Utilizing passive filters such as L, LC and LCL is preferred to cancel out high-frequency harmonics
caused by pulse width modulation of voltage source inverters. However, the LC and LCL filters have
shown better harmonic attenuation than the conventional L filter. Nevertheless, the control process
of LC and LCL filters is more complicated due to their higher-order dynamics. The problem gets more
challenging in the presence of uncertainties such as load and grid impedance variations. To overcome
these challenges, two novel adaptive neural dynamic surface controllers are proposed for LC and LCL
filters in the load and grid-connected modes, respectively. Meanwhile, the issue of computational
complexity inherent in the conventional backstepping method is avoided here by utilizing the dynamic
surface control technique. Furthermore, the matched and unmatched uncertainties of LC/LCL filters
are approximated via multi-input multi-output radial basis function neural networks. Stability of the
closed-loop systems is guaranteed by converging the tracking errors to a small neighborhood of the
origin. Simulations are given to illustrate the effectiveness and potential of the proposed adaptive
neural dynamic surface control methods under the load and grid impedance changes