The aim of this study was to bring forth a model-based control method for trajectory tracking of a normal human-like biped on descending and ascending stairs using the straight knee state and to verify the controller performance in the straight knee state periods. Contemplating a straight state in knee joints does not consent to each leg’s shank link to undertake angular rotations greater than thigh link’s rotations. For this purpose, among suggested methods, the self-impact joint constraint has been recommended for energy-efficient (normal) bipedal walking with the implementation of straight knee constraint. To reach this intention, dynamical motion equations are derived, developed and modified to comprise self-impact joint constraint at the knee joint. For gaining control over this complex dynamical system, accessible anthropometric stair gait cycle data are used to derive desired trajectories of thigh and shank links of self-impact biped. Furthermore, for taking the stair ascent and descent of the biped under control, a nonlinear intelligent controller with adaptive neural network control method was put forward. In proportion to simulation results on stair ascending and descending of biped robot, the biped could track the desired angular positions and velocities of the links very well despite having complex nonlinear terms in its governing dynamical equations due to the presence of self-impact constraints. Moreover, during the activation period of joint self-impact, an error of link position angles was evidently improved in comparison to unconstrained biped.

中文翻译：

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一种更逼真的步行双足动物复制品，通过考虑自冲击关节约束来暗示上下楼梯的动力学和控制

本研究的目的是提出一种基于模型的控制方法，用于使用直膝状态对正常类人双足动物上下楼梯的轨迹跟踪，并验证直膝状态期间的控制器性能。考虑膝关节处于伸直状态并不同意每条腿的小腿连杆进行比大腿连杆旋转更大的角度旋转。为此，在建议的方法中，自冲击关节约束已被推荐用于执行直膝约束的节能（正常）双足行走。为了达到这个目的，动态运动方程被推导出、发展和修改，以包括膝关节处的自冲击关节约束。为了控制这个复杂的动力系统，可访问的人体测量楼梯步态循环数据用于推导出自力式两足动物的大腿和小腿链接的所需轨迹。此外，针对两足动物的上下楼梯受控制，提出了一种具有自适应神经网络控制方法的非线性智能控制器。与双足机器人上下楼梯的仿真结果成正比，尽管由于存在自冲击约束，双足机器人在其控制动力学方程中具有复杂的非线性项，但仍能很好地跟踪链接的所需角位置和速度。此外，在关节自冲击激活期间，与无约束两足动物相比，链接位置角度的误差明显改善。