This study is concerned with an event-driven brain emotional online learning approach for finite control-set model predictive control (FCS-MPC) framework subject to system uncertainties and low switching frequency (SF). The developed framework consists of three key features: First, a bidirectional fuzzy brain emotional online learning approach along with a robustifying control term is leveraged to approximate the ideal controller; second, an event-driven- based mechanism that achieves the low SF operation by using a tube-like model predictive control point of view is embedded into the proposal; and third, an integral error term is introduced so as to enhance the tracking performance under low SF operation. Our method contributes to better attenuate capability of uncertainties and SF as well as tracking error without weighting factors. Further, the convergence analysis of the closed-loop control system is given. Finally, we underline its merits with different benchmark examples from the literature.

中文翻译：

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电源转换器的 FCS-MPC：事件驱动的大脑情感学习方法


本研究涉及一种受系统不确定性和低开关频率（SF）影响的有限控制集模型预测控制（FCS-MPC）框架的事件驱动的大脑情感在线学习方法。所开发的框架包含三个关键特征：首先，利用双向模糊大脑情感在线学习方法以及鲁棒控制项来逼近理想控制器；其次，提案中嵌入了一种基于事件驱动的机制，通过使用类管模型预测控制的观点来实现低SF操作；第三，引入积分误差项以增强低SF操作下的跟踪性能。我们的方法有助于更好地衰减不确定性和 SF 以及在没有加权因子的情况下跟踪误差的能力。进一步给出了闭环控制系统的收敛分析。最后，我们通过文献中的不同基准示例强调其优点。