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| Model predictive control: past, present, and future |
| NanLi1,HongChen2,MengLi1,ShuyouYu3,YanjunHuang1 |
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| (School of Automotive Studies, Tongji University, Shanghai
201804, China;College of Electronic and Information Engineering, Tongji
University, Shanghai 201804, China;Department of Control Science and Engineering, Jilin
University, Changchun 130012, Jilin, China) |
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| 摘要: |
| Model predictive control (MPC) has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted
control framework. This survey provides a structured account of MPC’s trajectory across three interrelated dimensions:
theoretical foundations, practical deployments, and future outlook. We first trace the emergence of core principles especially
around stability and robustness and highlight pivotal contributions that have shaped linear, nonlinear, robust, stochastic,
and adaptive MPC variants. The second part reviews computational advances, data-driven innovations, and widespread
deployments, with a particular emphasis on automotive applications. Finally, we articulate a forward-looking vision of MPC
as a general and unified paradigm for embodied intelligence. Throughout, we underscore contributions from international
and Chinese research communities and point to emerging research directions at the intersection of control theory, machine
learning, and intelligent systems. |
| 关键词: Model predictive control · Autonomous systems · Embodied intelligence |
| DOI：https://doi.org/10.1007/s11768-026-00320-x |
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| 基金项目:This work was supported by the National Natural Science Foundation
of China under Grant No. 62573318. |
|
| Model predictive control: past, present, and future |
| Nan Li1,Hong Chen2,Meng Li1,Shuyou Yu3,Yanjun Huang1 |
| (School of Automotive Studies, Tongji University, Shanghai
201804, China;College of Electronic and Information Engineering, Tongji
University, Shanghai 201804, China;Department of Control Science and Engineering, Jilin
University, Changchun 130012, Jilin, China) |
| Abstract: |
| Model predictive control (MPC) has evolved from an industry-originated heuristic to a rigorously grounded and widely adopted
control framework. This survey provides a structured account of MPC’s trajectory across three interrelated dimensions:
theoretical foundations, practical deployments, and future outlook. We first trace the emergence of core principles especially
around stability and robustness and highlight pivotal contributions that have shaped linear, nonlinear, robust, stochastic,
and adaptive MPC variants. The second part reviews computational advances, data-driven innovations, and widespread
deployments, with a particular emphasis on automotive applications. Finally, we articulate a forward-looking vision of MPC
as a general and unified paradigm for embodied intelligence. Throughout, we underscore contributions from international
and Chinese research communities and point to emerging research directions at the intersection of control theory, machine
learning, and intelligent systems. |
| Key words: Model predictive control · Autonomous systems · Embodied intelligence |
