多约束模型基于未知状态估计器和约束自适应层次规划的灵活联合驱动系统的预测控制
Chuanming Song1, Qinjun Du2, Xinchen Guo3
1School of Rail Transportation, Shandong Jiaotong University, Shandong, 250357, China. 230048@sdjtu.edu.cn.
Scientific reports
|November 4, 2025
概括
一种新的模型预测控制 (MPC) 方法使用未知状态估计器 (USE) 和约束自适应层次规划 (CAHP) 来提高灵活的联合控制精度和对干扰和约束的稳定性.
科学领域:
- 机器人和控制工程 机器人和控制工程
- 系统动力学和控制理论
背景情况:
- 灵活的联合系统面临的挑战是,由于不确定的干扰和多重约束,控制精度低,控制规律复杂.
- 现有的模型预测控制 (MPC) 方法可能会与这些问题作斗争,导致性能不佳.
研究的目的:
- 提出一种新的模型预测控制 (MPC) 方法,以提高灵活的联合系统的控制精度和稳定性.
- 解决现有方法在处理不确定的干扰和多重约束方面的局限性.
主要方法:
- 开发一个未知状态估计器 (USE),使用低通波器来估计一次性干扰,而不需要加速反或准确的动态模型.
- 基于命题逻辑的约束自适应层次规划 (CAHP) 方法的设计,用于动态约束优先级的自适应权重系数.
- 将USE和CAHP集成到一个MPC框架中 (USE-CAHP-MPC).
主要成果:
- 拟议的USE-CAHP-MPC方法实现了灵活的关节系统的高精度轨迹跟踪.
- 在不确定的干扰条件下证明了有效的避障能力.
- 满足所有指定的约束条件,包括和和避开障碍.
结论:
- 与传统的固定优先级MPC相比,USE-CAHP-MPC方法显著提高了控制精度和稳定性.
- 拟议的方法为复杂条件下的柔性关节提供了更可解决和更有效的控制策略.
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