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<p>Traditional optimization methods for robotic arm control are prone to getting stuck in local optima and unable to find global optima. In response to these issues, a series of improvement methods are studied to optimize the cuckoo algorithm. The innovation of the research lies in combining the improved cuckoo search algorithm (ICS) with model free adaptive control (MFAC) for attitude control of flexible articulated robotic arms (FRA). By introducing fuzzy neural networks and principal component analysis (PCA) to optimize the cuckoo search algorithm, the search efficiency and accuracy of the algorithm are improved. Meanwhile, a new intelligent algorithm based FRA control model was designed by combining the strong adaptability and robustness of MFAC with the precise tracking ability of sliding mode controller. The experiment demonstrated that the model had excellent performance, achieving an accuracy of 95.4% in just 150 iterations, which was superior to other models. When the robotic arm grasped lightweight and heavy objects, the system control was relatively accurate, with average errors of 0.25&#xa0;mm and 0.35&#xa0;mm, respectively. This study not only possesses essential theoretical value for the control of flexible robotic arms but also provides new ideas and methods for the development of related fields.</p>

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Combining MFAC with an improved cuckoo algorithm for flexible robotic rotary arm joint control

  • 期刊论文

  • 发表日期：

    2025年1月5日

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  • Song Liu

摘要

Traditional optimization methods for robotic arm control are prone to getting stuck in local optima and unable to find global optima. In response to these issues, a series of improvement methods are studied to optimize the cuckoo algorithm. The innovation of the research lies in combining the improved cuckoo search algorithm (ICS) with model free adaptive control (MFAC) for attitude control of flexible articulated robotic arms (FRA). By introducing fuzzy neural networks and principal component analysis (PCA) to optimize the cuckoo search algorithm, the search efficiency and accuracy of the algorithm are improved. Meanwhile, a new intelligent algorithm based FRA control model was designed by combining the strong adaptability and robustness of MFAC with the precise tracking ability of sliding mode controller. The experiment demonstrated that the model had excellent performance, achieving an accuracy of 95.4% in just 150 iterations, which was superior to other models. When the robotic arm grasped lightweight and heavy objects, the system control was relatively accurate, with average errors of 0.25 mm and 0.35 mm, respectively. This study not only possesses essential theoretical value for the control of flexible robotic arms but also provides new ideas and methods for the development of related fields.