Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Abstract

Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters

“…This technique is renowned for its advantages, which include its simple design and remarkable dynamic performance [ 26 , 27 ]. The underlying principle of FS-MPC revolves around selecting the most suitable switching state of the power converter in order to minimise the future deviation of the controlled variable [ 28 , 29 ].…”

Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives

“…Translation of flux and torque references into equivalent new stator flux vector PMSM [11,12,[21][22][23][24]33], Linear induction motor [13], 2L induction motor [34], Open-end winding induction motor [35] Redundant switching states/ voltage vectors Multilevel Inverters HVDC [36][37][38], Matrix converter-fed PMSM drives [39] Virtual voltage vectors 5Ph PMSM [40] Model reference adaptive system Induction motor [41] Logical control of each control objective Quasi-Z-Source inverter [42] Offset voltage between upper and lower capacitor arms 3Ph 3L NPC inverter [43] Fuzzy decision-making Nested NPC rectifier [44] Independent circulating current controller Multilevel inverters HVDC [45,46] Online Optimization: Non-AI-based (with weighting factor)…”

Generalized Model Predictive Torque Control of Synchronous Machines

“…There have been many attempts to enhance the performance of CCS-MPC and FCS-MPC in terms of delay compensation [14], computational efficiency [15], [16], [17], [18], and robustness [19], [20], [21]. However, most of those studies used model predictive current control (MPCC) [22], where the squared current error is included in the objective function for the currents to track the references.…”