Recurrent neural identification and an i-term direct adaptive control of nonlinear oscillatory plant
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Direct feedback control design for nonlinear systems
Automatica (Journal of IFAC)
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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This paper describes the use of Artificial Neural Networks for the control of vibrations of a mechanical system using its experimental direct inverse model. The neural controller is trained to model the experimental inverse model of the plant using the backpropagation algorithm with simulated annealing. The inverse model of the plant is obtained by the training mechanism that uses experimental input and output data. After the training, the neural network is used as a forward controller. The efficiency and the robustness of the controller are shown through experimental tests. The neural control algorithm is implemented in a computer and the performance of controller is evaluated under a set of experimental tests made to the active control of vibrations of a mechanical system of one degree of freedom actuated by magnetic actuators