Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems
Flexible-Time Receding Horizon Iterative Learning Control With Application to Marine Hydrokinetic Energy Systems
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DOI：
10.1109/tcst.2022.3165734
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发表时间：
2022-11
影响因子：
4.8
通讯作者：
Mitchell Cobb;James Reed;Maxwell J. Wu;K. Mishra;K. Barton;C. Vermillion
中科院分区：
文献类型：
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作者：
Mitchell Cobb;James Reed;Maxwell J. Wu;K. Mishra;K. Barton;C. Vermillion
本简介介绍了用于一类重复控制 (RC) 应用的迭代学习控制 (ILC) 框架，其特征为：1) 连续操作； 2）灵活的迭代时间； 3) 经济绩效指标。具体来说，由于操作的连续性而导致的迭代变化初始条件的影响是通过迭代域后退水平公式来解释的。为了满足灵活迭代时间的需求，时域动力学被转换为路径域动力学，其特征在于跨越迭代不变范围的无量纲参数。所得模型用于派生学习过滤器，以最小化多目标经济成本。所提出的方法应用于控制基于风筝的海洋流体动力（MHK）系统，该系统执行高速、重复的飞行路径，其目标是最大化其单圈平均功率输出。所提出的方法通过基于风筝的 MHK 系统的中保真度非线性模型的仿真进行了验证，结果表明风筝能够稳健且快速地收敛到功率最佳飞行模式。
This brief presents an iterative learning control (ILC) framework for a class of repetitive control (RC) applications characterized by: 1) continuous operation; 2) flexible iteration time; and 3) an economic performance metric. Specifically, the effect of iteration-varying initial conditions, resulting from the continuous nature of the operation, is accounted for through an iteration domain receding horizon formulation. To address the need for flexible iteration times, the time-domain dynamics are transformed into path-domain dynamics characterized by a non-dimensional parameter spanning an iteration-invariant range. The resulting model is used to derive learning filters that minimize a multi-objective economic cost. The proposed methodology is applied to the control a kite-based marine hydrokinetic (MHK) system, which executes high-speed, repetitive flight paths with the objective of maximizing its lap-averaged power output. The proposed approach is validated via simulations of a medium-fidelity nonlinear model of a kite-based MHK system, and the results demonstrate robust and fast convergence of the kite to power-optimal flight patterns.