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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Reinforcement Learning in Discrete Neural Control of the Underactuated System
verfasst von : Zenon Hendzel, Andrzej Burghardt, Marcin Szuster
Erschienen in: Artificial Intelligence and Soft Computing
Verlag: Springer Berlin Heidelberg
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The article presents a new approach to the problem of a discrete neural control of an underactuated system, using reinforcement learning method to an on-line adaptation of a neural network. The controlled system is of the ball and beam type, which is the nonlinear dynamical object with the number of control signals smaller than the number of degrees of freedom. The main part of the neural control system is the actor-critic structure, that comes under the Neural Dynamic Programming algorithms family, realised in the form of Dual Heuristic Dynamic Programming structure. The control system includes moreover the PD controller and the supervisory therm, derived from the Lyapunov stability theorem, that ensures stability. The proposed neural control system works on-line and does not require a preliminary learning. Computer simulations have been conducted to illustrate the performance of the control system.