基于GPSO-MPC的海底软坡的轮式机器人轨迹跟踪控制方法的研究
Dewei Li1,2, Zizhong Zheng1,2, Zhongjun Ding1,2
1College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究使用混合灰狼粒子群优化 (GPSO) 算法来增强水下机器人的控制,以适应调整模型预测控制 (MPC) 参数. 在具有挑战性的软海底环境中,GPSO-MPC显著提高了轨迹跟踪准确性和稳定性.
科学领域:
- 机器人技术
- 海洋工程
- 控制系统
背景情况:
- 轮式水下机器人在柔软的海底斜坡上面临着难以控制的挑战,
- 现有的模型预测控制 (MPC) 方法需要手动调整,并应对动态环境变化.
研究的目的:
- 为在复杂的海底地形中运行的轮式水下机器人制定适应性控制策略.
- 提高轨迹跟踪控制系统的准确性,稳定性和适应性.
主要方法:
- 开发了一种混合灰狼粒子群优化 (GPSO) 算法,以在线调整MPC参数.
- 创建了一个四轮差速驱动机器人的动力模型,并实现了带有错误状态线性化的MPC控制器.
- GPSO采用了层级领导和混乱的干扰来改善全球搜索和本地融合.
主要成果:
- 拟议的GPSO-MPC方法与标准的MPC和粒子群优化MPC (PSO-MPC) 相比显示出更高的性能.
- 在模拟过程中观察到跟踪精度,航向稳定性和控制流性的显著改善.
- GPSO-MPC的适应性在动态水下环境中被证明是有效的.
结论:
- 混合GPSO算法为水下机器人的MPC参数提供了有效的在线调整解决方案.
- GPSO-MPC提高了控制系统的性能和稳定性,在具有挑战性的软坡海底条件下.
- 这种方法支持智能海洋技术更可靠的海底测量,工程和环境监测.
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