A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control
Fuzzy Sets and Systems - Fuzzy control
Control of overhead cranes using a fuzzy logic controller
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Brief Second-order sliding-mode control of container cranes
Automatica (Journal of IFAC)
Brief Observer-controller design for cranes via Lyapunov equivalence
Automatica (Journal of IFAC)
A fuzzy Actor-Critic reinforcement learning network
Information Sciences: an International Journal
Control of the TORA system using SIRMs based type-2 fuzzy logic
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An application of ICA to BSS in a container gantry crane cabin's model
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Self-organizing state aggregation for architecture design of Q-learning
Information Sciences: an International Journal
Novel Approach for Adaptive Tracking Control of a 3-D Overhead Crane System
Journal of Intelligent and Robotic Systems
Transport control of underactuated cranes
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Nonlinear function approximation using fuzzy functional SIRMs inference model
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
SIRMs connected fuzzy inference method adopting emphasis and suppression
Fuzzy Sets and Systems
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A new fuzzy controller for anti-swing and position control of an overhead traveling crane is proposed based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as the input items, and the trolley acceleration as the output item. Each input item is given with a SIRM and a dynamic importance degree. The control system is proved to be asymptotically stable to the destination. The controller is robust to different rope lengths and has generalization ability for different initial positions. Control simulation results show that by using the fuzzy controller, the crane is smoothly driven to the destination in short time with small swing angle and almost no overshoot.