Decentralized output voltage tracking of cascaded DC–DC converters is an interesting topic to obtain
a high voltage conversion ratio. The control purpose is challenging due to the load resistance changes,
renewable energy supply voltage variations and interaction of the individual converters. In this paper,
four novel decentralized adaptive neural network controllers are designed on the cascaded DC–DC buck
and boost converters under load and DC supply voltage uncertainties. In the beginning, individual buck
and boost converter average models that can operate in both continuous and discontinuous conduction
modes are derived. Then, the interconnected and decentralized state-space models of cascaded buck
and boost converters are extracted. These models are highly nonlinear with unknown uncertainties
which can be estimated by neural networks. Further, two decentralized adaptive backstepping neural
network voltage controllers are proposed on cascaded buck converters to deal with uncertainties and
interactions. However, these control strategies are not applicable to a boost converter due to its nonminimum
phase nature. Then, two novel decentralized adaptive neural network with a conventional
proportional–integral reference current generator are developed on the cascaded boost converters.
Practical stability of the overall system is guaranteed for the proposed controllers using Lyapunov
stability theorem. Finally, four control strategies provide good quality of output voltage in the presence
of uncertainties and interactions. Comparative simulations are carried out on cascaded buck and boost
converters to validate the effectiveness and performance of the designed methods.