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In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non- linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known...
The paper presents an approach to system identification of input/output mappings of non-linear stochastic systems in accordance to an information-theoretic criterion. At that, a parameterized description of the system under study is utilized combined with a corresponding technique of estimation of the mutual information (in the Shannon sense), leading, finally, to a problem of the finite dimensional...
A new remote time-delay feedback controller is presented for a class of robot manipulator systems with unknown nonlinear dynamics and communication time-delay. The proposed control scheme consists of a local neural network (NN) compensation and a time-delay feedback controller. A NN-based identification is first employed to identify the robot manipulator system. Local linearization compensation is...
For a class of large-scale mechanical nonlinear systems, such as inverted pendulum, intervehicle spacing regulation in a platoon of automated highway systems and single-link robots with flexible joints, in this paper, we design a neural network (NN-based) decentralized adaptive control scheme to implement control them effectively. As a direct adaptive controller, neural network is used to approximate...
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