Adaptive Predictive Control Strategy Using Wavenet Based Plant Modeling

Nazaruddin, Yul Y.; Cahyadi, Ferdian

doi:10.3182/20080706-5-kr-1001.01846

Cited by 2 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the main point of an adaptive feedback ANC system is to measure the error signal and regenerate the reference signal d ðnÞ from the measurements. 14 One can see the basic block diagram of the feedback ANC system in Figure 3, where eðnÞ is the residual noise, d n ð Þ is the primary noise, yðnÞ is the anti-noise signal. Moreover, WðzÞ is the transfer function of the controller and SðzÞ is the transfer function of the secondary path from cancelling loudspeaker to the error microphone.…”

Section: Feedback Anc Systemmentioning

confidence: 99%

“…S z ð Þ ¼ z ÀÁ , then the feedback ANC system is equivalent to the standard adaptive predictor. 14 Figure 5 shows a block diagram of the standard adaptive predictor.…”

Section: Feedback Anc Systemmentioning

confidence: 99%

“…On the other hand, neural networks have nonlinear properties and these properties help them to handle nonlinear processes. [14][15][16] In Salmasi and Pourghassem's 17 study, it is shown that neural networks can perform more effective than the adaptive filter in the presence of nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

“…In feedback ANC systems, only one sensor is used, which acts as both reference and error sensor. 14 Figures 1 and 2 show the feedforward ANC system and feedback ANC system, respectively. The main idea of this paper was firstly proposed in Yamada et al 22 Feedback ANC system and its block diagram are studied in 'Feedback ANC system' section.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Active noise control using wavelet function and network approach

Rakhshan

Moula

Shabaninia

et al. 2016

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Active noise control (ANC) is based on the principle of superposition of waves. It means that an algorithm is used to tune a secondary source to make an anti-noise with equal amplitude but opposite phase with the primary source. In this paper, a wavelet function and network (WAVENET) approach is designed for ANC. The algorithm is used to train parameters of an anti-noise filter for omitting the undesired noise. FXLMS and NLMS are the conventional methods of ANC that need complex acoustic plant models and these necessities make the methods complex and inaccurate. In the WAVENET approach, this complexity can be accounted for. Numerical simulations for a WAVENET approach are presented to demonstrate the performance of the WAVENET approach scheme.

show abstract

Section: Feedback Anc Systemmentioning

confidence: 99%

“…S z ð Þ ¼ z ÀÁ , then the feedback ANC system is equivalent to the standard adaptive predictor. 14 Figure 5 shows a block diagram of the standard adaptive predictor.…”

Section: Feedback Anc Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active noise control using wavelet function and network approach

Rakhshan

Moula

Shabaninia

et al. 2016

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

show abstract

“…In recent years, investigation into optimal control for improving the controllability and operability of the high-purity distillation column has been reported. ,,− Model predictive control (MPC) is one of the successful implementations of needed control strategy. A model described as an ordinary differential equation, ,, a transfer function, or a state-space model − is typically used in the controller formulation.…”

Section: Introductionmentioning

confidence: 99%

Optimization-Based Control Strategy with Wavelet Network Input–Output Linearizing Constraint for an Ill-Conditioned High-Purity Distillation Column

Tanakunmas

Panjapornpon

Tawai

et al. 2017

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

A new nonlinear optimization control strategy is developed for multivariable control of an ill-conditioned, high-purity distillation column. A high-gain directional effect resulting from the ill-conditioned nature of the system causes difficulty in controllability and requires a higher performance control system. The developed optimal controller applies a minimization of energy consumption as the optimal objective function to treat the ill-conditioning effect, while wavelet neural network input/output linearizing constraints force the outputs to reach the desired set points. In this paper, ethylene dichloride purification is used as a case study. The process dynamics are evaluated based on relevant thermodynamic properties in Aspen Plus Dynamics and are controlled by the proposed controller in the MATLAB/Simulink platform. Control performances are investigated in this cosimulation environment for set point tracking and regulatory problems. The simulation results demonstrate that robust tracking is attained, while compensation of the input disturbances is effectively improved compared with a model predictive controller.

show abstract

Adaptive Predictive Control Strategy Using Wavenet Based Plant Modeling

Cited by 2 publications

References 7 publications

Active noise control using wavelet function and network approach

Active noise control using wavelet function and network approach

Optimization-Based Control Strategy with Wavelet Network Input–Output Linearizing Constraint for an Ill-Conditioned High-Purity Distillation Column

Contact Info

Product

Resources

About