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| Robust nonlinear MPC for tracking piece-wise constant reference
signals |
| HuahuiXie1,LiDai1,ZhongqiSun1,Di-HuaZhai1,YuanqingXia1,2 |
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| (School of Automation, Beijing Institute of Technology,
Beijing 100081, China;Zhongyuan University of Technology, Zhengzhou 450007,
Henan, China) |
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| 摘要: |
| This work provides a robust model predictive control framework tailored for tracking piece-wise constant reference signals
for nonlinear dynamics subject to additive disturbances. The approach integrates setpoint optimization and robust constraint
satisfaction into a unified optimization problem, guaranteeing the robust stability within a vicinity of an optimal admissible
setpoint. A crucial feature of the approach is its ability to preserve recursive feasibility despite abrupt variations in the target.
An offline implementation based on set-valued system representations is also discussed. Numerical examples demonstrate
the effectiveness of the controller. |
| 关键词: Nonlinear model predictive control · Setpoint tracking · Robust control |
| DOI：https://doi.org/10.1007/s11768-025-00313-2 |
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| 基金项目:This work was supported by the National Natural Science Foundation
of China under Grant 62503054, Grant U25A20460, Grant 62173036,
Grant 62173035, and Grant 62122014, and the Beijing Natural
Science Foundation Haidian Original Innovation Joint Fund Project
under Grant L252035. |
|
| Robust nonlinear MPC for tracking piece-wise constant referencesignals |
| Huahui Xie1,Li Dai1,Zhongqi Sun1,Di-Hua Zhai1,Yuanqing Xia1,2 |
| (School of Automation, Beijing Institute of Technology,
Beijing 100081, China;Zhongyuan University of Technology, Zhengzhou 450007,
Henan, China) |
| Abstract: |
| This work provides a robust model predictive control framework tailored for tracking piece-wise constant reference signals
for nonlinear dynamics subject to additive disturbances. The approach integrates setpoint optimization and robust constraint
satisfaction into a unified optimization problem, guaranteeing the robust stability within a vicinity of an optimal admissible
setpoint. A crucial feature of the approach is its ability to preserve recursive feasibility despite abrupt variations in the target.
An offline implementation based on set-valued system representations is also discussed. Numerical examples demonstrate
the effectiveness of the controller. |
| Key words: Nonlinear model predictive control · Setpoint tracking · Robust control |
