A stable adaptive neural-network-based scheme for dynamical system control

Xiao-li, XU; Lee, H.P.; Lin, W. Z.; Lim, Siak Piang; Shi, X H

doi:10.1016/j.jsv.2004.08.034

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…In Xu et al, 34 the authors have proposed a control method based on neural networks. The objective of the work by Fourati et al 35 is to stabilize the unknown nonlinear systems using a neural controller developed with backpropagation neural networks.…”

Section: Adaptive Neural Control Based On a Fuzzy Adapting Rate Neural Emulatormentioning

confidence: 99%

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

Rhili

Atig

Abdennour

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ensure stability and faster convergence, a neural controller adapting rate is established in the sense of the continuous Lyapunov stability method. Numerical simulations are included to illustrate the effectiveness of the proposed method. The performance of the proposed control strategy is also demonstrated through an experimental simulation.

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Section: Adaptive Neural Control Based On a Fuzzy Adapting Rate Neural Emulatormentioning

confidence: 99%

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

Rhili

Atig

Abdennour

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…The Neural Networks have been widely used mainly in connection with the finite element method for optimization of electromagnetic devices [1,5]. However, the optimization with Neural Networks was restricted to the magneto-dynamic phenomena [2,3]. In this work, we will use the Neural Networks method and we will propose a magneto-thermal calculation to optimize the throats distribution and their dimensions.…”

Section: Fig1 Classical Inductormentioning

confidence: 99%

“…The problem is to find the function which gives better results. For that, several tests must be carried out in order to determine the optimal architecture of the network [3].…”

Section: Algorithm Optimizationmentioning

confidence: 99%

Modelling and Optimization of Induction Cooking by the Use of Magneto-Thermal Finite Element Analysis and Neural Network

Allaoui

Kanssab

Matallah

et al. 2014

MSF

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The main advantage of induction cooking is the pan heating without thermal inertia.However to obtain best efficiency, it is essential to have an inductor geometry that gives ahomogeneous temperature on the pan bottom. In this paper, we will first model the magnetothermal phenomena of the system by a finite element method (FEM) in order to determine thedistribution of temperature on the pan bottom taking into consideration the nonlinearity of thesystem. This study shows that the temperature is not homogeneously distributed. Then, in the aim tohave homogeneous temperature distribution in the pan bottom, we propose an inductor structurewith four throats containing the coils and we will use Neural Networks to determine the optimalthroats distribution and their dimensions. The optimized structure permits us to achieve our goal.

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“…There have been previous applications of evolutionary algorithms [83]- [85] and neural networks [86], [87] within chaos control. In general, these were concerned with maintaining a system at a fixed operating point, aiming to improve upon the accuracy of local control techniques such as OGY, and often with explicit knowledge of dynamical information such as local Lyapunov exponents.…”

Section: A State Space Targetting In Mixed Chaotic/ordered Systemsmentioning

confidence: 99%

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

2014

IEEE Trans. Evol. Computat.

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Biological organisms exist within environments in which complex, non-linear dynamics are ubiquitous. They are coupled to these environments via their own complex, dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behaviour of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organisation of biochemical networks. We refer to these as artificial biochemical networks, and show how they can be evolved to control trajectories within three behaviourally diverse complex dynamical systems: the Lorenz system, Chirikov's standard map, and legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks. We find both approaches to be advantageous in certain situations, though note that the relative trade-offs between different models of artificial biochemical network strongly depend on the type of dynamical systems being controlled.

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A stable adaptive neural-network-based scheme for dynamical system control

Cited by 10 publications

References 23 publications

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

Modelling and Optimization of Induction Cooking by the Use of Magneto-Thermal Finite Element Analysis and Neural Network

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

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