通过凸优化实现混合动力船舶的集成监控与固定路径速度轨迹生成
Integrated supervisory control and fixed path speed trajectory generation for hybrid electric ships via convex optimization
AI总结：
针对混合动力船舶运行剖面未知导致传统控制性能受限的问题，提出集成轨迹生成与电源监控的GNC方法，通过变量变换和约束松弛将非凸规划转化为凸问题，并经模型尺度船舶数值验证。
摘要：
电池混合动力源架构能够降低具有多样化运行剖面船舶的燃料消耗和排放。然而，如果控制器对未来运行剖面未知，传统控制策略可能无法提升性能。本文提出一种制导、导航与控制（GNC）功能，该功能集成了轨迹生成和混合电源监控。我们聚焦于时间和燃料最优的路径约束轨迹规划。该问题是一个非线性、非凸的最优控制问题，这意味着它不易在船载平台上获得高效且可靠的求解。我们提出一种非线性变量变换和约束松弛，将非凸规划问题转化为凸最优控制问题。非凸的三自由度动力学、水动力、定距螺旋桨、电池以及通用能量转换器（例如燃料电池或发电机组）的耗散约束均以凸函数形式表示。由 Pontryagin 极小值原理导出的一个条件保证：当该条件满足时，松弛问题的解即为原问题的解。针对一艘模型尺度的电池混合动力船舶，通过数值算例说明了该方法的有效性。首先，利用拖曳水池试验数据验证了凸的船体和舵水动力模型。其次，在不同任务需求下评估了最优轨迹和监控方案。本文的凸化方案为采用成熟、计算稳健的凸优化方法铺平了道路，并为未来智能和无人水面艇的船载实时优化创造了新的可能性。
英文摘要：
Battery-hybrid power source architectures can reduce fuel consumption and emissions for ships with diverse operation profiles. However, conventional control strategies may fail to improve performance if the future operation profile is unknown to the controller. This paper proposes a guidance, navigation, and control (GNC) function that integrates trajectory generation and hybrid power source supervisory control. We focus on time and fuel optimal path-constrained trajectory planning. This problem is a nonlinear and nonconvex optimal control problem, which means that it is not readily amenable to efficient and reliable solution onboard. We propose a nonlinear change of variables and constraint relaxations that transform the nonconvex planning problem into a convex optimal control problem. The nonconvex three-degree-of-freedom dynamics, hydrodynamic forces, fixed pitch propeller, battery, and general energy converter (e.g., fuel cell or generating set) dissipation constraints are expressed in convex functional form. A condition derived from Pontryagin's Minimum Principle guarantees that, when satisfied, the solution of the relaxed problem provides the solution to the original problem. The validity and effectiveness of this approach are numerically illustrated for a battery-hybrid vessel in model scale. First, the convex hydrodynamic hull and rudder force models are validated with towing tank test data. Second, optimal trajectories and supervisory control schemes are evaluated under varying mission requirements. The convexification scheme in this work lays the path for the employment of mature, computationally robust convex optimization methods and creates a novel possibility for real-time optimization onboard future smart and unmanned surface vehicles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
