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<p>This paper introduces Incremental Learning MPC (ILMPC), a novel Model Predictive Control (MPC) approach designed to enhance the adaptability of control systems in dynamic environments with unpredictable disturbances. Traditional MPC methods are often limited by their reliance on static models and fixed optimization schemes, making them less effective in handling disturbances and model inaccuracies. To overcome these limitations, ILMPC integrates incremental learning, enabling continuous refinement of the control model using real-time data. This innovation improves prediction accuracy and control performance, allowing the system to adapt to changing operational conditions and unknown disturbances. Key advances include the development of a sequence prediction model that continuously updates the state-space model through incremental learning, improved disturbance suppression for more stable control, and a reduction in computational complexity by incrementally model parameters. Experimental results show that ILMPC enhances deviation suppression significantly compared to conventional methods and significantly reduces control input volatility, demonstrating its superior performance in real-time disturbance suppression and adaptability.</p>

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Predictive control approach incorporating incremental learning

  • 期刊论文

  • 发表日期：

    2025年2月3日

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  • Jian Chen,
  • Haiwei Pan,
  • Kejia Zhang,
  • Haiyan Lan,
  • Xu Xu,
  • Wenhui Luo

摘要

This paper introduces Incremental Learning MPC (ILMPC), a novel Model Predictive Control (MPC) approach designed to enhance the adaptability of control systems in dynamic environments with unpredictable disturbances. Traditional MPC methods are often limited by their reliance on static models and fixed optimization schemes, making them less effective in handling disturbances and model inaccuracies. To overcome these limitations, ILMPC integrates incremental learning, enabling continuous refinement of the control model using real-time data. This innovation improves prediction accuracy and control performance, allowing the system to adapt to changing operational conditions and unknown disturbances. Key advances include the development of a sequence prediction model that continuously updates the state-space model through incremental learning, improved disturbance suppression for more stable control, and a reduction in computational complexity by incrementally model parameters. Experimental results show that ILMPC enhances deviation suppression significantly compared to conventional methods and significantly reduces control input volatility, demonstrating its superior performance in real-time disturbance suppression and adaptability.