Robust nonlinear MPPT controller for PV energy systems using PSO-based integral backstepping and artificial neural network techniques

A PV system is subject to random variations in environmental conditions, and continuous tracking of the maximum power point is an indispensable step to improve the PV operational efficiency. Numerous techniques of maximum power point tracking have been reported in the literature. However, these techniques suffer from numerous problems such as oscillation around the maximum power point and do not provide satisfactory robustness. Taking into account the nonlinear nature of the PV module and power electronics converters in PV systems, nonlinear control represents a vital control solution to guarantee both an optimal and robust PV system. The nonlinear control strategy proposed in this work forms a closed-loop system between the PV module, boost converter, load, an artificial neural network model for reference prediction, and an integral backstepping controller. The stability of the controller has been verified by Lyapunov theory and the controller has been optimized using the particle swarm optimization (PSO) method. Numerical simulations with rigorous robust tests have proved the superior performance of the proposed controller as compared to perturb and observe, and PSO-terminal sliding mode controller. The proposed controller was further verified under real experimental environmental conditions and found to yield satisfactory performance.