Nonlinear momentum transfer control of a gyrostat with a discrete damper using neural networks

Seo, In-Ho; Leeghim, Henzeh; Bang, Hyochoong

doi:10.1016/j.actaastro.2008.01.014

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…In many cases, using the capability of ANNs in on-line learning, the designed ANN feedback linearization control system is adaptive [3,90,[92][93][94]. In these control systems, the ANN's structure and learning laws can be defined so that the control system is stable [3,[90][91][92].…”

Section: Ann Feedback Linearization Control Systemsmentioning

confidence: 99%

“…In contrast to the neuro-predictive method, ANN feedback linearization has been widely used to control second-order mechanical systems [87,88,90,92], and first-order processes/mechanical systems [86,91,94] have also been controlled by this method.…”

Section: Ann Feedback Linearization Control Systemsmentioning

confidence: 99%

“…for uncertainty compensation 23, 89; these cases are not considered as ANN feedback linearization systems in this paper. MLPs 86–88, 90, 91 and RBFNs 3, 92, both in the recurrent form, are the most popular neural networks in ANN feedback linearization. In many cases, using the capability of ANNs in on‐line learning, the designed ANN feedback linearization control system is adaptive 3, 90, 92–94.…”

Section: An Introduction To Neuro Controlmentioning

confidence: 99%

“…In many cases, using the capability of ANNs in on‐line learning, the designed ANN feedback linearization control system is adaptive 3, 90, 92–94. In these control systems, the ANN's structure and learning laws can be defined so that the control system is stable 3, 90–92. In contrast to the neuro‐predictive method, ANN feedback linearization has been widely used to control second‐order mechanical systems 87, 88, 90, 92, and first‐order processes/mechanical systems 86, 91, 94 have also been controlled by this method.…”

Section: An Introduction To Neuro Controlmentioning

confidence: 99%

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A critical review of the most popular types of neuro control

Mohammadzaheri

Chen

Grainger

2011

Asian Journal of Control

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In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well-known control technique. This attitude towards the extension of the application of well-known control methods using ANNs was followed by the development of ANN model-predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well-known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed.Key Words: Control, neural network, adaptive, feedback linearization, predictive, model reference, perceptron, radial basis. I. ARTIFICIAL NEURAL NETWORKS USABLE IN CONTROLArtificial neural networks (ANNs) are special mathematical models inspired by human neural networks. An ANN usually includes neurons, connections and biases. Neurons are arranged in 'layers'. There are a variety of neural networks suitable for different purposes [1,2]. In neuro control, it is a difficult and unsolved problem to find the best ANN structure for each specific application; thus, a fairly large ANN is usually employed to deal with relatively complex approximation problems [3]. In this paper, the most common types of artificial neural networks in the area of control are introduced: multi layer perceptrons (MLPs) and radial basis function networks (RBFNs). Both of these are known as universal approximators of systems [4,5]. The advantage of RBFNs is their quick training process compared with MLPs; however, for complicated systems with many inputs, the number of neurons in RBFNs are usually considerably higher than MLPs [1,2]. Regardless of the main structure, if the output of an ANN depends not only on the current q

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Section: Ann Feedback Linearization Control Systemsmentioning

confidence: 99%

Section: Ann Feedback Linearization Control Systemsmentioning

confidence: 99%

Section: An Introduction To Neuro Controlmentioning

confidence: 99%

Section: An Introduction To Neuro Controlmentioning

confidence: 99%

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A critical review of the most popular types of neuro control

Mohammadzaheri

Chen

Grainger

2011

Asian Journal of Control

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“…Other relevant studies have been researched, such as the dynamics of dual-spin spacecraft under effects of energy dissipation, where the damper masses in the platform and the rotor cause energy loss in the system (Nazari and Butcher, 2014), and the momentum transfer control of a torque-free gyrostat with a discrete damper, which uses an adaptive feedback linearisation method combined with neural networks (Seo et al, 2008). Zanardi and Moreira (2007) developed an analytical approach for the gyrostat attitude propagation using non-singular canonical variables to describe the rotational motion.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of a gyrostat satellite with the vector of gyrostatic moment tangent to the orbital plane

Morais

Santos

Silva

et al. 2022

Advances in Space Research

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Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

2009

Adaptive Control & Signal

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Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state-dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high-frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L 2 -gain performance. The resulting closed-loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty.

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Nonlinear momentum transfer control of a gyrostat with a discrete damper using neural networks

Cited by 14 publications

References 29 publications

A critical review of the most popular types of neuro control

A critical review of the most popular types of neuro control

Dynamics of a gyrostat satellite with the vector of gyrostatic moment tangent to the orbital plane

Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

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