| 引用本文: | 王秀博,段广仁.带有输入饱和的非线性全驱系统级联预测控制[J].控制理论与应用,2025,42(12):2419~2428.[点击复制] |
| WANG Xiu-bo,DUAN Guang-ren.Cascade predictive control for nonlinear fully-actuated systems with input saturation[J].Control Theory & Applications,2025,42(12):2419~2428.[点击复制] |
|
| 带有输入饱和的非线性全驱系统级联预测控制 |
| Cascade predictive control for nonlinear fully-actuated systems with input saturation |
| 摘要点击 186 全文点击 31 投稿时间：2024-05-24 修订日期：2025-07-05 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40297 |
| 2025,42(12):2419-2428 |
| 中文关键词 非线性系统 预测控制 全驱系统方法 级联优化 输入饱和 |
| 英文关键词 nonlinear systems predictive control fully actuated system approaches cascade optimization input satu ration |
| 基金项目 国家自然科学基金委基础科学中心项目(62188101),河北省自然科学基金项目(F2025501004),中央高校基本科研业务费专项资金项目(Z2025 041)资助. |
|
| 中文摘要 |
| 基于全驱系统方法的预测控制通过非线性输入变换,将原始输入映射为期望的线性闭环系统输入,进而构
造分散式线性预测模型,降低了优化问题求解的复杂度.然而,当系统具有输入饱和约束时,此类输入变换为期望的
预测模型引入强非线性的约束问题.为此,本文提出了一种级联预测控制方法,设计了双层优化结构的级联预测控
制器: 第1层优化结合上一时刻的预测输入序列,优化预测时域内输入变换后的线性边界;第2层优化则基于重新定
义的线性约束,求解带有松弛因子的分散式线性优化问题.该方法有效避免了因输入变换引发的非线性约束问题,
降低优化复杂度,并提高非线性优化问题的可解性,同时保证闭环系统的稳定性.最后,通过对全驱航天器姿态系
统和欠驱转动平移驱动系统的仿真,验证了所提算法的有效性. |
| 英文摘要 |
| Predictive control based on fully actuated system (FAS) approaches employs nonlinear input transformations
to map the original inputs into the desired linear closed-loop system inputs. This enables the construction of distributed
linear predictive models, effectively reducing the complexity of solving the optimization problem. However, when the
system owns input saturation, such input transformations introduce highly nonlinear constraint issues to the desired predic
tive model. To address this, this paper proposes a cascaded predictive control method and designs a cascaded predictive
controller with a two-layer optimization structure. In the first layer of optimization, the predictive input sequence from the
previous instant is used to optimize the linear boundaries of the transformed inputs within the predictive horizon. In the sec
ond layer of optimization, based on the newly defined linear constraints, a series of distributed linear optimization problems
with slack factors are solved. This cascaded predictive method effectively avoids the nonlinear constraint issues caused
by input transformations, reduces optimization complexity, improves the solvability of nonlinear optimization problems,
and ensures the stability of the closed-loop system. Finally, the effectiveness of the proposed algorithm is verified through
simulations on fully-actuated spacecraft attitude system and the under-actuated rotational translational actuator system. |
|
|
|
|
|