Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Modeling of a Rope-Driven Self-Levelling Crane
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Modeling and Optimal Control of Batch Processes Using Recurrent Neuro-Fuzzy Networks
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification
IEEE Transactions on Fuzzy Systems
Fuzzy logic based adjustment control of a cable-driven auto-leveling parallel robot
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Intelligent control of a four-rope-driven level-adjustment device with constrained outputs
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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To solve the level-adjusting problem of high accurate and costly payloads when loading and unloading, a rope-driven self-leveling device is developed, and a neurofuzzy controller is proposed. After a brief introduction of the configuration characteristics of the device and the fundamentals of neuro-fuzzy control, the construction of the neuro-fuzzy controller is set up, in which the angles of two diagonal inclinations which are measured from the two angle sensors are chosen as input variables, and the changes of two linear motion units' positions are the control variables. The neuro-fuzzy controller, whose rules are constructed based on human's regulating experience, was tuned by a hybrid algorithm, which is a combination of the least square estimate (LSE) method and the back-propagation (BP) algorithm. Experimental results show that the proposed neurofuzzy controller can achieve the control objective with high accuracy of regulation and short adjusting time, and is easily applied to the practical device.