Neural network based adaptive control of nonlinear plants using random search optimization algorithms

A method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics is presented. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.<
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