Adaptive control of LCL filter with time-varying parameters using reinforcement learning

Two main aspects of a LQ control of a single-phase converter with a LCL filter are studied in this paper. First, the choice of a penalization structure that prevents instability of the closed loop caused by input saturation due to limited voltage of the DC link. We show that all tested variants of the penalization can achieve stable performance of the system. Second, adaptation of the controller gains to a change of parameter of the LCL filter. We compare adaptation rules based on the classical Riccati equation and on the reinforcement learning method of the temporal difference gradient descent. The latter method has lower computational cost and the potential to be more extensible to non-linear systems. We show that the standard approach with a constant learning rate is inefficient and better results can be achievd using adaptive learning rate. Performance of the method with adaptive learning rate is tested in simulation of current tracking after step change of the output inductance.