Dynamic Balance Optimization and Control of Quadruped Robot Systems With Flexible Joints

This paper investigates dynamic balance optimization and control of quadruped robots with compliant/flexible joints under perturbing external forces. First, we formulate a constrained dynamic model of compliant/flexible joints for quadruped robots and a reduced-order dynamic model is developed considering the robot interaction with the environment through multiple contacts. A dynamic force distribution approach based on quadratic objective function is proposed for evaluating the optimal contact forces to cope with the external wrench, and fuzzy-based adaptive control of compliant/flexible joints for quadruped robots is proposed to suppress uncertainties in the dynamics of the robot and actuators. The dynamic surface control approaches and fuzzy learning algorithms are combined in the proposed framework. All the signals of the closed-loop system have proven to be uniformly ultimately bounded through Lyapunov synthesis. Simulation experiments were performed for a quadruped robot with compliant/flexible joints. The benefits of its tracking accuracy and robustness indicate that the proposed framework is promising for the robots with payload uncertainties and external disturbances.

本文研究了在外部扰动力作用下具有柔顺/柔性关节的四足机器人的动态平衡优化与控制。首先，我们为四足机器人建立了柔顺/柔性关节的约束动力学模型，并考虑机器人通过多个接触点与环境的相互作用，开发了一个降阶动力学模型。提出了一种基于二次目标函数的动态力分配方法，用于评估应对外部 wrench（此处可能指力和力矩，需根据具体学科背景确定准确释义）的最优接触力，并且提出了基于模糊自适应控制的四足机器人柔顺/柔性关节控制方法，以抑制机器人和执行器动力学中的不确定性。在提出的框架中结合了动态面控制方法和模糊学习算法。通过李雅普诺夫综合证明，闭环系统的所有信号最终一致有界。对具有柔顺/柔性关节的四足机器人进行了仿真实验。其跟踪精度和鲁棒性方面的优势表明，所提出的框架对于有效载荷不确定和存在外部干扰的机器人是有前景的。