Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems

@article{Parisini1998NeuralAF,
  title={Neural approximations for infinite-horizon optimal control of nonlinear stochastic systems},
  author={Thomas Parisini and Riccardo Zoppoli},
  journal={IEEE transactions on neural networks},
  year={1998},
  volume={9 6},
  pages={1388-408}
}
A feedback control law is proposed that drives the controlled vector vt of a discrete-time dynamic system (in general, nonlinear) to track a reference vt* over an infinite time horizon, while minimizing a given cost function (in general, nonquadratic). The behavior of vt* over time is completely unpredictable. Random noises act on the dynamic system and the state observation channel, which may be nonlinear, too. The random noises and the initial state are, in general, non-Gaussian; it is… CONTINUE READING
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