基于智能开关增益的滑模控制,优化协同机械手的功耗

Ali Parsai Kia, Moharam Habibnejad Korayem, Naeim Yousefi Lademakhi
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引用次数: 0

摘要

对于复杂任务至关重要的协同机械手正受到各行各业的广泛关注。认识到它们对功耗、成本和任务结果的影响,本文强调对控制方法、致动器功耗和机械手精度的关键研究。针对这些挑战,我们提出了一种神经网络-滑动模式控制器(NN-SMC),以优化致动器的功耗,并最大限度地减少合作机械手的误差。在轨迹和点对点模式下运行时,神经网络-滑动模式控制器(NN-SMC)会动态生成实时切换模式控制器(SMC)增益(L 和 K),以实现精确控制。将增益保持在允许范围内可确保稳定性。在点对点模式下,NN 协调生成最佳路径,并提供量身定制的增益。为了准确评估性能,引入了一种新的控制性能指标。3-DOF 合作机械手的实验结果表明,轨迹模式的控制性能指数显著提高了 28%,两种模式的计算复杂度都大幅降低。这项工作不仅解决了合作机械手固有的难题,还通过整合基于神经网络的控制技术标志着方法上的进步,有望提高效率和稳定性。
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Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators
Cooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.
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