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

Content Provider	IEEE Xplore Digital Library
Author	Parisini, T. Zoppoli, R.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr., Electron. & Comput. Eng., Trieste Univ., Italy (Parisini, T.)
Abstract	A feedback control law is proposed that drives the controlled vector v/sub t/ of a dynamic system (in general, nonlinear) to track a reference v/sub t/* over an infinite time horizon, while minimizing a given cost function (in general, nonquadratic). The behaviour of v/sub t/* 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 v/sub t//sup 0/ 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.
Starting Page	3294
Ending Page	3299
File Size	704861
Page Count	6
File Format	PDF
ISBN	0780335902
ISSN	01912216
DOI	10.1109/CDC.1996.573652
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-12-13
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimal control Stochastic systems Nonlinear dynamical systems Cost function Multi-layer neural network Feedback control Nonlinear control systems Control systems Neural networks Feedforward neural networks
Content Type	Text
Resource Type	Article

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