Floating Weighting Factors ANN-MPC Based on Lyapunov Stability for Seven-Level Modified PUC Active Rectifier
Floating Weighting Factors ANN-MPC Based on Lyapunov Stability for Seven-Level Modified PUC Active Rectifier
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DOI：
10.1109/tie.2021.3050375
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发表时间：
2022-01-01
影响因子：
7.7
通讯作者：
Al-Haddad, Kamal
中科院分区：
文献类型：
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作者：
Babaie, Mohammad;Mehrasa, Majid;Al-Haddad, Kamal
尽管具有成本效益，但七电平改进型U型电池（MPUC 7）有源整流器往往由于不相等的直流链路而不稳定。因此，除了保持效率和电能质量外，还需要一个多目标控制器来稳定电压和电流。传统的有限集模型预测控制（FSMPC）虽然可以处理多目标问题，但不能保证系统的稳定性，而且随着目标数目的增加，其权因子的整定变得非常繁琐。针对FSMPC的设计问题，提出了一种基于李雅普诺夫稳定性理论的低频自适应FSMPC（AMPC）。AMPC处理四个控制目标和一个解耦的稳定性目标。控制目标确保MPUC 7在开关损耗、dv/dt、THD和电容涟漪方面的标准性能。稳定性目标保证了整流器在不稳定条件下的可靠性。AMPC中的加权因子是浮动的，以解决调整的挑战，其中径向基函数神经网络控制器（RBFC）调整其变化。RBFC的训练是一种新的自训练方法，包括粒子群优化（PSO）算法和一些数学分析，而不使用任何训练数据。实验和模拟测试也评估AMPC在不同条件下，以确认其在实现预期目标的可靠性。
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, dv/dt, 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.