一个中心事件触发的非线性MPC方法来减少WMR的计算时间
M H Korayem1, Sh Ameri1, N Yousefi Lademakhi1
1Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.
ISA transactions
|November 14, 2025
概括
一种新的智能事件触发方法减少了机器人非线性模型预测控制 (NMPC) 的计算时间. 这种方法使用多层感知神经网络 (MLP-NN) 来提高机器人控制系统的准确性和效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 非线性模型预测控制 (NMPC) 面临着机器人应用中的计算挑战,原因是频繁的最佳控制问题 (OCP) 解决方案.
- 现有的事件触发NMPC (ET-NMPC) 方法可能会损害溶液的准确性,特别是热启动技术.
研究的目的:
- 开发一个智能事件触发的NMPC策略,可以降低计算负担,同时保持或提高跟踪精度.
- 为了减少NMPC中OCP计算的频率,以增强实时机器人控制.
主要方法:
- 提出了一个智能中心事件触发方法,利用最佳状态和实际状态之间的梯度变化.
- 采用多层感知神经网络 (MLP-NN) 来预测OCP输入,减少解决方案代和增强融合.
- 该方法通过在轮式移动机器人 (WMR) 平台上的模拟和实验来验证.
主要成果:
- 与传统的NMPC相比,拟议的智能事件触发机制减少了64.7%的NMPC计算时间.
- 使用事件触发方法,追踪错误提高了18%,减轻了准确性损失.
- 集成MLP-NN增强了融合和轨迹跟踪的准确性.
结论:
- 智能事件触发的NMPC方法有效地减少机器人系统中的计算负载.
- 这种方法为提高实时应用中NMPC的效率和准确性提供了可行的解决方案.
- 使用MLP-NN对于保持事件触发控制策略的准确性至关重要.
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