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<p>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.</p>

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Model predictive control: past, present, and future

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Nan Li,
  • Hong Chen,
  • Meng Li,
  • Shuyou Yu,
  • Yanjun Huang

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.