“半人马”机器人抗扰动平衡控制研究

在日常生活中人们经常有携带负载的需求，负载会增加人体行走的能量消耗。“半人马”机器人是一种新型的与人协同负重行走的系统，在人们需要携带负载行走时，“半人马”机器人可以与穿戴者共同承担负载，减少人体新陈代谢消耗。“半人马”机器人易受到外界的不确定性干扰影响，应对外界干扰的能力决定机器人的工作性能。侧向推力受扰是一种典型的外界扰动形式，本文将“半人马”机器人抗扰动问题缩小为研究“半人马”机器人在侧向受扰后与穿戴者协同运动恢复平衡的问题。本文的主要研究内容包含以下方面：

（1）搭建了“半人马”机器人抗扰动平衡控制的传感器系统。通过传感器系统，获取人体姿态角、机器人躯干姿态、加速度等运动信息，设计了状态估计器获得机器人的躯干位姿，并提出机器人感知外界扰动的方法。

（2）提出了人体受扰跨步运动预测方法。在受扰恢复过程中，“半人马”机器人控制算法要综合考虑机器人自身的抗扰动恢复和穿戴者的运动状态。本文进行了人体侧向受扰实验，获取到人体受扰冲量、大腿摆动角度和跨步步长等信息，使用回归模型和自适应模糊神经网络训练模型，预测人体受扰后的跨步步长，进一步估计人体质心运动位移。

（3）提出了“半人马”机器人抗扰动运动控制方法。机器人受扰后跨步运动过程中，双腿不断切换为支撑状态和摆动状态，对两种状态分别设计控制算法。对摆动腿的控制，基于倒立摆模型、捕获点理论和运动发散分量方程计算落足点，根据摆动腿运动性能优化步态周期，使用贝塞尔曲线规划足端轨迹；对支撑腿的控制，利用单刚体模型，提出了基于穿戴者运动信息的全状态模型预测控制算法规划支撑腿足端的地面作用力。

In daily life, people often need to carry a load, and the load will increase human energy expenditure. The Centaur robot is a new type of system that assists in human walking with a load. When people need to carry a load, the Centaur robot can share the load with the wearer, reducing the energy consumption of the human body. Uncertainty factors in the environment will affect the normal work of the Centaur robot, and the ability to deal with external disturbances determines the robot performance. Disturbance by lateral thrust is a typical form of external disturbance, and therefore this thesis narrows the problem of anti-disturbance to the problem of studying the Centaur robot and the wearer to restore balance after lateral disturbance. The main research content of the thesis includes the following aspects:

(1) The sensor system for the anti-disturbance balance control of the Centaur robot is built. Through the sensor system, the motion information such as human body posture angle, body posture and acceleration are obtained. On this basis, the state estimator is designed to obtain the robot's pose, and the method for the robot to perceive external disturbances is proposed.

(2) The prediction method for disturbed striding motion of human body is proposed. In the process of recovery from disturbance, the control algorithm of the Centaur robot should comprehensively consider the anti-disturbance recovery of the robot itself and the motion state of the wearer. In this thesis, the human body disturbance experiment was carried out, obtaining information such as the impulse of human body disturbance, the thigh swing angle, the stride length, etc., and the regression model and the adaptive network-based fuzzy inference system are used to predict the step length after the human body was disturbed, and further estimate the movement displacement of human body center of mass.

(3) The anti-disturbance motion control method for the Centaur robot is proposed. During the movement of the robot after being disturbed, the legs are constantly switched to the support state and the swing state, and the control algorithms are designed for the two states respectively. For the control of the swing leg, this thesis uses the inverted pendulum model, the capture point theory and the divergent component of motion equation to calculate the foothold point, and optimizes the gait cycle according to the swing leg motion performance, and uses the Bezier curve to plan the foot end trajectory. Regarding the control of the supporting leg, according to the wearer's motion information, this thesis uses the single rigid body model to proposes the full-state model predictive control algorithm that plans the ground force at the foot end of the supporting leg.

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