Hu Kuo
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, China.
PLoS One. 2025 Jul 22;20(7):e0328691. doi: 10.1371/journal.pone.0328691. eCollection 2025.
As modern industrial automation advances towards intelligence and flexibility, robotic arms are widely used in precision manufacturing, intelligent assembly, and medical surgery. To meet the high-precision motion control demand, this study proposes a robotic arm motion control technology integrating composite control and an improved sandcat swarm optimization algorithm. The algorithm is enhanced by introducing the Iterative chaotic iterative mapping and sparrow warning mechanism. The composite control strategy combines visual guidance with BP-PID control. Experimental results show Joint 1 has an average angle of about 50° and an average angular velocity of 0°/s. Joint 2 has an angle of about -80°, with an M-shaped angular velocity curve and large angular acceleration fluctuations, reaching a minimum angular jerk of -90/s³. Joint 3 has an angle of approximately 0°, with a W-shaped angular velocity curve and a maximum angular acceleration of 17°/s². The findings indicate the proposed technology accurately matches the kinematic and dynamic characteristics of robotic arm movements, ensuring precise joint motion trajectories and effectively suppressing shocks and vibrations. This research offers new ideas and methods for robotic arm motion control technology, with significant theoretical and practical value.
随着现代工业自动化向智能化和灵活性发展，机器人手臂在精密制造、智能装配和医疗手术中得到广泛应用。为满足高精度运动控制需求，本研究提出一种集成复合控制和改进的沙猫群优化算法的机器人手臂运动控制技术。该算法通过引入迭代混沌迭代映射和麻雀预警机制进行了增强。复合控制策略将视觉引导与BP-PID控制相结合。实验结果表明，关节1的平均角度约为50°，平均角速度为0°/s。关节2的角度约为-80°，角速度曲线呈M形，角加速度波动较大，最小角加加速度达到-90/s³。关节3的角度约为0°，角速度曲线呈W形，最大角加速度为17°/s²。研究结果表明，所提出的技术准确匹配了机器人手臂运动的运动学和动力学特性，确保了精确的关节运动轨迹，并有效抑制了冲击和振动。本研究为机器人手臂运动控制技术提供了新的思路和方法，具有重要的理论和实际价值。
