In this paper, we present a novel model predictive control (MPC) scheme that incorporates stability information derived from a control Lyapunov function (CLF) to dynamically prune suboptimal sequences from the search space and decrease the computational burden placed on the controller. The CLF used for pruning is then incorporated into a cost function that penalizes energy in the error system a...

To reduce the torque ripple in motors resulting from use of conventional direct control (DTC), a model predictive (MPC)-based DTC strategy for matrix converter-fed induction motor is proposed this paper. Two new look-up tables are proposed, these derived on basis electromagnetic and stator flux using all feasible voltage vectors their associated switching states. Finite set (FCS-MPC) has then b...

The performance of model predictive controllers (MPC) strongly depends on the quality their models. In field electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and often comes with parameter deviations. These issues particularly crucial in domain self-commissioning dri...

This paper presents a novel strategy for online parameter estimation in hybrid active power filter (HAPF). HAPF makes use of existing capacitor banks which it combines with an (APF) order to dynamically compensate reactive power. The equipment is controlled finite control set model predictive (FCS-MPC) due its already well-known fast dynamic response. similar grid-connected LCL-filtered convert...

In this, work the finite control set (FCS) model predictive direct current strategy with constraints, is applied to drive three-phase induction motor (IM) using well-known field-oriented control. As a modern algorithm approach of control, this kind decides suitable switching combination that brings error between desired command currents and predicated currents, as low possible, according proces...

Model predictive control (MPC) method has been recognized as one of the most promising technologies for modular multilevel converter (MMC) due to fast dynamic response and its simple realization. However, conventional finite set (FCS) MPC methods MMC are facing some challenges, such high computation burden, poor steady-state performance, dependence on weighting factors, variable switching frequ...

Doubly fed induction generator (DFIG) system suffers from complex control structure, slow dynamic response speed and cumbersome parameter design due to its traditional cascaded dual-loop strategy. A single-loop finite set model predictive (SLMPC) is proposed in the paper for DC-based DFIG DC grid, which simplifies structure design, enhances response. There are three improved aspects. First, eli...

This paper proposes a learning-based finite control set model predictive (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented address one ongoing challenges FCS-MPC converters, i.e., optimal design weighting coefficients appearing objective function for each...