Simultaneous hysteresis-dynamics compensation in high-speed, large-range trajectory tracking: A data-driven iterative control

Zhihua Wang, Qingze Zou

Research output: Contribution to journalArticlepeer-review


In this article, a data-driven difference-inversion-based iterative control (DDD-IIC) approach is proposed to compensate for both nonlinear hysteresis and dynamics of Hammerstein systems. Simultaneous hysteresis-dynamics compensation is needed in control of Hammerstein systems such as smart actuators, where effects of hysteresis and dynamics coexist and become pronounced in high-speed, large-range output tracking. Challenges, however, arises as hysteresis modeling, as needed in many existing control methods, can be complicated and prone to uncertainties, and the hysteresis and the dynamics are coupled and tend to change due to the variations of the system conditions (e.g., the aging of smart actuators). The proposed DDD-IIC technique aims to achieve simultaneous hysteresis-dynamics compensation with no need for modeling hysteresis and/or dynamics, and with both precision tracking and good robustness against hysteresis/dynamics variations. The convergence of the DDD-IIC algorithm in the presence of random output disturbance/noise is analyzed. It is shown that when the noise is negligible, exact tracking is achieved and the size of hysteresis accounted is given by the Golden ratio. The proposed DDD-IIC method is demonstrated via experiments of high-speed large-range output tracking on two different types of smart actuators with symmetric and asymmetric hysteresis behavior, respectively.

Original languageEnglish (US)
Pages (from-to)489-506
Number of pages18
JournalInternational Journal of Robust and Nonlinear Control
Issue number1
StatePublished - Jan 10 2023

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


  • data-driven
  • hysteresis and dynamics compensation
  • iterative learning control
  • nanopositioning control


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