Adaptive Optimal Control of CVCF Inverters With Uncertain Load: An Adaptive Dynamic Programming Approach

This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcement learning and dynamic programming (ADP). Different from existing literature, the load is treated as uncertainty robust state-feedback controller proposed. The stability of inverter-load system has been strictly analyzed. In order to obtain accurate output current differential signal, this designs tracking differentiator. It ensured that error asymptotically converges zero through output-feedback controllers. A standard proportional integral linear active disturbance rejection strategy are also designed purpose comparison. simulation results show inherent robustness does not require retuning with different applications.