Stable modeling based control methods using a new RBF network

Beyhan, Selami; Alcı, Musa

doi:10.1016/j.isatra.2010.04.005

Cited by 29 publications

(14 citation statements)

References 15 publications

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“…The output vector (C i (k)) for each subsystem is determined using the RLSs method. 34 A linear approximation of the nonlinear system is computed using an RLS algorithm to form the LDB, which is characterized using the Laguerre filters. Figure 1 shows the block diagram of the proposed model.…”

Section: Problem Definition and Identification Proceduresmentioning

confidence: 99%

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

Sadeghi¹,

Farrokhi

2018

Adaptive Control & Signal

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In this article, a real-time block-oriented identification method for nonlinear multiple-input-multiple-output systems with input time delay is proposed. The proposed method uses the Wiener structure, which consists of a linear dynamic block (LDB) followed by a nonlinear static block (NSB). The LDB is described by the Laguerre filter lattice, whereas the NSB is characterized using the neural networks. Due to the online adaptation of the parameters, the proposed method can cope with the changes in the system parameters. Moreover, the convergence and bounded modeling error are shown using the Lyapunov direct method. Four practical case studies show the effectiveness of the proposed algorithm in the open-loop and closed-loop identification scenarios. The proposed method is compared with the recently published methods in the literature in terms of the modeling accuracy, parameter initialization, and required information from the system. How to cite this article: Sadeghi M, Farrokhi M. Real-time identification of nonlinear multiple-inputmultiple-output systems with unknown input time delay using Wiener model with Neuro-Laguerre structure. Int J Adapt Control Signal Process. 2019;33:157-174. https://doi.

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Section: Problem Definition and Identification Proceduresmentioning

confidence: 99%

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

Sadeghi¹,

Farrokhi

2018

Adaptive Control & Signal

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show abstract

“…At any working point, by using the same method introduced in Section 3 we can design an infinite-time quadratic regulator based on the locally linear time-invariant state-space model (27). The objective function of LQR in discrete-time form is given by min ΔUðkÞ n J ¼…”

Section: Global Lqr Controllermentioning

confidence: 99%

“…By using a set of RBF networks to approximate the coefficients of a state-dependent ARX model, the RBF-ARX model is yielded, which has the advantage of the state-dependent ARX model in the description of nonlinear dynamics. It also has the advantage of RBF networks in function approximation [27,28]. In general, a RBF-ARX model uses far fewer RBF centers compared with a single RBF network model, because the complexity of the model is dispersed into the lags of the autoregressive parts of the model.…”

Section: Introductionmentioning

confidence: 99%

Modeling and control approach to a distinctive quadrotor helicopter

Peng

Chen

et al. 2014

ISA Transactions

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“…In recent years, special attention has been devoted to neural network methodologies for model and control of nonlinear dynamic systems in various areas [1][2][3][4]. Recurrent fuzzy neural networks were successfully employed in control and model of dynamic systems in [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

Çoban

Aksu

2018

Teh. vjesn.

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Abstract:In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the MultifeedbackLayer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network's output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems.

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Stable modeling based control methods using a new RBF network

Cited by 29 publications

References 15 publications

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

Modeling and control approach to a distinctive quadrotor helicopter

Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

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