Scholarlink

<p>Ensure stability and disturbance rejection are major challenges in nonlinear rehabilitation robot controller design; therefore, advanced nonlinear control methods must be developed. The main goal of this study is to propose a hybrid nonlinear PID with feedforward controller (HNPID-FFC) for a 2-link upper limb rehabilitation robot. This controller is designed to accurately track the desired repetitive trajectory while minimizing the effects of model uncertainties and external disturbances. The optimal parameters of the proposed controller are tuned using the grasshopper optimization algorithm (GOA) by minimizing the integral time square error (ITSE) fitness function between the desired and actual trajectories.</p><p>The performance of the proposed HNPID-FFC is explicitly compared with a standard Nonlinear PID (NPID) controller under four simulation scenarios (no uncertainty or disturbance, 10% model uncertainty only, external disturbance only, and both 10% uncertainty and external disturbance). Simulation results demonstrate that the HNPID-FFC outperforms the conventional NPID controller, achieving faster settling time, smaller steady-state error, and significantly reduced overshoot across all test cases, confirming its robustness and effectiveness for nonlinear rehabilitation robot control.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design hybrid nonlinear PID with feedforward controller and grasshopper optimization algorithm for upper limb rehabilitation robot

  • 期刊论文

  • 发表日期：

    2026年5月11日

查看全文
  • Aliaa A. Flaih,
  • Ekhlas H. Karam,
  • Yousra A. Mohammed

摘要

Ensure stability and disturbance rejection are major challenges in nonlinear rehabilitation robot controller design; therefore, advanced nonlinear control methods must be developed. The main goal of this study is to propose a hybrid nonlinear PID with feedforward controller (HNPID-FFC) for a 2-link upper limb rehabilitation robot. This controller is designed to accurately track the desired repetitive trajectory while minimizing the effects of model uncertainties and external disturbances. The optimal parameters of the proposed controller are tuned using the grasshopper optimization algorithm (GOA) by minimizing the integral time square error (ITSE) fitness function between the desired and actual trajectories.

The performance of the proposed HNPID-FFC is explicitly compared with a standard Nonlinear PID (NPID) controller under four simulation scenarios (no uncertainty or disturbance, 10% model uncertainty only, external disturbance only, and both 10% uncertainty and external disturbance). Simulation results demonstrate that the HNPID-FFC outperforms the conventional NPID controller, achieving faster settling time, smaller steady-state error, and significantly reduced overshoot across all test cases, confirming its robustness and effectiveness for nonlinear rehabilitation robot control.