多变量高斯式基于过程的学习模型预测控制与无气味的卡尔曼波器用于自主地表车辆
1College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, People's Republic of China.
ISA transactions
|January 11, 2026
概括
本研究引入了多变量高斯过程回归 (MVGPR) 用于建模自动地表车辆 (ASV) 动态. 开发的基于学习的模型预测控制 (MPC) 确保了强大的轨迹跟踪,即使是通信拒绝服务 (DoS) 攻击.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机器学习用于自主系统
- 海洋工程 海洋工程
背景情况:
- 由于水力动力学效应和环境不确定性,自动驾驶表面车辆 (ASV) 的非线性动力学建模具有挑战性.
- 传统方法在ASV系统中与高维数据和不确定性估计作斗争.
- 拒绝服务 (DoS) 攻击对ASV通信网络的可靠性构成重大威胁.
研究的目的:
- 使用先进的机器学习技术,为ASV开发一个强大的轨迹跟踪控制方案.
- 准确地建模ASV动态并估计复杂海上环境中的系统不确定性.
- 为了增强ASV对DoS攻击等通信中断的弹性.
主要方法:
- 多变量高斯过程回归 (MVGPR) 用于建模ASV的系统状态和观测动态,从而实现精确的多输入,多输出相关性和不确定性估计.
- 无气味卡尔曼波器 (UKF) 旨在改善状态估计,即使在无法测量的状态下也确保了稳定性.
- 使用MVGPR开发了一个基于学习的模型预测控制 (MPC) 框架,以处理轨迹跟踪并减轻没有外部补偿器的DoS攻击的影响.
主要成果:
- MVGPR方法有效地建模了复杂的ASV动态,在高维设置中表现优于传统方法.
- 集成的UKF提高了状态估计的准确性和稳定性.
- 基于MVGPR的学习MPC展示了强大而精确的轨迹跟踪性能,在不确定的条件下提高了系统稳定性,并模拟了DoS攻击.
结论:
- 拟议的基于MVGPR的学习MPC框架在自主地表车辆控制方面取得了重大进展,提供了准确的建模和强大的轨迹跟踪.
- 该方法有效地应对复杂动态,环境不确定性和通信安全威胁所带来的挑战.
- 通过模拟和硬件实验的验证证实了开发的控制策略的实际适用性和有效性.
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