Passivation and control of partially known SISO nonlinear systems via dynamic neural networks.
Reyes-Reyes, J., Yu, W., Poznyak, A.S. (2000)
Mathematical Problems in Engineering
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Reyes-Reyes, J., Yu, W., Poznyak, A.S. (2000)
Mathematical Problems in Engineering
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Stanisław Bańka, Paweł Dworak, Krzysztof Jaroszewski (2014)
International Journal of Applied Mathematics and Computer Science
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Ghania Debbache, Abdelhak Bennia, Noureddine Golea (2006)
International Journal of Applied Mathematics and Computer Science
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This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also...
Jimoh Olarewaju Pedro, Aarti Panday, Laurent Dala (2013)
International Journal of Applied Mathematics and Computer Science
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The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis...
Jiamei Deng, Victor M. Becerra, Richard Stobart (2009)
International Journal of Applied Mathematics and Computer Science
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The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear...
Sesh Commuri, Frank L. Lewis (1997)
Kybernetika
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Kenji Doya, Hidenori Kimura, Aiko Miyamura (2001)
International Journal of Applied Mathematics and Computer Science
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In this paper, we introduce several system theoretic problems brought forward by recent studies on neural models of motor control. We focus our attention on three topics: (i) the cerebellum and adaptive control, (ii) reinforcement learning and the basal ganglia, and (iii) modular control with multiple models. We discuss these subjects from both neuroscience and systems theory viewpoints with the aim of promoting interplay between the two research communities.
Hossein Rouhani, Arash Sadeghzadeh, Caro Lucas, Babak Araabi (2007)
Control and Cybernetics
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