Finite control set model predictive control (MPC) is a model-based control method that can include multi-objective optimization, constrained control, adaptive control, and online auto-tuning of weighting factors all in a single controller that exhibits fast dynamic tracking. This paper utilizes the model-based framework of MPC to develop a sensorless current maximum power point tracking (MPPT) algorithm. Eliminating the current sensor can reduce the cost and improve the reliability of the photovoltaic system. This paper also utilizes constrained control and online auto-tuning of MPC to develop an adaptive perturbation MPPT to reduce steady-state oscillation and improve dynamic performance. This paper builds in a single framework the different layers of the MPPT problem: control, estimation, and MPPT. The proposed adaptive perturbation sensorless current mode MPPT (ASC-MPPT) technique performance is compared to the well-known incremental conductance (InCon) MPPT technique. The EN50530 European industrial test standards were used to demonstrate performance.

中文翻译：

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光伏系统中无电流MPPT的自适应模型预测控制器

有限控制集模型预测控制（MPC）是一种基于模型的控制方法，可将多目标优化，约束控制，自适应控制和加权因子在线自动调整全部集中在一个具有快速动态跟踪功能的单个控制器中。本文利用基于模型的MPC框架来开发无传感器电流最大功率点跟踪（MPPT）算法。消除电流传感器可以降低成本并提高光伏系统的可靠性。本文还利用MPC的约束控制和在线自动调谐来开发自适应扰动MPPT，以减少稳态振荡并改善动态性能。本文在单个框架中构建了MPPT问题的不同层次：控制，估计和MPPT。将拟议的自适应无扰动电流模式MPPT（ASC-MPPT）技术性能与众所周知的增量电导（InCon）MPPT技术进行了比较。使用EN50530欧洲工业测试标准来演示性能。