基于模糊扰动自适应与控制器参数优化的遥控水下机器人轨迹跟踪控制
Trajectory tracking control of a Remotely Operated Underwater Vehicle based on Fuzzy Disturbance Adaptation and Controller Parameter Optimization
AI总结：
针对冰下探索中ROV精确轨迹跟踪需求，提出融合模糊自适应扰动抑制与PSO参数优化的MIMO非线性控制器，经仿真验证性能优于基线方法，助力冰下探索技术发展。
摘要：
冰下环境探索因科研准入受限面临独特挑战。本报告研究了将全驱动遥控水下机器人（Remotely Operated Vehicle, ROV）用于冰盖下浅海区域探索的潜力。借助海洋机器人技术的进步，ROV为拓展人类在偏远水下区域的活动范围提供了可行方案。为实现成功的冰下探索，ROV必须沿精确轨迹航行以有效接收定位信号。本研究开发了一种多输入多输出（multi-input-multi-output, MIMO）非线性系统控制器，具备基于Lyapunov的稳定性保证，并引入自适应律以抵消未知环境扰动。研究采用模糊逻辑动态调整自适应速率，提升了在高度非线性ROV动力学系统中的性能。此外，粒子群优化（Particle Swarm Optimization, PSO）算法可自动整定控制器参数，实现最优轨迹跟踪。报告详细阐述了ROV动力学模型、所提出的控制框架以及基于PSO的整定流程。仿真实验验证了该方法的有效性，实验结果表明其轨迹跟踪性能优于基线控制器。本研究推动了冰下探索能力的发展，为海洋机器人与自主水下系统的未来研究奠定了基础。
英文摘要：
The exploration of under-ice environments presents unique challenges due to limited access for scientific research. This report investigates the potential of deploying a fully actuated Remotely Operated Vehicle (ROV) for shallow area exploration beneath ice sheets. Leveraging advancements in marine robotics technology, ROVs offer a promising solution for extending human presence into remote underwater locations. To enable successful under-ice exploration, the ROV must follow precise trajectories for effective localization signal reception. This study develops a multi-input-multi-output (MIMO) nonlinear system controller, incorporating a Lyapunov-based stability guarantee and an adaptation law to mitigate unknown environmental disturbances. Fuzzy logic is employed to dynamically adjust adaptation rates, enhancing performance in highly nonlinear ROV dynamic systems. Additionally, a Particle Swarm Optimization (PSO) algorithm automates the tuning of controller parameters for optimal trajectory tracking. The report details the ROV dynamic model, the proposed control framework, and the PSO-based tuning process. Simulation-based experiments validate the efficacy of the methodology, with experimental results demonstrating superior trajectory tracking performance compared to baseline controllers. This work contributes to the advancement of under-ice exploration capabilities and sets the stage for future research in marine robotics and autonomous underwater systems.
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。
