Displaying similar documents to “A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling”

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.

Approach to the design of robust networked control systems

Michał Morawski, Antoni M. Zajączkowski (2010)

International Journal of Applied Mathematics and Computer Science

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The paper describes the application of the traffic engineering framework together with application layer procedures as mechanisms for the reduction of network latency lags. These mechanisms allow using standard and inexpensive hardware and software technologies typically applied for office networking as a means of realising networked control systems (NCSs) with high dynamic control plants, where a high dynamic control plant is the one that requires the sampling period several times shorter...

A backstepping approach to ship course control

Anna Witkowska, Mirosław Tomera, Roman Smierzchalski (2007)

International Journal of Applied Mathematics and Computer Science

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As an object of course control, the ship is characterised by a nonlinear function describing static manoeuvring characteristics that reflect the steady-state relation between the rudder deflection and the rate of turn of the hull. One of the methods which can be used for designing a nonlinear ship course controller is the backstepping method. It is used here for designing two configurations of nonlinear controllers, which are then applied to ship course control. The parameters of the...

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...

Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment

Farid Ferguene, Redouane Toumi (2009)

International Journal of Applied Mathematics and Computer Science

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Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness....

Input constraints handling in an MPC/feedback linearization scheme

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...