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<p>As core motion control units in high-end equipment, such as computer numerical control (CNC) machine tools and industrial robots, servo systems largely determine the overall precision and reliability of industrial automation. However, nonlinear factors such as friction, backlash, and time-varying disturbances in motor transmission systems pose persistent challenges to high-performance control, which readily degrade trajectory tracking and positioning accuracy. To meet the increasingly stringent requirements of precision manufacturing, control strategies have evolved from classical proportional-integral-derivative (PID) schemes to robust control and, more recently, to predictive and intelligent approaches. This review summarizes the mathematical principles, performance advantages, and applicable scenarios of the representative methods. Existing studies indicate that under complex conditions involving coupled disturbances and parameter variations, a single control strategy often struggles to simultaneously achieve dynamic response and robustness. Therefore, adaptive composite control architectures that integrate mechanism-based modeling with data-driven approaches, namely model-data fusion, have become a key trend. These architectures enable active compensation for unmodeled dynamics and online self-learning of control strategies throughout the lifecycle, effectively overcoming current limitations and enhancing the operational accuracy of advanced manufacturing systems.</p>

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Evolution of control strategies for servo systems under nonlinearities and uncertainties: towards model-data fusion

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Shuang Hou,
  • Shuaishuai Ge,
  • Zhigang Zhang,
  • Fang Tang

摘要

As core motion control units in high-end equipment, such as computer numerical control (CNC) machine tools and industrial robots, servo systems largely determine the overall precision and reliability of industrial automation. However, nonlinear factors such as friction, backlash, and time-varying disturbances in motor transmission systems pose persistent challenges to high-performance control, which readily degrade trajectory tracking and positioning accuracy. To meet the increasingly stringent requirements of precision manufacturing, control strategies have evolved from classical proportional-integral-derivative (PID) schemes to robust control and, more recently, to predictive and intelligent approaches. This review summarizes the mathematical principles, performance advantages, and applicable scenarios of the representative methods. Existing studies indicate that under complex conditions involving coupled disturbances and parameter variations, a single control strategy often struggles to simultaneously achieve dynamic response and robustness. Therefore, adaptive composite control architectures that integrate mechanism-based modeling with data-driven approaches, namely model-data fusion, have become a key trend. These architectures enable active compensation for unmodeled dynamics and online self-learning of control strategies throughout the lifecycle, effectively overcoming current limitations and enhancing the operational accuracy of advanced manufacturing systems.