Adaptive Neuralback Stepping Controller for MPPT in Photo Voltaic Systems

Govindharaj, Arunprasad; Mariappan, Anitha

doi:10.1109/incos45849.2019.8951363

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…In the neural network, the weights are updated online based on the uncertainty caused by the disturbances which do not require initial training that result in the minimum computational burden. The radial basis function (RBF) [28,29] polynomial is not preferred because every node in the hidden layer has to compute the RBF function based on the different centre values which result in the computation more intense and also it does not have orthogonal property. The two known orthogonal polynomials are Chebyshev polynomials [30][31][32] and Legendre polynomials [33].…”

Section: Introductionmentioning

confidence: 99%

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

Govindharaj

Mariappan

Ambikapathy

et al. 2022

IET Power Electronics

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A low‐cost battery‐less solar‐powered PMDC motor using an adaptive backstepping Chebyshev neural network controller to track the desired speed for any change in irradiance and load torque is proposed in this paper. The neural network is used for approximating the variable load torque because of its approximation property. The computational burden of the control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of neurons in accordance with the Lyapunov stability analysis. From the Lyapunov control function of backstepping control design procedure, the control law is obtained by an innovative way of elucidating the cubic equation, in place of resolving the derivative of the control law in the control function. This approach eliminates the constraints caused by the non‐strict feedback system for the backstepping control approach and also this reduces ripples in the duty cycle which makes its appropriateness in real‐time. To ensure its robustness in tracking the desired speed at a faster time and minimum overshoot, simulations are done for an extensive range of variations in irradiance and load torque, and the obtained results are assessed by comparing it with the PID controller and conventional backstepping controller. Because of the use of neural network the robustness of the proposed controller is ensured with enhanced transient and steady state responses. A prototype is developed in the laboratory and the obtained results are assessed by comparing it with the backstepping controller and PID controller.

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Section: Introductionmentioning

confidence: 99%

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

Govindharaj

Mariappan

Ambikapathy

et al. 2022

IET Power Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the neural network, the weights are updated online based on the uncertainty caused by the disturbances which do not require initial training that result in the minimum computational problem. The Radial Basis Function (RBF) [40,41] polynomial is not preferred because every node in the hidden layer has to compute the RBF function based on the different center values which result in the computation being more intense. Also, it does not have orthogonal property.…”

Section: Introductionmentioning

confidence: 99%

“…uS By solving for the cubic equation in(40) using Cardano formula and as 0 < u < 1, the duty cycle is given by…”

mentioning

confidence: 99%

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

et al. 2021

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The efficiency of the low-cost renewable energy source i.e. solar is very poor due to inadequate extraction of maximum power. By employing the proper maximum power point tracking algorithm, the efficiency can be increased. An innovative adaptive backstepping neural network controller is proposed in this paper to extract the maximum power from the solar panels by tracking the desired photovoltaic array voltage in real-time. The maximum power is extracted indirectly by tuning the PV voltage to the desired PV voltage where the maximum power is attained at the desired PV voltage point. The desired photovoltaic array voltage is obtained from the linear regression method. The change in photovoltaic current caused by varying irradiance and temperature is approximated using the Chebyshev polynomials. The quicker steady-state and transient responses are accomplished and the computational burden of the photovoltaic system control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of the neurons in accordance with the Lyapunov stability analysis. Also, Lyapunov control function of the backstepping control design procedure finds a control law by an innovative cubic equation interpretation, instead of resolving the first derivative of the control law, that diminishes the ripples in the duty cycle to make its appropriateness in real-time. A prototype is developed to validate the robustness of this controller in maximum power extraction at a faster time and the results confirm that adaptive backstepping neural network controller surpasses the performances of conventional backstepping controller and constant voltage PID controller.

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The Survey of MPPT under non-uniform atmospheric conditions for the Photovoltaic Generation Systems

Manisha

Gaur

2021

Int. j. inf. tecnol.

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Adaptive Neuralback Stepping Controller for MPPT in Photo Voltaic Systems

Cited by 4 publications

References 12 publications

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

Real-Time Implementation of Adaptive Neuro Backstepping Controller for Maximum Power Point Tracking in Photo Voltaic Systems

The Survey of MPPT under non-uniform atmospheric conditions for the Photovoltaic Generation Systems

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