Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Sharun, S. M.; Mashor, Mohd Yusoff; Norhayati, M. N.; Hadani, Wan Nur; Yaacob, Sazali

doi:10.1109/iccaie.2010.5735099

Cited by 2 publications

(2 citation statements)

References 13 publications

(13 reference statements)

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“…A few performance comparisons have been done between the adaptive neurocontroller based on the HMLP network and other controllers. The results show that the ANC based on the HMLP network have given significant improvement in the performance of controlling unstable systems (Sharun, Mashor, Jaafar, Yaacob & Norhayati, 2010a;Sharun et al, 2010b& Sharun et al, 2010c). In the current study, the advantages of the HMLP network and the WRLS algorithm are combined to improve the performance of tracking control techniques in various operating conditions such as noise, varying gain and disturbance torques.…”

Section: Monopole Uhf Dipolementioning

confidence: 99%

Innosat Attitude Control System Based on Adaptive Neuro-Controller

Sharun

Mashor

Nazid

et al. 2011

Journal of Information and Communication Technology

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The current research focuses on the designing of an intelligent controller for the Attitude Control System (ACS) of the Innovative Satellite (InnoSAT). The InnoSAT mission is to demonstrate local innovative space technology amongst the institutions of higher learning in the space sector. In this study, an Adaptive Neuro-controller (ANC) based on the Hybrid Multi Layered Perceptron (HMLP) network has been developed. The Model Reference Adaptive Control (MRAC) system is used as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model. The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output. The objective of this paper is to analyse the time response and the tracking performance of the ANC based on the HMLP network and the ANC based on the standard MLP network for controlling an InnoSAT attitude. These controllers have been tested using an InnoSAT model with some variations in operating conditions such as varying gain, measurement noise and disturbance torques. The simulation results indicated that the the ANC based on the HMLP network is adequate to control satellite attitude and give better results than the ANC based on the MLP network.

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Section: Monopole Uhf Dipolementioning

confidence: 99%

Innosat Attitude Control System Based on Adaptive Neuro-Controller

Sharun

Mashor

Nazid

et al. 2011

Journal of Information and Communication Technology

Self Cite

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show abstract

“…A few comparisons performance have been done between adaptive neuro-controller based on HMLP network and other controllers. The result shows that ANC based on HMLP network give significant improvement in the performance of controlling unstable system [15][16][17][18]. The comparison is based on a double integrator and a nonlinear plant.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-controller based on HMLP network for InnoSAT attitude control

Sharun

Mashor

Norhayati

et al. 2011

International Conference on Electrical, Control and Computer Engineering 2011 (InECCE)

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In this paper, an intelligence controller namely Adaptive Neuro-controller (ANC) based on Hybrid Multilayered Perceptron (HMLP) network is developed for the attitude control of a nano-satellite. The objective of this paper is to compare the tracking performance between ANC based on HMLP network and ANC based on standard MLP network for controlling a satellite attitude. Both ANC's use Model Reference Adaptive Control (MRAC) as a control scheme. The control scheme was used to control a time varying systems where the performance specifications are given in terms of a reference model. Weighted Recursive Least Square (WRLS) algorithm has been used to adjust the controller parameters to minimize the error between the actual output and the reference input. Y-Thompson spin control is adapted to the satellite system as the reference input throughout the simulation. These controllers have been tested using Innovative Satellite (InnoSAT) plant with some variations in operating conditions such as varying gain, noise and disturbance. The simulation results indicated that ANC based on HMLP network is adequate to control satellite attitude and gave better result than the ANC based on MLP network.

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Performance comparison between Adaptive Neuro-controller and Adaptive Parametric Black Box Controller

Cited by 2 publications

References 13 publications

Innosat Attitude Control System Based on Adaptive Neuro-Controller

Innosat Attitude Control System Based on Adaptive Neuro-Controller

Adaptive Neuro-controller based on HMLP network for InnoSAT attitude control

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