The increased integration of cyber and communication networks in microgrids for obtaining measurements and relaying controls results in a Cyber-Physical Microgrid (CPM) that paves the way for many essential applications. One of these applications is the Load Frequency Control (LFC), which is more challenging due to the stochastic behavior of renewable generation. The CPM also facilitates the participation of Electric Vehicle (EV) batteries in LFC. These information and communication technologies provide a myriad of advantages but also lead to a close synergy between heterogeneous physical and cyber components unlocking access points for cyber intrusions. A cyber intrusion camouflages and may present itself as uncertainty, eventually leading to astray controls and remedial actions. This leads to a need for an observer-based control strategy that can observe stochastic dynamic encumbrances and remunerate for them through a two-layer controller. The proposed controller is also investigated for its efficacy through Hardware-In-the-Loop (HIL) simulations in OPAL-RT that integrates the fidelity of physical simulations with the flexibility of numerical simulations.

In this paper, an optimal adaptive fuzzy sliding mode controller is presented for a class of nonlinear systems. In the proposed control, in the beginning, the boundaries of parametric uncertainties, disturbances and un-modeled dynamics are reduced using a feedback linearization approach. Next, in order to overcome the remaining uncertainties, a sliding mode controller is designed. Mathematical proof shows that the closed-loop system with the proposed control is globally asymptotically stable. Using sliding mode control causes the undesirable chattering phenomenon to occur in the control input. Next, in order to remove the undesirable chattering phenomenon, an adaptive fuzzy approximator is designed to approximate the maximum boundary of the remaining uncertainties. Another mathematical proof shows that the closed-loop system with the proposed control is globally asymptotically stable in the presence of structured and unstructured uncertainties, and external disturbances. Finally, the self-adaptive modified bat algorithm is used to determine the coefficients of the adaptive fuzzy sliding mode control and the coefficients of the membership functions of the adaptive fuzzy approximator. To investigate the performance of the proposed controller, an inverted pendulum system is used as a case study. Simulation results verify the desirable performance of the optimal adaptive fuzzy sliding mode control.