Optimal Model Predictive based on Super-Twisting Fractional Order Sliding Mode Control to Regulate DC-link Voltage of DC Microgrid

Abstract

This paper aims to essentially regulate the DC-link voltage of DC microgrid during the disturbance conditions in power system. Hence, a novel Optimal Model Predictive Super-Twisting Fractional Order Sliding Mode Control (OMP-STFOSMC) is proposed for three-phase AC-DC converter which can effectively enhance the stability and dynamic performance of microgrid. The conventional model-predictive controllers have severely imposed the dynamic stability which leads to high overshoot, undershoot and settling-time. The sliding mode controller can be replaced instead of these conventional controllers to appropriately triumph over this problem. A main drawback of conventional sliding mode controller is related to its high frequency chattering in the control signal which can affect the system and doesn’t make it satisfactory and feasible for real applications. The proposed OMP-STFOSMC can effectively enhance the control tracking performance and reduce the high frequency chattering problem. The Stochastic Fractal Search (SFS) algorithm due to its high exploration and good evasion of local optima is used to optimally tune the controller parameters. Different operation conditions are considered to evaluate the dynamic and chattering-free performance of proposed controller. As to the simulation results with comparative analysis, it is observed that the proposed OMP-STFOSMC offers better dynamic stability characteristics.

Model Predictive Sliding Mode Control of Direct Current Motors

“…Comparisons between extended MPSMC and Model Predictive proportional integral controller show that SMC reduces settling time, overshoot, and steady-state error, providing better performance in tracking DC reference voltage demand. Amiri et al [25] introduced an optimal model predictive super-twisting fractional order sliding mode control method to regulate DC-link voltage in DC microgrids during power system disturbance, improving stability and performance. Homaeinezhad et al [26] developed a control scheme that combines nonlinear MPC with discrete SMC to achieve robust stabilization and predictive tracking for a class of nonlinear uncertain multivariable systems.…”