Robust adaptive control
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Decentralized adaptive recurrent neural control structure
Engineering Applications of Artificial Intelligence
Decentralized adaptive fuzzy control of robot manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-time decentralized neural block controller for a robot manipulator
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a five degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the robot model, and based on this model a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural network learning is performed online using Kalman filtering. A controller is designed for each joint, using only local angular position and velocity measurements. These simple local joint controllers allow trajectory tracking with reduced computations. The applicability of the proposed scheme is illustrated via simulations.