Artificial neural network based inverse model control of a nonlinear process

Rajesh, R J; Preethi, R.; Mehata, Parth; Pandian, B. Jaganatha

doi:10.1109/ic4.2015.7375581

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…A similar problem is treated in [15] but with non-adaptive inverse control. Recently, Lyapunov-based DL adaptive online control of nonlinear systems, as opposed to AIC, was carried out [16].…”

Section: Adaptive Inverse Modelmentioning

confidence: 99%

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

Alwan

Hussain

2022

Sensors

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An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system.

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Section: Adaptive Inverse Modelmentioning

confidence: 99%

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

Alwan

Hussain

2022

Sensors

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“…A similar problem is treated in [14] but with non-adaptive inverse control. Recently, Lyapunov-based DL adaptive online control of nonlinear systems has been carried out rather than AIC [15].…”

Section: Adaptivementioning

confidence: 99%

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

Alwan¹,

Hussain²

2022

Preprint

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An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. Settling times and rise times of the step response are shown to improve in the DL-based AIC system.

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“…Neural networks based on an inverse model using for the identification and the control of nonlinear systems were proposed. 36 The weight adjustment is obtained according to the Lyapunov stability approach. In brief, the method is suitable for slowly varying processes but does not result in good closed-loop performance for general unknown processes with noise disturbances.…”

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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Artificial neural network based inverse model control of a nonlinear process

Cited by 9 publications

References 1 publication

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

Deep Learning for Robust Adaptive Inverse Control of Nonlinear Dynamic Systems: Improved Settling Time with an Autoencoder

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

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