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<p>In this work, an adaptive control problem is formulated for a class of parametric strict-feedback nonlinear systems with unknown hysteresis and time-varying disturbances. Hysteresis, a nonsmooth nonlinearity commonly present in physical systems, presents significant challenges for control design, especially when its parameters are unknown. The proposed control scheme utilizes command filters to improve tracking performance by compensating for nonlinearities and time-varying disturbances. Backlash hysteresis is modeled as an unknown nonlinear effect, while an adaptive algorithm is employed to estimate system parameters and adjust the control signals in real time. Based on Lyapunov stability theory, the approach guarantees both transient and asymptotic tracking performance despite the presence of unknown hysteresis and disturbances. Simulation results validate the effectiveness of the proposed method. Compared to an existing method, the proposed approach achieves up to 50% reduction in tracking error and improved performance metrics such as NMSE, RMSE, and BFR, thereby verifying its superior tracking accuracy and robustness.</p>

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Adaptive Command-Filtered Tracking Control for Nonlinear Systems with Unknown Hysteresis and Time-Varying Disturbances

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Simran Kharka,
  • Sandeep Sharma,
  • Arun Bali,
  • Uday Pratap Singh

摘要

In this work, an adaptive control problem is formulated for a class of parametric strict-feedback nonlinear systems with unknown hysteresis and time-varying disturbances. Hysteresis, a nonsmooth nonlinearity commonly present in physical systems, presents significant challenges for control design, especially when its parameters are unknown. The proposed control scheme utilizes command filters to improve tracking performance by compensating for nonlinearities and time-varying disturbances. Backlash hysteresis is modeled as an unknown nonlinear effect, while an adaptive algorithm is employed to estimate system parameters and adjust the control signals in real time. Based on Lyapunov stability theory, the approach guarantees both transient and asymptotic tracking performance despite the presence of unknown hysteresis and disturbances. Simulation results validate the effectiveness of the proposed method. Compared to an existing method, the proposed approach achieves up to 50% reduction in tracking error and improved performance metrics such as NMSE, RMSE, and BFR, thereby verifying its superior tracking accuracy and robustness.