Flatness-based adaptive fuzzy control for active power filters

A new method of adaptive control for active power filters is developed in this article. By proving that the active power filter is a differentially flat system, its transformation to the linear canonical (Brunovsky) form becomes possible. In this new description the control input of the active power filter comprises unknown nonlinear terms which are identified by neurofuzzy networks and through an adaptation / learning procedure. These estimated parts of the system’s dynamics are used in an indirect adaptive control scheme, which finally makes the outputs of the active power filter converge to the desirable setpoints. The learning rate in the aforementioned adaptation procedure is given a value which assures that a suitably chosen Lyapunov function will remain negative definite. Under the proposed control method, the closed loop of the active power filter is shown to satisfy the Hinfinity tracking criterion, which implies a maximum capability for rejection of external perturbations as well as of modelling errors. flatness-based adaptive fuzzy control based on differential flatness theory is a completely model-free control method. When designing the controller, there is no need for prior knowledge of the system’s parameters and state-space equations.