平动柔性机械臂抑振轨迹的代理优化算法与实验验证

Surrogate-based optimization algorithm for vibration trajectories of a translational flexible manipulator and experimental verification

  • 摘要: 轻质化、柔性化是未来航天航空、机器人等高端装备领域的发展趋势。然而,柔性结构极易在快速运动过程中出现残余振动等非期望动力学行为,严重影响控制精度和稳定性。为此,本文提出一种基于代理优化算法的抑振轨迹高效规划方法。首先,以平动柔性机械臂为对象,建立了刚柔耦合动力学方程,揭示了平移加速度、运动轨迹对结构残余振动的影响规律,并提出最小化残余振动的机械臂运动规划问题;进而,提出一种基于多项式样条曲线、Kriging模型和期望提升序列加点准则的高效求解方法,并通过与遗传算法对比验证了算法的高效性;最后,开展了实验设计与效果测试。结果表明,该方法利用200次以内的采样,即可获得最优抑振运动轨迹;相比于未优化前的五次多项式轨迹,机械臂的残余振动能量降低了1-2个数量级,最大残余振动振幅降低了79.14%以上。

     

    Abstract: Future advancements in robotics, aerospace, and precision equipment industries demand lightweight and flexible designs. However, structural properties of flexible structures result in undesirable dynamic behaviors such as residual vibration during rapid motion, which significantly affects the control accuracy and stability of the system. In light of this, this paper proposes a high-efficiency vibration suppression trajectory planning approach based on a surrogate-based optimization algorithm. Firstly, this paper establishes rigid-flexible coupled dynamic equations for a translational flexible manipulator system. Through the dynamic equation, we reveal the influence mechanism of translational acceleration and motion trajectory on the structural residual vibration. Then, this investigation formulates the motion planning problem for minimizing the residual vibration. Next, a high-efficiency planning algorithm is proposed to solve the issue based on the polynomial spline curves, the kriging model, and the expected improvement criterion. The efficiency of the algorithm was verified by comparison with the genetic algorithm. Finally, the experimental design and effect test are conducted. The experimental results demonstrate that the method can achieve the optimal vibration suppression motion trajectory with fewer than 200 samples. The optimized trajectory reduces the maximum residual vibration amplitude by over 79.14% and the residual vibration energy by 1-2 orders of magnitude, compared with the quintic polynomial trajectory.

     

/

返回文章
返回