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<p>Inspired by reference governor (RG), this paper proposes a novel adaptive robust trajectory generator (ARTG) for nonlinear systems to address uncertainties, nonlinearities, state and input constraints. An adaptive robust controller is presented to pre-compensate the nonlinear system, ensuring high tracking accuracy even under uncertainties and nonlinearities. A trajectory generator (TG) is designed to modify the time-varying trajectory whenever the constraint violations might occur. Considering the uncompensated uncertainties, a nominal system is introduced in TG to obtain fast and low-conservation predictions. To further improve the control performance, a novel undamped oscillation angular frequency adjustment algorithm (UOAFAA) is designed to tune the controller gains. Since the interaction between the adaptive robust controller and TG is well considered, the ARTG can achieve fast transient convergence speed and high tracking accuracy. Comparative experiments on a linear motor validate the effectiveness and superiority of the proposed method.</p>

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Novel adaptive robust trajectory generator for nonlinear systems under state and input constraints

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Jinna Fu,
  • Xiaoyu Zhang,
  • Wenjie Chen,
  • Zheng Chen

摘要

Inspired by reference governor (RG), this paper proposes a novel adaptive robust trajectory generator (ARTG) for nonlinear systems to address uncertainties, nonlinearities, state and input constraints. An adaptive robust controller is presented to pre-compensate the nonlinear system, ensuring high tracking accuracy even under uncertainties and nonlinearities. A trajectory generator (TG) is designed to modify the time-varying trajectory whenever the constraint violations might occur. Considering the uncompensated uncertainties, a nominal system is introduced in TG to obtain fast and low-conservation predictions. To further improve the control performance, a novel undamped oscillation angular frequency adjustment algorithm (UOAFAA) is designed to tune the controller gains. Since the interaction between the adaptive robust controller and TG is well considered, the ARTG can achieve fast transient convergence speed and high tracking accuracy. Comparative experiments on a linear motor validate the effectiveness and superiority of the proposed method.