This manuscript describes the hybrid learning algorithm for training the error optimisation in an MIMO nonlinear system. The automated controller is designed using lenient computation technique with a Levenberg-Marquardt training algorithm. The designed controller is interfaced to a microgrid which has renewable energy sources like solar, wind, fuel cell, or smart battery as input and the output power generated by these sources can be utilised for various grid and atomised applications. The erudition capability and designing methodology of adaptive networks and sturdiness of PID controllers are described. Finally, the study illustrates an offline mode comparison of PID-based ANFIS and neural controllers in terms of settling time, steady state error and overshoot.

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ISRA (India) = 1.344 ISI (Dubai, UAE) = 0.829 GIF (Australia) = 0.356 JIF = 1.500 SIS (USA) = 0.912 РИНЦ (Russia) = 0.179 ESJI (KZ) = 1.042 SJIF (Morocco) = 2.031 ICV (Poland) = 6.630

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