Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network

Zouari, Farouk

doi:10.4236/jsea.2012.53024

Cited by 13 publications

(7 citation statements)

References 47 publications

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“…Recently, nonlinear systems have become a research hotspot due to it can better describe the actual system. Usually, the fuzzy or NN technique are used to estimate the nonlinear function in the system, 1–8 and the backstepping control approach is utilized to design the controller 9–14 . Although many excellent research results have been reported, there are still many unsolved problems, such as disturbance and unknown gain function and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, the fuzzy or NN technique are used to estimate the nonlinear function in the system, [1][2][3][4][5][6][7][8] and the backstepping control approach is utilized to design the controller. [9][10][11][12][13][14] Although many excellent research results have been reported, there are still many unsolved problems, such as disturbance and unknown gain function and so forth. In Reference 15, for the affine nonlinear systems suffer from disturbances and uncertainties, a full-order observer which joins the ultimate uniform bounded stability and sliding-mode is presented.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered‐NN control for nonlinear system with unknown gain and disturbance

Chen

Shi

2022

Intl J Robust & Nonlinear

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In this article, the problem of tracking control for nonlinear system with unknown gain and disturbance is studied. First, to compensate for the adverse effects of disturbance, a disturbance observer via sliding‐mode is designed to estimate the disturbance, and the finite‐time convergence of estimation error is ensured under certain conditions. Then, an event‐triggered neural networks controller based on estimated disturbance is presented to achieve tracking control. Among which, the Nussbaum technique is employed to deal with the unknown gain, and the super‐twisting estimator is introduced to estimate the derivative of virtual control input. Then a time‐varying event‐triggered strategy is designed to reduce communication load. Furthermore, the stability analysis shows that the close‐loop signals are bounded, the tracking error converges to a small compact set, and no Zeno phenomenon occurs. Finally, two examples are provided to illustrate the effectiveness of the proposed new design schemes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event‐triggered‐NN control for nonlinear system with unknown gain and disturbance

Chen

Shi

2022

Intl J Robust & Nonlinear

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“…[11][12][13] To cope with practical systems with nonlinearities unmatched to local control inputs, the control problems of multiple-input-multiple-output (MIMO) nonlinear systems have been addressed via recursive control techniques. [14][15][16][17] Furthermore, adaptive tracking control approaches have been widely studied for several uncertain nonlinear systems [18][19][20][21] and practical nonlinear systems such as nonlinear servo systems, [22][23][24][25] active suspension systems, 26 direct-current motor drive systems, 27,28 and fractional-order systems. 29 Neural networks and fuzzy logic systems have been employed to compensate for unknown nonparametric uncertainties in approximation-based adaptive containment control schemes of nonlinear multi-agent systems in strict-feedback [30][31][32][33] and pure-feedback forms.…”

Section: Introductionmentioning

confidence: 99%

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Kim

Yoo

2022

Intl J Robust & Nonlinear

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This article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.

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“…3). As a result, the ERLS algorithm estimates the parameters of the DC motor, which is equivalent to the parameters of (13). Fig.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…These types of controllers are capable to improve the overall response of the controlled system [10]. Different techniques are presented in the literature, for instance the authors in [11,12] apply the fuzzy controller approach for digitally control of DC motor, while in [13][14][15][16] a neural network controller is proposed. Model reference method and adaptive techniques are also demonstrated in [9,17,18].…”

Section: Introductionmentioning

confidence: 99%

Real Time Self-Tuning Controller for Position Control of DC Motor System using Pole-Placement Technique

Mohammad¹,

Basim²,

Zaki³

et al. 2016

IJCA

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This paper presents a real time self-tuning controller for DC motor system. Pole-placement adaptive controller based on Exponentially Recursive Least Square (ERLS) algorithm is proposed. The parameters of the DC motor are estimated using ERLS algorithm. Once the estimation error is minimized, the identified parameters are forward to the supervisory control unit to find the corresponding PID gains. A PCI-6251 data acquisition card from National Instrument (NI) and the prototyped control system (33-100 & 33-110) from feedback device are deployed for real time implementation of the proposed solution. Simulation and experimental results verified the effectiveness of the ERLS algorithm, where the parameters of DC motor are estimated rapidly and accurately. Results, also shows the validation of the proposed self-tuning controller for position control of the DC motor system. General TermsControl Algorithm.

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Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network

Cited by 13 publications

References 47 publications

Event‐triggered‐NN control for nonlinear system with unknown gain and disturbance

Event‐triggered‐NN control for nonlinear system with unknown gain and disturbance

Distributed containment control of networked uncertain MIMO pure‐feedback nonlinear systems via quantized state feedback and communication

Real Time Self-Tuning Controller for Position Control of DC Motor System using Pole-Placement Technique

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