Stability analysis and optimization of Cuk converter based on delayed impulse comparable to spiking neural network control principle

In this paper, an impulsive control algorithm with time-delay is proposed to alleviate the issue of interrelated state variables and the adverse effect of input voltage or load perturbations on output in the highly nonlinearity of the DC converter. The impulsive control is introduced concerning the state information of the control system, while the stability criterion is established by employing the average dwell time. The control principle similarity of the delayed impulsive effect and the spiking neural network are elucidated. Unlike the already existing control methods, such as proportional–integral (PI) control and sliding mode (SM) control, we leverage the state information of the switching state drive system in recent history at every sampling moment to conduct impulsive control with accurate and swift error feedback. Three typical controls are executed on the output load and source voltage operating point, characterizing the advantages superior to the PI and SM control algorithms. Hence, the proposed algorithm outperforms others in system performance indicators such as output voltage step-up, step-down, settling time and ripple. In terms of the variation of source voltage and irradiance in the photovoltaic system, the verification of voltage control stability is given in the case of the maximum power point module of the photovoltaic system, exhibiting minor output voltage ripple and shorter transient change time.