Control of nonlinear dynamical systems using neural networks. II. Observability, identification, and control

By A. U. Levin, K. Narendra. 1996

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Deep learning in neural networks: An overview (2014, methodology): “1 RL Through NN World Models Yields RNNs With Deep CAPs In the special case of an RL FNN controller C interacting with a deterministic, predictable environment, a separate FNN called M can learn to become C’s world model through system identification, predicting C’s inputs from previous actions and ”
Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback (2003, methodology): “The feasibility of applying NNs to model unknown functions in dynamic systems has been demonstrated in several studies [19], [20].”
Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints (2016, background): “Artificial neural networks have been proven to be capable of approximating various nonlinear functions to any desired degree of accuracy under certain conditions [25], [26].”