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

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