Adaptive Fuzzy Q-Learning Control Design and Application to Grid-Tied Nine-Level Packed E-Cell (PEC9) Inverter

This letter applies a multiagent fuzzy Q-learning (FQL) algorithm, incorporated with a model-free nonlinear controller (MFNC), entitled FQL-MFNC for stabilized controlling of a recently introduced grid-connected nine-level Packed E-Cell (PEC9) inverter under dynamical operation. Unlike previous tuning schemes, which concentrate on extracting mathematical formulation of a controlled plant, this letter investigates a fuzzy Q-learning agent for optimal design of PEC9. In first step, the fuzzy reinforcement learning is adopted to tune the MFNC controller in the simulation environment. In fact, the FQL algorithm finds the optimal policy based on a reward function for adjustment of the MFNC control coefficients to guarantee the grid-connectivity requirements under PEC9 dynamical operation are met. The experimental tests are conducted to assure efficiency and practicability of the designed multi-agent FQL-MFNC scheme on the single-phase grid-connected PEC9 inverter.