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Infinite-horizon optimal control of nonlinear stochastic systems: a neural approach

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2 Author(s)
Parisini, T. ; Dept. of Electr., Electron. & Comput. Eng., Trieste Univ., Italy ; Zoppoli, R.

A feedback control law is proposed that drives the controlled vector vt of a 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 behaviour of vt* over time is completely unpredictable. Random noises (in general, non-Gaussian) act on both the dynamic system and the state observation channel, which may also be nonlinear. The proposed solution is based on three main approximating assumptions: 1) the optimal control problem is stated in a receding-horizon framework where vt0 is assumed to remain constant within a shifting-time window; 2) the control law is assigned a given structure (the one of a multilayer feedforward neural network) in which a finite number of parameters have to be determined in order to minimize the cost function; and 3) the control law is given a “limited memory”, which prevents the amount of data to be stored from increasing over time. The errors resulting from the second and third assumptions are discussed

Published in:

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on  (Volume:3 )

Date of Conference:

11-13 Dec 1996

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