Wavelet Neural Network Model Reference Adaptive Control Trained by a Modified Artificial Immune Algorithm to Control Nonlinear Systems

Lutfy, Omar Farouq

doi:10.1007/s13369-014-1088-5

Cited by 15 publications

(20 citation statements)

References 31 publications

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“…However, unlike other EAs, such as the genetic algorithm, the AIS includes a special mutation operation, which maintains the populations' diversity, and hence produces faster convergence rate. In this regard, the AIS algorithm, in particular the modified Micro-AIS [12], has achieved superior optimization results compared to the GA in this work. The procedure to apply the modified Micro-AIS algorithm as the IMC optimization method was achieved using the following steps [9]:…”

Section: Artificial Immune Systemmentioning

confidence: 84%

“…In this work, the SRWNN dilation and translation parameters are initially set using the following method [12]; Assume that a and b represent the minimum and the maximum values of a particular dataset, respectively. Using these variables, the translation and dilation of the j th wavelon are initialized as follows:…”

Section: Parameter Initialization In the Self-recurrent Wavelet Neuramentioning

confidence: 99%

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Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems

Lutfy¹,

Majeed²

2018

ETJ

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This paper presents a Self-Recurrent Wavelet Neural Network (SRWNN)-based Internal Model Control (IMC) for nonlinear systems. As the internal model, a Nonlinear Autoregressive Moving Average (NARMA-L2) is employed for obtaining a forward system model. Then, this model is directly used to formulate the control law. The proposed SRWNN-based IMC is an enhanced version of a previously published Wavelet Neural Network (WNN)-based IMC scheme. Particularly, the enhancement was attained by considering three modifications, which include the use of an initialization phase for the parameters of the wavelon layer, the utilization of self-feedback connections in the wavelon layer, and the exploitation of RASP1 as the mother wavelet function. The modified Micro Artificial Immune System (modified Micro-AIS) is employed as the training method. From the simulation results, the efficiency of the suggested methodology have been proved concerning control precision and disturbance rejection ability. Moreover, the superiority of the SRWNN over the WNN and the Multilayer Perceptron (MLP) as the IMC controllers has been confirmed from a comparative study. Furthermore, the modified Micro-AIS has accomplished better results compared to the Genetic Algorithm (GA) concerning control precision.

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Section: Artificial Immune Systemmentioning

confidence: 84%

Section: Parameter Initialization In the Self-recurrent Wavelet Neuramentioning

confidence: 99%

Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems

Lutfy¹,

Majeed²

2018

ETJ

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“…In the other hand, several researches have considered the impact of temperature on parameter, however, the challenge is to avoid degrading motor performance when it heats, adapting online control torque variations of the physical parameters of temperature-dependent motor. In so doing, these physical parameters must be determined on-line, either by direct estimation of these parameters, or by linking their variations with temperature variations [7]- [13].…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of PMSM using EKF with temperature variation tracking in automotive applications

Kerid¹,

Bourouina²,

Yahiaoui³

2018

PEN

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Permanent magnet synchronous machine is widely used for electric vehicles traction because of its high power density and its efficiency on a large flux weakening range. This paper focuses in particular on the estimation of PMSM parameters using EKF, we present a study assessing the temperature variations impact on the behavior of PMSM motor, and therefore we propose to estimate the temperature-dependent parameters. The main contribution in this work is an effective method for estimating parameters or their temperature variation, makes it possible to study and to avoid performance degradation by tracking and adapting the parameters in torque observer in order to find the same performance at any temperature and can be also used for thermal monitoring, which allows for better availability of motor, without causing damage, however, the knowledge of degradation mechanisms also gives insight for the design of this machine. Nowadays, there are essentially maps of reference currents according to the torque and speed that are used by car manufacturers and no account is then given of the parameter variations. The effectiveness of the proposed estimation method verified by both simulation and experiment.

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“…Compared to the GA, the AIS algorithm has a more efficient mutation operator which results in a better diversity of populations [19,20]. In this regard, utilizing this promising optimization method, Lutfy [21] proposed to use a modified micro artificial immune system (MMAIS) to train a WNN as the main controller in the MRAC scheme. However, in the above work, the WNN controller was treated as a black box approximator without using an initializing phase, which can affect the WNN approximation capability.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, suitable initialization of these factors plays an important role in improving the WNN approximation capability. However, despite this importance for the initialization process, several researchers did not consider a specific initialization approach for these parameters [11,13,16,21,23].To this end, aiming at enhancing the performance of the WNN controller, the motivation of the present work was to propose a more efficient version of the WNN structure. This modified WNN structure encompasses two amendments, namely; adopting an initialization phase to enhance the convergence to the optimal weights and including self-feedback connections to the wavelons in the wavelet layer.…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive Control based on a Self-Recurrent Wavelet Neural Network Utilizing Micro Artificial Immune Systems

Dawood¹,

Lutfy²

2017

alkej

Self Cite

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This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control performance of the SRWNN-based MRAC. As the training method, the recently developed modified micro artificial immune system (MMAIS) was used to optimize the parameters of the SRWNN. The effectiveness of this control approach was demonstrated by controlling several nonlinear dynamical systems. For each of these systems, several evaluation tests were conducted, including control performance tests, robustness tests, and generalization tests. From these tests, the SRWNN-based MRAC has exhibited its effectiveness regarding accurate control, disturbance rejection, and generalization ability. In addition, a comparative study was made with other related controllers, namely the original WNN, the artificial neural network (ANN), and the modified recurrent network (MRN). The results of these comparison tests indicated the superiority of the SRWNN controller over the other related controllers. Keywords: Artificial neural network, micro artificial immune system, model reference adaptive control, self-recurrent wavelet neural network , Wavelet neural network.

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Wavelet Neural Network Model Reference Adaptive Control Trained by a Modified Artificial Immune Algorithm to Control Nonlinear Systems

Cited by 15 publications

References 31 publications

Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems

Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems

Parameter identification of PMSM using EKF with temperature variation tracking in automotive applications

Model Reference Adaptive Control based on a Self-Recurrent Wavelet Neural Network Utilizing Micro Artificial Immune Systems

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