Smoothing the control action for NARMA-L2 controllers

Pukrittayakamee, A.; Jesús, Orlando De; Hagan, Martin T.

doi:10.1109/mwscas.2002.1186964

Cited by 21 publications

(16 citation statements)

References 5 publications

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“…From the result obtained it can be concluded that the neural network has shown better performances in rejecting impulse and sinusoidal disturbances from the engine and thus isolating the transmitted force from the chassis even though the overshoot of the controller is high. It was shown by other researches such as in [38] that the NARMA-L2 has the behavior of producing large overshoot and a phase shift when implemented as a controller. Therefore, the researcher in [37] has introduced appropriate design strategies to reduce the high overshoot and to smooth out the control action of the controller.…”

Section: Resultsmentioning

confidence: 97%

Active Engine Mounting Control Algorithm Using Neural Network

Darsivan

Martono

Faris

2009

Shock and Vibration

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Abstract. This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

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Section: Resultsmentioning

confidence: 97%

Active Engine Mounting Control Algorithm Using Neural Network

Darsivan

Martono

Faris

2009

Shock and Vibration

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“…Once the nonlinear function of f and g are modeled, the controller then rearrange of the two sub-networks of the plant model in an easy way to obtain the control. The closed loop system of this method is presented in Figure 12 [17].…”

Section: Ann Controllermentioning

confidence: 99%

Automatic Generation Control of Multi-area Interconnected Power Systems Using ANN Controller

Alzaareer¹,

Al-Shetwi²,

El-bayeh³

et al. 2020

RIA

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Load as well as power flow in tie-line are continuously varying in interconnection power systems. This paper presents an efficient method based on artificial intelligence control for automatic generation control (AGC) of a three-area power network. The control method implements Artificial Neural Network (ANN) to damp the frequency deviation and the fluctuation in the tie line power caused by load disturbances. The performance of the proposed controller is compared with classical control methods (PI and PID). The results showed that ANN-based control method is more efficient than others approaches. In this paper, MATLAB/SIMULINK package is used to investigate the results.

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“…An approximate NARAM-L2 model was used to represent the operation of the integrated system of the "Feedback" Torque & Speed Control module and the d.c shunt motor. The NARMA-L2 controller transforms nonlinear system dynamics into linear dynamics by cancelling the nonlinearities and this can be simply accomplished by Neural Network model [33]. The neural network was trained offline in batch form by back-propagation.…”

Section: Model Reference Controlmentioning

confidence: 99%

Adaptive linear prediction for optimal control of wind turbines

Narayana

Sunderland²,

Putrus

et al. 2017

Renewable Energy

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In order to obtain maximum power output of a Wind Energy Conversion System (WECS), the rotor speed needs to be optimised for a particular wind speed. However, due to inherent inertia, the rotor of a WECS cannot react instantaneously according to wind speed variations. As a consequence, the performance of the system and consequently the wind energy conversion capability of the rotor are negatively affected. This study considers the use of a time series Adaptive Linear Prediction (ALP) technique as a means to improve the performance and conversion efficiency of wind turbines. The ALP technique is introduced as a real time control reference to improve optimal control of wind turbines. In this study, a wind turbine emulator is developed to evaluate the performance of the predictive control strategy. In this regard, the ALP reference control method was applied as a means to control the torque/speed of the emulator. The results show that the employment of a predictive technique increases energy yield by almost 5%.

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Smoothing the control action for NARMA-L2 controllers

Cited by 21 publications

References 5 publications

Active Engine Mounting Control Algorithm Using Neural Network

Active Engine Mounting Control Algorithm Using Neural Network

Automatic Generation Control of Multi-area Interconnected Power Systems Using ANN Controller

Adaptive linear prediction for optimal control of wind turbines

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