Displaying similar documents to “Neural network-based NARX models in non-linear adaptive control”

Neural network-based MRAC control of dynamic nonlinear systems

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...

Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks

Zahir Ahmida, Abdelfettah Charef, Victor Becerra (2005)

International Journal of Applied Mathematics and Computer Science

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A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear...

Motor control neural models and systems theory

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.

Nonlinear predictive control based on neural multi-models

Maciej Ławryńczuk, Piotr Tatjewski (2010)

International Journal of Applied Mathematics and Computer Science

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated....

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

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...

Efficient nonlinear predictive control based on structured neural models

Maciej Ławryńczuk (2009)

International Journal of Applied Mathematics and Computer Science

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This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction...