Floating Weighting Factors ANN-MPC Based on Lyapunov Stability for Seven-Level Modified PUC Active Rectifier
Mohammad Babaie, Majid Mehrasa, Mohammad Sharifzadeh, Kamal Al‐Haddad
Abstract
Despite being cost-effective, seven-level Modified Packed U-Cell (MPUC7) active rectifier tends to be unstable due to unequal dc-links. Thus, a multiobjective controller is required to stabilize voltages and currents besides preserving efficiency and power quality. While conventional finite-set model predictive control (FSMPC) can deal with the multiobjective problem, it cannot assure the system stability, and its weighing factors tuning significantly becomes tiresome as the number of objectives increases. This article presents a low-frequency adaptive FSMPC (AMPC) stabilized based on Lyapunov stability theory to overcome the design problems of FSMPC. AMPC handles four control objectives and a decoupled stability objective. The control objectives assure the standard performance of MPUC7 in terms of switching losses, d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</i> /d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> , THD, and capacitors ripple. The stability objective guarantees the rectifier reliability under unstable conditions. The weighting factors in AMPC are floating to tackle the tuning challenges where a radial basis function neural network controller (RBFC) adjusts their variations. RBFC is trained by a novel self-training method including particle swarm optimization (PSO) algorithm and some mathematical analyses without using any training data. Experimental and simulation tests also evaluate AMPC in different conditions to confirm its reliability in fulfilling the desired objectives.