下肢外骨骼机器人系统参数辨识和控制方法研究
发布时间：2019-02-09 20:45
【摘要】：外骨骼机器人是一种可穿戴式机器人,其在行军作战、医疗康复和民用助力等方面具有极广泛的应用前景。本文就外骨骼机器人的传感系统和控制算法,从串联弹性驱动、机器人动力学模型、灵敏度放大控制器的设计和在线强化学习参数优化等角度对其展开研究。为增强穿戴者在佩戴机器人行走过程中的舒适性,提高机器人的仿生特性,本文在传统外骨骼关节基础上进行改进,在关节驱动电机和负载之间串联了弹性元件,用以减缓运动过程中的冲击作用并存储运动能量。先对弹性关节进行了数学建模,通过MATLAB仿真分析其动态跟随特性以确定合适本系统的弹性体刚度。为最大程度上简化机器人传感系统,本文采用灵敏度放大控制方法,此控制方法不需要任何检测人机交互力的传感器,但其对机器人动力学方程及动力学参数的准确性提出较高要求。本文使用拉格朗日方程推导机器人动力学方程,为最大程度上保证模型准确性,关节摩擦力矩和电机转子惯量等因素被统一考虑在模型当中,杆件质量和质心位置均采用实验的方法进行辨识。所设计的控制器采用通过实验辨识出来的机器人动力学参数,在定系数灵敏度放大控制实验成功之后,为进一步优化机器人随动效果,本文设计了强化学习在线优化灵敏度系数的算法。即下层仍采用灵敏度放大控制,上层采用DMP轨迹规划结合强化学习的在线参数优化算法对灵敏度系数进行在线优化。使用DMP算法对人体运动步态进行学习并给出预测轨迹,其与实际轨迹的偏差作为Q学习算法的实时奖励。通过MATLAB仿真验证了控制算法的稳定性。最后搭建外骨骼机器人实验平台,在外骨骼机器人实验系统上对所设计的算法进行验证,证实了动力学参数辨识的准确性和在线优化灵敏度系数算法的有效性。
[Abstract]:Exoskeleton robot is a wearable robot, which has a wide application prospect in marching, medical rehabilitation and civil assistance. In this paper, the sensing system and control algorithm of exoskeleton robot are studied from the point of view of series elastic drive, dynamic model of robot, design of sensitivity amplification controller and optimization of on-line reinforcement learning parameters. In order to enhance the comfortableness of the wearer in the walking process of the wearing robot and to improve the bionic characteristics of the robot, this paper improves on the traditional exoskeleton joint and makes a series of elastic elements between the joint driving motor and the load. Used to slow down the impact of motion and store motion energy. Firstly, the elastic joint is modeled by mathematical method, and its dynamic following characteristic is analyzed by MATLAB simulation to determine the stiffness of the elastic body suitable for the system. In order to simplify the robot sensor system to the greatest extent, the sensitivity amplification control method is adopted in this paper. This control method does not need any sensors to detect the human-computer interaction force. However, the accuracy of the dynamic equation and dynamic parameters of the robot is very high. In this paper, the dynamic equations of the robot are derived by using Lagrange equation. In order to ensure the accuracy of the model to the greatest extent, the joint friction moment and the moment of inertia of the motor rotor are considered in the model. The mass and centroid position of the member are identified by the experimental method. The designed controller adopts the robot dynamics parameters identified by experiments. After the experiment of constant coefficient sensitivity amplification control is successful, in order to further optimize the robot follow-up effect, In this paper, an algorithm for on-line optimization of sensitivity coefficient by reinforcement learning is designed. In other words, the lower layer still adopts sensitivity amplification control, and the upper layer optimizes the sensitivity coefficient by DMP trajectory planning and reinforcement learning online parameter optimization algorithm. The DMP algorithm is used to study the human moving gait and the prediction trajectory is given. The deviation from the actual track is the real time reward of Q learning algorithm. The stability of the control algorithm is verified by MATLAB simulation. Finally, the experimental platform of exoskeleton robot is built, and the designed algorithm is verified on the exoskeleton robot experimental system, which verifies the accuracy of dynamic parameter identification and the effectiveness of on-line optimization sensitivity coefficient algorithm.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242
本文编号：2419376
[Abstract]:Exoskeleton robot is a wearable robot, which has a wide application prospect in marching, medical rehabilitation and civil assistance. In this paper, the sensing system and control algorithm of exoskeleton robot are studied from the point of view of series elastic drive, dynamic model of robot, design of sensitivity amplification controller and optimization of on-line reinforcement learning parameters. In order to enhance the comfortableness of the wearer in the walking process of the wearing robot and to improve the bionic characteristics of the robot, this paper improves on the traditional exoskeleton joint and makes a series of elastic elements between the joint driving motor and the load. Used to slow down the impact of motion and store motion energy. Firstly, the elastic joint is modeled by mathematical method, and its dynamic following characteristic is analyzed by MATLAB simulation to determine the stiffness of the elastic body suitable for the system. In order to simplify the robot sensor system to the greatest extent, the sensitivity amplification control method is adopted in this paper. This control method does not need any sensors to detect the human-computer interaction force. However, the accuracy of the dynamic equation and dynamic parameters of the robot is very high. In this paper, the dynamic equations of the robot are derived by using Lagrange equation. In order to ensure the accuracy of the model to the greatest extent, the joint friction moment and the moment of inertia of the motor rotor are considered in the model. The mass and centroid position of the member are identified by the experimental method. The designed controller adopts the robot dynamics parameters identified by experiments. After the experiment of constant coefficient sensitivity amplification control is successful, in order to further optimize the robot follow-up effect, In this paper, an algorithm for on-line optimization of sensitivity coefficient by reinforcement learning is designed. In other words, the lower layer still adopts sensitivity amplification control, and the upper layer optimizes the sensitivity coefficient by DMP trajectory planning and reinforcement learning online parameter optimization algorithm. The DMP algorithm is used to study the human moving gait and the prediction trajectory is given. The deviation from the actual track is the real time reward of Q learning algorithm. The stability of the control algorithm is verified by MATLAB simulation. Finally, the experimental platform of exoskeleton robot is built, and the designed algorithm is verified on the exoskeleton robot experimental system, which verifies the accuracy of dynamic parameter identification and the effectiveness of on-line optimization sensitivity coefficient algorithm.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242
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