control problems in high performance systems (in aerospace, robotics and automotive areas).
Book
Adaptive Control
TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
Book
Control of Robot Manipulators
TL;DR: Control of robot manipulators , Control of robot Manipulators , مرکز فناوری اطلاعات و £1,000,000; اوشاوρز رسانی, کسورزی;
Journal ArticleDOI
A supervisory fuzzy neural network control system for tracking periodic inputs
TL;DR: Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance and the advantages of the proposedcontrol system are indicated in comparison with the sliding-mode control system.
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