scholarly journals Analisa Kinerja Teknik Reduksi PAPR Metode SLM dan HPA Predistorsi dengan Pemodelan Hammerstein pada Sistem OFDM

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ida Anisah
Keyword(s):  
Power Amplifier ◽  
Hammerstein Model ◽  
Non Linear ◽  
Selective Mapping

Abstrak: Teknik OFDM mempunyai kelemahan, salah satunya adalah tingginya PAPR yang akan menyebabkan distorsi non-linear pada Power Amplifier (PA), dikarenakan PA membatasi keluaran dengan nilai tertentu yang dapat mengurangi efisiensi daya amplifier. Dalam mengatasi masalah tersebut maka diperlukan teknik reduksi PAPR. Untuk teknik reduksi PAPR efisien yang digunakan adalah Selective Mapping (SLM). Teknik Reduksi PAPR SLM sendiri tidak dapat untuk memperbaiki daerah non-linear pada Power Amplifier, oleh karena itu diterapkan teknik pemodelan HPA Predistorsi untuk meningkatkan efisiensi dari PA dengan memperluas jangkauan linear dan mencegah sinyal jatuh ke wilayah saturasi PA. Untuk pemodelan HPA yang digunakan yaitu pemodelan Hammerstein. Model Hammerstein ini sederhana namun efektif untuk menggambarkan kinerja PA dan kinerja predistorter non-linear akan berlawanan dengan PA dengan menggunakan efek memori. Hasil kerja dari teknik gabungan tersebut dapat dianalisa berdasakan kurva BER dan kurva CCDF yang dihasilkan. Performansi BER dapat diperbaiki ketika menggunakan teknik reduksi SLM yang dikombinasikan dengan teknik predistorsi, dimana pada nilai SNR sebesar 15 dB sudah mampu mengatasi probabilitas BER. Namun, ketika tanpa teknik predistorsi baru bisa mengatasi probabilitas BER pada nilai SNR sebesar 22 dB. Untuk teknik reduksi SLM dan teknik predistorsi dengan subblok 16 mampu menghasilkan nilai lebih baik daripada saat menggunakan subblok 4 dan 8 yaitu dapat mencapai probabilitas BER hingga 10-6 dengan nilai SNR 14 dB, dan sudah dapat mengatasi probabilitas BER pada SNR 15 dB.  Dengan teknik predistorsi ini mampu meningkatkan linearisasi pada PA. Sehingga dapat dibuktikan untuk dapat melewati HPA, sinyal dengan reduksi menggunakan subblok 4 sudah dapat melewati HPA yang mempunyai linearisasi tinggi.  

A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akshaykumar Naregalkar ◽  
Subbulekshmi Durairaj
Keyword(s):  
Linear System ◽  
Regulatory Control ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Structure ◽  
Linear Dynamics ◽  
Vector Machines ◽  
Non Linear ◽  
Laguerre Filters ◽  
Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

scholarly journals Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 850
Author(s):  
Pietro Burrascano ◽  
Matteo Ciuffetti
Keyword(s):  
Early Detection ◽  
Additive Noise ◽  
Linear Models ◽  
High Stress ◽  
Damage Index ◽  
Hammerstein Model ◽  
Linear Behavior ◽  
Non Linear ◽  
Ultrasonic Techniques ◽  
Detection Of Defects

Ultrasonic techniques are widely used for the detection of defects in solid structures. They are mainly based on estimating the impulse response of the system and most often refer to linear models. High-stress conditions of the structures may reveal non-linear aspects of their behavior caused by even small defects due to ageing or previous severe loading: consequently, models suitable to identify the existence of a non-linear input-output characteristic of the system allow to improve the sensitivity of the detection procedure, making it possible to observe the onset of fatigue-induced cracks and/or defects by highlighting the early stages of their formation. This paper starts from an analysis of the characteristics of a damage index that has proved effective for the early detection of defects based on their non-linear behavior: it is based on the Hammerstein model of the non-linear physical system. The availability of this mathematical model makes it possible to derive from it a number of different global parameters, all of which are suitable for highlighting the onset of defects in the structure under examination, but whose characteristics can be very different from each other. In this work, an original damage index based on the same Hammerstein model is proposed. We report the results of several experiments showing that our proposed damage index has a much higher sensitivity even for small defects. Moreover, extensive tests conducted in the presence of different levels of additive noise show that the new proposed estimator adds to this sensitivity feature a better estimation stability in the presence of additive noise.

A pole placement controller for non-linear dynamic plants

2002 ◽  
Vol 216 (6) ◽  
pp. 467-476 ◽  
Author(s):  
Q M Zhu ◽  
L Z Guo
Keyword(s):  
Control System ◽  
System Design ◽  
Discrete Time ◽  
Linear Control System ◽  
Linear Control ◽  
Pole Placement ◽  
Hammerstein Model ◽  
Control System Design ◽  
Non Linear ◽  
Pole Placement Controller

In this study a control-oriented model is proposed to represent a wide range of non-linear discrete-time dynamic plants. As a testimony to the efficiency of the model structure for control system design, a pole placement controller is designed for non-linear discrete-time plants. Mathematically the solution of the controller output is converted into resolving a polynomial equation in the current control term u( t), which significantly reduces the difficulties encountered in non-linear control system synthesis and computational complexities. The integrated procedure provides a straightforward methodology to use in linear control system design techniques when designing non-linear control systems. For a demonstration of the effectiveness of the proposed methodology used to deal with practical problems, pole placement controllers are designed for three non-linear plants, including the Hammerstein model, a laboratory-scale liquid level system and a continuous stirred tank reactor. The simulation results are presented with graphical illustrations.

scholarly journals A New Hammerstein Model for Non-Linear System Identification

2012 ◽  
pp. 6-12
Author(s):  
Ayush Mittal ◽  
P Aadaleesan
Keyword(s):  
Orthonormal Basis ◽  
Linear Time ◽  
Basis Functions ◽  
Hammerstein Model ◽  
Linear Time Invariant ◽  
Wavelet Networks ◽  
Non Linear ◽  
Non Linear System ◽  
Linear Time Invariant System ◽  
Time Invariant

In the present work a newer type of black box nonlinear model in Hammerstein structure is proposed. The model has Wavelet Network coupled with Orthonormal Basis Functions which is capable of modeling a class of non-linear systems with acceptable accuracy. Wavelet basis functions have the property of localization in both the time and frequency domains which enables wavelet networks to approximate severe non-linearities using few number of parameters. Orthonormal Basis functions possess the ability to approximate any linear time invariant system using appropriate basis functions. The efficacy of the model in modeling is demonstrated using numerical examples.

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