Scholarlink

Problem <p>Nonlinear vibrations in helicopter systems present considerable challenges to performance and stability.</p> Control Scheme <p>This paper presents a novel control framework tailored for a fuzzy-proportional-integral-derivative (FPID) controller, specifically focusing on nonlinear vibration management and helicopter rotor dynamics control. The constraints of controller are optimized using a hybrid Giza Pyramid Construction Teaching Learning Based Optimization algorithm. We utilize a nonlinear helicopter hardware model as a benchmark, subjecting it to external disturbances created by high-speed fans to replicate real-world scenarios.</p> Computation <p>By employing the MATLAB/Simulink platform, our computational technique effectively mitigates disturbances while minimizing critical fitness functions: Integral-Time-Square-Error (ITSE), Integral-Square-Error (ISE), and Integral-Absolute-Error (IAE).</p> Conclusion <p>The results demonstrate that our hybridized algorithm outperforms existing optimization techniques, showcasing improved stability and reliability in both simulations and real-time applications. This research significantly advances helicopter control methodologies and enhances the overall performance of helicopter systems under challenging conditions.</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

Stabilizing a Nonlinear Helicopter Model: Advanced Hybrid Optimization Technique for Controlled Rotor Dynamics and Vibration Minimization Under External Disturbances

  • 期刊论文

  • 发表日期：

    2025年2月17日

查看全文
  • Abhishek Chaudhary,
  • Rohan Pillai,
  • Shreyansh Upadhyaya,
  • Gaurav Kaushik

摘要

Problem

Nonlinear vibrations in helicopter systems present considerable challenges to performance and stability.

Control Scheme

This paper presents a novel control framework tailored for a fuzzy-proportional-integral-derivative (FPID) controller, specifically focusing on nonlinear vibration management and helicopter rotor dynamics control. The constraints of controller are optimized using a hybrid Giza Pyramid Construction Teaching Learning Based Optimization algorithm. We utilize a nonlinear helicopter hardware model as a benchmark, subjecting it to external disturbances created by high-speed fans to replicate real-world scenarios.

Computation

By employing the MATLAB/Simulink platform, our computational technique effectively mitigates disturbances while minimizing critical fitness functions: Integral-Time-Square-Error (ITSE), Integral-Square-Error (ISE), and Integral-Absolute-Error (IAE).

Conclusion

The results demonstrate that our hybridized algorithm outperforms existing optimization techniques, showcasing improved stability and reliability in both simulations and real-time applications. This research significantly advances helicopter control methodologies and enhances the overall performance of helicopter systems under challenging conditions.