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RISS 인기검색어
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- 저자 : Gualberto Solís-Perales (검색결과 2 건)
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Activation of Neuronal Ensembles via Controlled Synchronization
Gualberto Solís-Perales,Juan Gonzalo Barajas-Ramírez 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.1
In this contribution we present the activation of neuronal ensembles of Hindmarsh-Rose neurons bycontrolled synchronization. The main problem consists in to impose a particular spiking-bursting behavior in allthe neurons of the network. We consider a network where the neurons are in its resting state, it is desired that theneurons change their resting state to a particular behavior of activation, dictated by a neuron called the referenceneuron. The goal is reached by controlling some neurons in the network controlling only the membrane potential(electrical synapse). The key feature of the present contribution is that by controlling a small number of neuronsin the network a desired behavior is induced in all the neurons in the network despite its network topology. Theimportant parameters are the control gain and the coupling strength, thus the activation of the network lays downon a compromise between the control gain and the coupling strength.
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Gualberto Solís-Perales,Jairo Sánchez-Estrada,Ricardo Femat 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.9
This contribution presents a gain adaptation, which allows us to tune a robust asymptotic feedback linearization (RAFL). The gain adaptation allows the RAFL to attenuate the measurement noise sensitivity. The RAFL is considered here because it ensures tracking without prior information about the system’s nonlinearities and parameter bounds. Also, the RAFL only has the system output available for feedback. In this work, the robust tracking problem is faced considering: modeling errors, parametric variations, external perturbations, and noisy output measurement. On one side, the RAFL control faces modeling errors, parametric variations, and external perturbations through an observer that estimates uncertainties using an extra state, which lumps all the unknown nonlinearities and uncertainties. On the other hand, the proposed adaptive gain function allows the observer’s high gain to vary to have a fast observer’s convergence while simultaneously avoiding amplifying the measurement noise in the steadystate. The adaptive gain function provides the RAFL control robustness against noisy measurement. Thereby, the RAFL control with adaptive gain function becomes a robust feedback linearizing against to measurement noise. Finally, the RAFL controller with the adaptive gain function is illustrated by a numerical simulation of a tracking problem for a DC-motor and a chemical oxygen demand regulation in an anaerobic digestion process.
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