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To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation, a control method involving flexible multistate switches (FMSs) is proposed in this study. This approach is based on an improved double-loop recursive fuzzy neural network (DRFNN) sliding mode, which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults. First, an improved DRFNN sliding mode control (SMC) method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system. To improve the robustness of the system, an adaptive parameter-adjustment strategy for the DRFNN is designed, where its dynamic mapping capabilities are leveraged to improve the transient compensation control. Additionally, a quasi-continuous secondorder sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability. The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem. A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink. The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis. [ABSTRACT FROM AUTHOR]

We demonstrated herein an electrochemical dearomatizative alkylation of Katritzky salts, wherein Katritzky salts were harnessed as both radical acceptors and donors. A wide range of privileged dihydropyridine scaffolds was constructed with good to excellent yields. Cyclic voltammetry (CV) and electron paramagnetic resonance (EPR) results confirmed the key intermediates-dihydropyridine radicals and gram-scale reaction highlighted the practical and sustainable feature of the newly developed protocol. [ABSTRACT FROM AUTHOR]