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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Summary An indirect approach is employed to track the desired output voltage of the boost converter by controlling the inductor current in the proposed adaptive backstepping Chebyshev neural network controller, because of the non‐minimum phase nature of the converter. The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev neural network is designed for the closed‐loop system based on the Lyapunov stability analysis to obtain an asymptotically stable system. In the proposed work, the required duty cycle is obtained by a novel method of solving the quadratic equation of the control function instead of the first derivative of the duty cycle to get the desired output voltage from the Lyapunov control function. Detailed analyses of simulations are carried out for a wide range of variations in the set point and load, and the results are compared with that of backstepping and PID controllers. The proposed controller exhibits superior performance than other controllers for the uncertainties caused by disturbances. To ensure its suitability in real time, a prototype is designed for the proposed controller, and the obtained results are compared with that of backstepping and PID controllers. Investigation of experimental results confirms the adaptability of the proposed control scheme as it exhibits accurate and fast response irrespective of disturbances acting upon it.

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