A new methodology for the design of adaptive controllers using “state-strict passivity”: Application to neural network controllers
Sesh Commuri, Frank L. Lewis (1997)
Kybernetika
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Sesh Commuri, Frank L. Lewis (1997)
Kybernetika
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Stanisław Bańka, Paweł Dworak, Krzysztof Jaroszewski (2014)
International Journal of Applied Mathematics and Computer Science
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Reyes-Reyes, J., Yu, W., Poznyak, A.S. (2000)
Mathematical Problems in Engineering
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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...
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...
Lei Yu, Shumin Fei, Jun Huang, Yongmin Li, Gang Yang, Lining Sun (2015)
Kybernetika
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In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical...
Sándor Hajdu, Péter Gáspár (2016)
International Journal of Applied Mathematics and Computer Science
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In the frame structure of stacker cranes harmful mast vibrations may appear due to the inertial forces of acceleration or the braking movement phase. This effect may reduce the stability and positioning accuracy of these machines. Unfortunately, their dynamic properties also vary with the lifted load magnitude and position. The purpose of the paper is to present a controller design method which can handle the effect of a varying lifted load magnitude and position in a dynamic model and...
Chutiphon Pukdeboon, Anuchit Jitpattanakul (2017)
Kybernetika
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This paper presents a composite controller that combines nonlinear disturbance observer and second order sliding mode controller for attitude tracking of flexible spacecraft. First, a new nonsingular sliding surface is introduced. Then, a second order sliding mode attitude controller is designed to achieve high-precision tracking performance. An extended state observer is also developed to estimate the total disturbance torque consisting of environmental disturbances, system uncertainties...
Jimoh Olarewaju Pedro, Olurotimi Akintunde Dahunsi (2011)
International Journal of Applied Mathematics and Computer Science
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This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output...
Mirosław Tomera (2017)
International Journal of Applied Mathematics and Computer Science
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The paper presents the design of a hybrid controller used to control the movement of a ship in different operating modes, thereby improving the performance of basic maneuvers. This task requires integrating several operating modes, such as maneuvering the ship at low speeds, steering the ship at different speeds in the course or along the trajectory, and stopping the ship on the route. These modes are executed by five component controllers switched on and off by the supervisor depending...